International Journal of Application or Innovation in Engineering & Management (IJAIEM)

Web Site: www.ijaiem.org Email: editor@ijaiem.org

Volume 3, Issue 10, October 2014

ISSN 2319 - 4847

Optimization Of Anycast Packet Forwarding

Approach For WSN
Netranand Pathak1 Pratiksha2 Mukund Madhav3 Ketandeep Jamwal 4 Aditya Madhav5
M-Tech, Electrical Engineering (Power system) GALGOTIA UNIVERSITY-NOIDA
B.Tech, Computer Science Engineering,Punjab College of Engg.& Tech.Lalru
B-Tech, Electronics & Communication Engineering, BHSBIET-Lehragaga
B-Tech, Electronics & Communication Engineering, BHSBIET-Lehragaga
Diploma Electronics & Communication Engineering, B.Tech, Civil Engg, M.B.U

ABSTRACT
This paper describes the topic based on minimizing the delay and maximizing the lifetime of event-driven wireless sensor
networks, for which events occur in frequently. In such systems, most of the energy is consumed when the radios are on,
waiting for an arrival to occur. Sleep-wake scheduling is an effective mechanism to prolong the lifetime of these energyconstrained wireless sensor networks. However, sleep-wake scheduling could result in substantial delays because a transmitting
node needs to wait for its next-hop relay node to wake up. An interesting line of work attempts to reduce these delays by
developing any cast.-based packet forwarding schemes, where each node opportunistically forward]s a packet to the
neighboring node that wakes up among multiple candidate nodes. Wireless sensor network (WSNs) consists of many sensor
nodes and these networks are deployed in different classes of applications for accurate monitoring. Wireless sensor nodes are
limited energy supply has constrained the lifetime of a sensor network. Nodes in wireless sensor network are densely located
and there is duplication of sensed data. This happens because of multiple nodes sensing same event. Such data duplication is
responsible for wastage of node energy. Since energy conservation is one of the key issue in WSNs. So, data fusion and data
aggregation should be used in order to save energy. Data aggregation is effective method to eliminate redundancy and to
minimize the number of transmission. In this paper we present an efficient data aggregation strategy based on tree & cluster
formation which eliminates such data duplication and improves node energy efficiency provides the best aggregation quality
when compared to other existing systems.

KEYWORDS-COMPONENT:- Anycast, Sleep-wake scheduling, Sensor network, Energy-efficiency, Delay

1.INTRODUCTION
Wireless sensor networks (WSNs) have a significant potential in applications interacting with the physical world, such
as surveillance and environmental monitoring. In many of these applications, the use of battery-powered sensor nodes
greatly eases the deployment of the network, but the limited capacity of these batteries substantially limits the network
lifetime. One of the largest sources of energy consumption in wireless nodes is the use of idle listening, and many
solutions to reducing this problem in WSNs have been proposed based on the use of duty cycling . In duty cycling,
sensor nodes periodically alternate between being active and sleeping. When active, a node is able to transmit or receive
data, whereas when sleeping, the node completely turns off its radio to save energy; duty cycles of 110% (percentage
of time in the active state) are typical in order to maximize energy savings. In order to transmit a packet from one node
to another, the radios of both nodes must be on, motivating the use of synchronization between the operational cycles of
different nodes. Examples of protocols using synchronized approaches include S-MAC, T-MAC, and RMAC.
Duty cycling is a widely used mechanism in wireless sensor networks (WSNs) to reduce energy consumption due to idle
listening, but this mechanism also introduces additional latency in packet delivery. Several schemes have been proposed
to mitigate this latency, but they are mainly optimized for light traffic loads. A WSN, however, could often experience
busty and high traffic loads, such as due to broadcast or converge cast traffic. A new MAC protocol, called Demand
Wakeup MAC (DW-MAC),that introduces a new low-overhead scheduling algorithm that allows nodes to wake up on
demand during the Sleep period of an operational cycle and ensures that data transmissions do not collide at their
intended receivers. This demand wakeup adaptively increases effective channel capacity during an operational cycle as
traffic load increases, allowing DW-MAC to achieve low delivery latency under a wide range of traffic loads including
both unicast and broadcast traffic. Decreasing the hold on and improving the life-time of event-driven wsn signal
methods, for which actions occur irregularly. The awaken organizing is a useful process to proceed the life-time of these
energy-constrained wsn signal methods but due to this the shifting node needs to hold on for its next-hop connect node
to activate. To decrease these difficulties there is a need to make any cast-based package delivering methods, where
each node capably forward a packet to the first close by node that activates among several candidate nodes. In this

