An expert system for low‐power and lossy indoor sensor networks. Issue 4 (4th November 2020)

• Record Type: Journal Article
• Authors: Habib, Sami J.; Marimuthu, Paulvanna N.; Renold, Pravin; Ganesh Athi, Balaji
• Other Names: Chakraborty Tanmoy guestEditor.; Bhatia Sumit guestEditor.; Caragea Cornelia guestEditor.; Moreira Fernando guestEditor.; Rocha Álvaro guestEditor.; Dubey Ashwani Kumar guestEditor.
• Is Part Of: Expert systems. Volume 38:Issue 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-04
• Subjects: analytical modelling -- adaptive routing protocol -- expert systems -- indoor monitoring -- low‐power network -- lossy networks -- multi‐decision criterion model -- self‐aware; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12650
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: We have developed an expert system comprising a self‐aware framework for resource‐efficient and accurate data transmission within a low‐power lossy sensor network (LLN) deployed for indoor monitoring. We derived both individual and group awareness, which could ensure the awareness of each sensor regarding its resources, neighbours and network environment. The proposed expert system facilitates decision‐making under dynamic environmental conditions and employs a multi‐criteria decision‐making (MCDM) model to determine the selection of the best path towards the sink node with awareness of the existing network environment. The proposed system is validated by constructing a 6LoWPAN network in the Contiki Cooja simulator. MCDM is applied to generate an adaptive objective function for the IPv6 routing protocol for the LLN (RPL) and to aid in ranking the nodes to select the best available neighbouring node, while the data accuracy is ensured by the cluster head through data correlation among its associated members. The network performance is assessed by analyzing the packet delivery rate, throughput and energy consumption against varying sensors and by comparing our proposed MCDM‐RPL with a standard RPL and a fuzzy‐based RPL, where the results show that our framework is found to be better with gains of 13, 25 and 13%, respectively.