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Energy consumption mitigation  routing protocols for large wsn's final Energy consumption mitigation routing protocols for large wsn's final Document Transcript

  • National Conference on Current Trends in Computer Science and Engineering - CSECONF2012 Energy consumption mitigation Routing Protocols for Large-Scale Wireless Sensor Networks Anil Kumar H1 , Suma 2, Manjunath CR3 , Dr Nagaraj GS4 1,2 Mtech 2nd sem,Dept of CSE,SET,Jain University 3 Asst prof,Dept of CSE,Jain University 4 Prof,Dept of CSE,RVCE,VTU suma_vaidya@yahoo.comAbstract: With the advances in micro-electronics, wireless energy of a sensor reaches a certain threshold, the sensorsensor devices have been made much smaller and more will become faulty and will not be able to functionintegrated, and large-scale wireless sensor networks properly, which will have a major impact on the network(WSNs) based the cooperation among the significant performance[1,2].amount of nodes have become a hot topic. “Large-scale” The routing protocols for large scale WSNs canmeans mainly large area or high density of a network. categorized as controlAccordingly the routing protocols must scale well to the • overhead reduction-based,network scope extension and node density increases. A • energy consumption mitigation-based andsensor node is normally energy-limited and cannot be • energy balance-based.recharged, and thus its energy consumption has a quitesignificant effect on the scalability of the protocol. In a II-Energy consumption mitigation-basedhierarchical routing protocol, all the nodes are divided into category: The routing protocols in this class aim toseveral groups with different assignment levels. The nodes mitigate the energy consumption. They exploit variouswithin the high level are responsible for data aggregation means to achieve this target, such as dynamic eventand management work, and the low level nodes for sensing clustering, multi-hop communication, cooperativetheir surroundings and collecting information. With focus communication and so on. These methods can consume theon the hierarchical structure, in this paper we provide an energy appropriately and avoid wasted energy [1].insight into Energy consumption mitigation routingprotocols designed specifically for large-scale WSNs. III - Data Gathering algorithm based on Mobile According to the different objectives, the protocols aregenerally classified based on different criteria such as Agent (DGMA)[3]control overhead reduction, energy consumption mitigation In terms of energy consumption reduction andand energy balance. This paper focuses on the study of network end-to-end delay decrease, a Data Gatheringenergy consumption mitigation to show how to mitigate the algorithm based on Mobile Agent (DGMA) is proposed forenergy consumption. the cluster-based wireless sensor network. where an emergent event occurs is clustered dynamically based onKeywords: large-scale wireless sensor networks, routing the event severity, by which the scale and lifetime ofprotocol. clusters are determined. In each cluster a mobile agent is utilized to traverse every member node to collect sensed data. In the higher level of the network, a virtual cluster is formed among the cluster heads and the base station, andI- Introduction multi-hop communication is adopted for sensed data WSN is widely considered as one of the most delivery to the base station (BS).important technologies for the twenty-first century. A WSN In DGMA, all the sensor nodes are in “restraining” statetypically consists of a large number of low-cost, low- and they are activated only when some emergent eventpower, and multifunctional wireless sensor nodes, with occurs. Then the nodes having monitored the event aresensing, wireless communications and computation clustered. After the event intension gets reduced, thecapabilities . These sensor nodes communicate over short clustered nodes will change to a “restraining” state for thedistance via a wireless medium and collaborate to sake of energy consumption reduction. In the cluster, theaccomplish a common task, for example, environment tree structure is used to save energy instead of single hopmonitoring, military surveillance, and industrial process communication between the sensor nodes and the clustercontrol.In many WSN applications, the deployment of1 head. After the cluster construction is complete, a route forsensor nodes is performed in an ad hoc fashion without the mobile agent, which is equipped on the cluster head, iscareful planning and engineering. Once deployed, the used to traverse all the member nodes for collecting thesensor nodes must be able to autonomously organize sensed event data. This process is started up by the clusterthemselves into a wireless communication network. Sensor head and repeated at every cluster member by broadcastingnodes are battery-powered and are expected to operate a request packet, and anticipating a reply from its eachwithout attendance for a relatively long period of time. In neighbor for getting residual energy, path loss, and eventmost cases it is very difficult and even impossible to intension information of the neighbor. To deliver thechange or recharge batteries for the sensor nodes. When the sensed data to the final destination (here the base station) in 57
