scholarly journals Chain‐routing scheme with compressive sensing‐based data acquisition for Internet of Things‐based wireless sensor networks

IET Networks ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 43-58
Author(s):  
Ahmed Aziz ◽  
Walid Osamy ◽  
Ahmed M. Khedr ◽  
Ahmed Salim
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Data Acquisition ◽  
Compressive Sensing ◽  
Wireless Sensor ◽  
Routing Scheme

scholarly journals Deterministic clustering based compressive sensing scheme for fog-supported heterogeneous wireless sensor networks

2021 ◽  
Vol 7 ◽  
pp. e463
Author(s):  
Walid Osamy ◽  
Ahmed Aziz ◽  
Ahmed M Khedr
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Acquisition ◽  
Compressive Sensing ◽  
Network Lifetime ◽  
Computational Cost ◽  
Base Station ◽  
Random Selection ◽  
Wireless Sensor ◽  
Heterogeneous Wireless Sensor Networks

Data acquisition problem in large-scale distributed Wireless Sensor Networks (WSNs) is one of the main issues that hinder the evolution of Internet of Things (IoT) technology. Recently, combination of Compressive Sensing (CS) and routing protocols has attracted much attention. An open question in this approach is how to integrate these techniques effectively for specific tasks. In this paper, we introduce an effective deterministic clustering based CS scheme (DCCS) for fog-supported heterogeneous WSNs to handle the data acquisition problem. DCCS employs the concept of fog computing, reduces total overhead and computational cost needed to self-organize sensor network by using a simple approach, and then uses CS at each sensor node to minimize the overall energy expenditure and prolong the IoT network lifetime. Additionally, the proposed scheme includes an effective algorithm for CS reconstruction called Random Selection Matching Pursuit (RSMP) to enhance the recovery process at the base station (BS) side with a complete scenario using CS. RSMP adds random selection process during the forward step to give opportunity for more columns to be selected as an estimated solution in each iteration. The results of simulation prove that the proposed technique succeeds to minimize the overall network power expenditure, prolong the network lifetime and provide better performance in CS data reconstruction.

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