X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/c5833cac353b334ae2450046541cb61c6b5b54e3..433012874584d03b48d32d4cdba05eeb3b28dbe6:/CONCLUSION.tex

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-\chapter*{Conclusion and Perspectives\markboth{Conclusion and Perspectives}{Conclusion and Perspectives}}
-\label{chII}
-\addcontentsline{toc}{chapter}{Conclusion and Perspectives}
+%\chapter*{Conclusion and Perspectives\markboth{Conclusion and Perspectives}{Conclusion and Perspectives}}
+%\label{ch7}
+%\addcontentsline{toc}{chapter}{Conclusion and Perspectives}
 
-\section*{Conclusion}
+\chapter{Conclusion and Perspectives}
+\label{ch07}
+
+\section{Conclusion}
 
 In this dissertation, we have concentrated on proposing a distributed optimization protocols so as to prolong the lifetime of wireless sensor networks. We have addressed the problem of the area coverage and the lifetime optimization in wireless sensor networks, where the ultimate goal is the coverage preservation and the extension of the network lifetime continuously and effectively when monitoring a certain area (or region) of interest. 
 
-\section*{Perspectives}
+The first part of the dissertation has presented the scientific background including WSNs, brief survey of related works, and evaluation tools as well as optimization solvers.  
+
+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 nodesâ 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, 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 has been 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 so as to provides 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 other two existing and known in the literature, DESK and GAF. The experimental results have validated our protocol and showed their efficiency in the optimization of the coverage and the lifetime compared to existing methods.
+
+
+Next, we propose a method called Multiround Distributed Lifetime Coverage Optimization protocol (MuDiLCO)  in chapter 5, to maintain the coverage and to improve the lifetime in wireless sensor networks. MuDiLCO protocol is an extension of the DiLCO protocol introduced in chapter 4.  In MuDiLCO, the protocol  has implemented activity scheduling based optimization in order provides a multiple set of active sensor nodes for several rounds in the sensing phase. We have introduced an improved coverage optimization model that make a multiround optimization, whilst it was a single round optimization in DiLCO. We have conducted several sets of simulations comparing the proposed MuDiLCO protocol for different number of rounds as well as with other existing coverage methods like DESK and GAF. 
+
+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. PeCO protocol is distributed among sensor nodes in each subregion. The novelty of our approach lies essentially in the formulation of a new mathematical optimization model based on the perimeter coverage level to schedule sensorsâ activities. The 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 in comparison with some other protocols.
+
+Finally, we outline some interesting issues that we will consider in our perspectives which are discussed in more detail next.
 
 
 
+\section{Perspectives}
 
+In this dissertation, we have focused on the lifetime area coverage optimization problem and we are interested only in energy-efficient, distributed and parallel protocol. Various scenarios might need to be taken into consideration such as fault-tolerance, k-coverage, $\alpha$-coverage, adjustable sensorâs sensing range network, heterogeneous network, mobility, etc. In the future, we will concentrate on the following work:
 
+In chapter 4,  We have studied the impact of the number of subregions chosen to subdivide the area of interest, considering different network sizes. The optimal number of subregions will be investigated in the future. We also plan to study and propose a coverage protocol, which computes all active sensor schedules in one time, using optimization methods such as swarms optimization or evolutionary algorithms. The round will still consist of 4 phases, but the decision phase will compute the schedules for several sensing phases which, aggregated together, define a kind of meta-sensing phase. The computation of all cover sets in one time is far more difficult, but will reduce the communication overhead.
 
 
+In chapter 5, we plan to design and propose a heterogeneous integrated optimization protocol in WSNs. This protocol integrates three energy-efficient (coverage, routing and data aggregation) protocols so as to extend the network lifetime in WSNs. The sensing, routing, and  aggregation jobs are also challenges in WSNs. This integrated optimization protocol will be executed by each cluster head in the wireless sensor network. The cluster head will be selected in a distributed way and based on local information.
 
+In chapter 6, We plan to extend our framework so that the schedules are planned for multiple sensing periods. We also want to improve our integer program to take into account heterogeneous sensors from both energy and node characteristics point of views. Finally, it would be interesting to implement our protocol using a sensor-testbed to evaluate it in real world applications.
 
 
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