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Web Site: www.ijaiem.org Email: editor@ijaiem.org
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program, we first research how to enhance the delivering methods for lowering the expected packet-delivery difficulties
from the signal nodes to the strain. Depending on the result, we then provide a solution to the mixed control problem of
how to successfully control the program aspects of the sleep-wake organizing technique and the any throw packetforwarding technique to improve the program life-time, subject to a limitation on the expected end-to-end packetdelivery hold on or wait. Our computation indicates that the recommended solution can outperform prior heuristic
solutions in the fictional works, especially under the genuine conditions where there are challenges, e.g., a lake or a
mountain, in the security area of wsn signal methods.
METHODOLOGY
To understand the concept of wireless sensor networking.
To understand the anycast packet forwarding technique.
Literature survey related to various wireless sensor techniques.
Place the nodes randomly to form a network.
Obtain the position of each node in the network.
Calculate the transmission cost by sending data from node to node.
Compare the various costs with previous technologies

2.RESULTS
2.1SIMULATION EVALUATION
We evaluated DW-MAC using version 2.29 of the ns-2 simulator, under both unicast and broadcast trafc. Under
unicast trafc, we compared DW-MAC against S-MAC, S-MAC with adaptive listen- ing, and RMAC. Under
broadcast trafc, because broadcast is not supported in S-MAC with adaptive listening or in RMAC, we com- pared
DW-MAC only against S-MAC, in which a broadcast packet is transmitted during a Data period without using
RTS/CTS [27]. Table 1 summarizes the key networking parameters used in our simulations. In our simulations,
each sensor node has a single omni-directional antenna, and we use the common ns-2 combined free space and
two-ray ground reection radio propagation model. Except for the parameters on radio power consumption above,
which are typical values for Mica2 radios (CC1000) [28], we used the default settings in the standard S-MAC
simulation module dis- tributed with the ns-2.29 package, also used for evaluations of S- MAC and RMAC in
previous work [5]. The transition time of the CC1000 radio between sleep and active states is around 2.47 ms [3],
but the state transition power is not available in the data sheet. Although the state transition power is normally
much lower than Tx or Rx power, in order not to favor DW-MAC, which re- quires more state transitions than SMAC in this aspect, we set the state transition power to the same value of Tx power. We observed similar trends in
our results even if the state transition power is 0. In evaluating power efciency, we focus on energy consumed by radios but ignore energy consumed by other components such as CPU and memory [23]. The transmission range and
the carrier sensing range are modeled after the 914MHz Lucent WaveLAN DSSS ra- dio interface, which is not
typical for a sensor node, but we use these parameters to make our results comparable to those reported in previous
work, and since measurements have shown that similar proportions of the carrier sensing range to the transmission
range are also observed in some state-of-art sensor nodes [1]. In our simulations, we keep the same duty cycle of
5% for S- MAC, RMAC, and DW-MAC. The durations for the Sync, Data, and Sleep periods we used are shown
in Table 2. For DW-MAC, we use the same duty cycle-related parameters that were used in the evaluation of RMAC
in [5] for generating comparable results. The data packet size used in our simulations was 100 bytes, although a
maximum packet size of 256 bytes is supported by the CC1000 radios [13] and by the parameters used in our
simulations. To simplify our evaluations, we do not include routing trafc in the simulations and assume that
there is a routing protocol de- ployed to provide the shortest path between any two nodes. We also ensure that
every network we used in our simulations is a con- nected network. In addition, we do not include any synchronization
trafc and assume all the nodes in the network have already been synchronized to use a single wake-up and sleep
schedule. For simulations under unicast trafc, each run contains unicast packets toward a sink node that are
triggered by a series of 500 events, and each average value is calculated from the results of 10 random runs. For
simulations under broadcast trafc, each run con- tains 500 broadcast packets generated by a sink node, and each average value is calculated from the results of 30 random runs. Con- dence intervals of the average values are not
shown because even 99% condence intervals are so close to average values that they overlap with the data point
markers.

