IOT UNIT 3

TO DO:
1. Wireless Medium access issues
2. MAC protocol survey
3. Survey routing protocols
4. Sensor deployment & Node discovery
5. Data aggregation & dissemination

# Wireless Medium Access Issues in IoT

In IoT, devices and sensors often communicate wirelessly to share data. Since these devices
share a common wireless medium (e.g., Wi-Fi, Bluetooth, ZigBee), efficient medium access
is crucial to ensure smooth communication and prevent issues like data collisions or
interference.

Key Issues in Wireless Medium Access

1. Limited Bandwidth
o IoT devices compete for a finite wireless spectrum.
o Bandwidth congestion occurs when many devices try to communicate
simultaneously, leading to slow data rates and poor performance.
2. Data Collisions
o When two or more devices transmit data at the same time, the signals
interfere, causing data corruption.
o This is a major problem in shared communication channels like Wi-Fi or ZigBee.
3. Energy Constraints
o IoT devices (e.g., sensors) are often battery-powered, so minimizing energy
consumption during wireless communication is critical.
o Repeated retransmissions (due to collisions) or idle listening waste energy.
4. Heterogeneous Devices
o IoT systems involve devices with different communication protocols, speeds,
and ranges (e.g., ZigBee, LoRa, Wi-Fi, BLE).
o Ensuring compatibility and seamless communication between these diverse
devices is challenging.
5. Latency (time delay in transmitting data from sender -> receiver)
o Real-time IoT applications (e.g., smart traffic systems, healthcare monitoring)
require low latency.
o Medium access delays caused by contention (devices waiting to transmit) can
affect system performance.
 6. Scalability
o With potentially thousands of IoT devices connected, managing medium access
becomes complex.
o Wireless protocols must scale efficiently without degrading performance as the
number of devices increases.
7. Interference
o External wireless devices, environmental obstacles, and radio frequency (RF)
noise can interfere with IoT communications.
o Such interference reduces signal quality and reliability.

Techniques to Address Wireless Medium Access Issues

To address these challenges, various Medium Access Control (MAC) protocols are used:
1. Carrier Sense Multiple Access (CSMA)
o Before sending data, a device “listens” to check if the channel is free. If the
channel is busy, the device waits and retries.
o Used in Wi-Fi networks (IEEE 802.11).
2. Time Division Multiple Access (TDMA)
o Devices are assigned specific time slots to transmit data.
o This avoids collisions and improves energy efficiency.
3. Frequency Division Multiple Access (FDMA)
o The frequency spectrum is divided into separate channels, and each device is
assigned a unique frequency band.
o Reduces interference but requires more spectrum resources.
4. Code Division Multiple Access (CDMA)
o Devices share the same frequency but use unique codes to differentiate their
signals.
o Offers better scalability and resistance to interference.
5. Slotted ALOHA
o Devices transmit only at fixed time slots to reduce collisions.
o Useful for low-traffic scenarios.
6. ZigBee and LoRaWAN
o Protocols like ZigBee and LoRa optimize energy-efficient communication for
low-power, low-data IoT applications.

Practical Example
Imagine a smart home IoT system:
• Devices like smart lights, sensors, and thermostats communicate via Wi-Fi or ZigBee.
• If multiple devices send data simultaneously, there could be collisions and delays.
• Using protocols like CSMA ensures devices listen for free channels before
transmitting, minimizing collisions and improving communication efficiency.
# MAC Protocol Survey in IoT

The Medium Access Control (MAC) layer is part of the Data Link Layer in the OSI model. Its
main role is to control how multiple devices share a communication medium (wireless or
wired) efficiently and without interference. In IoT, where numerous resource-constrained
devices (sensors, actuators, etc.) operate, choosing an appropriate MAC protocol is critical
for energy efficiency, reliability, and scalability.

Why MAC Protocols Are Important for IoT

• IoT devices share a common medium (e.g., radio frequencies in wireless networks).
Without proper access control, communication can experience data collisions and
delays.
• Many IoT devices are battery-powered, requiring MAC protocols to minimize energy
consumption.
• IoT networks often operate under low data rates and limited bandwidth, requiring
protocols that optimize throughput.

