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An Adaptive Energy Efficient Reliable Routing Protocol for Wireless Sensor Networks
An Adaptive Energy Efficient Reliable Routing Protocol for Wireless Sensor Networks
An Adaptive Energy Efficient Reliable Routing Protocol for Wireless Sensor Networks
An Adaptive Energy Efficient Reliable Routing Protocol for Wireless Sensor Networks
An Adaptive Energy Efficient Reliable Routing Protocol for Wireless Sensor Networks
An Adaptive Energy Efficient Reliable Routing Protocol for Wireless Sensor Networks
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An Adaptive Energy Efficient Reliable Routing Protocol for Wireless Sensor Networks

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A reliable routing protocol for wireless sensor …

A reliable routing protocol for wireless sensor
networks (WSN) should be capable of adjusting to
constantly varying network conditions while conserving
maximum power. Existing Routing protocols provide
reliability at the cost of high energy consumption. In this
paper, we propose to develop an Adaptive Energy Efficient
Reliable Routing Protocol (AEERRP) with the aim of
keeping the energy consumption low while achieving high
reliability. In our proposed protocol, the data forwarding
probability is adaptively adjusted based on the measured
loss conditions at the sink. So only for high loss rates, a node
makes use of high transmission power to arrive at the sink.
Whenever the loss rate is low, it adaptively lessens the
transmission power. Since the source rebroadcasts the data,
until the packet loss is minimized, high data reliability is
achieved. By simulation results we show that the proposed
protocol achieves high reliability while ensuring low energy
consumption and overhead.

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  • 1. ACEEE International Journal on Network Security, Vol 1, No. 1, Jan 2010 An Adaptive Energy Efficient Reliable Routing Protocol for Wireless Sensor Networks Basavaraj S.Mathapati1, Dr.V.D.Mytri2 and Dr.Siddarama R. Patil3 1 APPA Institute of Engineering and Technology /Computer Science and Engineering, Gulbarga, Karnataka, India Email: basavarajphd@gmail.com 2 GND college of Engineering, Bidar, Karnataka, India Email: mytrivd@gmail.com 3 PDA College of Engineering/Electronics and Communication Engineering, Gulbarga, Karnataka, India Email: pdapatil@yahoo.comAbstract— A reliable routing protocol for wireless sensor single sensor would allow unless the correct location ofnetworks (WSN) should be capable of adjusting to the specified phenomenon is unknown. The multipleconstantly varying network conditions while conserving sensor nodes are needed in most of the situations tomaximum power. Existing Routing protocols provide surmount over environmental hindrances namelyreliability at the cost of high energy consumption. In thispaper, we propose to develop an Adaptive Energy Efficient obstructions, line of sight constraints etc. Also, theReliable Routing Protocol (AEERRP) with the aim of environment under supervision does not possess ankeeping the energy consumption low while achieving high infrastructure for energy or communication. It is veryreliability. In our proposed protocol, the data forwarding essential that the sensor nodes have to persist on minute,probability is adaptively adjusted based on the measured finite energy sources and communicate by means of aloss conditions at the sink. So only for high loss rates, a node wireless communication channel.makes use of high transmission power to arrive at the sink. Sensor networks are applied in a number of ways inWhenever the loss rate is low, it adaptively lessens the several areas. For instance, it consist of environmentaltransmission power. Since the source rebroadcasts the data, monitoring –that includes examining air, soil and water,until the packet loss is minimized, high data reliability isachieved. By simulation results we show that the proposed condition based maintenance, habitat monitoringprotocol achieves high reliability while ensuring low energy (estimating the population and behavior of plant andconsumption and overhead. animal species), military surveillance, seismic detection, inventory tracking, smart spaces and so on. In fact sensorIndex Terms—Sensor Networks, Reliability, overhead, networks have the capability of converting a better way toEnergy Consumption, Routing Protocol comprehend and assemble complex physical system [1] because of the pervasive nature of micro-sensors. I. INTRODUCTION B. Routing Protocols for Sensor NetworksA. Wireless Sensor Networks Routing in sensor networks is difficult for the reason that numerous features distinguish them from the modern In recent years, the advancement of technologies has communication and wireless ad-hoc networks.resulted in the deployment