An efficient networks’ energy consumption and Quality of Services (QoS) are considered the most important issues, to evaluate the route quality of the designed routing protocol in Wireless Sensor Networks (WSNs). This study is presented an evaluation performance technique to evaluate two routing protocols: Secure for Mobile Sink Node location using Dynamic Routing Protocol (SMSNDRP) and routing protocol that used K-means algorithm to form Data Gathered Path (KM-DGP), on small and large network with Group of Mobile Sinks (GMSs). The propose technique is based on QoS and sensor nodes’ energy consumption parameters to assess route quality and networks’ energy usage. The evaluation technique is conducted on two routing protocols in two phases: The first phase is used to evaluate the route quality and networks’ energy consumption on small WSN with one mobile Sink Node (SN) and GMSs. The second phase, is used to evaluate the route quality and networks’ energy consumption on large network (four WSNs) with GMSs. The two phases are implementated by creating five sceneries via using NS2.3 simulator software. The implementation results of the proposed performance evaluation technique have demonstrated that SMSNDRP gives better networks’ energy consumption on small single network in comparison with KM-DGP. Also, it gives high quality route in large network that used four mobile SN, in contrast to KM-DGP that used sixteen mobile SNs. While in large network, it found that KM-DGP with sixteen mobile SNs gives better networks’ energy consumption in comparison with SMSNDRP with four mobile SNs.
Achieving energy-efficient Wireless Sensor Network (WSN) that monitors all targets at
all times is an essential challenge facing many large-scale surveillance applications.Singleobjective
set cover problem (SCP) is a well-known NP-hard optimization problem used to
set a minimum set of active sensors that efficiently cover all the targeted area. Realizing
that designing energy-efficient WSN and providing reliable coverage are in conflict with
each other, a multi-objective optimization tool is a strong choice for providing a set of
approximate Pareto optimal solutions (i.e., Pareto Front) that come up with tradeoff
between these two objectives. Thus, in the context of WSNs design problem, our main
contribution is to
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