In:
Journal of Intelligent & Fuzzy Systems, IOS Press, Vol. 41, No. 1 ( 2021-08-11), p. 19-35
Abstract:
Data acquisition problem on large distributed wireless sensor networks (WSNs) is considered as a challenge in the growth of Internet of Things (IoT). Recently, the combination of compressive sensing (CS) and routing techniques has attracted much attention of researchers. An open question of this combination is how to integrate these techniques effectively for specific tasks. On the other hand, CS data reconstruction process is considered as one of the CS challenges because it requires to recover N data from only M measurement where M 〈 〈 N. Through this paper, we propose a new scheme for data gathering in IoT based heterogeneous WSN that includes a new effective Deterministic Clustering using CS technique (DCCS) to handle the data acquisition problem. DCCS reduces the total overhead computational cost needed to self-organize WSN using a simple approach and then uses CS at each sensor node to decrease the overall energy consumption and increase the network lifetime. The proposed scheme includes also an effective CS reconstruction algorithm called Random Selection Matching Pursuit (RSMP) to improve the recovery process at the base station (BS). RSMP adds a random selection process during the forward step to give the opportunity for more columns to be selected as an estimated solution in each iteration. The simulation results show that the proposed scheme succeeds to minimize the overall network power consumption and prolong the network lifetime beside provide better performance in CS data reconstruction.
Type of Medium:
Online Resource
ISSN:
1064-1246
,
1875-8967
Language:
Unknown
Publisher:
IOS Press
Publication Date:
2021
detail.hit.zdb_id:
2070080-5
SSG:
11
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