Abstract
In this paper we propose an improvement to the GRPW algorithm for wireless sensor networks called GRPW-M , which collects data in a wireless sensor network (WSN) using a mobile nodes. Performance of GRPW algorithm algorithm depends heavily on the immobile sensor nodes . This prediction can be hard to do. For that reason, we propose a modified algorithm that is able to adapt to the current situation in the network in which the sensor node considered mobile. The goal of the proposed algorithm is to decrease the reconstruction cost and increase the data delivery ratio. In comparing the GRPW-M protocol with GRPW protocol in simulation, this paper demonstrates that adjustment process executed by GRPW-M does in fact decrease the reconstruction cost and increase the data delivery ratio . Simulations were performed on GRPW as well as on the proposed Routing algorithm. The efficiency factors that were evaluated was total number of transmissions in the network and total delivery rate. And in general the proposed Routing algorithm may perform reasonable well for a large number network setups.
Highlights
Remarkable advances have been made in microelectronicsmechanical system (MEMS) and wireless communication technologies
Greedy forwarding may lead into a dead end when there is no neighbor closer to the destination, and recovery strategy such as GPSR [10] is necessary to guaranty data packets can be delivered to the destination
In [12], Nazir proposes the Mobile Sink based Routing Protocol (MSRP) in which the sink movement strategy depend on the residual energy information from the cluster-heads and takes the movement based on the residual energy of the cluster-heads [12]
Summary
Remarkable advances have been made in microelectronicsmechanical system (MEMS) and wireless communication technologies This development has enabled sensors to collect contexts from the real world. It is important to choose a routing protocol for WSN with a mobile sink, because the efficient routing paths between the sensor node and the sink change with time. In greedy forwarding, each node just needs to know three pieces of information: its location, the location of neighbors, and the location of the sink. Many applications for sensor networks such as monitoring of forest fires, the remote meter reading,...For these cases,The Geographic routing of data in this type of network is an important challenge, Geographic routing uses nodes locations as their addresses, and forwards packets (when possible) in a greedy manner towards the destination. Since location information is often available to all nodes in a sensor network (if not directly, through a network localization algorithm) in order to provide location-stamped data or satisfy location-based queries, geographic routing techniques are often a natural choice
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