A Survey on Bio-inspired Networking

Falko Dressler∗,a , Ozgur B. Akanb

a Computer Networks and Communication Systems, Dept. of Computer Science, University of Erlangen, Germany
b Dept. of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey

Abstract
The developments in the communication and networking technologies have yielded many existing and en-
visioned information network architectures such as cognitive radio networks, sensor and actor networks,
quantum communication networks, terrestrial next generation Internet, and InterPlaNetary Internet. How-
ever, there exist many common significant challenges to be addressed for the practical realization of these
current and envisioned networking paradigms such as the increased complexity with large scale networks,
their dynamic nature, resource constraints, heterogeneous architectures, absence or impracticality of cen-
tralized control and infrastructure, need for survivability, and unattended resolution of potential failures.
These challenges have been successfully dealt with by Nature, which, as a result of millions of years of
evolution, have yielded many biological systems and processes with intrinsic appealing characteristics such
as adaptivity to varying environmental conditions, inherent resiliency to failures and damages, successful
and collaborative operation on the basis of a limited set of rules and with global intelligence which is larger
than superposition of individuals, self-organization, survivability, and evolvability. Inspired by these char-
acteristics, many researchers are currently engaged in developing innovative design paradigms to address
the networking challenges of existing and envisioned information systems. In this paper, the current state-
of-the-art in bio-inspired networking is captured. The existing bio-inspired networking and communication
protocols and algorithms devised by looking at biology as a source of inspiration, and by mimicking the
laws and dynamics governing these systems is presented along with open research issues for the bio-inspired
networking. Furthermore, the domain of bio-inspired networking is linked to the emerging research domain
of nanonetworks, which bring a set of unique challenges. The objective of this survey is to provide better un-
derstanding of the potentials for bio-inspired networking which is currently far from being fully recognized,
and to motivate research community to further explore this timely and exciting topic.

1. Introduction

The last few decades have witnessed striking developments in communication and networking technologies
which have yielded many information network architectures. One prominent product of this evolution, the
Internet, is itself an unprecedented success story which has shown the enormous potential of information
networks in terms of impact on society, economy and quality of life. While this potential is, in the Internet
case, still only partially exploited as it continues to diffuse into every aspect of our daily lives in many
different forms; the next generation of information systems with salient offsprings ranging from quantum
communication networks [1] to InterPlaNetary Internet [2] is beginning to make its way, posing phenomenal
challenges to researchers and engineers.
These next generation information networks are envisioned to be characterized by an invisible and ubiq-
uitous halo of information and communication services, which should be easily accessible by users in a
transparent, location-independent, and seamless fashion [3]. Therefore, the result will be a pervasive and,

∗ Correspondingauthor.
Email addresses: dressler@informatik.uni-erlangen.de (Falko Dressler), akan@eee.metu.edu.tr (Ozgur B. Akan)

To appear in Elsevier Computer Networks

in fact, living network extending the current Internet capabilities. This ubiquitous networking space will
include, in addition to the traditional Internet-connected devices, networked entities which are in much
closer interaction with us such as wearable networks [4], in-body molecular communication networks [5],
unattended ground, air, and underwater sensor networks [6], self-organizing sensor and actor networks [7, 8]
and locally intelligent and self-cognitive devices exploiting the communication resources with the help of
cognitive capabilities, e.g., cognitive radio networks [9]. Clearly, this vision implies that almost every object
will be able to effectively and collaboratively communicate, thus becoming, to some extent, a node of the
future pervasive Internet-like global network.
The evolution in communication and networking technologies brings many such potential advantages to
our daily lives. At the same time, the complexity of the existing and envisioned networked information
systems has already gone far beyond what conventional networking paradigms can do in order to deploy,
manage, and keep them functioning correctly and in an expected manner. Self-organization techniques are
demanded to overcome current technical limitations [10]. In fact, there exist many common significant
challenges that need to be addressed for practical realization of these existing and next generation network-
ing architectures, such as increased complexity with large scale networks, their dynamic nature, resource
constraints, heterogeneous architectures, absence or impracticality of centralized control and infrastructure,
need for survivability, and unattended resolution of potential failures.
Clearly, most of the existing and next generation communication systems cannot be handled according
to the conventional networking paradigms, which are not able to accommodate the scale, heterogeneity and
complexity of such scenarios. Novel paradigms are needed for designing, engineering and managing these
communication systems.
While the challenges outlined above such as scalability, heterogeneity and complexity are somehow
new by-products of the evolution in the communication technologies in the last few decades, they have
been successfully dealt with by Nature for quite some time. Unlike the evolution in the communication
technologies which have brought these challenges about, the evolution in Nature have yielded artifacts
which are actually the solution approaches that can handle many of these challenges with an elegance and
efficiency still far beyond current techniques.
In fact, when we look carefully into nature, it is clearly observed that the dynamics of many biological
systems and laws governing them are based on a surprisingly small number of simple generic rules which
yield collaborative yet effective patterns for resource management and task allocation, social differentiation,
synchronization (or de-synchronization) without the need for any externally controlling entity. For example,
by means of these capabilities, billions of blood cells which constitute the immune system can protect the
organism from the pathogens without the any central control of the brain [11]. Similarly, an entire organism is
autonomously maintained in a relatively stable equilibrium state via a major functionality, i.e., homeostasis,
for the operation of vital functions without any need for a central biological controller [12]. Task allocation
process in the insect colonies is collaboratively decided and performed according to the willingness of an
individual such that the overall task is optimized with a global intelligence comprised of simple individual
responses [13].
These examples and, in general, as a result of millions of years of evolution, biological systems and
processes have intrinsic appealing characteristics. Among others, they are

• adaptive to the varying environmental circumstances,

• robust and resilient to the failures caused by internal or external factors,
• able to achieve complex behaviors on the basis of a usually limited set of basic rules,

• able to learn and evolve itself when new conditions are applied,
• effective management of constrained resources with an apparently global intelligence larger than the
superposition of individuals
• able to self-organize in a fully distributed fashion, collaboratively achieving efficient equilibrium,

2
 • survivable despite harsh environmental conditions due to its inherent and sufficient redundancy.
These characteristics lead to different levels of inspiration from biological systems towards the derivation
of different approaches and algorithm designs at each of the networking layers for efficient, robust and
resilient communication and information networks. Therefore, in order to keep pace with the evolution in
networking technologies, many researchers, members of this very young research community, are currently
engaged in developing innovative design paradigms inspired by biology in order to address the networking
challenges of existing and envisioned information systems. The common rational behind this effort is to
capture the governing dynamics and understand the fundamentals of biological systems in order to devise new
methodologies and tools for designing and managing communication systems and information networks that
are inherently adaptive to dynamic environments, heterogeneous, scalable, self-organizing, and evolvable.
Besides bio-inspired networking solutions, communication on nano-scale is being investigated with two
important but conceptually different goals. On the one hand, bio-inspired nano machinery is investigated
to build machines on nano level using communication and actuation capabilities derived from biological
counterparts. More specifically, the most promising communication mechanism between nano-machines
forming nano-scale networks is currently envisioned to be molecular communication, i.e., coding and transfer
of information in terms of molecules, which is also mainly inspired by the cellular signaling networks observed
in the living organisms. On the other hand, such nano-machines can also be used in the main field of
molecular biology to study biological systems. Thus, we aim to link both bio-inspired research and nano-
communication by looking at state-of-the-art solutions in both domains.
In this paper, we present the survey of the bio-inspired networking and communication protocols and
algorithms devised by looking at biology as a source of inspiration, and by mimicking the laws and dynamics
governing these systems. It should be also noted that we leave the existing and quite comprehensive literature
on the communication and computing algorithms based on evolutionary techniques, e.g., genetic algorithm,
out of our scope, and mainly focus on the networking paradigms and solution approaches inspired by the
biological systems and processes and their governing dynamics. Furthermore, in spite of the many successful
applications of bio-inspired research, we emphasize that the main challenge is neither the inspiration nor the
application, but is understanding the biological system and its behavior, the modeling of the system, and
the conceptual derivation of technical solutions. Therefore, our objective is to provide better understanding
of the current state-of-the-art and the research issues in the broad field of bio-inspired networking and help
research community to find appealing hints for future explorative activities on this timely and exciting topic.
The remainder of the paper is organized as follows. In Section 2, we summarize the most challenging
questions in networking and provide pointers to biological similarities and solutions. We explore the proposed
biological systems and processes whose models can be exploited towards the design of novel networking
paradigms in Section 3. In Section 4, we investigate the current and proposed protocols and algorithms
based on and inspired by biological systems for diverse set of networking architectures. This includes
a summary of activities in the field of bio-inspired networking. After that, we connect the bio-inspired
networking domain to the upcoming field of nanonetworks in Section 5, which also focuses on establishing
communication networks within biological systems. Finally, we state the concluding remarks in Section 6.

