Fast and energy efficient data storage for information discovery in multi-dimensional WSNs

Multidimensional WSNs are deployed in complex environments to sense and collect data relating to multiple attributes (multi-dimensional data). An efficient information discovery for multi-dimensional WSNs deployed in mission-critical environments has become an essential research consideration. Timely and energy efficient information discovery is very important to maintain the QoS of such mission critical applications. An inefficient information discovery mechanism will result in high transmission of data packets over the network creating bottlenecks leading to unbalanced energy consumption over the network. High latency and inefficient energy consumption will have a direct effect on the QoS of mission-critical applications of particular importance in this regard is the minimization of hotspots. In this paper, we propose a perimeter-based multi-dimensional, multi-resolution storage approach for information discovery that balances the energy consumption by balancing the traffic load as equally as possible. Thus ensuring a maximum network lifetime. We present simulation results to show that the proposed approach to information discovery offers significant improvements on information discovery latency compared with current approaches. In addition, the results prove that the Quality of Service QoS improvements come with reduction of hotspots and significant network-wide energy saving and increased network lifetime.