  • National Conference on Current Trends in Computer Science and Engineering - CSECONF2012the higher level of the network a virtual cluster is formed b) The Data Aggregation on Mobile Agentwherein the base station acts as the cluster head. As in the The mobile agent consists of identification ID, routelocal cluster, a multi-hop communication is adopted. The information, data buffer and processing codes, in whichcurrent cluster head will select the node which is the closest data buffer mainly load the data distilled or fused data fromto the base station in the neighboring nodes as its next hop. sensor nodesIf the distance from all neighbor nodes to the base station islonger than that from the node itself, the node will IV - Dynamic Minimal Spanning Tree Routingcommunicate with the base station directly. When the Protocol (DMSTRP)[4]number of the sensor nodes increases, the energy DMSTRP is a cluster-based routing protocol,consumption in DGMA increases more slowly. uses Minimal Spanning Tree (MSTs) to replace clubs toFurthermore, the dynamic cluster formation feature further connect the node in the clusters in two layers of thereduces the energy consumption. The use of a mobile agent network: intra-cluster and inter-cluster. Because clubs arereduces energy consumption, but extends the delay for the less effective than a spanning tree in connecting the nodescluster head to collect all the sensed data from all the if the network area is larger, DMSTRP is an elegantmember nodes. The chain-like route delivery of data by the solution in larger network areas.cluster head makes the node closest to the base station (Low Energy Adaptive Clustering Hierarchy)LEACHoverloaded and destroys the reliability. chooses clubs as the basic topology of the network, asCluster-based wireless sensor network saves energy by shown in Figure 1 and managing clubs does not need multi-reducing the number of nodes communicating with base hops and thus makes the routing path simple. One stepstation. Compared to direct communication, cluster-based further in (Base Station Controlled Dynamic Clusteringmethod has a remarkable improving in energy-efficient. Protocol )BCDCP, the CHs are connected by a tree insteadDGMA includes dynamic clustering and Data Gathering of a club and the BS functions as the manager of the wholeBased on Mobile Agent for Emergent Event Monitoring network, so BCDCP is more energy-efficient than LEACH. DMSTRP improves BCDCP further by connecting nodes inDynamic Clustering clusters by MSTs. In each cluster, all the nodes includinga) Dynamic Clustering Based on Event Severity Degree: the CH are connected by a MST and then the CH acts asAfter wireless sensor network is deployed into the the leader to collect data from the nodes on the tree. On themonitoring environment, all nodes will be set to higher level, all the CHs connected by another MST“restraining” state rather than clustered. And they’re cooperate to route data towards the BS. The data fusionactivated just when some emergent event occurs. Then the process is handled during the packet transmission along thenodes will be clustered. The scale and lifetime of the tree route.clusters lie on the event severity degree. After the Obviously, DMSTRP consumes energy more efficientlystimulating intension is reduced, those activated nodes will than LEACH and BCDCP, because the averagechange to “restraining” state over again. The cluster-tree transmission distance between nodes is reduced through thestructure is used to save energy , with multi-hop rather than multi-hop intra-cluster and inter-cluster communications,single hop from the member nodes to the cluster head. and thus the energy dissipation of transmitting data is potentially reduced. Furthermore, due to the reasonable schedule, the transmission collision is alleviated andb) The Construction of Virtual Cluster: Generally, single- DMSTRP can achieve shorter delay compared withhop communication is taken between the cluster heads and LEACH and BCDCP. But the transmission schedulethe base station in spite of long distance, in which those creates more overhead.cluster heads away from the base station always have aweak lifetime because of more energy consumption led by V - Hierarchical Geographic Multicast Routinglong-distance. A multi-hop virtual cluster is formed with (HGMR).