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Research On Placement Problem Of Node In Wireless Sensor Networks

Posted on:2013-01-04Degree:DoctorType:Dissertation
Country:ChinaCandidate:N CheFull Text:PDF
GTID:1118330362462091Subject:Computer software and theory
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Wireless sensor networks (WSNs) consist of hundreds or even thousands of sensor nodes connected through wireless communications to form a multi-hop self-organization network, it is widely used in environmental monitoring, biological monitoring, battlefield rescue, etc.Since sensor nodes are often deployed in harsh conditions and even the areas where the human can not access, sensor nodes only can be deployed in random way which leads to the overall network performance is difficult to be guaranteed, thus there are a large number of researchers working on how to deploy some mobile nodes to improve network performance. Most of the previous deployment strategies utilize the mobility of sensor nodes to optimize the sense area coverage, the lack of deployment strategies associated with the distribution of existing fixed nodes makes deployment strategies can not work effectively in the area where sensor networks has existed, thus based on the analysis of the location of fixed nodes and the network topology, this paper studies on maximizing network life, network connectivity optimization, maximizing real-time coverage, minimizing real-time response these four issues and proposes corresponding mobile nodes'deployment strategies.Because sensor nodes in WSNs often die due to energy depletion, WSNs can not provide long-term reliable sense service, this paper focuses on how to extend reliable network sense service time through the mobility of the base station, in other words how to maximize the network life. This paper first introduces the concept of point energy density to measure residual sense energy of each point in area, through the analysis of point energy density distribution and point energy density consumed along with the mobility of the base station, we proposes Energy Density Balance Base Station Movement strategy (EDB-BSM) to balance the whole energy density distribution of sense area in order to extend the life of sensor networks. Through the Glomosim simulation, We compares our strategy with the fixed base station and base station movement in random way in terms of point energy density distribution and network life to verify the effectiveness of our strategy.Because sensor nodes in WSNs are usually randomly deployed, sensor nodes often die due to energy depletion or accidents happened, making the entire network connectivity is difficult to guarantee, but the connectivity of WSNs can determine the service quality of WSNs that information can be effectively delivered, then the second problem: network connectivity optimization problem is introduced in this paper. We focus on how to add minimum number of relay nodes into heterogeneous networks to restore connectivity of networks in order to decrease the network restoration costs. This paper re-derives approximation ratio of Graph Augmentation Based Relay Node Deployment (GA-RND) proposed in previous work when used in heterogeneous WSNs, and proposes Weighted Graph Augmentation Based Relay Node Deployment (WGA-RND) and Iterative Weighted Graph Augmentation Based Relay Node Deployment (IWGA-RND), then we prove the approximation ratio of our deployment strategies is 10. Finally, through the experiment in terms of varying the number of sensor nodes, varying high-capacity node communication radius RHA, and varying the number of HA-sensor nodes, the result shows IWGA-RND algorithm performs best, WGA-RND algorithm secondly, GA-RND algorithm worst, then we give the theoretical analysis of the result produced by these three algorithms used in different environment.Because whether sensor nodes in Wireless Sensor and actor Networks (WSANs) can be real-time responded determines whether the WSANs are applied effectively. When the number of actors is not enough that all the sensor nodes can achieve real-time response, we should make as many sensor nodes as possible can achieve real-time response, then the third problem: maximizing real-time coverage problem is introduced in this paper. We focus on how to deploy actor nodes in WSANs to maximize the number of real-time covered senor nodes in order to improve WSANs performance. Through the WSANs real-time analysis based on Voronoi diagram, we proposes the VORonoi-Based Maximize Real-Time Coverage deployment strategy (MRTC-VOR), and compare MRTC-VOR with the VORonoi-Based Maximize Sense Field deployment strategy (MSF-VOR) and the VECtor-Based Maximize Sense Field deployment strategy (MSF-VEC) these two typical sense area coverage strategies through the experiment. The result shows that MRTC-VOR can make WSANs achieves better real-time performance in terms of convergence speed, energy consumption, etc. MRTC-VOR can perform better and can be more effectively applied in WSANs.In fact it is another situation when the number of actor nodes is enough or even redundant, then providing an actor node deployment strategy which can guarantee that all sensor nodes are real-time covered is certainly not enough, we should optimize response delay achieved by sensor nodes, then the fourth problem: minimizing real-time response problem is introduced in this paper. We focus on how to deploy actor nodes in WSANs to minimize the maximal response delay achieved by sensor nodes in order to improve WSANs performance. Through the WSANs real-time analysis based on Voronoi diagram, we proposes the the VORonoi-Based Minimize Real-Time Response deployment strategy (MRTR-VOR), and compares MRTC-VOR with MSF-VOR and MSF-VEC these two typical sense area coverage strategies through the experiment, the result shows that MRTR-VOR can make WSANs achieves better real-time performance, in terms of convergence speed, energy consumption, etc. MRTC-VOR can perform better and can be more effectively applied in WSANs. Furthermore, judging whether the number of actor nodes is redundant or not is NP-hard, thus we provide the performance under the same experimental scenario of MRTR-VOR and MRTC-VOR in terms of application effect, convergence speed, energy consumption, etc.
Keywords/Search Tags:WSNs, WSANs, deployment strategy, network life, network connectivity, network real time
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