Two-Fold Spatiotemporal Regression Modeling in Wireless Sensor Networks

Distributed data and restricted limitations of sensor nodes make doing regression difficult in a wireless sensor network. In conventional methods, gradient descent and Nelder Mead simplex optimization techniques are basically employed to find the model incrementally over a Hamiltonian path among the nodes. Although Nelder Mead simplex based approaches work better than gradient ones, compared to Central approach, their accuracy should be improved even further. Also they all suffer from high latency as all the network nodes should be traversed node by node. In this paper, we propose a two-fold distributed cluster-based approach for spatiotemporal regression over sensor networks. First, the regressor of each cluster is obtained where spatial and temporal parts of the cluster’s regressor are learned separately. Within a cluster, the cluster nodes collaborate to compute the temporal part of the cluster’s regressor and the cluster head then uses particle swarm optimization to learn the spatial part. Secondly, the cluster heads collaborate to apply weighted combination rule distributively to learn the global model. The evaluation and experimental results show the proposed approach brings lower latency and more energy efficiency compared to its counterparts while its prediction accuracy is considerably acceptable in comparison with the Central approach.