2. Challenges in Networking

Clearly, there exist many challenges for the realization of the existing and the envisioned next generation
network architectures. At the same time, similar problems and their naturally evolved biological solution
approaches also exist for these networking paradigms. In this section, we review the most challenging
fundamental issues for networking and highlight the analogies with their counterparts and corresponding
solution approaches which already exist in the biological systems. Most of the listed challenges relate to
problems in wireless networks such as mobile ad hoc networks or sensor networks. With the increasing use
of ubiquitous computing, many of the most important networking issues relate to such networks. At the
same time, due to vast amount of research efforts over wireless and mobile networking domains, the existing
examples of bio-inspired solutions addressing these common major challenges are, hence, observed in the
current literature of these research areas as well.
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 Furthermore, some challenges explored here, e.g., large-scale networking, heterogeneous architectures,
also stand as important barriers for the realization of future Internet architectures including the Internet
of things [14]. Moreover, some security aspects such as the spreading of Internet worms is covered by the
examples discussed in Section 4. It needs to be noted that this section cannot be seen as a full reference of
challenges in networking, but as a list that can finally be addressed by bio-inspired solutions.
Here, instead of exploring networking problems in terms of functionalities and algorithms in each layer
of communication protocol stacks for diverse set of network architectures,1 we overview the main common
challenges of the existing and the next generation networks brought about by the evolution in communication
technologies and the increasing demand posed upon them.

2.1. Large scale networking

One of the main challenges is related to the sheer size exhibited by the networking systems, which connect
huge numbers of users and devices in a single, omni-comprehensive preferably always-on network. The size
of this omni-comprehensive network, in terms of both number of constituent nodes and running services, is
expected to exceed by several orders of magnitude that of current Internet.
For example, Wireless Sensor Networks (WSNs) having a broad range of current and future applications
are generally envisioned to be composed of a large number, e.g., in numbers ranging between few hundreds
to several hundred thousands, of low-end sensor nodes [15]. The first direct consequence of such large scales
is the huge amount the traffic load to be incurred over the network. This could easily exceed the network
capacity, and hence, hamper the communication reliability due to packet losses by both collisions in the local
wireless channel as well as congestion along the path from the event field towards the sink [17]. Consequently,
the difficulty level for the selection of appropriate set and number of nodes and their reporting frequency
for reliable yet efficient communication also increases with the network size [18].
Similarly, it becomes more important to find the optimal routes, if possible, in order to keep the commu-
nication overhead at acceptable levels during the dissemination of large amount of information over a large
scale network. As the network scale expands, the number of possible paths, and hence, the search space for
the optimal route in terms of a preset criteria, also drastically enlarges. The number of routing tables to
maintain, and, regardless of a specific routing mechanism, the amount of traffic for table updates experience
the same increase as the network scales up.
Clearly, deployment, effective communication, and management in large scale networks, e.g., sensor
networks and mobile ad hoc networks, cannot be manually realized. Hence, networking mechanisms must
be scalable and adaptive to variations in the network size. Fortunately, there exist many biological systems
that inspire for the design of effective communication solutions for large scale networks. For example, as
discussed in Section 4.1.1 in detail, based on optimizing global behavior in solving complex tasks through
individual local means, Ant Colony Optimization (ACO) techniques [19] provide efficient routing mechanisms
for large-scale mobile ad hoc [20]. In addition, information dissemination over large scales can be handled
with the help of epidemic spreading [21, 22, 23], which is the main transmission mechanism of viruses over
the large and scale-free organism populations. Similar examples, as presented in Section 4, clearly show that
the potential adverse effects of large scale networking may be handled with bio-inspired mechanisms.

2.2. Dynamic nature

Unlike the early communication systems composed of a transmitter / receiver pair and communication
channel, which are all static, the existing and the envisioned networking architectures are highly dynamic
in terms of node behaviors, traffic and bandwidth demand patterns, channel and network conditions.
According to the mobility patterns of the nodes, network dimensions, and radio ranges; communication
links may frequently be established and become obsolete in mobile ad hoc networks [24]. Furthermore, due
to mobility of the nodes, and environmental variations as a result of movement, the channel conditions and
hence link qualities may be highly dynamic. Similarly, in the target tracking applications of sensor networks,
based on the target behaviors and the area to be monitored, the amount of traffic created by the sensor

1 Exhaustive surveys of network and communication challenges for some of these architectures can be found in [6, 7, 9, 15, 16].
4
nodes may drastically increase at the time of detection and may decay with time. This imposes varying load
on the network which may result in inefficient capacity utilization if static approaches are employed.
Dynamic spectrum access and its management in cognitive radio networks is another important case
where the dynamic nature of the user behaviors, channel requests and application-specific bandwidth de-
mands pose significant challenges on the network design [9]. The objective of cognitive radio networks itself
is to leverage the dynamic usage of spectrum resources in order to maximize the overall spectrum utilization.
The list of examples could be expanded, which, however, would only further reinforce the fact that com-
munication techniques need to be adaptive to the dynamics of the specific networking environment. To this
end, the biological systems and processes are known to be capable of adapting themselves to varying circum-
stances towards the survival. For example, Artificial Immune System (AIS), inspired by the principles and
processes of the mammalian immune system [25], efficiently detects variations in the dynamic environment
or deviations from the expected system patterns. Similarly, activator-inhibitor systems and the analysis of
reaction-diffusion mechanisms in biological systems [26] also capture dynamics of the highly interacting sys-
tems through differential equations. As will be explored in Section 4, many biologically inspired approaches,
e.g., activator-inhibitor mechanisms [27], AIS [28], can be exploited to develop communication techniques
which can adapt to varying environmental conditions.

2.3. Resource constraints

As the communication technologies evolve, demands posed upon the networks also drastically increase
in terms of set of available services, service quality including required bandwidth capacity, and network
lifetime. For example, the current Internet can no longer respond to every demand as its capacity is almost
exceeded by the total traffic created, which lays a basis for the development of next generation Internet [29].
At the same time, with the increased demand from wireless networking, fixed spectrum assignment-based
traditional wireless communications has become insufficient in accommodating wide range of radio commu-
nication requests. Consequently, cognitive radio networks with dynamic spectrum management and access
has been proposed and is currently being designed in order to improve utilization of spectrum resources [9].
On the other hand, some next generation networking architectures, e.g., InterPlaNetary Internet [2],
intrinsically possess resource constraints due to their physical and structural limitations. More specifically,
for the networks composed of nodes that are inherently constrained in terms of energy and communication
resources, e.g., WSNs [15], Mobile Ad Hoc Networks (MANETs) [24], nano-scale and molecular communi-
cation networks [5], these limitations directly bound their performance and mandate for intelligent resource
allocation mechanisms.
The biological systems yet again help researchers by providing pointers for mechanisms and solution ap-
proaches which address the trade-off between the high demand and limited supply of resources. For example,
in the foraging process [30], ants use their individual limited resources towards optimizing the global behav-
ior of colonies in order to find food source in a cost-effective way. As explained in Section 4.1.1, the behavior
of ant colonies in foraging process inspire many resource-efficient networking techniques. Furthermore, cel-
lular signaling networks, and its artificial counterpart, represent and capture the dynamics of interactions
contributing to the main function of a living cell. Hence, they might also enlighten important avenues to
obtain efficient communication techniques for resource constrained nano-scale and molecular communication
networks.

2.4. Need for infrastructure-less and autonomous operation

With significant increase in network dimensions both spatially and in the number of nodes, centralized
control of communication becomes unpractical. On the other hand, some networks are by definition free
from infrastructure such as wireless ad hoc networks [24], Delay Tolerant Networks (DTNs) [2], WSNs [15],
and some have a heterogeneous, mostly distributed and non-unified system architecture such as cognitive
radio networks [9], wireless mesh networks and WiMAX [16]. These networking environments mandate for
distributed communication and networking algorithms which can effectively function without any help from
a centralized unit.
At the same time, communication networks are subject to failure either by device malfunction, e.g., nodes
in a certain area may run out of battery in sensor networks, or misuse of their capacity, e.g., overloading
5
the network may cause heavy congestion blocking the connections. In most cases, networks are expected to
continue their operation without any interruption due to these potential failures. Considering the dynamic
nature, lack of infrastructure, and impracticality of centralized communication control, it is clear that
networks must be capable of re-organizing and healing themselves to be able to resume their operation.
Hence, the existing and next generation information networks must have the capabilities of self-organization,
self-evolution and survivability.
In order to address these needs, networks must be equipped with similar set of intelligent algorithms
and processes as largely observed in biological systems. In fact, inherent features of many biological systems
stand as promising solutions for these challenges.
For example, epidemic spreading mechanism could be inherited towards efficient information dissemi-
nation in highly partitioned networks and for opportunistic routing in delay tolerant networking environ-
ments [23]. Ant colonies, and in general insect colonies, which perform global tasks without the control
of any centralized entity, could also inspire the design of communication techniques for infrastructure-less
networking environments [31]. Furthermore, synchronization principles of fireflies [32] could be applied to
the design of time synchronization protocols as well as communication protocols requiring precise time syn-
chronization. Activator-inhibitor systems may be exploited for distributed control of sensing periods and
duty cycle of target tracking sensor network [33, 34]. The autonomous behavior of artificial immune system
may be a good model for the design of effective algorithms for unattended and autonomous communica-
tion in sensor networks [28]. Thus, as discussed in Section 4 in detail, the potential handicaps of lack of
infrastructure and autonomous communication requirements in various networking environments could be
addressed through careful exploration of biological systems.

