Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 8, Issue 2, Pages 1109-1122Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2020.3010834
Keywords
Optimization; Internet of Things; Sensors; Evolutionary computation; Quality of service; Immune system; Sociology; Decomposition; evolutionary algorithm; Internet of Things (IoT); multiobjective optimization; service
Categories
Funding
- National Natural Science Foundation of China [61972456, 41772123]
- Natural Science Foundation of Tianjin [19JCYBJC15800]
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This paper presents an improved decomposition-based multiobjective evolutionary algorithm for optimizing the allocation of IoT services. By designing appropriate operators, better solutions can be achieved in agricultural IoT services, reducing service time and lowering service costs.
Internet of Things (IoT) aims to provide ubiquitous services in real life. When different service requests arrive, how to assign them to proper service providers has become a challenging problem, especially in large-scale IoT service circumstances. In order to obtain the best service matching scheme, it is crucial to minimize total service cost and service time. Since both goals are conflicting, we have modeled IoT service as a multiobjective problem. Thus, we propose an improved decomposition-based multiobjective evolutionary algorithm for the IoT service (I-MOEA/D-IoTS). We have designed appropriate operators, such as array encoding, population initialization, Tchebycheff decomposition approach, local improvement, simulated binary crossover, and Gaussian mutation. In order to verify the effectiveness of the proposed algorithm, we apply it in three different scenarios of the agricultural IoT service. The simulation experimental results show that the proposed algorithm can achieve better tradeoff of solutions for IoT service and reduce total service cost and shorten service time.
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