Sensor networks are characterized by limited energy, processing power, and bandwidth capabilities. These limitations become particularly critical in the case of event-based sensor networks where multiple collocated nodes are likely to notify the sink about the same event, at almost the same time. The propagation of redundant highly correlated data is costly in terms of system performance, and results in energy depletion, network overloading, and congestion. Data aggregation is considered to be an effective technique to reduce energy consumption and prevent congestion in wireless sensor networks. In this paper, we derive a number of important insights concerning the data aggregation process, which have not been discussed in the literature so far. We first estimate the conditions under which aggregation is a costly process in comparison to a non aggregation approach, by considering a realistic scenario where the processing costs related to aggregation of data are not neglected. We also consider that aggregation should preserve the integrity of data, and therefore, the entropy of the correlated data sent by sources can be considered in order to both decrease the amount of redundant data forwarded to the sink and perform an overall lossless process. We also derive the cumulative and the probability distribution functions of the delay in an aggregator node queue, which can be used to relate the delay to the amount of aggregation being considered. The framework we present in this paper serves to investigate the tradeoff between the increase in data aggregation required to reduce energy consumption, and the need to maximize information integrity, while also understanding how aggregation impacts the network propagation delay of a data packet.
Wireless sensor networks Data aggregation Entropy Energy consumption Delay