2.5. Heterogeneous architectures

The other critical aspect of many of the existing and envisioned communication networks is linked to
their heterogeneity and its resultant extremely complex global behavior, emerging from the diverse range of
network elements and large number of possible interactions among them. Next generation communication
systems are generally envisioned to be composed of a vast class of communicating devices differing in their
communication / storage / processing capabilities, ranging from Radio Frequency Identification (RFID)
devices and simple sensors to mobile vehicles equipped with broadband wireless access devices.
For example, as one of the emerging and challenging future networking architectures, the Internet of
things (IoT) is defined as a vision of network of objects which extends the Internet capabilities into our daily
lives transforming our immediate environment into a large-scale wireless networks of uniquely identifiable
objects. One of the main research problems for the realization of the vision of IoT is that it will exhibit
high degrees of heterogeneity in terms of node types, e.g., ranging from smart household appliances to even
consumer goods such as a yogurt can identified with RFID tags [14].
Similarly, cognitive radio networks involve the design of new communication techniques to realize the
co-existence of different wireless systems communicating on overlapping spectrum bands with an ultimate
objective of maximizing the spectrum utilization. Wireless mesh networks and WiMAX are also expected
to be composed of heterogeneous communication devices and algorithms [16].
Sensor and Actor Networks (SANETs) architecturally incorporate both heterogeneous low-end sensor
nodes and highly capable actor nodes [7, 10]; and Vehicular Ad Hoc Networks (VANETs) [35] exhibit sig-
nificant levels of heterogeneity in terms of wireless communication technologies in use and mobility patterns
of ad hoc vehicles.
Such heterogeneity and asymmetry in terms of capabilities, communication devices and techniques need
to be understood, modeled and effectively managed, in order to allow the realization of heterogeneous novel
communication networks. Different levels of heterogeneity is also observed in biological systems. For ex-
ample, in many biological organisms, despite external disturbances, a stable internal state is maintained
through collaborative effort of heterogeneous set of subsystems and mechanisms, e.g., nervous system, en-
docrine system, immune system. This functionality is called “homeostasis”, and the collective homeostatic
behavior [36] can be captured towards designing communication techniques for networks with heterogeneous
architectures. On the other hand, insect colonies are composed of individuals with different capabilities and

6
abilities to respond to a certain environmental stimuli. Despite this inherent heterogeneity, colonies can
globally optimize the task allocation and selection processes via their collective intelligence [13]. Similar ap-
proaches can be adopted to address task assignment and selection in SANETs [37, 38], for spectrum sharing
in heterogeneous cognitive radio networks [31], as well as multi-path routing in overlay networks [39, 40].

2.6. Communication on the micro level

With the advances in micro- and nano-technologies, electro-mechanical devices have been downscaled
to micro and nano levels. Consequently, there exist many micro- (MEMS) and nano-electro-mechanical
systems (NEMS) and devices with a large spectrum of applications. Clearly, capabilities for communication
and networking in micro even in nano scales become imperative in order to enable micro and nano devices
to cooperate and hence collaboratively realize certain common complex task which cannot be handled
individually. With this regard, “nanonetworks” could be defined as a network composed of nano-scale
machines, i.e., nano-machines, cooperatively communicating with each other and sharing information in
order to fulfill a common objective [41].
While the communication and networking requirements at these scales might be similar from the func-
tional perspective, there exist significant differences between the communication in the traditional and
micro / nano scales. The dimensions of nano-machines render conventional communication technologies
such as electromagnetic wave, acoustic, inapplicable at these scales due to antenna size and channel limita-
tions. Furthermore, the communication medium and channel characteristics also show important deviations
from the traditional cases due to the rules of physics governing these scales.
The main idea of nano-machines and nano-scale communications and networks have also been motivated
and inspired by the biological systems and processes. Hence, it is conceivable that the solutions for the
challenges in communication and networking at micro and nano-scales could also be developed through
inspiration from the existing biological structures and communication mechanisms.
In fact, many biological entities in organisms have similar structures with nano-machines. For example,
every living cell has the capability of sensing the environment, receiving external signals, performing certain
tasks at nano-scales. More importantly, based on transmission and reception of molecules, cells in a biologi-
cal organism may establish cellular signaling networks [42], through which they can communicate in order to
realize more complex and vital tasks, e.g., immune system responses. Therefore, as will be explored in Sec-
tion 5 in details, the inspiration from cellular signaling networks, and hence, molecular communication [43],
provide important research directions and promising design approaches for communication and networking
solutions at micro and nano-scales.

3. Biological Models Inspiring Communication Network Design(er)s

The main intention of this survey is to introduce and to overview the emerging area of bio-inspired
networking. Therefore, the scope of this section is first to introduce the general approach to bio-inspired
networking by discussing the identification of biological structures and techniques relevant to communication
networks, modeling the systems and system properties, and finally deriving optimized technical solutions.
Secondly, we try to classify the field of biologically inspired approaches to networking – selected examples
are presented in more detail in Section 4. Bio-inspired algorithms can effectively used for optimization
problems, exploration and mapping, and pattern recognition. Based on a number of selected examples, we
will see that bio-inspired approaches have some outstanding capabilities that motivate their application in
a great number of problem spaces.
As this paper focuses on recent approaches to bio-inspired networking, we explicitly exclude the broad
field of evolutionary algorithms, which are successfully applied to optimization problems in many areas
of computer science and engineering. As a further remark, it should be noted that self-organization of
(massively) distributed systems is also not in our scope – whereas many of the discussed biological examples
also provide solutions to this problem [10].

7
 Bio-inspired engineering

Understanding Engineering
Identification of Model simplification
Modeling of realistic
analogies between and tuning for ICT
biological behavior
biology and ICT applications

Figure 1: Necessary steps to adapt biological mechanisms to technical solutions

3.1. Modeling approaches

Before introducing the specific biological models that have are/can be exploited towards the development
and realization of bio-inspired networking solutions, we need to briefly study the general modeling approach.
First modeling approaches date back to the early 1970ies [44, 45]. Since that time, quite a number of
technical solutions mimicking biological counterparts have been developed and published. Typical bio-
networking architectures showing the complete modeling approach are described in [46, 47]. This bio-
networking architecture can be seen as a catalyzer or promoter for many other investigations in the last
decade. A more recent work of this architecture shows that there is still room for further improvements [48].
Looking at many papers and proposals that have been derived in recent years, some of this can be
understood as attempts to present (engineering) technical solutions with some similarities to biological
counterparts without really investigating the key advantages or objectives of the biological systems. Obvi-
ously, many methods and techniques are really bio-inspired as they follow principles that have been studied
in nature and that promise positive effects if applied to technical systems. Three steps can be identified
that are always necessary for developing bio-inspired methods that have a remarkably impact in the domain
under investigation:
1. Identification of analogies – which structures and methods seem to be similar
2. Understanding – detailed modeling of realistic biological behavior
3. Engineering – model simplification and tuning for technical applications
These primary principles of investigating and exploiting biologically inspirations are depicted in Figure 1.
First, analogies between biological and technical systems such as computing and networking systems must
be identified. It is especially necessary that all the biological principles are understood properly, which is
often not yet the case in biology. Secondly, models must be created for the biological behavior. These models
will later be used to develop the technical solution. The translation from biological models to the model
describing bio-inspired technical systems is a pure engineering step. Finally, the model must be simplified
and tuned for the technical application. As a remark, it should be mentioned that biologists already started
looking at bio-inspired systems to learn more about the behavioral pattern in nature [49]. Thus, the loop
closes from technical applications to biological systems.

