X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/fc2e307dd138aca4c04532a3e6bed0aadff80225..b526efd6a44846d1d89a70fedae4bf9bd2058885:/CONCLUSION.tex

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 \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. The ultimate goal is the coverage preservation and the extension of the network lifetime continuously and effectively when monitoring a certain area of interest. 
+In this dissertation, we have concentrated on the design of 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. The ultimate goal is the coverage preservation and the extension of the network lifetime continuously and effectively when monitoring a certain area of interest. 
 
 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.  
 
@@ -19,7 +19,7 @@ representative active nodes that will optimize the network lifetime while taking
 
 
 
-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 references.
+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.
 
 
 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. 
@@ -31,10 +31,10 @@ Finally, we outlined some interesting issues that will be considered in our pers
 
 
 \section{Perspectives}
+In this dissertation, we have focused on the lifetime area coverage optimization problem and we were interested only in energy-efficient distributed protocols, considering static homogeneous sensor nodes. Several parameters, constraints, and requirements can have an important impact on the coverage performance in WSNs.
+Thus, various scenarios parameters might need to be taken into consideration in the future, such as fault-tolerance, k-coverage, $\alpha$-coverage, adjustable sensorâs sensing range network, heterogeneous network, mobility, etc.
 
-In this dissertation, we have focused on the lifetime area coverage optimization problem and we have interested only in energy-efficient distributed protocols. 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 particle swarm optimization or evolutionary algorithms. A period will still consist of 4 phases, but the decision phase will compute the schedules for several sensing rounds which, aggregated together, define a kind of meta-sensing round. The computation of all cover sets in one step is far more difficult, but will reduce the communication overhead.
+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 particle swarm optimization or evolutionary algorithms. A period will still consist of 4 phases, but the decision phase will compute the schedules for several sensing rounds which, aggregated together, define a kind of meta-sensing round. The computation of all cover sets in one step is far more difficult, but will reduce the communication overhead.
 
 
 We also plan to design and propose a heterogeneous integrated optimization protocol in WSNs. This protocol would integrate 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, a leader node in our protocols, in the wireless sensor network. The cluster head will be selected in a distributed way and based on local information.
