期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2016
卷号：14
期号：4
页码：1368-1375
DOI：10.12928/telkomnika.v14i4.4013
语种：English
出版社：Universitas Ahmad Dahlan
摘要：Wireless Sensor Network is a collection of independent nodes and create a network for monitoring purposes in various scenarios like military operation, environmental operation etc. WSN network size is increasing very rapidly these days, due to large network size energy consumption is also increased and it has small battery, lifetime of network decreases due to early death of nodes and it impact the overall system performance. Clustering is a technique for enhance the network lifetime in WSN. Here in this paper we propose a new multi-objective adaptive swarm optimization (MASO) technique for clustering and computes the maximum number of clusters, which is best suited for the network. Each cluster has cluster head and cluster members and performed the task of local information extraction. Cluster head gathers all the extracted information from member nodes and send it to the base station, where base station performed global information extraction from all the cluster head nodes and generate some useful result. In MASO technique, object is used to find the best global position for the node and compare with existing position value. If new value is better than the old value, than node moves to a new position and object update their table for this new position. We are considering error probability in transmission of data packet in one hop communication. Here obtained the results are compared with other research in terms of overall network lifetime and effect on network lifetime when the size of the network is changed. We have fine tuned the node’s decay rate and throughput of the network.
其他摘要：Wireless Sensor Network is a collection of independent nodes and create a network for monitoring purposes in various scenarios like military operation, environmental operation etc. WSN network size is increasing very rapidly these days, due to large network size energy consumption is also increased and it has small battery, lifetime of network decreases due to early death of nodes and it impact the overall system performance. Clustering is a technique for enhance the network lifetime in WSN. Here in this paper we propose a new multi-objective adaptive swarm optimization (MASO) technique for clustering and computes the maximum number of clusters, which is best suited for the network. Each cluster has cluster head and cluster members and performed the task of local information extraction. Cluster head gathers all the extracted information from member nodes and send it to the base station, where base station performed global information extraction from all the cluster head nodes and generate some useful result. In MASO technique, object is used to find the best global position for the node and compare with existing position value. If new value is better than the old value, than node moves to a new position and object update their table for this new position. We are considering error probability in transmission of data packet in one hop communication. Here obtained the results are compared with other research in terms of overall network lifetime and effect on network lifetime when the size of the network is changed. We have fine tuned the node’s decay rate and throughput of the network.
