Sum Versus Vote Fusion in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distributed regression: an efficient framework for modeling sensor network data
Proceedings of the 3rd international symposium on Information processing in sensor networks
Distributed optimization in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
A high-accuracy, low-cost localization system for wireless sensor networks
Proceedings of the 3rd international conference on Embedded networked sensor systems
A survey on clustering algorithms for wireless sensor networks
Computer Communications
Regional and on-farm wireless sensor networks for agricultural systems in Eastern Washington
Computers and Electronics in Agriculture
WASA '08 Proceedings of the Third International Conference on Wireless Algorithms, Systems, and Applications
Particle swarm optimized multiple regression linear model for data classification
Applied Soft Computing
Machine Learning and Data Mining: Introduction to Principles and Algorithms
Machine Learning and Data Mining: Introduction to Principles and Algorithms
Expert Systems with Applications: An International Journal
A survey of particle swarm optimization applications in electric power systems
IEEE Transactions on Evolutionary Computation
Optimal Power Scheduling for Correlated Data Fusion in Wireless Sensor Networks via Constrained PSO
IEEE Transactions on Wireless Communications
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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.