3.2. Classification and categorization

Basically, the following application domains of bio-inspired solutions to problems related to computing
and communications can be distinguished:

• Bio-inspired computing represents a class of algorithms focusing on efficient computing, e.g. for opti-
mization processes and pattern recognition
• Bio-inspired systems constitute a class of system architectures for massively distributed and collabo-
rative systems, e.g. for distributed sensing and exploration

• Bio-inspired networking is a class of strategies for efficient and scalable networking under uncertain
conditions, e.g. for autonomic organization in massively distributed systems
Looking from biological principles, several application domains in networking can be distinguished. Ta-
ble 1 summarizes the biological domains that are, together with specific examples of successful application
to networking, detailed in Section 4.
8
 Biological Application fields in networking Selected references
principle
Swarm distributed search and optimization; routing in computer net- [50, 51, 13, 52, 19, 30, 53,
Intelligence and works, especially in MANETs, WSNs, and overlay networks; 20, 54, 39, 40, 37, 38, 55,
Social Insects task and resource allocation 56]
Firefly robust and fully distributed clock synchronization [32, 57, 58, 59, 60, 61, 62]
Synchronization
Activator- (self-) organization of autonomous systems; distributed coordi- [63, 26, 64, 27, 65, 33, 34]
Inhibitor nation; continuous adaptation of system parameters in highly
Systems dynamic environments
Artificial Immune network security; anomaly and misbehavior detection [66, 25, 67, 68, 69, 18, 70,
System 11, 36]
Epidemic content distribution in computer networks (e.g. in DTNs); over- [21, 23, 71, 72, 73, 74, 75,
Spreading lay networks; analysis of worm and virus spreading in the In- 76, 77, 78, 22, 79, 80, 72,
ternet 81, 71]
Cellular Signaling coordination and control in massively distributed systems; pro- [82, 42, 83, 84, 85, 86, 87,
Networks gramming of network-centric operating sensor and actor net- 88, 34, 89, 90, 91, 92]
works

Table 1: Categorization of biological phenomena and networking algorithms mimicking these concepts

Arterial blood pressure ↓ Kidney Liver

Arterial blood pressure ↑

Angiotensinogen
Renin
Promoter

Increase of Suppressor Angiotensin I

blood volume ACE

Angiotensin II

Kidney: aldosterone
Smooth muscle cells: → Na+ retention → Adenohypophysis
contraction regulation of blood (brain):
volume vasopressin
ligand target cell
ligand G protein-coupled
Receptor-tyrosine kinase receptor DNA
Cell membrane
Gap junctions
Ras GG
Adapter
proteins target cell DNA
Raf-kinase
Signalling
MEK Other DNA
signalling cell
MAPK cascades

target cell
DNA
Gene transcription DNA
nucleus

Figure 2: Communication and coordination on micro and macro level. Depicted is the information exchange within a cell,
between cells, within the human body, among people, and around the globe

Besides these specific algorithms that are mimicking biological mechanisms and behavior, the general
organization of biological systems, i.e. the structure of bodies down to organs and cells, can be used as
an inspiration to develop scalable and self-organizing technical systems, in particular computer networks.
Respective control frameworks and complete bio-networking architectures have been investigated [46, 48].
Figure 2 depicts another interesting property of many biological communication and coordination mech-
anisms. If studying those techniques on the micro level, i.e. on a cellular basis or the signaling pathways
between cells, similar mechanisms can be identified compared to studies of the macro level, i.e. the coor-
dination among people in a group or even around the globe. In summary, many models are similar on the
micro and macro level – basically exploiting similar communication and coordination mechanisms.
This degree of similarity has advantages. First of all, the precise modeling of specific communication
aspects can frequently be done using existing models for other domains. For example, the diffusion of proteins
to neighboring cells can be described with a similar communication model like the epidemic spreading of
viruses between different people. Mathematical models are often the same. On the other hand, such
similarity requires especial care when selecting the right biological model as source for inspiration to solve
a technical problem. If the models do not perfectly match, the technical solution may be limited in its
functionality or effectiveness.

9
 Further summaries in this field can be found in form of book chapters in [93] and in [94]. Additionally,
the book “Advances in Biologically Inspired Information Systems - Models, Methods, and Tools” can be
recommended as a source of general bio-inspired solutions to technical systems [95].

4. Approaches to Bio-inspired Networking

In this section, we introduce the current state-of-the-art in bio-inspired networking based on examples for
the various networking paradigms. The following list is not meant to be comprehensive and to completely
represent all approaches in the domain of bio-inspired networking. However, we selected a number of tech-
niques and methods for more detailed presentation that clearly show advantages in fields of communication
networks. In the discussion, we try to highlight the necessary modeling of biological phenomena or principles
and their application in networking.

4.1. Swarm Intelligence and Social Insects

The coordination principles studied in the fields of swarm intelligence [13] and especially those related to
social insects give insights into principles of distributed coordination in Nature. In many cases, direct com-
munication among individual insects is exploited, e.g., in the case of dancing bees [51]. However, especially
the stigmergic communication via changes in the environment is as fascinating as helpful to coordinate mas-
sively distributed systems. For example, Ma and Krings studied the chemosensory communication systems
in many of the moth, ant and beetle populations [50]. The difference between the “wireless” network of
an insect population and an engineered wireless sensor network is that insects encode messages with semio-
chemicals (also known as infochemicals) rather than with radio frequencies. Application examples of the
bees’ dance range from routing to intruder detection [51]. Another typical example is the communication
between ants for collaborative foraging. We discuss the ACO and its application for routing, task allocation,
and search in peer-to-peer networks in the following.

4.1.1. Ant Colony Optimization

Ant Colony Optimization (ACO) is perhaps the best analyzed branch of swarm intelligence based algo-
rithms. In general, swarm intelligence is based on the observation of the collective behavior of decentralized
and self-organized systems such as ant colonies, flocks of fishes, or swarms of bees or birds [13]. Such systems
are typically made up of a population of simple agents interacting locally with one another and with their
environment.
In most cases, swarm intelligence based algorithms are inspired by the behavior of foraging ants [13].
Ants are able to solve complex tasks by simple local means. There is only indirect interaction between
individuals through modifications of the environment, e.g. pheromone trails are used for efficient foraging.
Ants are “grand masters” in search and exploration.
ACO is based on the principles of the foraging process of ants.2 Ants perform a random search (random
walk) for food. The way back to the nest is marked with a pheromone trail. If successful, the ants are
returning to the nest (following their own trail). While returning, an extensive pheromone trail is produced
pointing towards the food source. Further ants are recruited that follow the trail on the shortest path
towards the food. The ants therefore communicate based on environmental changes (pheromone trail), i.e.
they use stigmergic communication techniques for communication and collaboration.
The complete ACO algorithm is described in [19, 30]. The most important aspect in this algorithm
is the transition probability pij for an ant k to move from i to j. This probability represents the routing
information for the exploring process.

2 Other foraging methods, e.g. E.coli bacteria have also been used as inspiration for efficient communication in ad hoc
networks, e.g. for data harvesting in vehicular networks [52].

10
  α β
[τ (t)] × [ηij ]
 P ij if j ∈ Jik


α β
pkij = [τil (t)] × [ηil ] (1)
k
 l∈Ji


0 otherwise
Each move depends on the following parameters:
• Jik is the tabu list of not yet visited nodes, i.e. by exploiting Jik , an ant k can avoid visiting a node i
more than once
• ηij is the visibility of j when standing at i, i.e. the inverse of the distance

• τij is the pheromone level of edge (i, j), i.e. the learned desirability of choosing node j and currently
at node i
• α and β are adjustable parameters that control the relative weight of the trail intensity τij and the
visibility ηij , respectively
k
After completing a tour, each ant k lays a quantity of pheromone ∆τij (t) on each edge (i, j) according
to the following rule, where T (t) is the tour done by ant k at iteration t, Lk (t) is its length, and Q is a
k

parameter (which only weakly influences the final result).

Q/Lk (t) if (i, j) ∈ T k (t)


k
∆τij (t) = (2)
0 otherwise
Dynamics in the environment are explicitly considered by the ant foraging scheme. The pheromone
slowly evaporates. Thus, if foraging ants are no longer successful, the pheromone trail will dissolve and the
ants continue with their search process. Additionally, randomness is also a strong factor during successful
foraging. A number of ants will continue the random search for food. This adaptive behavior leads to an
optimal search and exploration strategy. Pm k
This effect is provided by the pheromone update rule, where ∆τij (t) = k=1 ∆τij (t). The decay is
implemented in form of a coefficient ρ with 0 ≤ ρ < 1.

τij (t) ← (1 − ρ) × τij (t) + ∆τij (t) (3)

According to [19], the total number of ants m is an important parameter of the algorithm. Too many
ants would quickly reinforce suboptimal tracks and lead to early convergence to bad solutions, whereas too
few ants would not produce enough decaying pheromone to achieve the desired cooperative behavior. Thus,
the decay rate needs to be carefully controlled.
In the following, selected applications in networking are discussed that are based on the main concepts
of ACO.