[5]base station as the cluster head. The path from the cluster HGMR aims at enhancing data forwarding efficiency andhead to base station can be searched as follows. The cluster increasing the scalability to a large-scale network. HGMRheads always select the node which is the closest to base seamlessly incorporates the key design concepts of thestation in the neighbor nodes as its next hop. If the distance Geographic Multicast Routing (GMR) and Hierarchicalfrom all neighbor nodes to base station is longer than that Rendezvous Point Multicast (HRPM) protocols, andfrom the node itself to base station, the node will optimizes the two routing protocols in the wireless sensorcommunicate with base station directly. network environment. HGMR starts with a hierarchical decomposition of a multicast group into subgroup of manageable size using HRPM’s key concept of mobileData Gathering Based on Mobile Agent for Emergent geographic hashing. Within each subgroup, HGMR usesEvent Monitoring GMR’s local multicast scheme to forward a data packeta) Dynamic Route Planning of Mobile Agent: along multiple branches of the multicast tree in oneFor an emergent event monitoring scene, when some event transmission. In HGMR, the multicast group is divided intooccurs, only those nodes in event area would be activated subgroups using the mobile geographic hashing idea: theto cluster. The selection of the next hop for mobile agent deployment area is recursively partitioned into equal-sizednot only bases on energy consumption and path loss, but square sub-domains called cells, where d is decompositionalso the stimulated intension received by the nodes, in index depending on the encoding overhead constraints, andwhich the discrete emergent event is under consideration. each cell consists of a manageably-sized subgroup of 58
  • National Conference on Current Trends in Computer Science and Engineering - CSECONF2012members. An Access Point (AP) is responsible for all joint clustering and optimal cooperative routing, wheremembers in its cell, and APs are managed in turn by a neighboring nodes dynamically form coalitions andRendezvous Point (RP). The role of each AP or RP is cooperatively transmit packets to the next hop destination.mapped to some unique geographic location by a simple The cooperative sensor network can be modeled as anhash function. The node that is currently closest to that edge-weighted graph, based on which minimum energylocation then serves the role of AP/RP, and routing to the cooperative routing is characterized by using the standardAP/RP is conveniently achieved by geographic routing. To shortest path algorithm.We study two interesting cases: 1)join a hierarchically decomposed multicast group, a node For the case where the delay can be expressed in terms offirst hashes the multicast group identifier (GID) to obtain the number of hops, we use the bi-section method to findthe hashed location of the RP via a hashed function and the maximum throughput routing; 2) For large scalesends a JOIN message to the RP, which is the same as in networks where the end-to-end delay can be approximatedthe flat domain scenario. After receiving the value of the as the product of the number of hops and the average one-current d of the hierarchy from the RP, the node utilizes the hop delay, we present a polynomial time algorithm to findhash function with d and the node’s location to compute the the maximum throughput routing. the energy efficienthashed location of the AP belonging to its cell. Note that cooperative routing can enhance the performance of WSNscomputing the hashed location assumes that all nodes know significantly.the approximate geographic boundaries of the network.After that the source builds an overly tree, the Source → We have taken some initial steps to investigate distributedAPs tree, whose the vertices are active APs in a topology cooperative geographic routing, building on nodegraph; and an AP → Members overly tree is also built from cooperation and traditional geographic routing. Asthe AP, considering each member as the vertex. 2 d illustrated in Fig. 1, for a given source-destination pair, theWhen a source needs to send data packets, it utilizes the routing problem in a coalition-aided network was treated asunicast-based forwarding strategy belonging to HRPM to a multiplestage decision problem, where at stage i, thepropagate data packets to each AP along the Source → APs coalition head, denoted as CHi, first broadcasts datatree. In each cell, adjusting the value of d, the number of packets to all nodes within its coalition and looks for themembers for which an AP is responsible does not increase next stage coalition to forward the packet to. Once the nexttoo much. Therefore, GMR’s cost over progress optimizing stage CH, denoted by CHk, was chosen, CHi coordinatesthe broadcast algorithm, which is used to select the next the nodes within its coalition to cooperatively forward therelay node at each hop, contributes to reduce the number of packet to CHk. This process continued until the data weredata transmissions while maintaining a low encoding forwarded to the destinationoverhead compared with the unicast communication.Sensor nodes running GMR use the position of theirneighbors to select the subgroup which is the best one todeliver the message towards the destination, and theselected neighbors can reduce most the total route todestination. When no neighbor of the current node canreduce the route to the destination, face routing is used tocircuitously search the path to the destination. In HGMR,the geographic hashing algorithm makes the membershipmanagement very simple with almost zero cost. Accordingto the number of the nodes which play the different roles,HGMR selects the transmission methods for differenthierarchies in reason, which makes the routing energy-efficient and scalable. However, the