A scalable model for energy load balancing in large-scale sensor networks

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