Akademik Veri Yönetim Sistemi
1st International Conference on Bio-Inspired Models of Network, Information and Computing Systems, Madonna di Campiglio, İtalya, 11 - 13 Aralık 2006, ss.92-93
Wireless sensor networks (WSNs) are event-based systems that rely on the collective effort of dense deployed sensor nodes. Due to the dense deployment, since sensor observations are spatially correlated with respect to spatial location of sensor nodes, it may not be necessary for every sensor node to transmit its data. Therefore, due to the resource constraints of sensor nodes it is needed to select the minimum number of sensor nodes to transmit the data to the sink. Furthermore, to achieve the application-specific distortion bound at the sink it is also imperative to select the appropriate reporting frequency of sensor nodes to achieve the minimum energy consumption. In order to address these needs, we propose the new Distributed Node and Rate Selection (DNRS) method which is based on the principles of natural immune system. Based on the B-cell stimulation in immune system, DNR selects the most appropriate sensor nodes that send samples of the observed event, are referred to as designated nodes. The aim of the designated node selection is to meet the event estimation distortion constraint at the sink node with the minimum number of sensor nodes. DNRS enables each sensor node to distributively decide whether it is a designated node or not. In addition, to exploit the temporal correlation in the event data DNRS regulates the reporting frequency rate of each sensor node while meeting the application-specific delay bound at the sink. Based on the immune network principles, DNRS distributively selects the appropriate reporting frequencies of sensor nodes according to the congestion in the forward path and the event estimation distortion periodically calculated at the sink by Adaptive LMS Filter. Performance evaluation shows that DNRS provides the minimum number of designated nodes to reliably detect the event properties and it regulates the reporting frequency of designated nodes to exploit the temporal correlation in the event data whereby it provides the significant energy saving.