Energy Efficient Data Aggregation for Wireless Sesor Networks
Rabindra Bista, Jae‐Woo Chang · InTech eBooks · 2010
IntroductionA Wireless Sensor network (WSN) (Heinzelman et al., 2000;Yick et al., 2008) consists of a large number of spatially distributed autonomous resource-constrained tiny sensor devices which are also known as sensor nodes (Horton et al., 2002).WSNs have some unique features, for instance, limited power, ability to withstand harsh environmental conditions, ability to cope with node failures, mobility of nodes, dynamic network topology, communication failures, heterogeneity of nodes, large scale of deployment and unattended operation.Although sensor nodes forming WSNs are resource-constrained, i.e., limited power supply, slow processor and less memory, they are widely used in many civilian application areas, including environment and habitat monitoring, healthcare applications, home automation, traffic control and in military applications such as battlefield surveillance (Pottie & Kaiser, 2000).Because data from sensor nodes are correlated in terms of time and space, transmitting only the required and partially processed data is more meaningful than sending a large amount of raw data.In general, sending raw data wastes energy because duplicated messages are sent to the same node (implosion) and neighboring nodes receive duplicate messages if two nodes share the same observing region (overlapping).Thus, data aggregation, which combines data from multiple sensor nodes, has been actively researched in recent years.An extension of this approach is in-network aggregation (Considine et al., 2004;Madden et al., 2002;Bista et al., 2009) which aggregates data progressively as it is passed through a network.In-network data aggregation can reduce the data packet size, the number of data transmissions and the number of nodes involved in gathering data from a WSN.The most dominating factor for consuming precious energy of WSNs is communication, i.e., transmitting and receiving messages.Therefore, reducing generation of unnecessary traffics in WSNs enhances their lifetime.In addition, involving as many sensor nodes as possible during data collections by the sink node can utilize maximum resources of every sensor node.As a result, an adverse scenario will not happen in a WSN in which the sensor nodes closer to the sink run out of energy sooner than other nodes and the network loses its service ability, regardless of a large amount of residual energy of the other sensor nodes.