A Scalable Model for Energy Load Balancing in Large-scale Sensor Networks

In this paper we propose a stochastic geometric model to study the energy burdens seen in a large scale hirarchical sensor network. The network makes a natural use of aggregation nodes, for compression, filtering or data fusion of local sensed data. Aggregation nodes (AGN) then relay the traffic to mobile sinks. While aggregation may substantially reduce the overall traffic on the network it may have a deleterious effect of concentrating loads on paths between AGNs and the sinks— such inhomogeneities in energy burdens may in turn lead to nodes with depleted energy reserves. To remedy this problem we consider how one might achieve more balanced energy burdens across the network by spreading traffic, i.e., using a multiplicity of paths between AGNs and sinks. The proposed model reveals, how various aspects of the task at hand impact the characteristics of energy burdens on the network and in turn the likely lifetime for the system. We show that the scale of aggregation and degree of spreading might need and can be optimized. Additionally if the sensing activity involves large amounts of data flowing to sinks, then inhomogeneities in the energy burdens seen by nodes around the sinks will be hard to overcome, and indeed the network appears to scale poorly. By contrast if the sensed data is bursty in space and time, then one can reap substantial benefits from aggregation and balancing.