of minute, low-power, cheap,distributed devices that can be subjected to local • It is not feasible for constructing a globalprocessing and wireless communication in a real time [1]. addressing scheme for the deployment of pureThese nodes are referred as sensor nodes. Each sensor number of sensor nodes. Consequently, classicalnode processes to a limited level. But these nodes possess IP-based protocols cannot be employed to sensorthe capability of evaluating a physical environment networks.completely when managed by the particulars obtained • By contrasting to characteristic communicationfrom a number of other nodes. Hence, a sensor network networks nearly the entire applications of sensorcan be identified as a set of sensor nodes which organizes networks necessitates the sensed data flow fromto execute certain functions. In comparison the multiple regions (sources) to a specific sink.conventional networks the sensor networks rely on dense • Multiple sensors may generate similar dataco-ordination and deployment to perform their functions. within the adjacent area of a phenomenon andTypically, the sensor networks comprise of few sensor this leads to a main redundancy in the generatednodes that are connected to a central processing station. data traffic. Such redundancies have to beHowever, these days the spotlight is on wireless, utilized by the routing protocols to enhancedistributed sensing nodes. The distributed sensor enables energy and bandwidth exploitation.a closer allocation as per the phenomenon whereas a 31© 2010 ACEEEDOI: 01.ijns.01.01.07
  • 2. ACEEE International Journal on Network Security, Vol 1, No. 1, Jan 2010 • Sensor nodes are forcefully bounded in terms of algorithms to compute approximations to the idealized transmission power, processing capacity, on- disjoint and braided paths. Second, they have evaluated board energy, and storage and therefore they the relative performance of disjoint and braided necessitate cautious resource management. multipaths. • Node failures and packet losses are anticipated Vidhyapriya and Vanathi [12] have proposed an to be general in several sensor networks. These energy efficient adaptive multipath routing technique failures or losses could be for the short-term in which utilized multiple paths between source and the nature, for instance because of the temporary sink, adaptive because they have low routing overhead. wireless interference. That protocol was intended to provide a reliable Accordingly, a routing protocol for such challenged transmission environment with low energy consumption,networks could be competent of adjusting to constantly by efficiently utilizing the energy availability and thevarying network conditions while conserving maximum received signal strength of the nodes to identify multiplepower. routes to the destination. Normally, power save protocols offers two choices to Matthew J. Miller and Indranil Gupta [13] havethe user based on the broadcast. First, the broadcast can discussed that the devices became more reliant on batteryattain a comparatively low latency, if no power save is power, it was essential to design energy efficientemployed, although at the sacrifice of large energy costs protocols. In their previous work, they have proposedto listen for broadcasts. The second choice is to employ Probability-Based Broadcast Forwarding (PBBF) tothe power save protocol. This option conserves extra address broadcast power save by allowing users to selectenergy than the first option; however it possesses high a desired tradeoff between energy consumption, latency,latency which is not suitable to a few applications. and reliability. In their paper they have extended their Every single data or request packet is blindly previous work. They have introduced a parameter thatrebroadcasted or forwarded by the other nodes, in the allowed a tradeoff between reliability and packetblind flooding which augments the energy utilization and overhead to give users more options.communication overhead. Each mobile node rebroadcasts Michele Zorzi and Ramesh R. Rao [14] have proposeda packet on the basis of a predetermined forwarding a novel forwarding technique based on geographicalprobability p, in the traditional probabilistic broadcast location of the nodes involved and random selection ofschemes. So as to create rebroadcast decisions, global the relaying node via contention among receivers. Theytopological information on the network is not necessary have focused on the multihop performance of such ain the probabilistic broadcast schemes. However, general solution, in terms of average number of hops to reach aprobabilistic methods had concentrated on pure destination as a function of the distance and of theprobabilistic state of affairs with comparatively modest average number of available neighbors.inspection on the