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(a) Life time of sensor based on delay

(c)

Energy consumed

ISSN 2319 - 4847

(c) Total energy consumed

(d) End-to-end delay

Figure1: Performance for unicast trafc in 49-node (7 7) grid network scenarios.

3.EVALUATION UNDER UNICAST TRAFC

We compare DW-MAC with S-MAC, S-MAC with adaptive lis- tening (shown as S-MAC-AL in all gures), and
RMAC both in a 49-node (7 7) grid network and in random networks. In the grid network, each node is 200
meters from its neighbours, and the sink node is at the center. Based on a correlated-event workload [10], we
introduce a Random Correlated-Event (RCE) trafc model to simulate the impulse trafc triggered by spatiallycorrelated events commonly observed in detection and tracking ap- plications. RCE picks a random (x, y) location for
each event. If every node has a sensing range R, only nodes that are within the circle centered at (x, y) with radius R
generate packets to report this event. We adjust the sensing rage R to simulate different degrees of workload in a
network. In our experiments, a new event is gen- erated once every 200 seconds, and each node having sensed the
event sends one packet to the sink node. We vary R from 100 me- ters to 500 meters; the average number of
packets generated per event is listed in Table 3. Note that an event triggers at most one packet when R is 100
meters. The lengths of paths traversed by these packets range from 1 to 6 hops, and the average is 3.05. In this
way, we explore how efciently S-MAC, S-MAC with adaptive listening, RMAC, and DW-MAC handle different
degrees of trafc load. The performance of these protocols for unicast trafc in the 49-node grid network scenarios is
shown in Figure 8. Figure 8(a) shows the average and maximum end-to-end latency of packets in the RCE model as
the sensing range (and thus trafc load) increases. DW-MAC has a much smaller rate of increase than do S-MAC
and RMAC. When there are around 15 packets generated for each event with the 500-meter sensing range, DWMAC reduces average end-to-end delay by around 70% compared to S- MAC and RMAC. DW-MAC outperforms
S-MAC because DW- MAC allows more transmissions in a cycle by using the Sleep pe- riod for actual data
transmissions. RMAC experiences more delay than DW-MAC as workload increases, because of increased packet
collisions caused by scheduling conicts. It is the retransmission effort to recover these collided packets that results
in larger end-to- end delay. When the sensing range is 500 meters, the maximum end-to-end delay with RMAC is
374.95 seconds, which is off the top of the graph. This extreme delay occurs when a packet gen- erated for one
event failed to reach the sink before the next event happened. Under the light trafc with the 100-meter sensing
range, DW-MAC shows slightly larger delay than RMAC, due to the time that a received data packet is forwarded to
the next hop in multihop forwarding. In RMAC, a data packet is forwarded immediately, whereas in DW-MAC,
forwarding starts at a later time determined by the corresponding SCH frame. This extra delay experienced by DWMAC, however, is less than the duration of a Sleep period. S- MAC with adaptive listening shows slightly larger

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delay compared to DW-MAC. This low delay achieved by adaptive listening, how- ever, comes at the cost of lower
packet delivery ratio and increased energy consumption as shown next. The packet delivery ratios corresponding to
Figure 8(a) are shown in Figure 8(b). DW-MAC maintains close to 100% packet delivery ratio and outperforms the
other protocols across all sens- ing ranges. The delivery ratio with S-MAC with adaptive listening drops quickly,
since with larger the sensing ranges, more collisions are caused by transmissions from hidden nodes, as we discussed
in Section 2; in addition, a node may transmit a packet when its in- tended receiver is in sleep state, further
decreasing packet delivery ratio. DW-MAC and RMAC outperform S-MAC mainly for two reasons. First, they only
transmit short scheduling frames during a Data period, avoiding collisions between a control frame and a long data
frame. Second, a node does more retransmission attempts for a data packet in DW-MAC and RMAC. Specically, a
sched- uling frame sent by both as RTS and as CTS; even if this frame fails to reach the next-hop neighbor, the
intermediate node does not increase its retry count, as the node has not received the corresponding data packet yet,
although the node has attempted to reserve the medium to forward the incoming data packet once. Even with such
extra retransmission attempts, the delivery ratio of RMAC drops more quickly than that of DW-MAC beyond a
400-meter sensing range, as retransmissions are not enough to recover the increased colli- sions due to RMACs
scheduling conicts.