Types of MAC Protocols in IoT

There are different MAC protocols designed to suit the specific requirements of IoT
networks. These can be categorized based on how they access the channel:

Types of MAC protocols

Contention based Schedule based Hybrid

CSMA TDMA SMAC

ALOHA (slotted FDMA TMAC

& pure)

CDMA ZMAC

1. Contention-Based MAC Protocols

In contention-based protocols, devices compete for access to the communication channel. If
two devices try to send data simultaneously, a collision occurs. These protocols are simple
and suitable for low-traffic networks.
• Carrier Sense Multiple Access (CSMA):
o Devices "sense" the medium before transmitting to check if it is free.
o Example: CSMA/CA (Collision Avoidance) used in Wi-Fi.
o Pros: Simple and easy to implement.
 oCons: High collision probability in dense IoT networks.
• ALOHA (Pure and Slotted):
o Devices transmit data whenever they have data to send (Pure ALOHA), or at
specific time slots (Slotted ALOHA).
o Pros: Works in simple IoT setups with low traffic.
o Cons: High collision rates reduce efficiency.

2. Scheduled-Based MAC Protocols

These protocols assign dedicated access to devices, avoiding contention and collisions.
Scheduling ensures that each device knows when to transmit.

• Time Division Multiple Access (TDMA):

o Devices are assigned fixed time slots for communication.
o Example: Used in Bluetooth and industrial IoT systems.
o Pros: Collision-free, energy-efficient for periodic transmissions.
o Cons: Not suitable for dynamic or asynchronous traffic.
• Frequency Division Multiple Access (FDMA):
o Divides the frequency spectrum into separate channels, and each device gets a
unique frequency band.
o Pros: Collision-free, suitable for long-range IoT communication.
o Cons: Requires more spectrum resources.
• Code Division Multiple Access (CDMA):
o Devices share the same frequency but use unique codes to differentiate their
signals.
o Pros: CDMA allows multiple users on the same channel, making it highly
bandwidth-efficient.
o Cons: Requires advanced coding and decoding algorithms.

3. Hybrid MAC Protocols

Hybrid MAC protocols combine features of contention-based and scheduled-based
protocols to balance energy efficiency, latency, and throughput.
• Z-MAC (Zebra MAC):
o Combines CSMA for low-traffic networks and TDMA for high-traffic situations.
o Suitable for IoT networks with varying traffic loads.
o Pros: Adaptive and energy-efficient.
• SMAC (Sensor-MAC):
o Designed for IoT sensor networks with periodic data transmissions.
o Implements sleep cycles to save energy.
o Pros: Great for low-power IoT applications.
 • TMAC (Timeout-MAC):
o Similar to SMAC but allows dynamic adjustment of sleep cycles based on
network traffic.
o Pros: Improved energy efficiency compared to SMAC.

MAC Protocol Comparison Based on IoT Requirements

MAC Protocol Energy Efficiency Latency Scalability Collision Avoidance

CSMA Low Medium Moderate Partial
TDMA High Low High Yes
ALOHA Low High Low No
Z-MAC High Low High Yes
SMAC High Low Moderate Partial

Practical Example of MAC Protocols in IoT

1. Smart Agriculture:
o Sensors monitor soil moisture and temperature.
o For periodic, low-energy data transmissions, protocols like TDMA or SMAC are
used to save power.
2. Smart Home (Wi-Fi-based IoT):
o Devices like smart bulbs, thermostats, and cameras use CSMA/CA to avoid
collisions.
3. Smart Traffic Systems:
o Real-time communication between vehicles and traffic lights requires low-
latency protocols like Z-MAC or TDMA.

Conclusion
The MAC protocol survey in IoT highlights the importance of choosing an appropriate
protocol based on the application's requirements (energy efficiency, latency, and scalability).
Contention-based protocols like CSMA are suitable for low-traffic networks, while
scheduled-based protocols like TDMA excel in time-sensitive applications. Hybrid protocols
like Z-MAC offer flexibility for dynamic IoT networks.
# Survey Routing Protocols

Survey routing protocols deal with collecting information about the nodes, network, and
communication paths in a wireless sensor network (WSN). These protocols help identify and
optimize the routing paths for transmitting data efficiently.

Key Concepts of Survey Routing Protocols

1. Purpose:
o These protocols are designed to survey the network to gather information
about:
▪ Node availability
▪ Node location
▪ Energy levels of nodes
▪ Communication links between nodes
o Based on this information, optimal routing paths are selected.
2. Importance:
o Helps identify the most reliable and energy-efficient paths for data
transmission.
o Reduces packet loss and ensures network longevity, which is crucial for sensor
networks.

