PMID- 37420692
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20230710
LR  - 20230718
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 23
IP  - 12
DP  - 2023 Jun 12
TI  - Variable-Length Multiobjective Social Class Optimization for Trust-Aware Data 
      Gathering in Wireless Sensor Networks.
LID - 10.3390/s23125526 [doi]
LID - 5526
AB  - Data gathering in wireless sensor networks (WSNs) is vital for deploying and 
      enabling WSNs with the Internet of Things (IoTs). In various applications, the 
      network is deployed in a large-scale area, which affects the efficiency of the 
      data collection, and the network is subject to multiple attacks that impact the 
      reliability of the collected data. Hence, data collection should consider trust 
      in sources and routing nodes. This makes trust an additional optimization 
      objective of the data gathering in addition to energy consumption, traveling 
      time, and cost. Joint optimization of the goals requires conducting 
      multiobjective optimization. This article proposes a modified social class 
      multiobjective particle swarm optimization (SC-MOPSO) method. The modified 
      SC-MOPSO method is featured by application-dependent operators named interclass 
      operators. In addition, it includes solution generation, adding and deleting 
      rendezvous points, and moving to the upper and lower class. Considering that 
      SC-MOPSO provides a set of nondominated solutions as a Pareto front, we employed 
      one of the multicriteria decision-making (MCDM) methods, i.e., simple additive 
      sum (SAW), for selecting one of the solutions from the Pareto front. The results 
      show that both SC-MOPSO and SAW are superior in terms of domination. The set 
      coverage of SC-MOPSO is 0.06 dominant over NSGA-II compared with only a mastery 
      of 0.04 of NSGA-II over SC-MOPSO. At the same time, it showed competitive 
      performance with NSGA-III.
FAU - Saad, Mohammed Ayad
AU  - Saad MA
AUID- ORCID: 0000-0001-9527-2647
AD  - Department of Electrical, Electronics & System Engineering, Faculty of 
      Engineering & Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 
      43600, Selangor, Malaysia.
AD  - Department of Medical Instrumentations Technique Engineering, Al-Kitab 
      University, Kirkuk 36001, Iraq.
FAU - Jaafar, Rosmina
AU  - Jaafar R
AUID- ORCID: 0000-0001-8019-0446
AD  - Department of Electrical, Electronics & System Engineering, Faculty of 
      Engineering & Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 
      43600, Selangor, Malaysia.
FAU - Chellappan, Kalaivani
AU  - Chellappan K
AUID- ORCID: 0000-0002-2618-216X
AD  - Department of Electrical, Electronics & System Engineering, Faculty of 
      Engineering & Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 
      43600, Selangor, Malaysia.
LA  - eng
PT  - Journal Article
DEP - 20230612
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - *Algorithms
MH  - Awareness
MH  - Data Collection
MH  - Reproducibility of Results
PMC - PMC10302200
OTO - NOTNLM
OT  - data gathering
OT  - multi-objective
OT  - social class optimization
OT  - trust aware
OT  - variable length
COIS- The authors declare no conflict of interest.
EDAT- 2023/07/08 10:42
MHDA- 2023/07/10 06:42
PMCR- 2023/06/12
CRDT- 2023/07/08 01:11
PHST- 2023/01/09 00:00 [received]
PHST- 2023/05/26 00:00 [revised]
PHST- 2023/05/27 00:00 [accepted]
PHST- 2023/07/10 06:42 [medline]
PHST- 2023/07/08 10:42 [pubmed]
PHST- 2023/07/08 01:11 [entrez]
PHST- 2023/06/12 00:00 [pmc-release]
AID - s23125526 [pii]
AID - sensors-23-05526 [pii]
AID - 10.3390/s23125526 [doi]
PST - epublish
SO  - Sensors (Basel). 2023 Jun 12;23(12):5526. doi: 10.3390/s23125526.