A Loss Inference Algorithm for Wireless Sensor Networks to Improve Data Reliability of Digital Ecosystems

Digital ecosystems (DEs) are based on a large amount of distributed data, and these data are gathered from physical devices, particularly from wireless sensor networks (WSNs). Due to the inherent stringent bandwidth and energy constraints, energy-efficient mechanisms of performance measurement are the key to the proper operation of WSNs and thereby important for the data reliability of DEs. This paper presents a novel algorithm, i.e., Loss Inference based on Passive Measurement (LIPM), to infer WSN link loss performance. The LIPM algorithm passively monitors the application traffic between sensor nodes and the sink (base station), and then uses network tomography technology to infer the network internal performance. Furthermore, contour maps, the well-known representation of data, are first taken into account in WSN loss performance inference, which can help the LIPM algorithm identify lossy areas rapidly. Finally, the algorithm is validated through simulations and exhibits good performance and scalability.