4.1.2. Routing
Perhaps the best known examples of ACO in networking are the AntNet [53] and AntHocNet [20]
routing protocols. Both protocols follow the concepts of ant routing. In particular, so called agents are used
to concurrently explore the network and exchange collected information in the same way as ants explore
the environment. The communication among the agents is indirect, following the stigmergy approach, and
mediated by the network itself.
AntNet provides a proactive routing approach that relies on the idea to periodically launch mobile
agents towards randomly selected destination nodes. The key objective for these explorer agents is to find
a minimum cost path, i.e. a shortest path, between the source and the destination, and to update the
path-related routing entries in the network. Following the ACO algorithm, so called forward ants randomly
search for the destination using a greedy stochastic policy. After locating the destination, the agents turn
into backward ants and travel home on the same path used for exploration. On this way, all routing tables
11
of traversed nodes are updated with the most current information about the destination node. In order to
avoid congestion, AntNet maintains a probability pd for creating explorer agents according to the current
traffic conditions.
The routing tables as used by AntNet and AntHocNet are represented by Tk , which defines the prob-
abilistic routing policy currently adopted for node k. For each destination d and for each neighbor n, Tk
stores a probabilistic value Pnd expressing the quality (desirability)
P of choosing n as a next hop towards
destination d. The outgoing probabilities are constrained by Pnd = 1.
n∈Nk
Similar to AntNet, AntHocNet [20] is based on the ACO algorithm used in the context of ad hoc
networks. AntHocNet sets up paths when they are needed at the start of a session. Thus, AntHocNet
represents a reactive routing approach. Improved scalability compared to AntHocNet has been achieved by
HOPNET [54], an algorithm based on ants hopping between so called zones. It consists of local proactive
route discovery within a node’s neighborhood and reactive communication between the neighborhoods.
Another work to be named in the domain of routing is the self-adaptive multi-path routing in overlay
networks [39, 40]. Again, randomness is exploited to find optimal solutions in selecting network paths.
Even though this approach is namely focusing on adaptive responses from attractors, the attractors can be
compared to the explorer ants and the probabilistic routing decisions.

4.1.3. Task Allocation

Based on the same concepts, integrated task allocation and routing in SANETs has been investigated
[37, 38]. The proposed architecture is completely based on probabilistic decisions. During the lifetime of the
SANET, all nodes maintain and adapt a probability P (i) to execute a task out i of a given set. Reinforcement
strategies are exploited to optimize the overall system behavior. It needs to be mentioned that the integrated
task allocation and routing approach represents a typical cross-layer solution. Application layer and network
layer are both responsible for operating the entire SANET.
Task selection is performed by the nodes according to a probabilistic scheme. It is assumed that all the
agents know a priori a list of possible tasks Tagent = {T1 , T2 , . . . , Tn } that they can perform. Each agent k
associates a task i ∈ Tagent to a real number τik , which is representing the pheromone level. Heterogeneity is
inherently supported. Therefore, the task lists of different agents will be different. The probability to chose
a task P (i) can now be calculated as (with βtask ≥ 1 used for improved exploitation of good solutions):

(τik )βtask
P (i) = (4)
(τj )βtask
P
j∈Tagent

All agents initialize their pheromone level τik = τinit . Afterwards, this level is updated according to the
achieved task:
min(τmax , τik + ∆τ ) if task i was successful

τik = (5)
max(τmin , τik − ∆τ ) otherwise
The routing is performed similar to the techniques proposed in AntNet and AntHocNet except of one
major difference. In order to support the task specific communication, the routing table is extended to
cover different forwarding probabilities for the defined tasks, i.e. a class parameter c is added for each
routing entry for destination d. Accordingly, the forwarding probability is denoted as c Rnd . This allows the
exploitation of task specific communication paths. Basically, this technique can be also used for supporting
different message priorities in the routing process.

4.1.4. Search in Peer-2-Peer Networks

Search in Peer-2-Peer (P2P) networks is usually provided by centralized or decentralized lookup tables.
However, the effort to find data in unstructured decentralized P2P networks can easily become the dom-
inating factor. The use of ant-based approaches in this domain is expected to solve some of the typical
problems.
A self-organized approach for search in P2P networks has been proposed in [55]. The resulting algorithm
is a typical ant-based approach to query routing in P2P networks. It is based on results from addressing
12
the exploitation-exploration dilemma, i.e. the question when to exploit available information and when to
explicitly explore the network. In particular, it exploits the best results known so far for path selection,
or it can explore a path that is not currently known as the best one in order to possibly find an improved
solution to the problem. If it succeeds, this will enhance the performance of the system.
Similarly, Antares, which is also an ant-inspired P2P information system for a self-structured grid [56],
maintains information in a distributed system. Antares uses agents to manage the storage and replication of
data. These agents follow again the concepts of ACO by computing optimized pick and drop probabilities.

4.2. Firefly Synchronization

Precise synchronization in massively distributed systems is a complex issue and hard to achieve. Recently,
new models for clock synchronization have been proposed based on the synchronization principles of fireflies.
In this context, early biological experiments have been conducted by Richmond who also discovered the
underlying mathematical synchronization model [32].
Basically, the firefly synchronization is based on pulse-coupled oscillators [57]. The simple model for
synchronous firing of biological oscillators consists of a population of identical integrate-and-fire oscillators.
A local variable xi is integrated from zero to one and the oscillator fires when xi = 1. Then, the xi jumps
back to zero.
dxi
= S0 − γxi (6)
dt
Multiple oscillators are assumed to interact in form of simple pulse coupling: when a given oscillator
fires, it pulls the others up by a fixed amount , or brings them to the firing threshold, whichever is less.

xi (t) = 1 ⇒ ∀j 6= i : xj (t+ ) = min(1, xj (t) + ) (7)

As a result, for almost all initial conditions the population evolves to a state in which all the oscillators
are firing synchronously.
The presented concept of self-organized clock synchronization has been successfully applied to synchro-
nization in ad hoc networks [58, 59]. Using a linearly incrementing phase function φi , the local pulse of a
node is controlled: when φi reaches a threshold φth , the local oscillator fires. For a period of T , this can be
described as follows:
dφi (t) φth
= (8)
dt T
When coupling identical oscillators, the phase can be controlled according to Equation 7. Additional
effort is needed to compensate the transmission delays in ad hoc and sensor networks. This can be done
by selecting appropriate values for . In particular, the phase shift is dynamically updated according to the
estimated transmission delay.
The general application of this clock synchronization technique for wireless networks is discussed in [60].
The main result is the identification of the so called “deafness problem”, i.e. the problem that nodes cannot
receive and transmit simultaneously. This can be solved by dividing the synchronization cycle into two
parts, one for listening to other firing nodes and one for local phase update and pulse firing. This can easily
be achieved by doubling the original period T to 2T .
Furthermore, synchronization-based data gathering in sensor networks has been presented in [61]. The
idea is to optimize the energy efficiency for periodic data gathering in WSNs. In the described approach,
a base station centric sensor network is issues consisting of concentrically placed sensors. Then, the firefly
based synchronization is used to distribute stimuli for the sensors to measure data and to transmit the
results to the base station. Thus, fully self-organized coordinated sensing can be achieved.
A similar synchronization scheme has been proposed for application in overlay networks [62]. In order
to address the synchronization issue in P2P networks as a result of network dynamics, failures, and scale,
firefly based clock synchronization has been applied as a robust and scalable heartbeat synchronization.

13
4.3. Activator-Inhibitor Systems
The basis for exploiting the characteristics of activator-inhibitor systems in technical systems is the
analysis of reaction-diffusion mechanisms. In the 1950ies, the chemical basis of morphogenesis has been
analyzed [63]. The underlying reaction and diffusion in a ring of cells has been successfully described in
form of differential equations. Assuming that for concentrations of X and Y chemical reactions are tending
to increase X at the rate f (X, Y ) and Y at the rate of g(X, Y ), the changes of X and Y due to diffusion
also take into account the behavior of the entire system, i.e. all the neighboring N cells. Thus, the rate of
such chemical reactions can be described by the 2N differential equations [63] (where r = 1, . . . , N , µ is the
diffusion constant for X and ν is the diffusion constat for Y ):

dXr
= f (Xr , Yr ) + µ(Xr+1 − 2Xr + Xr−1 )
dt
dYr
= g(Xr , Yr ) + ν(Xr+1 − 2Xr + Xr−1 ) (9)
dt
For general application (independent of the shape of the generated pattern or the structure of interacting
systems, this set of differential equations can be written as (with F and G being nonlinear functions for
(chemical) reactions, Du and Dv describe the diffusion rates of activator and inhibitor, and ∇2 is the
Laplacian operator):

du
= F (u, v) − Du ∇2 u
dt
dv
= G(u, v) − Dv ∇2 v (10)
dt
A direct application of Turing’s formula is described in [26]. In this approach, reaction-diffusion pattern
formation is used to support high-level tasks in smart sensor networks. In particular, on-off patterns in
large-scale deployments for forest fire scenarios have been investigated. As a key result, different shapes
have been detected such as stripes, spots, and ring patterns, that can be exploited for high-level activities
such as navigating robots to the source of the fire.
Further experiments and considerations on reaction-diffusion based pattern generation in sensor networks
are described in [64]. Again, reaction-diffusion based control mechanisms have been investigated. Similarly,
cooperative control can be achieved based on reaction-diffusion equation for surveillance system [27].
As can be seen from the mentioned approaches, sensor coordination is one of the primary application
fields for employing activator-inhibitor mechanisms. In the following, two further solutions are depicted
that coordinate sensing activities in WSNs to achieve improved energy performance, i.e. to maximize the
network lifetime [96].
In [65], pattern formation models are used to coordinate the on-off cycles of sensor nodes. In particular,
sensors are allowed to control their sensory and their radio transceiver while, at the same time, the network
needs to be able to transmit sensor data over a multi-hop network to one or more data sinks. In order to
achieve this objective, the sensor field operates as a discrete approximation, in space and in time, of equation
system 10. Each sensor stores its own activator and inhibitor values and broadcasts them every τ seconds.
Using the received data, the neighboring nodes re-evaluate the reaction-diffusion equations. Sensors with a
activator value exceeding some (given) threshold are becoming active by turning on their sensing circuitry.
As shown in [65], the performance of the system achieves astonishing good results.
Similarly, the distributed control of processing periods is investigated in [33, 34]. Using the programming
system Rule-based Sensor Network (RSN) [87], a sensor network is configured for target tracking. In this
example, the duty cycle is controlled by a promoter / inhibitor system that takes into account the efficiency
of the local observations and the results from neighboring nodes. By exploiting the information transmitted
towards a sink node, each node can estimate the need for further local measurements and adequately update
the local sampling period.