effects of broadcast algorithms on Dandan Liu et al. [15] have considered a distributedparticular applications namely route discovery. and efficient information dissemination and retrieval Routing Protocols can be categorized on the basis of system for wireless sensor networks. In such a systemsubsequent techniques [2]: each sensor node operates autonomously with no central • Flooding protocols such as SPIN [4] , node of control in the network, and it can be a data source • Gradient protocols like Directed Diffusion [5] (it produces data) as well as a data sink (it consumes and GRAB [8] , data). They have aimed at developing energy efficient • Clustering protocols namely LEACH [3] and protocols that disseminate information sensed at a source HEED [10] node to any other nodes that are interested in the • Geographic protocols namely GPSR [7], GAF information. They have proposed two protocols, one was [6] and GEAR [9]. based on the quorum scheme and the other was based on In this paper, we propose to develop an Adaptive the home agent scheme. Their protocols have threeEnergy Efficient Reliable Routing Protocol (AEERRP) advantages: (1) Fully distributed. (2) high success rate forwith the aim of keeping the energy consumption low data retrieval; (3) capable of dealing with mobile sensorswhile achieving high reliability in order to ensure high as well as static sensors.overall network connectivity. A priority-based multi-path routing protocol (PRIMP) was proposed by Yuzhe Liu and Winston K.G. Seah [16] II. RELATED WORK for sensor networks to offer extended network lifetime and robust network fault tolerance. Extensive simulations Deepak Ganesan et al. [11] have addressed two issues. have validated that PRIMP exhibits significantly betterFirst, they have defined localized algorithms for the performance in energy conservation, load-balancing andconstruction of alternate paths. For reasons of robustness data delivery than its comparable schemes. Moreover,and energy-efficiency, sensor network data dissemination PRIMP addresses the slow startup issue occurred inmechanisms used localized decisions for path setup and datacentric routing schemes.for recovery from failure. They have proposed localized 32© 2010 ACEEEDOI: 01.ijns.01.01.07
  • 3. ACEEE International Journal on Network Security, Vol 1, No. 1, Jan 2010 A reliable energy-efficient routing (REER) protocol needed. Sensor nodes endure unpredictable and recurrentwas proposed by Min Chen et al. [17] to achieve the failures because of the disturbing environment [8].goals for dense wireless sensor networks (WSNs). Based In our proposed protocol, we estimate the density of aon the geographical information, REER’s design region by employing the neighborhood information ofharnesses the advantage of high node density and relies nodes located in that region. The neighborhoodon the collective efforts of multiple cooperative nodes to information is obtained using a topology discoverydeliver data, without depending on any individual ones. scheme. Based on this, the current number of forwardingThey have initially selected reference nodes (RNs) nodes is kept in a forward node count (CFN), at each node.between source and sink. Then, multiple cooperative If the packet loss ratio at the neighbor of the sink is morenodes (CNs) are selected for each RN. The reliability was than a maximum-threshold value, CFN is incrementedattained by cooperative routing: each hop keeps multiple adaptively until the loss ratio is less than the maximum-CNs among which any one may receive the broadcast threshold value. This guarantees the reliability of data.data packet from the upstream hop to forward the data When the loss ratio value becomes less than thesuccessfully. The distance between two adjacent RNs maximum-threshold value, it specifies the successfulprovides a control knob to trade off robustness, total packet delivery. In this scenario, the FNC is decrementedenergy cost and end-to-end data latency. until the CFN is equal to its minimum forwarding node Zijian Wang et al. [18] have proposed an energy count.efficient and collision aware (EECA) node-disjoint In contrast to existing routing protocols, our protocol ismultipath routing algorithm for wireless sensor networks. neither single-path nor multi-path; rather each nodeWith the aid of node position information, the EECA adapts the paths based on the estimated loss conditions.algorithm attempts to find two collision-free routes using In this protocol, only for high loss rates, a node makesconstrained and power adjusted flooding and then use of high transmission power to arrive at the sink.transmits the data with minimum power needed through Whenever the loss rate is low, it adaptively lessens thepower control component of the protocol. transmission power. Since energy consumption is Kavitha, C. and