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.
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 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.
