Computation Power and Energy Optimized Task Allocation in Internet of Things

Kazanci I., ÖZDEMİR S., TOSUN S.

IEEE Transactions on Network and Service Management, vol.19, no.4, pp.4424-4433, 2022 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 19 Issue: 4
  • Publication Date: 2022
  • Doi Number: 10.1109/tnsm.2022.3161074
  • Journal Name: IEEE Transactions on Network and Service Management
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC
  • Page Numbers: pp.4424-4433
  • Keywords: Computation power, energy efficiency, Internet of Things (IoT), multi-objective optimization, resource management, task allocation
  • Hacettepe University Affiliated: Yes


IEEEthe aim of most Internet of things (IoT) networks is to allow different devices with various capabilities to share their resources and cooperate to perform a demanded task. Most of IoT objects are heterogeneous and are equipped with limited energy and computational capabilities. Therefore, distributing tasks to these objects represents a big challenge. In the literature, most of the existing works use heuristic optimizations to deal with different aspects of task allocation problem without considering the heterogeneity nature of the objects and the effect of their limited resources. In this paper, we first model the problem of task allocation in Internet of things by adopting the concept of task groups and virtual objects. Then, we develop a multi-objective optimization algorithm to solve the task allocation problem. The algorithm simultaneously optimizes two contradictory objectives: computational power and energy efficiency. The first objective considers the fact that virtual objects are the basic units of computations and attempts to maximize the mean value of the computational power of virtual objects. On the other hand, the second objective aims to minimize the total dissipated energy in the network to ensure maximum operational and stability periods. In order to verify and test the effectiveness of the proposed algorithm, we performed extensive MATLAB based analysis as well as application layer simulations, based on OMNeT++, with various scenarios and compared our results with the most relevant algorithm in the literature.