Viswanatha, K.V. [19] have proposed lowered, the network lifetime is maximized. Since thean energy efficient fault-tolerant multipath routing source rebroadcasts the data, until the packet loss istechnique which utilized multiple paths between source minimized, high data reliability is achieved.and the sink. Their protocol was intended to provide a B. Topology Discovery Phasereliable transmission environment with low energyconsumption, by efficiently utilizing the energy In this phase, the sink broadcasts a topology discoveryavailability and the available bandwidth of the nodes to (TOPDIS) packet in the network. This packet is employedidentify multiple routes to the destination. To achieve to determine the cost of each forwarding node. A node’sreliability and fault tolerance, their protocol selects cost is defined as the minimum power needed to reach thereliable paths based on the average reliability rank (ARR) sink by this node. Thus, the nodes which are nearer to theof the paths. Average reliability rank of a path was based sink have smaller cost while nodes which are far awayon each nodes reliability rank (RR), which represents the from the sink have larger cost. We presume each nodeprobability that a node correctly delivers data to the can estimate the cost of sending data to its nearbydestination. In case the existing route encounters some neighbors on the basis of the signal-to-noise-ratio (SINR)unexpected link or route failure, their algorithm selects of the neighbors. The packets trace the direction ofthe path with the next highest ARR, from the list of lessening cost to reach the sink. When multiple paths ofselected paths. lessening cost exist, they develop a forwarding mesh. As soon as a topology request packet is sent to all the III. PROPOSED PROTOCOL sensor nodes by the AP, the next phase begins. After acquiring this packet, a node first settles whether it comesA. System Design and Protocol Overview from a neighbor or interferer. It makes use of the received signal strength information from its interference model, In this paper, we assume the following sensor network to fix on the origin of the packet. If the transmitting nodemodel. Many minute, stationary sensor nodes are occurs to be the next hop of the receiving node, withdeployed over a field. The user acquires the sensing data minimum cost, the receiving node appends its own costby means of the stationary sink which communicates information to the packet and rebroadcasts it. Thewithin the network. Each event is identified by multiple receiving node maintains an array to store the cost andsensor nodes which are closer and one among them signal strength of this transmitting node. Once this phaseproduces the reports as a source. Reports are forwarded has been completed, the energy efficient forwardingover several hops before arriving at the sink owing to the phase begins, which is discussed in the next section.limited radio range. Nodes are competent to tune theirtransmitting powers to manage how long thetransmissions may travel. These power adjustments areable to conserve energy and lessen collisions when it is 33© 2010 ACEEEDOI: 01.ijns.01.01.07
  • 4. ACEEE International Journal on Network Security, Vol 1, No. 1, Jan 2010C. Energy Efficient Forwarding C FN =C FN +γ , until C FN ≥ C FN min After the topology discovery phase, each node Nmaintains a Neighbor Information Table (NIT), which IV. PERFORMANCE EVALUATIONcontain the fields Node Id, Distance and Cost. Node Id isthe id of the neighbor node, Distance is the distance A. Simulation Setupbetween that node with N and Cost is the power required We evaluate our AEERRP scheme through NS2to send a packet from that node to the sink. simulation. We considered a random network deployed in Let N i , i = 1,2,...n be the neighbors of N . Then N an area of 500 X 500 m. The number of nodes is varied assorts the NLT based on the distances of N i . (i.e.) the 25, 50, 75 and 100. Initially the nodes are placednodes with shortest distance with N are listed first. randomly in the specified area. The sink is assumed to be situated 100 meters away from the above specified area. Each node maintains a forward node count ( C FN ), The initial energy of all the nodes assumed as 5 joules. Inwhich denotes the broadcast or rebroadcast probability. our simulation, the channel capacity of mobile hosts is set Initially C FN [ Nk ] = C FN min , for all nodes to the same value: 2 Mbps. We use the distributed Nk , k = 1,2, L C FN min is the minimum number of coordination function (DCF) of IEEE 802.11 for wirelessforwarding nodes. Without loss of generality, we can LANs as the MAC layer protocol. The simulated traffic isassume that CBR with UDP source and sink. All experimental results C FN min = 1 . The steps involved in the adaptive presented in this section are averages of five runs on different randomly chosen scenarios. The following tableenergy efficient forwarding phase are given below: summarizes the simulation parameters used. 1) Suppose N wants to send the collected data to the sink, it attaches its cost to the data packet and broadcast the packet to the nearest neighbors. 