Lossless In-Network Processing in WSNs for Domain-Specific Monitoring Applications

• Published in: IEEE transactions on industrial informatics Vol. 13; no. 5; pp. 2130 - 2139
• Main Authors: Guo, Peng, Cao, Jiannong, Liu, Xuefeng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Application programming interface; Chains; Computer networks; Computer simulation; Customization; Feature extraction; In-network processing; Informatics; Internet of Things; matrix computation; Monitoring; Network management systems; Object recognition; Optimization; Partitioning algorithms; Remote sensors; Routing (telecommunications); routing scheme; Sensors; Singular value decomposition; Smart grid; Vibrations; wireless sensor network (WSN); Wireless sensor networks
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2017.2691586

Internet of things (IOT) is emerging as sensing paradigms in many domain-specific monitoring applications in smart cities, such as structural health monitoring (SHM) and smart grid monitoring. Due to the large size of the monitoring objects (e.g., civil structure or the power grid), plenty of sensors need to be deployed and organized to be a large scale of multihop wireless sensor networks (WSNs), which tends to have quite high transmission cost. In-network processing is an efficient way to reduce the transmission cost in WSNs. However, implementing in-network processing for above domain-specific monitoring usually requires to losslessly distribute a dedicate domain-specific algorithm into WSNs, which is much different from most existing in-network processing works. This paper conducts a case study of a classic centralized SHM algorithm, i.e., eigensystem realization algorithm (ERA), and shows how to losslessly and optimally in-network process ERA, especially the typical feature extraction method, i.e., that is singular value decomposition (SVD) therein, in a WSN. Based on whether the intermediate data can be processed together or not by sensor nodes, we respectively implement tree-based in-network processing of SVD and chain-based in-network processing of SVD in WSNs. We prove that using an appropriate shallow light tree as routes for tree-based in-network processing of SVD, can achieve the approximation ratio {\text{1}}+\sqrt{2} (in terms of transmission cost), while for the chain-based in-network processing of SVD, we design two efficient heuristic algorithms for searching the optimal routes. Extensive simulation results validate the efficiency of these proposed schemes that are customized for SVD-based IOT applications.