14
4.4. Artificial Immune System
The term Artificial Immune System (AIS) refers to a terminology that refers to adaptive systems inspired
by theoretical and experimental immunology with the goal of problem solving [66]. The primary goal of
an AIS, which is inspired by the principles and processes of the mammalian immune system [25], is to
efficiently detect changes in the environment or deviations from the normal system behavior in complex
problems domains.
The role of the mammalian immune system can be summarized as follows: It protects the body from
infections by continuously scanning for invading pathogens, e.g. exogenous (non-self) proteins. AIS based
algorithms typically exploit the immune system’s characteristics of self-learning and memorization. The
immune system is, in its simplest form, a cascade of detection and adaptation, culminating in a system
that is remarkably effective. In nature, two immune responses were identified. The primary one is to
launch a response to invading pathogens leading to an unspecific response (using Leucoytes). In contrast,
the secondary immune response remembers past encounters, i.e. it represents the immunologic memory. It
allows a faster response the second time around showing a very specific response (using B-cells and T-cells).
An AIS basically consists of three parts, which have to be worked out in the immune engineering pro-
cess [66]:
• Representations of the system components, i.e. the mapping of technical components to antigens and
antibodies
• Affinity measures, i.e. mechanisms to evaluate interactions (e.g., stimulation pattern and fitness func-
tions) and the matching of antigens and antibodies
• Adaptation procedures to incorporate the system’s dynamics, i.e. genetic selection
A first AIS has been developed by Kephart [67], and early approaches showing the successful application
of such AISs in computer and communication systems have been presented in [25, 68]. Meanwhile, a number
of frameworks are available. Focusing on the design phase of an AIS, de Castro and Timmis [66] proposed an
immune engineering framework. A similar conceptual frameworks for Artificial Immune Systems for generic
application in networking has been presented in [69]. Again, three steps for designing the framework have
been emphasized: representation, selection of appropriate affinity measures, and development of immune
algorithms. In this framework, Markov chains are used to describe the system’s dynamics.
Data analysis and anomaly detection represent typical application domains [66]. The complete scope of
AISs is widespread. Sample applications have been developed for fault and anomaly detection, data mining
(e.g., machine learning, pattern recognition), agent based systems, control, and robotics. Pioneering work
by Timmis and co-workers needs to be mentioned who conceptually analyzed the AIS and applied it to
several problem domains [69, 11, 36].
An application of an immune system based distributed node and rate selection in sensor networks has been
proposed in [18]. Sensor networks and their capabilities, in particular their transmission rate, are modeled
as antigens and antibodies. The distributed node and rate selection (DNRS) algorithm for event monitoring
and reporting is achieved by B-cell stimulation, i.e. appropriate node selection.. This stimulation depends
on the following influences: (1) the affinity between the sensor node (B-cell) and event source (pathogen),
(2) the affinity between the sensor node and its uncorrelated neighbor nodes (stimulating B-cells), and (3)
the affinity between the sensor node and its correlated neighbor nodes (suppressing B-cells). Thus, this
algorithm exploits also an activator-inhibitor scheme for optimizing the affinity measure in an AIS.
An Artificial Immune System approach to misbehavior detection in MANETs is described in [70]. In
particular, an AIS has been designed to detect misbehavior in Dynamic Source Routing (DSR), a typical
reactive MANET protocol. For the representation of routing events, letters from the alphabet are used,
e.g. “A=RREQ sent” or “E=RREQ received”. Antibodies are represented as received sequences of such
routing events. Then, a matching function can be defined using sequences of those letters, e.g. “Gene 1=#E
in sequence” (refer to [70] for more details). Then, the AIS is used to identify a node as “suspicious” if a
corresponding antigen is matching any antibody. Furthermore, a node is classified as “misbehaving” if the
probability that the node is suspicious, estimated over a sufficiently large number of data sets, is above a
threshold.
15
4.5. Epidemic Spreading
Epidemic spreading is frequently used as an analogy to understand the information dissemination in wire-
less ad hoc networks. Information dissemination in this context can refer to the distribution of information
particles (as usually provided by ad hoc routing techniques) [21, 23] or to the spread of viruses in the Inter-
net [71, 72] or on mobile devices [73]. Biological models of virus transmission provide means for assessing
such emerging threats and to understand epidemics as a general purpose communication mechanism.
A number of mathematical models of the different networks have been investigated that lie at various
points on a broad conceptual spectrum. At one end are network models that reflect strong spatial effects,
with nodes at fixed positions in two dimensions, each connected to a small number of other nodes a short
distance away. At the other end are scale-free networks, which are essentially unconstrained by physical
proximity, and in which the number of contacts per node are widely spread. The main difference is in the
epidemic spread. In scale-free networks, epidemics can persist at arbitrarily low levels, whereas in simple
two-dimensional models a minimum level of virulence is needed to prevent them from dying out quickly [73].
The system model for epidemic communication relies on a population, i.e. a number of nodes that
represent the network. Information entities are exchanges among the nodes using a diffusion algorithm.
All transmissions are usually assumed to be atomic, i.e. there will be no split during diffusion. Then, all
the nodes can be distinguished into two groups: susceptible nodes, S(t) describes this set at a certain time
t, and infective nodes I(t) [74]. The diffusion algorithm is then a process that converts susceptible nodes
into infective nodes with a rate α = βx N I(t), where β is the probability of information transmission, i.e.
the infection probability, x describes the number of contacts among susceptible nodes, and N is the total
number of nodes. The infection rate can then be described as:
dI βx
= α × S(t) = I(t) × S(t) (11)
dt N
A measure for the connectedness of the nodes is termed eigenvector centrality. Let us consider a graph
model of the network topology and denote by A the corresponding adjacency matrix. The eigenvector
centrality of a node i is defined being proportional to the sum of the eigenvector centralities of i’s neighbors,
where e represents the vector of nodes’ centrality scores. Otherwise stated, e is the eigenvector of A relative
to the eigenvalue λ:
A×e
ei = (12)
λ
Depending on the particular application scenario, the healing rate, i.e. the non-negative rate of converting
infective nodes, also needs to be considered in this equation.
There is a wide application range for epidemic communication in computer networks. Primarily, the
focus is on routing in mobile ad hoc networks with growing interest in opportunistic routing [75], in which
messages are passed between devices that come into physical proximity, with the goal of eventually reaching
a specified recipient.
For example, the understanding of the spread of epidemics in highly partitioned mobile networks has been
studied in [23]. The main application field in this work was the use of epidemic communication in DTNs. As
a conclusion, the paper outlines possibility to roughly measure the importance of a node to the process of
epidemic spreading by the node’s eigenvector centrality. Regions, as defined by the steepest-ascent rule, are
clusters of the network in which spreading is expected to be relatively rapid and predictable. Furthermore,
nodes whose links connect distinct regions play an important role in the (less rapid, and less predictable)
spreading from one region to another.
The characteristics of epidemic information dissemination have been carefully modeled to investigate the
inherent characteristics [76]. For example, the buffer management plays and important role and a stepwise
probabilistic buffering has been proposed as a solution [77].
Detailed models have been built to study the performance impact of epidemic spreading [78] Whereas
Markov models lead to quite accurate performance predictions, the numerical solution becomes impractical
if the number of nodes is large. In [78], an unified framework based on ordinary differential equations is
presented that provides appropriate scaling as the number of nodes increases. This approach allows to derive
16
closed-form formulas for the performance metrics while obtaining matching results compared to the Markov
models.
In this view, the power of epidemics for robust communication in large-scale networks has been investi-
gated by quite a number of approaches [21, 22, 79]. The interesting result is that the network topology plays
an important role whether epidemics can be applied for improved robustness and efficiency. In particular,
the scale-free property must be ensured in order to overcome possible problems with transmissions that
quickly die out.
A slightly different problem (and solution) has been addressed in [80]. The targeted question is that the
problem of determining the right information collection infrastructure can be viewed as a variation of the
network design problem – including additional constraints such as energy efficiency and redundancy. As the
general problem is NP-hard, the authors propose a heuristic based on the mammalian circulatory system,
which results in a better solution to the design problem than the state-of-the-art alternatives. The resulting
circulatory system approach for wireless sensor networks is quite similar to the epidemics approach even
though only the communication within an organism is used as an analogy.
Besides efficient routing solutions, the application to network security is probably the most important
aspect of epidemic models. The spread of Internet worms has been studied recently with astonishing re-
sults [72, 81, 71].