(a). New life time of nodes

(b). New Total Energy Consumed

(c). New Energy consumed

(d). New node-to-node delay

Figure2: Performance for random correlated-event trafc in 50-node networks with sensing range of 250 m.
Figure 8(c) shows the average energy consumption of nodes versus sensing ranges in the 49-node grid network
scenarios. Under light workload, when the sensing range is 100 meters, all four MAC protocols show almost the
same power consumption, but when traf- c load increases as the sensing range gets larger, average energy
consumption in all protocols except DW-MAC increases quickly (energy consumption for DW-MAC does
increase, but increases very slowly). When the sensing range is 500 meters, DW-MAC consumes less than 50%
of the energy consumed by S-MAC with adaptive listening to achieve even lower packet delivery latency. We also

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compare S-MAC, S-MAC with adaptive listening, RMAC, and DW-MAC in 100 random networks, each with
50 nodes randomly located in a 1000 m 1000 m area. For each network, one random node is chosen as the sink,
and the RCE model with 250-meter sensing range is used to generate 500 events, once every 200 seconds. We
conduct one simulation run for each net- work, and 3845 packets are generated in each run on average. The
results are plotted in Figure 9. For the same reasons discusses above, DW-MAC outperforms the other three
protocols in delivery latency, delivery ratio, and energy consumption. Figure 9(a) show the CDF of end-to-end
latency for all packets in all 100 runs. Aver- age end-to-end latency with S-MAC, S-MAC with adaptive listen- ing,
RMAC, and DW-MAC are 61.8%, 21.6%, 36.7%, and 15.7%, respectively. Although adaptive listening greatly
reduces end-to- end latency for S-MAC, this gain is at the cost of lower delivery ratio and more energy
consumption. Figure 9(b) shows the CDF of delivery ratios in these 100 runs. The average delivery ratios of SMAC, S-MAC with adaptive listening, RMAC, and DW-MAC are 99.63%, 95.03%, 99.99%, and 99.99%,
respectively. The aver- age energy consumptions of the sensors are plotted in Figure 9(c), where the average
values with S-MAC, S-MAC with adaptive lis- tening, RMAC, and DW-MAC are 1.386, 2.666, 1.724, and 1.163
mW, respectively. The trends observed in these random networks are consistent with those observed in the 49-node
grid network.

4.EVALUATION UNDER BROADCAST TRAFC

We compared DW-MAC with S-MAC, both in regular grid net- works and in random networks, under broadcast
trafc In the grid network, the sink node is at the center, and each node is 200 meters from its neighbors. We vary
the grid size from 3 3 (9 nodes) to 11 11 (121 nodes). The sink node generates a broadcast packet once every
100 seconds so that transmissions for one packet complete before the next packet is generated. Due to space limits,
we evaluate DW-MAC under only two categories of broadcast protocols: simple ooding (all nodes that have received a broadcast packet rebroadcast it exactly once, indicated by ALL) and Connected Dominating Set (CDS)
based ooding (only nodes in a CDS that have received a broadcast packet rebroadcast it exactly once, indicated by
CDS). The CDS is formed by the algorithm by Gandhi et al. [8], with a slight modication to al- ways include
the sink node in the CDS; the results for our opti- mized multihop forwarding for broadcast trafc are indicated
by DW-MAC CDS-MH. Note that this CDS algorithm is designed to minimize broadcast latency, and the resulting
CDS is not neces- sarily an minimum CDS.

