Wireless Sensor Network Lifetime Maximization

In recent years,Wireless Sensor Network(WSN)is widely investigated,due to its cheapcost,robustness and flexibility.It is comprised of a set of sensor nodes equipped with sensors(one or more),micro-processor,power source(battery in general),and radio transceivers.With these four components,sensors in WSNs can well accomplish two main tasks: 1)to monitor or control the desired physical phenomena,2)to transmit the sensed data in the appropriate state through single-hop or multi-hop toward the sink for further processing,through a designed protocol under some constraints like;power limitation,sensing coverage limitation and memory capacity.Due to the importance of WSNs provided tasks,and its advantages such as: Cheapness,scalability,robustness,reliability,high responsiveness,mobility,and power efficiency,it is necessary to keep the WSN functionality as long as possible.This thesis addresses the issue of maximizing the lifetime of Wireless Sensor Networks(WSNs).First,it proposes a new technique to find and extract the Overlapped Sub-regions C2 & C3,and the Maximum Covered Regions in WSN by using Net Arcs(NA)Method.NA method uses the idea of graph theory,and transforms the graph model from theory state to the real time,additionally,it uses some algebra techniques,to find all overlapped covered sub-regions(C2)and(C3),and the maximum covered regions in the given network,finding these sub-regions and ensuring the connectivity between them,helps the researchers to use other optimization methods based on coverage to maximize the network lifetime.Second,at the same aim,the Energy Efficient Balanced Tree-Based Routing Protocol called EEBTR has been proposed.EEBTR is designed to achieve two mains goals: 1)A load-energy balance between nodes located at the same level,by giving in each level of the tree,a same number of children to nodes.2)Extensive study and implementation,in both random and deterministic deployments.The simulation results have shown a save of energy of more than 40% when sending data via EEBTR,instead of GTR,it can be explained that,unlike the nodes in GTR protocol,the nodes located at the same level in EEBTR protocol,have approximately the same number of children,which means the same probability to be loaded.Consequently,EEBTR improves GTR performance in terms of both network lifetime and energy-efficiency.Furthermore,the study has addressed different schemes(random and triangular),the simulation results proved the effectiveness,the robustness and the adaptiveness of the proposed EEBTR for both random and deterministic deployment.Third,a balanced tree-based using energy saver algorithm protocol called BTRES is proposed.The proposed BTRES aims to let all sensors located at the same level in the tree,consume a similar energy,it uses the same idea proposed in EEBTR,but with the investigation of a new energy saver strategy,to improve the performance of the balanced tree-based protocol.BTRES protocol based on two-tiers process.The first tier,builds a BTR(the balanced tree-based)by processing on three phases: Building the tree phase,Children Level Balancing(CLB)phase,and Node Degree Balancing(NDB)phase.The second tier,for each time any node loses E_loss of its energy,the Energy Saver Algorithm(ESA)starts running to conserve the residual energy in this current node.The BTRES protocol with the idea of making packets travelling via a balanced tree,and with the idea of updating the tree by running the Energy Saver Algorithm(ESA),in each time the node loses E_lossof its energy,such that this value is concluded from the simulation for better results,and with the consideration of the three criteria: depth,transmission distance and residual energy.The simulation results show that BTRES improves ETR performance in term of energy balancing and network lifetime by more than 70%,while it improves the EEBTR lifetime by approximately 30%,moreover BTRES increases the network lifetime by more than 30% in DHA protocol.At the other side,BTRES improves DHA performance in term of energy consumption by more than 40%,while it improves the EEBTR lifetime by approximately 15%,while BTRES consumes approximately the same energy like in ETR protocol.Thus,BTRES proved better performance than ETR,EEBTR,and DHA in terms of energy efficiency and network lifetime.Fourth,to maximize the wireless sensor network lifetime,a novel Probabilistic and Deterministic Tree-based Routing protocol(PDTR)is proposed.PDTR builds a tree from the leaves(source nodes)to the head(sink node),according to the best elements in the initial probabilistic routing table,measured by the product of hops-count distribution,and transmission distance distribution,to select the best tree-paths.Each sender node forwards the received data to the next hop via the deterministic built tree.After that,when any node loses l_eof its energy,PDTR updates the tree at that node.This update links probabilistically one of that node's children to a new parent,according to the updated probabilistic routing table,measured by the product of the updated: Hops-count distribution,transmission distance distribution,and residual energy distribution at the loss of l_eenergy.By implementing the control parameters in each distribution,PDTR shows the impact of each distribution in the routing path.These control parameters are oriented by the user for different performances.The simulation results prove that selecting the initial best paths to root the packets via unicast,then improving the tree at the node with loss of energy by rooting the packets via anycast,leads to better performance in terms of energy consumption and network lifetime.Finally,this thesis proposed a novel Three-Distribution Tree-based Routing protocol called(TDTR),designed for the heterogeneous wireless sensor networks HWSNs with Multi-levels Energy,to achieve better performance in term of energy consumption and network lifetime.The present protocol consists of two phases: First phase,builds the tree based on the minimum product of three factors: distance-to-sink,transmission distance and proportional residual energy.Each sender node in the network selects the best parent with the minimum value,among its strong parent set,to gather the collected data.After each data transmission round,to maintain the load balancing,the second phase starts to run,from the last level L=l-1 upwards to the level L=2,such that: the tree has |L|=l levels,then when L=2,the process starts to run from level L=2,downwards to level L=l-1.The second phase process,aims to find in each level the over-worker node and make it selecting probabilistically its forwarder,according to its forwarding probability.The simulation results proved showed that TDTR improved BEENISH protocol lifetime NL with more than 70%,while it lives two-times more than EDEEC protocol lifetime NL.Moreover,we can conclude that our proposed TDTR sends 20% more data than in BEENISH protocol,while it sends two times more quantity of data,comparatively than the quantity sent it EDEEC.Hence,our proposed protocol offers more robustness,effectiveness,lifetime,and data deliverance,comparatively than EDEEC and BEENISH.