Update by Ali

[ThesisAli.git] / CONCLUSION.tex

diff --git a/CONCLUSION.tex b/CONCLUSION.tex
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+++ b/CONCLUSION.tex
@@ -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 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.
 
 
 representative active nodes that will optimize the network lifetime while taking the responsibility of covering the corresponding subregion.
 
 


@@ -28,7 +28,7 @@ In chapter 6, we have proposed an approach called Perimeter-based Coverage Optim
 the objective of maintaining a good  coverage ratio while maximizing the network
 lifetime. 
 %in order to optimize the lifetime coverage, so that it provides activity scheduling which ensures sensing coverage as long as possible.
 the objective of maintaining a good  coverage ratio while maximizing the network
 lifetime. 
 %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, the 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.
+ Like DiLCO and MuDiLCO, the 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 mixed-integer program during the decision phase.  The extensive simulation experiments have demonstrated that PeCO can offer longer lifetime coverage for WSNs.

 
 Finally, we outline some interesting issues that will be considered in our perspectives which are discussed in more detail next.
 
 
 Finally, we outline some interesting issues that will be considered in our perspectives which are discussed in more detail next.
 


@@ -38,14 +38,13 @@ Finally, we outline some interesting issues that will be considered in our persp
 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 sensor’s sensing range network, heterogeneous network, mobility, etc.
 
 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 sensor’s sensing range network, heterogeneous network, mobility, etc.
 

-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.  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. The cluster head will be selected in a distributed way and based on local information.
 
 
 
 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. The cluster head will be selected in a distributed way and based on local information.
 

-We plan to extend our PeCO protocol so that the schedules are planned for multiple sensing periods. We also want to improve our mathematical model to take into account heterogeneous sensors from both energy and node characteristics point of views. 
+We plan to extend our PeCO protocol so that the schedules are planned for multiple sensing periods. We consider the use of optimization methods such as particle swarm optimization or evolutionary algorithms to obtain quickly near optimal solutions. The computation of all cover sets in one step is far more difficult, but will reduce the communication overhead.  We also want to improve our mathematical model to take into account heterogeneous sensors from both energy and node characteristics point of views. 

 
 Finally, it would be interesting to implement our protocols using a sensor-testbed to evaluate it in real world applications.
 
 
 Finally, it would be interesting to implement our protocols using a sensor-testbed to evaluate it in real world applications.