| Wireless Sensor networks (WSNs) are composed of a plenty of tiny sensor nodes with theability of sensing, computing and wireless communication. They are mainly for informationcollection of different environment and monitoring objects, which are finally tranmitting to sinkthrough wireless way. In recent years, researching of WSNs has become an enthusiasm. WSNshave come to licensing revenue in many industries. However, the key technologies are still inprobing.Due to limited power allocation of sensor node as well as inconvience of changing battery inmass-deployed or special applied situation, lifetime of sensor nodes depends on the life span ofbattery in a large degree. Therefore, drawdown of energy consumption and optimization of energyutilization of sensors plays a vital role for deep and overall practice of WSNs. It can greatlypromote the development process.This dissertation researches on energy optimization of WSNs, mainly from cooperativetransmission of WSNs, energy recovery and energy scavenging of sensor node, energycost-sharing among nodes, etc. It investigates many theories like computational intelligence,quantum theory, MAC resource allocation, and game theory, etc., concerns MAC layer andnetwork layer. Besides, it presents some protocols for network optimization, and makes detailedperformance analysis and verification through simulations and tests.The main contributions are as follows:A cooperative communication protocol in clustered WSNs as EEDC and its improvedversion EEIDC is presented.EEDC (Energy-Efficient Dynamic Cooperative Protocol) implements data transmission in adynamic clustering style. EEDC aims for monitoring the burst incidents. To improve theperformance of EEDC, it adds intelligence to EEDC with computational intelligence. Anintelligent Quantum Ant Colony Algorithm (IQACO) is proposed, whose intelligence originatesfrom strong neighborhood and high searching speed. IQACO is applied in EEDC, which makesEEDC update to Energy-Efficient Intelligent and Dynamic Cooperative Protocol (EEIDC).EEIDC has two crucial characteristics as cooperative and intelligent. Clustering method is furtheroptimized through computational intelligence: the conception of strong neighborhood based on ACO is proposed. When choosing cooperative nodes, neighborhood energy of node is taken intoaccount. Cooperation vacuum in case of node failure is avoided. On the base of our clusteredmodel, the relations among packet failuer rate, power consumption, and inter-cluster distance indifferent cluster size and numbers are analyzed. Besides, comparisons between our algorithm andother multi-hop algorithms are made.An Online Multi-Energy Optimization Based on Combined Access Mechanism(OMEOCA) for WSNs is designed.OMEOCA integrates battery recovery and environmental energy (RF energy) scavengingtechnology. RF energy scavenging is relevant to position of sensor node and activities ofneighboring nodes. Battery recovery aims to reclaim some available energy in battery aftersleeping, which can be applied through MAC duty-cycling schedule. A novel duty-cyclingschedule according to saturate threshold of battery recovery is proposed. Nodes can carry onenergy recovery in an appropriate sleeping period and increase its energy through RF energyscavenging from its active neighbors. The advantage of this method is: This method obtainsadditional energy for nodes by physical characteristics without disturbing other nodes. Throughadjusting of duty cycle, consideration on both costs of energy scavenging and saturation thresholdof energy recovery, total capacity of enengy recovery and energy scavenging can be maximizedfor this scheme. Beisdes, energy recovery is maximized in slight sleep as well. Simplicity andeffectiveness is satisfied in this scheme.An energy-efficient routing scheme based on clustered multi-cooperative model is given.Energy-saving in WSNs through cost-sharing cooperative game is investigated. Two separatedcost-sharing schemes are designed, which are among cluster heads and cluster membersrespectively. The distinct cooperation patterns and attributes of cost-sharing for the twocost-sharing schemes coincide with selection of cluster heads and cluster members respectively.Cluster member selection process chooses cluster members through a stragegyproof cost-sharingplan, which aims for optimal energy routing. Cluster head selection is carried out by constructionof cluster head coalition, which realizes cost sharing of clusters among candidate cluster heads.On the base of two cost-sharing schemes, a Cost Sharing Game-based CooperativeEnergy-Efficient Clustering algorithm (CSG-CEEC) is presented. The significance of CSG-CEECis: both cost of single node and whole network are pondered on. Thus tradeoff between individualcost and overall cost should be made when making decision. The main innovations of this thesis are:A bi-directional cooperating clustered architecture is proposed.Such architecture does not claim for the direct communication capability with sink node forcluster heads. Multi-cooperative transmission through cooperative cluster members can reduceenergy consumption. Traditional routes from source node to sink node are substituted by multiplecooperative paths. Clustered routing and data transmission are overlapped, which achievesdynamic and realtime transmission. Strong neighborhood is proposed to promote efficiency ofclustering, where neighborhood energy is considered during the selection of cluster head andcluster members. Quantum Ant Colony Algorithm is originally added into the cooperativetransmission protocol to provide intelligence.A novel on-line multi-energy optimization scheme is designed.Two energy recovery technologies as battery recovery effect and RF energy scavenging arecombined. Node operation is designed according to boundary conditions, which aims to maximizeenenrgy utilization ratio and prolong network lifetime. Based on physical characteristics, thismethod researches MAC layer and combines two energy reclaiming technologies into a MACscheduling based on TDMA/CSMA combined access. So far as we know, there are hardly anyresearches on this aspect.An energy cost allocation that originates from game theory is carried out.Based on clustered architecture, two cost sharing games for cooperative transmission andcommmon clustering affairs are designed. These two games are among cluster members andcluster heads respectively. Both do not care for profit but cost, which follows the single-objectivenature of WSNs. Cost sharing game among cluster members emphasises on cost sharing duringdata transmission. To guarantee competitive (efficiency), the sharing cost should be partly budgetbalanced. Cost sharing game among cluster heads requires cluster head to undertake all cost ofcommon affairs in clustering. A fair cost allocation is carried out according to position of clusterheads and residual energy. This cost sharing scheme is distinctly different with others and canchoose cluster heads and members wiser. |
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