Figure 3: ThroughPut graph for anycast broadcasting

Figure 4: Node-to node delay graph for new thesis of anycast network

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The interval between trafc bursts is changed from 200 seconds to 100 seconds to show the differences among
protocols more clearly. DW-MAC reduces average energy consumption over S-MAC by about 26% under simple
ooding and by about 18% under CDS- based ooding. DW-MAC achieves these savings by not overhear- ing data
transmissions. In DW-MAC, a node only attempts to re- ceive an incoming packet after receiving an SCH that
indicates the packet has not been received. Simple ooding consumes more en- ergy because of more rebroadcasts.
Whether or not the optimized multihop forwarding is used, a ooding results in the same num- ber of
transmissions, so this optimization does not affect energy consumption much. Finally, we compare these broadcast
protocols in 100 random networks, the same networks used for evaluations under unicast trafc. The sink in each
network generates 500 broadcast packets in each run, one packet every 100 seconds. Figure 11(a) shows the CDF of
end-to-end latency for all packets in the 100 runs. All DW- MAC based broadcast protocols show much smaller endto-end la- tency than those based on S-MAC. The average end-to-end latency for S-MAC ALL, S-MAC CDS, DWMAC ALL, DW-MAC CDS and DW-MAC CDS-MH are 49.1, 34.8, 24.2, 20.8, and 16.0 sec- onds, respectively.
On average, end-to-end latency is reduced by more than 50% both in simple ooding and in CDS-based ood- ing.
Unlike the results in grid networks, DW-MAC shows lower average end-to-end latency in CDS-based ooding
than those in simple ooding, because the speedup gained by fast propagation along CDS nodes is often greater
than the slowdown caused by defers in these networks. For these 100 runs, the CDF of deliv- ery ratios is shown
in Figure 11(b), and the CDF of average en- ergy consumption is shown in Figure 11(c). S-MAC ALL, S-MAC
CDS, DW-MAC ALL, DW-MAC CDS, and DW-MAC CDS-MH, respectively, show average delivery ratios of 98.6%,
92.1%, 99.0%, 95.0% and 96.4% and average energy consumption of 1.785, 1.355, 1.288, 1,185, and 1.183 mW. The
difference in energy consumption between DW-MAC CDS and DW-MAC CDS-MH is almost invisi- ble because the
optimized multihop forwarding does not affect the number of data transmissions much. Overall, DW-MAC achieves
lower end-to-end delays, higher delivery ratios, and more energy savings for broadcast trafc in these random
networks.

5.DISCUSSION/ANALYSIS
Joohwan Kim, Xiaojun Lin, Ness B, Shroff, Prasun Sinha, IEEE-2010 , in this paper Minimizing Delay and
Maximizing Lifetime for Wireless Sensor Networks With Anycast,explained that lifetime of nodes in wireless sensor
networks can be increased by using sleep wake scheduling algorithm and event reporting delay can be reduced by using
selective traditional anycast packet forwarding technique.
Yanjun Sun, Shu Du, Omer Gurewitz, David B. Johnson (MobiHoc-2008) , in this paper "DW-MAC:A Low
Latency, Energy Efficient Demand- Wakeup MAC Protocols for Wireless Sensor Networks", explained that Duty
cycling is a widely used mechanism in wireless sensor networks (WSNs) to reduce energy consumption due to idle
listening, but this mechanism also introduces additional latency in packet delivery. Several schemes have been
proposed to mitigate this latency, but they are mainly optimized for light traffic loads. A WSN, however, could often
experience bursty and high traffic loads, such as due to broadcast or convergecast traffic. In this paper, they present a
new MAC protocol, called Demand Wakeup MAC (DW-MAC), that introduces a new low-overhead scheduling
algorithm that allows nodes to wake up on demand during the Sleep period of an operational cycle and ensures that
data transmissions do not collide at their intended receivers. This demand wakeup adaptively increases effective
channel capacity during an operational cycle as traffic load increases, allowing DW-MAC to achieve low delivery
latency under a wide range of traffic loads including both unicast and broadcast traffic. They compare DW-MAC with
S-MAC (with and without adaptive listening) and with RMAC using ns-2 and show that DW-MAC outperforms these
protocols, with increasing benefits as traffic load increases. For example, under high unicast traffic load, DW-MAC
reduces delivery latency by 70% compared to S-MAC and RMAC, and uses only 50% of the energy consumed with SMAC with adaptive listening. Under broadcast traffic, DWMAC reduces latency by more than 50% on average while
maintaining higher energy efficiency.
Sha Liu, Kai-Wei Fan and Prasun Sinha,The Ohio State University(2007) in this paper CMAC: An Energy
Efficient MAC Layer Protocol Using convergent Packet Forwarding for Wireless Sensor Networks explained that low
duty cycle operation is critical to conserve energy in wireless sensor networks. Traditional wake-up scheduling
approaches either require periodic synchronization messages or incur high packet delivery latency due to the lack of
any synchronization. In this paper, they present the design of a new low duty-cycle MAC layer protocol called
Convergent MAC (CMAC). CMAC avoids synchronization overhead while supporting low latency. By using zero
communication when there is no traffic, CMAC allows operation at very low duty cycles. When carrying traffic, CMAC
first uses any cast to wake up forwarding nodes, and then converges from route-suboptimal any cast with
unsynchronized duty cycling to route-optimal unicast with synchronized scheduling. To validate design and provide a
usable module for the community, they implement CMAC in TinyOS and evaluate it on the Kansei testbed consisting
of 105 XSM nodes. The results show that CMAC at 1% duty cycle significantly outperforms BMAC at 1% in terms of
latency, throughput and energy efficiency. They also compare CMAC with other protocols using simulations. The