2) When a neighbor N1 receives the packet from N , it first checks its cost is less than that TABLE I. of N . If it is less, it further forwards the packet. SIMULATION PARAMETERS Otherwise it drops the packet, since N1 is not No. of Nodes 25,50,75 and 100 towards the direction of the sink. Area Size 500 X 500 3) When the packet reaches the destination D , it Mac 802.11 measures the loss ratio (LR), which is the ratio of number of packets dropped and total packets Simulation Time 50 sec broadcast from the source. Traffic Source CBR 4) Then D sends this LR value as a feed back to Packet Size 512 the source N . Transmit Power 0.360 w 5) When N receives this value, it checks the value Receiving Power 0.395 w of LR. It then modifies the value of C FN as Idle Power 0.335 w C FN =C FN +γ , if LR > LR max . Initial Energy 5J Where γ is the minimum increment of decrement Transmission Range 75mcount and LR max is the maximum threshold value of B. Performance Metricsloss rate. 6) It then rebroadcast the data packets with the We compare AEERRP with the extended PBBF [13] incremented C FN , so that increasing the scheme. We evaluate mainly the performance according to the following metrics. reachability of the sink. The total power required Control overhead: The control overhead is defined as to reach the sink is thus calculated based on the the total number of routing control packets normalized by cost field of all the nodes in C FN . For example, the total number of received data packets. if C FN = 4 , then the minimum required power Average end-to-end delay: The end-to-end-delay is will be 4 * cost of each neighbor node in the averaged over all surviving data packets from the sources NIT. to the destinations. 7) When the rebroadcast packets reach the Average Packet Delivery Ratio: It is the ratio of the destination D , it again calculates the losses ratio number .of packets received successfully and the total LR and sends back to N . number of packets transmitted. 8) It then reassigns the value of C FN , depending on Loss Ratio: It is the average energy consumption of all nodes in sending, receiving and forward operations. the value of LR. Once LR < LR max , then 34© 2010 ACEEEDOI: 01.ijns.01.01.07
  • 5. ACEEE International Journal on Network Security, Vol 1, No. 1, Jan 2010 The simulation results are presented in the next scheme achieves more delivery ratio than the PBBFsection. scheme since it has both reliability features.C. Simulation Results Nodes Vs Overhead Nodes Vs Delay 2000 0.002 1500 0.0015 AEERRP AEERRP 0.001 1000 PBBF PBBF 0.0005 500 0 0 25 50 75 100 25 50 75 100 N o d es N o d es Figure 1. Nodes Vs End-to-End Delay Figure 4.Nodes Vs Overhead Figure 1 shows the results of average end-to-end delay Figure 4 shows the results of routing overhead for thefor the 25, 50, 75 and 100. From the results, we can see nodes 25, 50, .100. From the results, we can see thatthat AEERRP scheme outperforms the PBBF scheme by AEERRP scheme outperforms the PBBF scheme byattaining low delay. attaining low overhead. Nodes Vs Energy Nodes Vs Packet Loss 0.4 1400 Energy(J) 1200 0.3 AEERRP 1000 0.2 800 AEERRP PBBF 0.1 600 PBBF 400 0 200 25 50 75 100 0 25 50 75 100 Nodes N o d es Figure 2. Nodes Vs Energy Figure 5. Nodes Vs Packet Loss Next, we measure the average energy consumption of Finally, we measure the average packet loss. Fromthe network. From Figure 2, we can see that, our Figure5, we can see that, our AEERRP has low packetAEERRP consumes less energy when compared with the loss when compared with the PBBF.PBBF. Nodes Vs Delivery Ratio V. CONCLUSION 100 80 In order to achieve high data reliability in wireless 60 sensor networks, most of the data forwarding protocols AEERRP 40 PBBF uses blind flooding or probability based broadcast forwarding, at the cost of high energy consumption. In 20 this paper, we have developed an Adaptive Energy 0 Efficient Reliable Routing Protocol (AEERRP) with the 25 50 75 100 aim of keeping the energy consumption low while N o d es achieving high reliability. In our proposed protocol, we estimate the density of a region using the neighborhood Figure 3. Nodes Vs Delivery Ratio information of nodes located in that region. The neighborhood information is collected using a topology Figure 3 shows the results of average packet delivery discovery scheme. The data forwarding probability isratio for the nodes 25, 50, .100. Clearly our AEERRP adaptively determined based on the measured loss conditions. So only for high loss rates, a node uses high 35© 2010 ACEEEDOI: 01.ijns.01.01.07