4.6. Cellular Signaling Networks

Basically, the term signaling describes the interactions between single signaling molecules [82]. Such
communication, also known as signaling pathways [42, 83], is an example for very efficient and specific
communication. Cellular signaling occurs at multiple levels and in many shapes.
Briefly, cellular interactions can be viewed as processing in two steps. Initially, an extracellular molecule
binds to a specific receptor on a target cell, converting the dormant receptor to an active state. Subsequently,
the receptor stimulates intracellular biochemical pathways leading to a cellular response [83]. In general,
the following two cellular signaling techniques can be distinguished [84].
Intracellular signaling – The signal from the extracellular source is transferred through the cell mem-
brane. Inside of the target cell, complex signaling cascades are involved in the information transfer (signal
transduction), which finally result in gene expression or an alteration in enzyme activity and, therefore,
define the cellular response.
Intercellular signaling – Cells can communicate via cell surface molecules. In this process, a surface
molecule of one cell or even a soluble molecule, which is released by one cell, directly binds to a specific
receptor molecule on another cell. Soluble molecules such as hormones can also be transported via the blood
to remote locations.
A key challenge for biology is to understand the structure and the dynamics of the complex web of
interactions that contribute to the structure and function of a living cell. In order to uncover the structural
design principles of such signaling networks, network motifs have been defined as patterns of interconnec-
tions occurring in complex networks at numbers that are significantly higher than those in randomized
networks [85].
A couple of approaches have been discussed using artificial signaling networks. Most of this work is
targeting programming schemes for massively distributed systems such as sensor networks. In the following,
two of the most successful approaches will be introduced: RSN and Fraglets. Another approach is on
parallel execution of IF-THEN constructs using artificial cell signaling networks with molecular classifier
systems [86].

4.6.1. Rule-based Sensor Network

Rule-based Sensor Network (RSN) is a light-weight programming scheme for SANETs [87, 88]. It is
based on an architecture for data-centric message forwarding, aggregation, and processing, i.e. using self-
describing messages instead of network-wide unique address identifiers. It has been shown that RSN can
outperform other SANET protocols for distributed sensing and network-centric data pre-processing in two
dimensions: (a) reactivity of the network, i.e. the response times for network-controlled actuation can

17
 Rsn node behavior

return
modify
Incoming messages

Message
buffer Working Action send
set 1 set

Working drop actuate

set 2
Source
set

Working
Δt set n

Figure 3: The working behavior of a single RSN node. Received messages are stored in a buffer. After ∆t, they are selected to
a working set according to specific criteria, and finally being processed, i.e. forwarded, dropped, etc.

be reduced, and (b) communication overhead, i.e. the bandwidth utilization on the wireless transmission
channels was improved.
Figure 3 depicts the working behavior of a single RSN node. After receiving a message, it is stored in a
message buffer. The rule interpreter is either started periodically (after a fixed ∆t) or after the reception of
a new message. An extensible and flexible rule system is used to evaluate received messages and to provide
the basis for the node programming scheme. The specific reaction on received data is achieved by means of
predicate-action sequences of the form if PREDICATE then { ACTION }.
First, all messages matching the predicate are stored in so called working sets. Finally, the specified
action is executed on all the messages in the set. Using such rule-sets, complex and dynamic behavior can
be modeled. Examples are event monitoring applications in sensor networks or target tracking under energy
constraints. In biological systems such behavior can be modeled (or studied) using signaling networks and
repetitive patterns, or motifs.
The period of RSN execution ∆t has been identified as a key parameter for controlling the reactivity
vs. energy performance of the entire RSN-based network. Basically, the duration of messages stored in the
local node introduces an artificial per-hop delay. The optimal value for ∆t affects the aggregation quality
vs. real-time message processing. A promoter-inhibitor system has successfully been applied to solve this
issue [34] (see Section 4.3).

4.6.2. Fraglets
A metabolistic execution model for communication protocols was named Fraglets [89]. Similar to RSN,
this model is also based on the concept of data-centric communication. Furthermore, the execution relies
on the unification of code and data, featuring a single unit called “fraglets” that are operands as well
as operators. Fraglets have surprising strong ties to formal methods as well as to molecular biology. At
the theory level, fraglets belong to string rewriting systems. In particular, fraglets are symbol strings
[s1 : s2 : . . . tail] that represent data and/or logic, where tail is a (possibly empty) sequence of symbols. Each
node in the network has a fraglet store to which incoming fraglets are added. The node continuously examines
the fraglet store and identifies which fraglets need to be processed. Simple actions lead to transformations
of a single fraglet. More complex actions combine two fraglets. If several actions are possible at a time, the
system randomly picks one action, atomically removes the involved fraglets from the store, processes them,
and puts potential results back into the store [89].
Table 2 lists some typical (selected) rules for fraglet transformation and reaction on events.
Using the fraglet system, network-centric operations can be specified to be executed by participat-
ing nodes after reception of a specific fraglet. A simple example of a fraglet program is the following
confirmed-delivery protocol (CDP) that transfers received [cdp : data] fraglets from A to B, with per packet
acknowledgments [89]:
A [matchP : cdp : send : B : deliver]
B [matchP : deliver : split : send : A : ack : ∗]
Further research on fragets has been conducted w.r.t. resilience and robustness [90],self-modifying and
self-replicating programs using fraglets [91], and the extensibility of the fraglet system, e.g. cryptographic
primitives have been added to provide security measures for the fraglets system [92].
18
 Op Input Output
Transformation rules
nul [nul : tail] - (fraglet is removed)
dup [dup : t : u : tail] [t : u : u : tail]
split [split : t : . . . : ∗ : tail] [t : . . .], [tail]
send A [send : B : tail] B [tail]
Reaction rules
match [match : s : tail1 ], [tail1 : tail2 ]
(merge) [s : tail2 ]
matchP [matchP : s : tail1 ], [tail1 : tail2 ]
(persist) [s : tail2 ] [matchP : s : tail1 ]

Table 2: Typical fraglet transformation and reaction rules [89], where ∗ is a position marker for splitting fraglets and X [. . .]
specifies the place where a fraglet is stored