Mechanism:
• In Survey Routing Protocols, nodes periodically send control packets or status
messages to neighbouring nodes.
• These control packets include:
o Node ID
o Energy status
o Link quality information
o Distance or hop count to other nodes
• The network uses this information to update routing tables dynamically.
For example:
• Nodes survey their neighbours to discover:
o Who is in range?
o What is the energy status of other nodes?
o Which link provides the best quality?

Role in IoT and Wireless Networks:

• Survey routing plays a key role in improving data transmission efficiency in networks
like:
o Wireless Sensor Networks (WSN): Nodes collect and send environmental data
efficiently.
 o Ad-hoc Networks: Helps manage dynamic connections in networks without
fixed infrastructure.

Advantages:
• Energy Efficiency: Identifies energy-efficient routes to reduce node energy
consumption.
• Reliability: Improves data delivery reliability by selecting strong links.
• Adaptability: Adjusts to changing network conditions, such as node failures or energy
depletion.

Routing Protocols
Routing protocols define the rules and procedures to determine the path for data
transmission between nodes in a network. They play a crucial role in enabling efficient,
reliable, and optimized delivery of data, especially in Wireless Sensor Networks (WSNs) and
IoT systems.
Routing protocols are categorized based on their functionality, objective, and design
philosophy.

Types of Routing Protocols

1. Node-Centric Routing Protocols
• Focuses on the nodes as the primary participants in routing.
• The destination node is identified by unique numeric identifiers like Node IDs.
• Nodes often have to coordinate and manage their resources for efficient data routing.
Example:
• LEACH (Low Energy Adaptive Clustering Hierarchy):
o Organizes nodes into clusters to balance energy consumption.
o A cluster head collects and aggregates data from member nodes, reducing
redundant transmissions.

2. Data-Centric Routing Protocols

• Focuses on the data rather than specific nodes.
• Instead of targeting a specific node, routing is done based on attributes or content of
the data.
• Designed to eliminate redundancy and optimize data transmission.
Example:
• SPIN (Sensor Protocol for Information via Negotiation):
o Nodes negotiate before sending data to eliminate redundant information.
o Helps overcome issues like flooding and gossiping.

3. Source-Initiated Routing Protocols

• The routing process is initiated by the source node.
 • Routes are discovered dynamically on-demand when data needs to be sent.
• These protocols are reactive because they do not maintain routes unless required.
Examples:
• DSR (Dynamic Source Routing):
o Uses source routing where the sender specifies the full path to the destination.
• AODV (Ad-hoc On-demand Distance Vector):
o Discovers routes only when necessary.
o Particularly useful for mobile ad-hoc networks where the network topology
changes frequently.

4. Destination-Initiated Routing Protocols

• The routing process is initiated by the destination node.
• The destination generates the route request, and nodes in the network respond.
Examples:
• Directed Diffusion:
o The destination node broadcasts its interest in specific types of data.
o Nodes with matching data reply, and the best path is selected.
• LEACH (can also fit here):
o Balances energy among nodes for routing efficiency.

Key Differences Between Routing Protocols

Type Focus Trigger Examples
Node-Centric Nodes as primary participants Node ID-based routing LEACH
Data-Centric Data attributes/content Data-focused routing SPIN
Source-Initiated Source node starts process On-demand routing DSR, AODV
Destination-Initiated Destination node starts Interest from destination Directed Diffusion, LEACH

Conclusion
Routing protocols are essential in any network, particularly in Wireless Sensor Networks
and IoT systems, to determine optimal data transmission paths.
• Node-centric protocols focus on node IDs.
• Data-centric protocols prioritize data content.
• Source-initiated and destination-initiated protocols differ based on which node starts
the routing process.
The choice of routing protocol depends on the application requirements, such as energy
efficiency, scalability, and network dynamics.
# Sensor Deployment and Node Discovery in IoT and Wireless Sensor
Networks (WSNs)

In IoT systems and Wireless Sensor Networks (WSNs), sensor deployment and node
discovery are critical components for ensuring efficient communication, proper coverage,
and network functionality. These processes help establish connectivity among nodes,
enabling reliable data transfer within the network.