  • 6. ACEEE International Journal on Network Security, Vol 1, No. 1, Jan 2010transmission power to reach the sink and whenever the International Journal of Computer Science, Vol.34, No.8,loss rate is low, it adaptively reduces the transmission 2007.power. Since the source rebroadcast the data, until the [13] Matthew J. Miller and Indranil Gupta, "Practicalpacket loss is minimized, high data reliability is achieved. Exploitation of the Energy-Latency Tradeoff for Sensor Network Broadcast", in proceedings of Fifth Annual IEEEBy simulation results we have shown that the proposed International Conference on Pervasive Computing andprotocol achieves high reliability while ensuring low Communications Workshops, pp.318-322, 2007.energy consumption and overhead. [14] Michele Zorzi and Ramesh R. Rao,"Geographic Random Forwarding (GeRaF) for adhoc and sensor networks: REFERENCES multihop performance", IEEE Transactions on Mobile Computing, vol. 2, n. 4, Oct.-Dec. 2003, Doi:[1] Archana Bharathidasan and Vijay Anand Sai Ponduru, 10.1109/TMC.2003.1255650. "Sensor Networks: An Overview" technical report, [15] Dandan Liu , Xiaodong Hu , Xiaohua Jia, " Energy SURVEY paper. IEEE Infocom 2004. efficient information dissemination protocols by[2] Kemal Akkaya and Mohamed Younis, "A Survey on negotiation for wireless sensor networks", in proc. of Routing Protocols for Wireless Sensor Networks" Ad Hoc Journal on Computer Communications, vol. 29, no. 11, pp: Networks, Vol.3, pp.325-349, 2005. 2136- 2149, 2006.[3] Wendi Rabiner Heinzelman, Anantha Chandrakasan, and [16] Yuzhe Liu, Winston K.G. Seah,"A Priority-based Multi- Hari Balakrishnan, "Energy-Efficient Communication path Routing Protocol for Sensor Networks", in proc. of Protocol for Wireless Microsensor Networks", in 15th IEEE Intl. Symposium on Personal, Indoor and proceedings of 33rd Annual Hawaii International Mobile Radio Communications, vol. 1, pp: 216- 220, 5-8 Conference on System Sciences, Vol.2, pp.10,January September 2004. 2000. [17] Min Chen, Taekyoung Kwon, Shiwen Mao, Yong Yuan[4] Wendi Rabiner Heinzelman, Joanna Kulik, and Hari and Victor C.M. Leung, "Reliable and Energy-Efficient Balakrishnan, "Adaptive Protocols for Information Routing Protocol in Dense Wireless Sensor Networks",in Dissemination in Wireless Sensor Networks", in proc. of Intl. Journal on Sensor Networks, vol. 4, no. 12, proceedings of 5th annual ACM/IEEE international pp: 104- 117, 2007, Doi: 10.1504/IJSNET.2008.019256. conference on Mobile computing and networking, pp.174- [18] Zijian Wang, Eyuphan Bulut, and Boleslaw K. Szymanski, 85, 1999. "Energy Efficient Collision Aware Multipath Routing for[5] Chalermek, Ramesh Govindan and Deborah Estrin, Wireless Sensor Networks", in proc. of International "Directed Diffusion: A scalable and robust communication Conference on Communications, Dredsen, Germany, June paradigm for sensor networks", in proceedings of the 2009. International Conference on Mobile Computing and [19] Kavitha, C. Viswanatha, K.V. "An Energy Efficient Fault Networking, pp.56-67, 2000. Tolerant Multipath (EEFTM) Routing Protocol for[6] Ya Xu, John Heidemann and Deborah Estrin, "Geography- Wireless Sensor Networks", in proc. of IEEE Intl. Con. on informed Energy Conservation for ad hoc routing" in Advance Computing, pp: 746- 751, 6-7 March, Patiala, proceedings of 7th annual international conference on 2009, Doi: 10.1109/IADCC.2009.4809106. Mobile computing and networking, pp.70-84, 2001.[7] Brad Karp and H.T. Kung, "GPSR: Greedy Perimeter Stateless Routing for Wireless Networks", Proceedings of the 6th Annual ACmIEEE International Conference on Mobile computing and Networking, pp: 243- 254, Boston, Massachusetts, United States, August 2000.[8] Fan Ye, Gary Zhong, Songwu Lu and Lixia Zhang, "GRAdient Broadcast: A Robust Data Delivery Protocol for Large Scale Sensor Networks", in proc. of Wireless Networks, vol. 11, no. 3, pp: 285- 298, May 2005.[9] Yan Yu, Ramesh Govindan and Deborah Estrin, "Geographical and Energy Aware Routing: a recursive data dissemination protocol for wireless sensor networks", UCLA Computer Science Department Technical Report, TR-01-0023, pp: 1-11, May 2001.[10] Ossama Younis and Sonia Fahmy, "HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad- hoc Sensor Networks", IEEE Transactions on Mobile Computing, vol. 3, no. 4, pp: 366- 379, Oct- Dec 2004, Doi: 10.1109/TMC.2004.41.[11] Deepak Ganesan, Ramesh Govindan, Scott Shenker and Deborah Estrin “Highly-Resilient Energy-Efficient Multipath Routing in Wireless Sensor Networks", ACM SIGMOBILE Mobile Computing and Communications, Vol.5, No.4, pp.11-25, 2001.[12] Vidhyapriya and Vanathi, "Energy Efficient Adaptive Multipath Routing for Wireless Sensor Networks", 36© 2010 ACEEEDOI: 01.ijns.01.01.07

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