X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/eeb8b4a44243858ec0e0c5e0828883afa38ecb67..e17c9931e4c1607fff951e7ac261dcc949249ead:/CONCLUSION.tex

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@@ -11,7 +11,10 @@ The first part of the dissertation has presented the scientific background inclu
 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 the second part of the dissertation, We have designed a new three 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 in WSNs. This part proposes a 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 the coverage and lifetime performances.
+In the second part of the dissertation, We have designed a new three 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 in WSNs. This part proposes a 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 the 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 and the best
+representative active nodes that will optimize the network lifetime while taking the responsibility of covering the corresponding subregion.
+
+
 
 In chapter 4,