-In chapter 1, We have began with a general overview on wireless sensor networks. We have described various concepts, mechanisms, types, applications, and challenges in WSNs. We have presented several energy-efficient techniques so as to improve the network lifetime of WSNs. The coverage problem, the network lifetime, and the energy consumption modeling in WSNs have explained. A brief survey about coverage algorithms in literature is achieved in chapter 2.
-We have classified those works into centralized and distributed algorithms. We have given a brief comparison of the main characteristics of each approach. This part finally included in chapter 3, a comparative study of different evaluation tools dedicated to WSNs. In addition, we have illustrated a various commercial and free optimization solvers considering the main features of each one.
+In chapter 1, We started with a general overview on wireless sensor networks. We have described various concepts, mechanisms, types, applications, and challenges in WSNs. Several energy-efficient techniques so as to improve the network lifetime of WSNs have been presented. The coverage problem, the network lifetime, and the energy consumption modeling in WSNs have been explained. A brief survey about literature on coverage algorithms is achieved in chapter 2.
+We have classified those works into centralized and distributed algorithms. We have given a brief comparison of the main characteristics of each approach. Finally we have included in chapter 3 a comparative study of different evaluation tools dedicated to WSNs. In addition, we have illustrated various commercial and free optimization solvers considering the main features of each one.
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+In the second part of the dissertation, We have designed three new different optimization protocols, which schedules nodes’ activities (wake up and sleep stages) with the objective of maintaining a good coverage ratio while maximizing the network lifetime. We propose two-step approaches. Firstly, the field of sensing is divided into smaller subregions using the concept of divide-and-conquer method. Secondly, one of the proposed optimization protocols is applied in each subregion in a distributed parallel way to optimize both coverage and lifetime performances. The proposed protocols combine two efficient mechanisms: network leader election and sensor activity scheduling, where the challenges include how to select the most efficient leader in each subregion, the best
+representative active nodes that will optimize the network lifetime while taking the responsibility of covering the corresponding subregion.
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+In chapter 4, we have proposed an optimization protocol called Distributed Lifetime Coverage Optimization protocol (DiLCO), which optimizes the coverage and the lifetime of a wireless sensor network. DiLCO protocol is distributed in each sensor node in the subregions of the sensing field. It is implemented in each subregion simultaneously and independently. The proposed DiLCO protocol is a periodic protocol where each period consists of 4 phases: information exchange, leader election, decision, and sensing. The sensor nodes collaborate in each subregion to elect the leader. The leader applies activity scheduling based optimization in order to provide only one optimal set of active sensor nodes that takes the mission of sensing during this period. The performance of our DiLCO protocol has been evaluated by a series of simulations. We have presented a comparison between our proposed protocol and two other existing protocols known in the literature: DESK and GAF. The experimental results have validated our protocol and showed its efficiency in the optimization of the coverage and the lifetime compared to the two benchmarking methods.
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+Next, we propose in chapter 5 a method called Multiround Distributed Lifetime Coverage Optimization protocol (MuDiLCO), which is an extension of the DiLCO protocol introduced in chapter 4. MuDiLCO implemented an activity scheduling based optimization in order to provide multiple sets of active sensor nodes, for several rounds in the sensing phase. We have thus introduced an improved coverage optimization model that make a multiround optimization, whilst it was a single round optimization in DiLCO. We have conducted many simulations comparing the proposed MuDiLCO protocol for different number of rounds, as well as with DiLCO, DESK, and GAF.
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+In chapter 6, we have proposed an approach called Perimeter-based Coverage Optimization protocol (PeCO) in order to optimize the lifetime coverage, so that it provides activity scheduling which ensures sensing coverage as long as possible. Like DiLCO and MuDiLCO, PeCO protocol is distributed among sensor nodes in each subregion. The novelty of our approach, in comparison with DiLCO and MuDiLCO, lies essentially in the formulation of a new mathematical optimization model based on the perimeter coverage level to schedule sensors’ activities. A leader provides one schedule during the current period by executing the new integer program during the decision phase. The extensive simulation experiments have demonstrated that PeCO can offer longer lifetime coverage for WSNs.
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+Finally, we outlined some interesting issues that will be considered in our perspectives which are discussed in more detail next.
