X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/c46884fa798f3a13361244344e58a641ff9e6ad8..756ad5dc8d9d6c233545dc73899b374b9fce2618:/CONCLUSION.tex

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@@ -14,7 +14,7 @@ The first part of the dissertation has presented the scientific background inclu
 In chapter 1, we have 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.
 
-In the second part of the dissertation, We have designed three new different optimization protocols, which schedule 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
+In the second part of the dissertation, we have designed three new different optimization protocols, which schedule 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.