With a view to prolong the duration of the wireless sensor network, many battery lifetime prediction algorithms run on individual nodes. If not properly designed, this approach may be detrimental and even accelerate battery depletion. Herein, we provide a comparative analysis of various machine-learning algorithms to offload the energy inference task to the most energy-rich nodes, to alleviate the nodes that
are entering the critical state. Taken to its extreme, our approach may be used to divert the energy-intensive tasks to a monitoring station, enabling a cloud-based approach to sensor network management. Experiments conducted in a controlled environment with real hardware have shown that RSSI can be used to infer the state of a remote wireless node once it is approaching the cutoff point. The ADWIN algorithm was used for smoothing the input data and for helping a variety of machine
learning algorithms particularly to speed up and improve their prediction accuracy.
|Name||Lecture Notes in Computer Science|
|Conference||conference; The 2013 International Workshop on Cloud-assisted Smart Cyber-Physical Systems (C-SmartCPS), jointly held with ICA3PP-2013 and UIC-2013, Vietri sul Mare, Italy.; 2013-12-18; 2013-12-20|
|Period||18/12/13 → 20/12/13|
|Other||The 2013 International Workshop on Cloud-assisted Smart Cyber-Physical Systems (C-SmartCPS), jointly held with ICA3PP-2013 and UIC-2013, Vietri sul Mare, Italy.|