1. Sensor Deployment
Sensor deployment refers to positioning sensor nodes in a WSN to maximize coverage,
energy efficiency, and network longevity. Sensors are placed in a target area to monitor
environmental conditions, such as temperature, pressure, or motion.
Types of Sensor Deployment
1. Deterministic Deployment:
o Sensor nodes are placed manually or using a pre-determined pattern.
o Example: In applications like smart buildings, sensors are placed in fixed
positions to ensure proper coverage.
o Advantages: Controlled placement, uniform coverage.
o Disadvantages: Not scalable for large, inaccessible areas.
2. Random Deployment:
o Sensors are deployed randomly (e.g., dropped by drones or scattered in
remote areas).
o Example: In forest fire detection or military applications where manual
placement is not possible.
o Advantages: Suitable for large-scale, unstructured environments.
o Disadvantages: May result in uneven coverage and require post-deployment
adjustments.
Challenges in Sensor Deployment
• Coverage: Ensuring that all areas are monitored without any gaps.
• Connectivity: Maintaining communication between nodes to ensure data transfer.
• Energy Efficiency: Optimizing the placement to minimize energy consumption for
data transmission.
• Scalability: Accommodating additional nodes without disturbing the existing
network.
• Cost Constraints: Reducing costs for sensor installation and maintenance.

2. Node Discovery
Node discovery is the process of identifying and registering nodes (sensor devices) within
the network. It is essential to establish communication and ensure proper operation of the
network.
Steps in Node Discovery
 1. Node Initialization:
o When a sensor node is powered on, it broadcasts a HELLO message or a
beacon signal.
o This message includes the node’s unique identifier (Node ID), position, and
status.
2. Neighbor Discovery:
o Nodes detect other nodes within their communication range.
o A node maintains a list of neighbors by exchanging HELLO messages
periodically.
3. Topology Formation:
o Based on the discovered nodes, a network topology is formed, such as a star,
mesh, or tree topology.
o This step establishes routes for data transmission.
4. Node Registration:
o The discovered nodes register themselves with a central sink node (gateway)
or controller, completing the discovery process.

Types of Node Discovery Protocols

Node discovery protocols are essential for identifying neighbors and ensuring connectivity
in dynamic networks.
1. Proactive Node Discovery:
o Nodes periodically broadcast messages to discover neighbors, even if no data
transfer is required.
o Suitable for static or low-mobility networks.
o Example: Table-driven protocols like Destination-Sequenced Distance Vector
(DSDV).
2. Reactive Node Discovery:
o Nodes initiate the discovery process only when data needs to be transmitted.
o Saves energy compared to proactive methods.
o Example: Ad-hoc On-demand Distance Vector (AODV) protocol.
3. Hybrid Node Discovery:
o Combines proactive and reactive approaches for better performance.
o Nodes periodically exchange messages but also initiate discovery when
required.

Challenges in Node Discovery

• Energy Consumption: Broadcasting messages consumes power, which is critical in
battery-powered nodes.
• Scalability: In large networks, handling many nodes increases complexity.
• Dynamic Topology: In mobile networks, frequent changes in node positions require
constant updates.
 • Latency: Delays in discovering nodes can affect real-time applications.
• Collision of Messages: Multiple nodes sending discovery messages simultaneously
can cause interference.

Importance of Sensor Deployment and Node Discovery

• Network Efficiency: Proper deployment ensures complete coverage, and node
discovery establishes efficient communication.
• Energy Optimization: Reduces redundant communication and prolongs the network
lifetime.
• Scalability: Supports the addition of new nodes seamlessly.
• Reliability: Ensures the network can adapt to dynamic changes like node failure or
mobility.

Conclusion
Sensor deployment and node discovery are fundamental for designing and operating IoT
systems and Wireless Sensor Networks.
• Sensor deployment focuses on strategic placement to ensure coverage and
connectivity.
• Node discovery establishes connections between nodes and forms the network
topology.
These processes directly impact the performance, efficiency, and lifespan of the network,
making them essential for applications such as environmental monitoring, smart cities,
healthcare systems, and industrial IoT.
Data Aggregation and Dissemination in IoT and Wireless Sensor Networks
(WSNs)

In IoT systems and Wireless Sensor Networks (WSNs), data aggregation and data
dissemination are critical techniques for efficient data collection, processing, and
distribution. These processes help optimize the use of network resources like bandwidth,
energy, and computational power while ensuring the effective delivery of useful data to end
devices or systems.