RP is in charge of toomuch missions in HGMR, which may bring the problem ofrapid energy consumption and make the entire networkcollapse. It focus on joint optimal clustering and cooperative routing.HGMR starts with a hierarchical decomposition of a Consider a cooperative sensor network, where a node withmulticast group into subgroups of manageable size (i.e. data would first multicast the packet to a subset of itsencoding overhead) using HRPM’s key concept mobile neighbors, and then ask them to dynamically form ageographic hashing. Within each subgroup, HGMR uses coalition, and cooperatively transmit the packet to the next-GMR’s local multicast scheme to forward a data packet hop destination. The corresponding energy consumption isalong multiple branches of the multicast tree in one the sum of the multicast cost and thetransmission. Thus, HGMR can simultaneously achieve cooperative transmission cost. Intuitively, when the numberenergy efficiency (through higher forwarding efficiency of nodes in a coalition increases, the cooperativeutilizing multicast advantage) and scalability (through low transmissionoverhead hierarchical decomposition). cost would decrease, but the multicast cost would increase, and vice versa.VI - Joint Clustering and Optimal CooperativeRouting (JCOCR):[6] 59
  • National Conference on Current Trends in Computer Science and Engineering - CSECONF2012JOINT CLUSTERING AND MINIMUM ENERGYCOOPERATIVE ROUTING includes a) Optimal Coalition Size: Consider a sensor network,where each node has a strict power constraint Pmax. Dataneed to be routed from a source node S to a destination VII- CONCLUSIONnode D. In each transmission, an intermediate node wouldmulticast the packet to a subset of its neighbors, and ask the At present, routing in large-scale WSNs is a hot researchnodes in the subset to dynamically form a coalition and topic with a limited but rapidly growing set of efforts beingcooperatively transmit the packet to next stage destination published. This paper is contribution to study on 4 various(point-to-multiple-point transmission first, and then routing protocols of Energy-Consumption Mitigation inmultiple-point-to-point transmission). During the routing large-scale WSN’s.process, the number of neighboring nodes that participate With the increasing functionalities available to a wirelessin the cooperative transmission, i.e., the size of the dynamic sensor node, more complicated tasks which involve morecoalition, plays a key role. Note that the energy cost of each energy consumption and network overhead may betransmission is the sum of the multicast cost and the assigned to the sensor nodes. To increase energy efficiencycooperative cost. Intuitively, a larger coalition would and scalability of the network still remains a challengingreduce the cooperative cost, but may require more multicast research to reach nodes further away, whereas a smallercoalition would require less multicast energy but highercooperative cost. Thus motivated, we characterize the REFERENCESoptimal coalition size to minimize the transmission cost. [1] Changle Li *, Hanxiao Zhang, Binbin Hao and Jiandong Li, “A Survey on Routing Protocols for Large-Scale Wireless Sensorb) Minimum Energy Cooperative Routing: The minimum Networks”, cooperative routing problem (MECR) can be [2] Al-Karaki, J.N.; Kamal, A.E., “ Routing techniques in wirelessdefined as follows. Definition : (MECR) The Instance is sensor networks: A survey”. IEEE Wirel. Commun. 2004, 11, 6-given by an edge-weighted directed graph G = (V, E,C, γ) 28.and a source destination pair S-D. Let p be a path in G and [3] Lingyun Yuan, Xingchao Wang, “Study on Data GatheringC(p) be the sum of the costs over the edges on p, C(p) Algorithm Based on MobileAgent and WSN for Emergent Event=∑e∈ C(e). The Problem is to find the optimal path po p Monitoring”, Yunnan Normal University Kunming, China.such that C(po) is minimized. The routing problem [4] Guangyan Huang1, Xiaowei Li1, and Jing He2, “Dynamic Minimal Spanning Tree Routing Protocol forformulated above is a shortest path routing problem on the Large Wireless Sensor Networks” Advanced Test Technologynew directed graph G, and can be solved by the well- Lab., Institute of Computing Technology,known Dijkstra’s algorithm. The minimum energy Chinese Academy of Sciences, Beijing, China, 2006 IEEEcooperative routing would achieve better energy saving, [5] Dimitrios Koutsonikolas1, Saumitra Das1, Y. Charlie Hu1, andbecause of the following reasons. 1) It exploits optimal Ivan Stojmenovic2,3, “Hierarchical Geographic Multicast Routingpower allocation within each coalition to reduce the forWireless Sensor Networks”, The University of Birmingham,cooperative transmission cost. 2) It characterizes the United Kingdomoptimal coalition size to minimize the energy cost of each 3SITE, University of Ottawa, Ontario, Canada, 2007 IEEEtransmission. 3) It chooses the routing path based on global [6] Weiyan Ge and Junshan Zhang, Guoliang Xue, “Joint Clustering and Optimal Cooperative Routinginformation instead of local information. In wireless sensor network”,IEEE 2008. 60
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