In-network context inference in IoT sensory environment for efficient network resource utilization. (15th March 2019)

• Record Type: Journal Article
• Authors: Verma, Rahul Kumar; Pattanaik, K.K.; Bharti, Sourabh; Saxena, Divya
• Is Part Of: Journal of network and computer applications. Volume 130(2019)
• Page Start: 89
• Page End: 103
• Publication Date: 2019-03-15
• Subjects: IoT sensory environment -- Wireless sensor networks -- Context-awareness -- Closeness centrality -- Edge computing; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2019.01.013
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: Context-awareness refers to the ability of an application to understand the situation from the sensed information and provides service(s) accordingly with the minimum human intervention. Context-aware IoT applications collect high-level contexts (HLCs) instead of the raw sensor data from the sensor networks to take the required actions in real-time. Inferring contexts accurately with low network traffic, latency, and energy consumption is a challenging task. Existing on-node data processing mechanisms provide only low-level events which do not support the fusion of multi-variate sensor data collected from multiple sensor nodes. On the other hand, inferring the HLCs at gateway, requires transmission of sensed data from the edge of the network to gateway (out-network) which incurs high network traffic and energy consumption. In this paper, we propose a naive computationally lightweight in-network context inference mechanism, named InContextIoT, that can process the multi-variate sensor data collected from different sensors for inferring the HLCs inside the sensor network efficiently. A data collection node, named Inode, is selected for the HLCs inference on the basis of node's residual energy, closeness centrality, degree, and availability of computation resources. Then, we use the Bayesian classification for inferring the HLCs at Inode from the collected low-level events of various sensor nodes. The selection of Inode is dynamic and depends on the region of interest (RoI) of application queries. Experiments show that InContextIoT reduces network energy consumption to 73% by reducing the network traffic approximately two times (2x) in compared to existing centralized state-of-art approaches.