Open AccessChain‐routing scheme with compressive sensing‐based data acquisition for Internet of Things‐based wireless sensor networks
Author(s) -
Aziz Ahmed,
Osamy Walid,
Khedr Ahmed M.,
Salim Ahmed
Publication year - 2021
Publication title -
iet networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.466
H-Index - 21
eISSN - 2047-4962
pISSN - 2047-4954
DOI - 10.1049/ntw2.12002
Subject(s) - computer science , compressed sensing , wireless sensor network , routing (electronic design automation) , energy consumption , real time computing , algorithm , data acquisition , data compression , efficient energy use , internet of things , energy (signal processing) , computer network , process (computing) , engineering , embedded system , mathematics , statistics , electrical engineering , operating system
Abstract The emerging Internet of Things (IoT)‐based systems that integrate diverse types of sensors, mobiles and other technologies to physical world is becoming increasingly popular for use in wide varieties of applications. Compressive sensing (CS)‐based information acquisition and in‐network compression provide an effective method for accurate data recovery at the base station (BS) with reduced cost of communication. In this study, how CS can be combined with routing protocols for gathering data in IoT‐based Wireless Sensor Networks (WSNs) in an energy‐efficient manner are examined. A novel chain‐routing scheme with CS based data acquisition is introduced that includes the following new algorithms: (1) Seed Estimation Algorithm (SEA) to find the best measurement matrix by selecting the best‐estimated seed, (2) Chain Construction Algorithm (CCA) to organise the network nodes during transmitting and receiving process, (3) Compression approach with reduced consumption of energy that improves the lifetime of the network by minimising the local data traffic, and (4) Reconstruction Algorithm (RA) that reconstructs the original data with minimum reconstruction error. Here, extensive simulation and analysis results prove the performance of our proposed method in improving the network lifetime 35% better than the ECST algorithm and 93% better than the PEGASIS algorithm. In addition, the proposed reconstruction algorithm exceeds the other reconstruction algorithms performance.