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Immune system-based energy efficient and reliable communication in wireless sensor networks

Atakan, Baris
Akan, Oezguer B.
Wireless sensor networks (WSNs) are event-based systems that rely on the collective effort of densely deployed sensor nodes. Due to the dense deployment, since sensor observations are spatially correlated with respect to 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 imperative 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 of great significance to determine the appropriate sampling frequency of sensor nodes to minimize energy consumption. In order to address these needs, the Distributed Node and Rate Selection (DNRS) algorithm which is based on the principles of natural immune system is developed. Based on the B-cell stimulation in immune system, DNRS selects the: most appropriate sensor nodes that send samples of the observed event, i.e., designated nodes. The aim of the designated node selection is to meet the event signal reconstruction 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 sampling 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 sampling frequencies of sensor nodes according to the congestion in the forward path and the event signal reconstruction distortion periodically calculated at the sink by Adaptive LMS Filter. Performance evaluation shows that DNRS provides the minimum number of designated nodes to reliably reconstruct the event signal and it regulates the sampling frequency of designated nodes to exploit the temporal correlation in the event signal with significant energy saving.