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results show for 1% duty cycle, CMAC exhibits similar throughput and latency as CSMA/CA using much less energy,
and outperforms SMAC and GeRaF in all aspects.
David Culler, Jonathan Hui, Philip Levis, Scott Shenker, Ion Stoica, and Jerry Zhao Joseph Polastre (October
20, 2005) in this paper A unifying link abstraction for wireless sensor networks explained technological advances
and the continuing quest for greater efficiency have led to an explosion of link and network protocols for wireless
sensor networks. These protocols embody very different assumptions about network stack composition and, as such,
have limited interoperability. It has been suggested that, in principle, wireless sensor networks would benefit from a
unifying abstraction (or "narrow waist" in architectural terms), and that this abstraction should be closer to the link
level than the network level. This paper takes that vague principle and turns it into practice, by proposing a specific
unifying sensornet protocol (SP) that provides shared neighbor management and a message pool.The two goals of a
unifying abstraction are generality and efficiency: it should be capable of running over a broad range of link-layer
technologies and supporting a wide variety of network protocols, and doing so should not lead to a significant loss of
efficiency. To investigate the extent to which SP meets these goals, we implemented SP (in TinyOS) on top of two very
different radio technologies: B-MAC on mica2 and IEEE 802.15.4 on Telos. They also built a variety of network
protocols on SP, including examples of collection routing , dissemination and aggregation . Measurements show that
these protocols do not sacrifice performance through the use of our SP abstract.
ROY ET AL, ROUTING WITH ANYCASTING IN AD-HOC NETWORKS (2004)
Wireless ad hoc networks are infrastructureless multi-hop networks in which nodes behave as mobile routers. The
intermediate node is often faced with the decision to choose between two of its neighbors, both of which may be equally
good for forwarding the packet to the final destination. Selection is then made randomly, without respecting the
possibility that one of the nodes may not be suitable for immediate transmission. Anycasting paradigm can be quite
useful in such scenario. Roy et al. (2004) propose MAC layer anycasting and claim that it can make educated decisions
in such scenarios, leading to potential benefits in performance. However they argue that MAC-layer anycasting can
introduce several tradeoffs and can be disadvantageous in certain aspects. To avoid these tradeoffs we propose to
enhance MAC layer anycasting with the use of metric based filters in anycasting. Their work is greatly influenced by
the research of Zegura et al. (2000). They believe this technique can enhance the performance (with added advantage of
some sort Of QoS to be delivered) without much overheads as involved in MAC layer anycasting.