1. Data Aggregation
Definition:
Data aggregation is the process of combining and summarizing data collected from multiple
sensor nodes to eliminate redundancy and reduce the amount of transmitted data in the
network. This reduces energy consumption and enhances network performance.
Why is Data Aggregation Important?
• Energy Efficiency: Wireless nodes typically run on batteries; reducing data
transmission saves energy.
• Bandwidth Optimization: Aggregating data minimizes the data size sent to the base
station or sink node, conserving bandwidth.
• Redundancy Elimination: Raw sensor data often includes redundant or overlapping
information; aggregation removes duplicates.

Techniques of Data Aggregation

1. Tree-Based Aggregation:
o Nodes form a tree-like structure with a root (sink node) and multiple child
nodes.
o Data is aggregated hierarchically as it moves from the leaf nodes up to the root.
o Example: Aggregating temperature data in a forest using a hierarchical
approach.
2. Cluster-Based Aggregation:
o The network is divided into clusters, and each cluster has a Cluster Head (CH).
o Sensor nodes send data to the CH, which performs aggregation and forwards
the result to the sink node.
o Example: LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol.
3. Centralized Aggregation:
o All raw data is sent to a central node or base station, where aggregation occurs.
o Drawback: This can create a bottleneck and consume more energy during data
transmission.
4. In-Network Aggregation:
o Aggregation is performed within the network, as nodes forward data to the
sink node.
 o Each intermediate node aggregates data from its child nodes before forwarding
it.

Benefits of Data Aggregation

• Reduces energy consumption by minimizing communication overhead.
• Improves scalability by managing large amounts of sensor data efficiently.
• Enhances the accuracy of data through summarization (e.g., averages, sums).
• Extends the lifetime of the sensor network.

2. Data Dissemination
Definition:
Data dissemination refers to the process of distributing and propagating data from one or
more sensor nodes to other nodes, base stations, or IoT devices in the network.
Purpose of Data Dissemination
• To ensure reliable delivery of data to intended recipients.
• To propagate control messages (e.g., routing updates) or query requests to nodes.
• To distribute aggregated or raw data to base stations or external systems for decision-
making.

Techniques of Data Dissemination

1. Flooding:
o A simple technique where data is broadcast to all nodes in the network.
o Advantages: Ensures that all nodes receive the data.
o Disadvantages: Causes redundancy, collisions, and high energy consumption.
2. Gossiping:
o A node sends data to a randomly selected neighboring node, which repeats the
process.
o Advantages: Reduces collisions compared to flooding.
o Disadvantages: Slower delivery of data due to sequential forwarding.
3. Query-Based Dissemination:
o Data is disseminated based on queries initiated by the base station or sink
node.
o Example: The sink node may request temperature data, and only relevant
sensor nodes respond.
o Advantages: Reduces unnecessary communication.
4. Data-Centric Dissemination:
o Data is disseminated based on attributes (e.g., location, sensor type).
o Nodes identify and send data that matches specific requirements.
o Example: Sensor Protocol for Information via Negotiation (SPIN).
Challenges in Data Dissemination
• Energy Efficiency: Repeated broadcasting consumes node energy.
• Latency: Delays in disseminating critical data can impact real-time applications.
• Network Congestion: High data volumes may overload the network, causing packet
drops.
• Scalability: Ensuring efficient dissemination in large-scale networks is challenging.

Differences Between Data Aggregation and Data Dissemination

Aspect Data Aggregation Data Dissemination
Purpose Combines and summarizes data from nodes. Distributes data or queries to nodes.
Process Reduces redundancy and minimizes data size. Propagates data or control messages.
Techniques Tree-based, cluster-based, in-network. Flooding, gossiping, query-based methods.
Energy Impact Saves energy by reducing transmissions. May consume more energy due to broadcasting.
Output Aggregated (summarized) data. Disseminated (distributed) data or queries.

Conclusion
• Data Aggregation reduces the amount of data sent to the sink node, optimizing
energy usage and bandwidth. Techniques like clustering and tree-based aggregation
are widely used in IoT and WSNs.
• Data Dissemination ensures that the required data or control information is
effectively distributed across the network using methods like flooding, gossiping, or
query-based dissemination.
Together, these processes ensure efficient operation, scalability, and energy conservation in
IoT and WSN environments, making them critical for real-world applications such as smart
cities, environmental monitoring, and industrial IoT systems.