Genetic Algorithm based Cluster Head Selection for Optimimized Communication in Wireless Sensor Network

Wireless Sensor Network (WSNs) utilizes conveyed gadgets sensors for observing physical or natural conditions. It has been given to the steering conventions which may contrast contingent upon the application and system design. Vitality administration in WSN is of incomparable significance for the remotely sent vitality sensor hubs. The hubs can be obliged in the little gatherings called the Clusters. Clustering is done to accomplish the vitality effectiveness and the versatility of the system. Development of the group likewise includes the doling out the part to the hub based on their borders. In this paper, a novel strategy for cluster head selection based on Genetic Algorithm (GA) has been proposed. Every person in the GA populace speaks to a conceivable answer for the issue. Discovering people who are the best proposals to the enhancement issue and join these people into new people is a critical phase of the transformative procedure. The Cluster Head (CH) is picked using the proposed technique Genetic Algorithm based Cluster Head (GACH). The performance of the proposed system GACH has been compared with Particle Swarm Optimization Cluster Head (PSOCH). Simulations have been conducted with 14 wireless sensor nodes scattered around 8 kilometers. Results proves that GACH outperforms than PSOCH in terms of throughput, packet delivery ratio and energy efficiency

Genetic Algorithm based Cluster Head Selection for Optimimized Communication in Wireless Sensor Network

Wireless Sensor Network (WSNs) utilizes conveyed gadgets sensors for observing physical or natural conditions. It has been given to the steering conventions which may contrast contingent upon the application and system design. Vitality administration in WSN is of incomparable significance for the remotely sent vitality sensor hubs. The hubs can be obliged in the little gatherings called the Clusters. Clustering is done to accomplish the vitality effectiveness and the versatility of the system. Development of the group likewise includes the doling out the part to the hub based on their borders. In this paper, a novel strategy for cluster head selection based on Genetic Algorithm (GA) has been proposed. Every person in the GA populace speaks to a conceivable answer for the issue. Discovering people who are the best proposals to the enhancement issue and join these people into new people is a critical phase of the transformative procedure. The Cluster Head (CH) is picked using the proposed technique Genetic Algorithm based Cluster Head (GACH). The performance of the proposed system GACH has been compared with Particle Swarm Optimization Cluster Head (PSOCH). Simulations have been conducted with 14 wireless sensor nodes scattered around 8 kilometers. Results proves that GACH outperforms than PSOCH in terms of throughput, packet delivery ratio and energy efficiency

AUV Data Gathering in Underwater Wireless Sensor Networks

[[notice]]補正完畢[[conferencetype]]兩岸[[conferencedate]]20150712~20150714[[ispeerreviewed]]Y[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]安徽省滁州市[[countrycodes]]CH

Analysis on Data Collection with Multiple Mobile Elements

Abstract-Exploring mobile elements to conduct data collection in wireless sensor networks offers a new approach to reducing and balancing the energy consumption of sensor nodes; however, the resultant data collection latency may be large due to the limited travel speed. Many research efforts have been made on reducing the data collection latency with the scenario where a single mobile element is available. A potential problem with this approach is the scalability, and a straightforward solution is to employ multiple mobile elements to collect data collaboratively. In this paper, the network where multiple homogeneous mobile elements are available is modeled as an M/G/c queuing system, and insights on the data collection performance are obtained through theoretically analyzing the measures of the queue. In addition, a heuristic formula to determine the optimal number of mobile elements is proposed based on this model. The accuracy of our modeling and analysis, along with the performance evaluation of the proposed heuristic formula, is verified through extensive simulation

Sink Mobility Schemes in Wireless Sensor Networks for Network Lifetime Extension

Sensor nodes in Wireless Sensor Networks (WSNs) are normally battery-powered and remain stationary after deployment. When a sensor node runs out of energy it will no longer provide sensing and data processing. This can lead to a huge loss in the network due to the routing path re-allocation and failure of sensing and reporting events in the environment. Hence energy conservation has been receiving increased attention in WSN research works. The concept of mobile sink has been recently introduced for WSNs in order to improve the overall performance of WSNs as it shifts the burden of energy consumption from the sensor nodes to sink nodes, which are typically considered to have unconstrained energy supply and larger computational power. In this thesis we present two sink mobility schemes: Load Base sink Movement (LBM) and Residual Energy Aware Routing (REAR) to prolong network lifetime in a random event-driven scenario. LBM computes the optimal tentative sink node position considering both the geographical distance from sensors to sink and transmission load of sensors as well. REAR is a routing strategy that considers the residual energy of sensors when establishing routing paths. Experimental results confirm that the proposed schemes can significantly extend the network lifetime, compared to existing techniques