5. Nano-scale and Molecular Communication

Incredible improvements in the field of nano-technologies have enabled the nano-scale machines that
promise new solutions for several applications in biomedical, industry and military fields. Some of these
applications require or might exploit the potential advantages of communication and hence cooperative
behavior of these nano-scale machines to achieve a common and challenging objective that exceeds the ca-
pabilities of a single device. At this point, the term “nanonetworks” is defined as a set of nano-scale devices,
i.e., nano-machines, communicating with each other and sharing information to realize a common objective.
Nanonetworks allow nano-machines to cooperatively communicate and share any kind of information such as
odor, flavor, light, or any chemical state in order to achieve specific tasks required by wide range of applica-
tions including biomedical engineering, nuclear, biological, and chemical defense technologies, environmental
monitoring.
Despite the similarity between communication and network functional requirements of traditional and
nano-scale networks, nanonetworks bring a set of unique challenges. In general, nano-machines can be
categorized into two types: one type mimics the existing electro-mechanical machines and the other type
mimics nature-made nano-machins, e.g., molecular motors and receptors. In both types, the dimensions
of nano-machines render conventional communication technologies such as electromagnetic wave, acoustic,
inapplicable at these scales due to antenna size and channel limitations. In addition, the available memory
and processing capabilities are extremely limited, which makes the use of complex communication algorithms
and protocols impractical in nano regime.
Furthermore, the communication medium and the channel characteristics also show important deviations
from the traditional cases due to the rules of physics governing these scales. For example, due to size
and capabilities of nano-machines, traditional wireless communication with radio waves cannot be used to
communicate nano-machines that may constitute of just several moles of atoms or molecules and scale on
the orders of few nanometers. Hence, these unique challenges need to be addressed in order to effectively
realize the nano-scale communication and nanonetworks in many applications from nano-scale body area
networks to nano-scale molecular computers.
The motivation behind nano-machines and nano-scale communications and networks have also originated
and inspired by the biological systems and processes. In fact, nanonetworks are significant and novel
artifacts of bio-inspiration in terms of both their architectural elements, e.g., nano-machines, and their
principle communication mechanism, i.e., molecular communication. Indeed, many biological entities in
organisms have similar structures with nano-machines, i.e., cells, and similar interaction mechanism and
vital processes, cellular signaling [42], with nanonetworks. Within cells of living organisms, nano-machines
called molecular motors, e.g., dynein, myosin [97], realize intracellular communication through chemical
energy transformation. Similarly, as explained in Section 4.6, within a tissue, cells communicate with each
other through the release over the surface and the diffusion of certain soluble molecules, and its reception
as it binds to a specific receptor molecule on another cell [84].
19
 Apparently, cellular signaling networks are the fundamental source of inspiration for the design of
nanonetworks. Therefore, the solution approaches for the communication and networking problems in
nanonetworks may also be inspired by the similar biological processes. The main communication mecha-
nism of cellular signaling is based on transmission and reception of certain type of molecules, i.e., molecular
communication, which is, indeed, the most promising and explored communication mechanism for nanonet-
works.
In nature, molecular communication between biological entities takes place according to the ligand re-
ceptor binding mechanism. Ligand molecules are emitted by one biological phenomenon then, the emitted
ligand molecules diffuse in the environment and bind the receptors of another biological phenomenon. This
binding enables the biological phenomenon to receive the bound molecules by means of the diffusion on
cell membrane. The received ligand molecules allow the biological phenomenon to understand the biolog-
ical information. For example, in biological endocrine system, gland cells emit hormones to intercellular
environment then, hormone molecules diffuse and are received by corresponding cells. According to the
type of emitted hormone, the corresponding cells convert the hormone molecule to biologically meaning-
ful information. This natural mechanism provides the molecular communication for almost all biological
phenomena.
Following the main principles of this mechanism, a number of studies have been performed on the design of
nano-scale communication. Molecular communication and some design approaches are introduced [98],and
its fundamental research challenges are first manifested in [99]. Different mechanisms are proposed for
molecular communication including a molecular motor communication system [100], intercellular calcium
signaling networks [43], an autonomous molecular propagation system to transport information molecules
using DNA hybridization and bio-molecular linear motors. An information theoretical analysis of a single
molecular communication channel is performed in [5]. An adaptive error compensation mechanism is devised
for improving molecular communication channel capacity in [101]. In [102], molecular multiple-access, relay
and broadcast channels are modeled and analyzed in terms of capacity limits and the effects of molecular
kinetics and environment on the communication performance are investigated. Based on the use of vesicles
embedded with channel forming proteins, a communication interface mechanism is introduced for molecular
communication in [103, 104]. In addition, wide range of application domains of molecular communication
based nanonetworks are introduced from nano-robotics to future health-care systems [105].
Clearly, inspired by biological systems, molecular communication, which enable nano-machines to com-
municate with each other using molecules as information carrier, stands as the most promising communica-
tion paradigm for nanonetworks.3 While some research efforts and initial set of results exist in the literature,
many open research issues remain to be addressed for the realization of nanonetworks.
Among these, first is the thorough exploration of biological systems, communications and processes,
in order to identify different efficient and practical communication techniques to be inspired by and ex-
ploited towards innovative nanonetwork designs. The clear set of challenges for networked communication
in nano-scale environments must be precisely determined for these different potential bio-inspired solution
avenues. Applicability of the traditional definitions, performance metrics and well-known basic techniques,
e.g., Time Division Multiple Access (TDMA), random access, minimum cost routing, retransmission, error
control, congestion, must be studied. Furthermore, potential problems for the fundamental functionalities
of nanonetworks, such as modulation, channel coding, medium access control, routing, congestion control,
reliability, must be investigated without losing the sight of the bio-inspired perspective in order to develop
efficient, practical and reliable nanonetwork communication techniques through inspiration from the existing
biological structures and communication mechanisms.

6. Conclusion

The realization of most of the existing and the next generation networks, e.g., cognitive radio networks,
sensor and actor networks, quantum communication networks, vehicular communication networks, terrestrial

3A comprehensive survey of nanonetworks with molecular communication can be found in [41].

20
 Table 3: Current research projects on bio-inspired networking
Project name Funding Research area URL
ANA EU FET Autonomic network architecture and http://www.ana-project.org/
principles
BioNet NSF, DARPA Bio-networking architecture for design http://netresearch.ics.uci.edu/bionet/
and implementation of scalable, adap-
tive, survivable/available network ap-
plications
BIONETS EU FET Bio-inspired service evolution for the http://www.bionets.eu/
pervasive age
CASCADAS EU FET Autonomic and situation-aware com- http://www.cascadas-project.org/
munications, and dynamically adapt-
able services
ECAgents EU FET Embodied and communicating agents http://ecagents.istc.cnr.it/
interacting directly with the physical
world
Haggle EU FET Situated and autonomic communica- http://www.haggleproject.org/
tions
MC NSF, DARPA Molecular communication as a solution http://netresearch.ics.uci.edu/mc/
for communication between nanoma-
chines
Swarmanoid EU FET Design, implementation and control of http://www.swarmanoid.org/
a novel distributed robotic system
Swarm-bots EU FET Design and implementation of self- http://www.swarm-bots.org/
organizing and self-assembling arti-
facts
WASP EU IP Self-organization of nodes and services http://www.wasp-project.org/
in WSNs

next generation Internet, and InterPlaNetary Internet, have many common significant barriers such as
the increased complexity with large scale networks, dynamic nature, resource constraints, heterogeneous
architectures, absence or impracticality of centralized control and infrastructure, need for survivability and
unattended resolution of potential failures. At the same time, there exist many biological systems and
processes with intrinsic appealing characteristics such as adaptivity to varying environmental conditions,
inherent resiliency to failures and damages, successful and collaborative yet practical and simple operation,
self-organization, survivability, and evolvability.
In this paper, the common fundamental networking challenges, the current status of research efforts
to address them from the perspective of bio-inspired networking is captured. Researchers have started to
realize the significance and potentials of bridging the gap between the these two distinct domains under the
cross-disciplinary field of bio-inspired networking. Through the existing research results, it has been shown
that the inspiration from biology is, indeed, a powerful source of innovative network design.
A list of current active related research projects and dissemination tools for the related research results
are provided in Table 3 and 4, respectively. Inevitably, these lists cannot cover all projects and activities
related to bio-inspired networking. Furthermore, some of the major conferences and workshops as well as
journals and special issues specifically devoted to the field are listed in Table 4. As the topic of bio-inspired
networking is meanwhile listed in the scopes and programs of most networking conferences, the list aims to
emphasize on the events that have been specifically established by the bio-inspired research community.
Despite the considerable amount of ongoing research in this direction, the bio-inspired networking re-
search community is quite young, and there still remains significantly challenging tasks for the research
community to address fundamental challenges for the realization of many existing and most of the emerging
networking architectures. With this regard, a vast space of biological systems, which still remains unex-
plored, needs to be thoroughly investigated in order to discover their artifacts to be used towards accelerating
the evolution in the information and communication technologies domain. We anticipate that this survey
will provide better understanding of the potentials for bio-inspired networking, which is currently far from
being fully utilized, and to motivate research community to further explore this timely and exciting topic.
21
 Table 4: Conferences, workshops, journals and special issues on bio-inspired networking
Name of the event URL
Conferences and workshops
Bionetics International Conference on Bio inspired http://www.bionetics.org/
Models of Network, Information and Com-
puting Systems
Biowire Workshop on Bio-inspired Design of Wire- http://www.usukita.org/?q=node/225
less Networks and Self-Organising Net-
works
EvoCOMNET European Workshop on Nature-inspired http://www.evostar.org/
Techniques for Telecommunications and
other Parallel and Distributed Systems
Bionetworks Workshop on Socially and Biologically In- http://san.ee.ic.ac.uk/bionets07/
spired Wired and Wireless Networks (co-
located with IEEE MASS 2007)
BLISS The 2008 ECSIS Symposium on Bio- http://www.see.ed.ac.uk/bliss08/
inspired, Learning, and Intelligent Systems
for Security
BADS International Workshop on Bio-Inspired http://bads.icar.cnr.it/
Algorithms for Distributed Systems (co-
located with IEEE ICAC 2009)
Journals and special issues
ICST Transactions on Bio-Engineering and Bio- http://www.icst.org/
inspired Systems
Journal of Bio-Inspired Computation Research http://www.ripublication.com/jbicr.
(JBICR) htm
Inderscience International Journal of Bio-Inspired http://www.inderscience.com/ijbic
Computation (IJBIC)
Elsevier Ad Hoc Networks
http://www.elsevier.com/locate/adhoc
Special Issue on Bio-inspired Computing
and Communication in Wireless Ad Hoc
and Sensor Networks
IEEE Journal on Selected Areas in Communications
http://www.jsac.ucsd.edu/
(JSAC)
Special Issue on Bio-inspired Networking
Springer Transactions on Computational Systems Bi-
http://www.springer.com/series/7322
ology (TCSB)
Special Issue on Biosciences and Bio-
inspired Information Technologies
Springer Soft Computing http://www.springer.com/engineering/
Special Issue on Distributed Bio-inspired journal/500
Algorithms
Springer Swarm Intelligence http://www.springer.com/computer/
Special Issue Swarm Intelligence for artificial/journal/11721
Telecommunications Networks
Inderscience International Journal of Autonomous
http://www.inderscience.com/ijaacs
and Adaptive Communications Systems (IJAACS)
Special Issue on Bio-inspired Wireless Net-
works

22
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