6.CONCLUSIONS
However, in synchronized sleep wake scheduling such synchronization procedure could incur additional
communication overhead, and consume a considerable amount of energy.
However, this on demand sleep-wake scheduling can significantly increase the cost of sensor motes due to the
additional receivers.
However, in asynchronized sleep wake scheduling, because it is not practical for each node to have complete
knowledge of the sleep-wake schedule of other nodes, it incurs additional delays along the path to the sink because
each node needs to wait for its next-hop node to wake up before it can transmit. This delay could be unacceptable
for delay-sensitive applications, such as fire detection or tsunami alarm, which require that the event reporting
delay be small.
Under traditional packet-forwarding schemes, every node has one designated next-hop relaying node in the
neighborhood, and it has to wait for the next-hop node to wake up when it needs to forward a packet. Each node
has multiple next-hop relaying nodes in a candidate set (we call this set a forwarding set). A sending node can
forward the packet to the first node that wakes up in the forwarding set. Anycast forwarding reduces the eventreporting delay and minimizes the power consumption by using optimum methods.
The route distance between nodes of wireless sensor network will be optimum.
The power consumption between location points and sensors will be least optimum.
The Bandwidth utilization will be maximum.
The event reporting delay will be small.

REFERENCES
[1] Joohwan Kim,Xiaojun Lin,Ness B,Shroff,Prasun Sinha,Minimizing Delay and Maximizing Lifetime for Wireless
Sensor Networks With Anycast,pages 14,IEEE-2010.
[2] Yanjun Sun,Shu Du,Omer Gurewitz,David B. Johnson,"DW-MAC:A Low Latency,Energy Efficient DemandWakeup MAC Protocols for Wireless Sensor Networks",pages 10,MobiHoc-2008.
[3] Sha Liu, Kai-Wei Fan and Prasun Sinha,CMAC: An Energy Efficient MAC Layer Protocol Using convergent
Packet Forwarding for Wireless Sensor Networks The Ohio State University(2007) .

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[4] Qing Cao, Tarek Abdelzaher, Tian He, and John Stankovic. Towards Optimal Sleep Scheduling in Sensor
Networks for Rare-Event Detection. In Proceedings of the Fourth International Symposium on Information
Processing in Sensor Networks (IPSN 2005), pages 2027April2005. David Culler, Jonathan Hui, Philip Levis,
Scott Shenker, Ion Stoica, and Jerry Zhao Joseph Polastre A unifying link abstraction for wireless sensor
networks (October 20, 2005).
[5] G. Anastasi, A. Falchi, A. Passarella, M. Conti, and E. Gregori. Performance Measurements of Motes Sensor
Networks.In Proceedings of the 7th ACM International Symposium on Modeling, Analysis and Simulation of
Wireless and Mobile Systems (MSWiM 2004), pages 174181, October 2004.
[6] T. van Dam and K. Langendoen, An Adaptive Energy-Efficient MAC Protocol for Wireless Sensor Networks, in
Proc. SenSys, pp. 171180, November 2003.
[7] M. Zorzi and R. R. Rao, Geographic Random Forwarding (GeRaF) for Ad Hoc and Sensor Networks: Energy and
Latency Performance, IEEE transactions on Mobile Computing, vol. 2, pp. 349365, October 2003.
[8] J. Elson, L. Girod, and D. Estrin, Fine-grained network time synchronization using reference
broadcasts,SIGOPS Oper. Syst. Rev., vol. 36,no. SI, pp. 147163, 2002.

AUTHOR
Netrananad Pathak was born in India. He graduated in Electrical Engineering from BHSBIET Lehragaga
in year 2012. He is pursuing his M.Tech in Power System from Galgotias University Noida. His special
fields of interest are Power System.

Pratiksha was born in India. She graduated in Computer Science Engineering from Punjab College of
Engineering & Technology, Lalru in year 2013. She is presently working as Software Developer in
Mumbai.

Mukund Madhav was born in India. He graduated in Electronics & Communication Engineering from
BHSBIET Lehragaga in year 2013. He is presently working in telecommunication in 4G installation and
commissioning
Engineering
with
Harpy
Network
for Reliance Telecom.
Ketandeep Jamwal was born in India. He graduated in Electronics & Communication Engineering from
BHSBIET Lehragaga in year 2013. He is presently working in telecommunication in 4G installation and
commissioning Engineering with Harpy Network for Reliance Telecom.

Aditya Madhav was born in India. He has done his Diploma in Electronics & Communication
Engineering, B-Tech in Civil Engineering from M.B.U.

Volume 3, Issue 10, October 2014

Page 43