An Effective Multi-Objective Optimization Algorithm for Spectrum Allocations in the Cognitive-Radio-Based Internet of Things

Ren, Han; Gao, Yang; Wu, Chunxue; Lu, Dianjie

doi:10.1109/access.2017.2789198

Cited by 66 publications

(38 citation statements)

References 25 publications

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“…In [18], SA can achieve high task execution efficiency when dealing with task allocation issue. In [19], authors proposed a GA scheme to improve system performance for better total system efficiency in WSNs. GA is adaptable but has many problems, computational complexity for instance.…”

Section: Related Workmentioning

confidence: 99%

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Elite Immune Ant Colony Optimization-Based Task Allocation for Maximizing Task Execution Efficiency in Agricultural Wireless Sensor Networks

Zhou

2020

Journal of Sensors

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The research of agricultural wireless sensor networks (AWSNs) plays an important role in the field of facility agricultural technology. The temperature and humidity nodes in AWSNs are so tiny that they are limited on computation, network management, information collection, and storage size. Under this condition, task allocation plays a key role in improving the performance of AWSNs to reduce energy consumption and computational constraints. However, the optimization of task allocation is a nonlinearly constrained optimization problem whose complexity increases when constraints such as limited computing capabilities and power are undertaken. In this paper, an elite immune ant colony optimization (EIACO) is proposed to deal with the problem of task allocation optimization, which is motivated by immune theory and elite optimization theory. The EIACO uses ant colony optimization (ACO) to combine the clone operator and elite operator together for the optimization of task allocation. The performances of EIACO with different numbers of temperature and humidity sensor nodes and tasks have been compared by both genetic algorithm (GA) and simulated annealing (SA) algorithm. Simulation results show that the proposed EIACO has a better task execution efficiency and higher convergence speed than GA and SA. Furthermore, the convergence speed of EIACO is faster than GA and SA. Therefore, the whole system efficiency can be improved by the proposed algorithm.

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Section: Related Workmentioning

confidence: 99%

“…ρ is the evaporation coefficient of pheromone to avoid infinite growth of pheromone on the path. And Δτ k ef ðt, t + 1Þ in equation (19) represents the increment of pheromone information that the k th ant left on the path ðe, f Þ during the ðt, t + 1Þ generation.…”

Section: Path Selectionmentioning

confidence: 99%

Elite Immune Ant Colony Optimization-Based Task Allocation for Maximizing Task Execution Efficiency in Agricultural Wireless Sensor Networks

Zhou

2020

Journal of Sensors

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“…A trading-based spectrum optimization strategy is introduced in [27], in which IoT users try to optimize their access to the spectrum by ensuring that then spectrum price will not exceed the access benefits. A multi-objective optimization algorithm is presented in [28] for spectrum allocation cognitive radio-based IoT, aiming to maximize both end-to-end throughput and spectrum utilization. The problem is NP-hard and genetic algorithm is used to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

Fair Resource Allocation in Cooperative Cognitive Radio Iot Networks

2020

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Due to the growing demand for spectral resources with the emergence of IoT networks and applications, cooperative cognitive radio IoT networks (CCR-IoTN) are expected to play an essential role in the world of smart technology. In this paradigm, the benefits of cooperative communication and cognitive radio networks (CRN) are merged to meet IoT networks' needs. In this work, we employ the hybrid overlayunderlay CRN to guarantee both the secondary user (SU) stability and provide acceptable total throughput. In our CCR-IoTN, SUs may act as a relay to help the primary transmission. If the primary user (PU) is active, the secondary non-relay nodes transmit concurrently with the PU in underlay mode. In return for their cooperation, SUs are allowed to access the spectrum to transmit their data in overlay mode. In this context, we propose several fair resource allocation frameworks and compare well-known problems such as spectrum efficiency, energy efficiency as an objective function for CCR-IoTN. This kind of problem is inherently nonconvex. To solve the problems, therefore, we reformulate them as a convex problem using quadratic transform. Finally, we use simulation to evaluate the performance of the proposed hybrid resource allocation method. The resource allocation framework based on proportional fairness policy is shown to have acceptable performance in terms of throughput, fairness, and energy efficiency.

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“…As an effective method to solve MOP, MOEAs could obtain solutions with good diversity [3]. Consequently, they are widely found in various practical applications, such as power dispatching [4], financial management [5], electric machine designing [6], and spectrum allocation [7].…”

Section: Introductionmentioning

confidence: 99%

Multi‐Swarm Multi‐Objective Optimizer Based on p‐Optimality Criteria for Multi‐Objective Portfolio Management

Chen

et al. 2019

Mathematical Problems in Engineering

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Portfolio management is an important technology for reasonable investment, fund management, optimal asset allocation, and effective investment. Portfolio optimization problem (POP) has been recognized as an NP-hard problem involving numerous objectives as well as constraints. Applications of evolutionary algorithms and swarm intelligence optimizers for resolving multi-objective POP (MOPOP) have attracted considerable attention of researchers, yet their solutions usually convert MOPOP to POP by means of weighted coefficient method. In this paper, a multi-swarm multi-objective optimizer based on p-optimality criteria called p-MSMOEAs is proposed that tries to find all the Pareto optimal solutions by optimizing all objectives at the same time, rather than through the above transforming method. The proposed p-MSMOEAs extended original multiple objective evolutionary algorithms (MOEAs) to cooperative mode through combining p-optimality criteria and multi-swarm strategy. Comparative experiments of p-MSMOEAs and several MOEAs have been performed on six mathematical benchmark functions and two portfolio instances. Simulation results indicate that p-MSMOEAs are superior for portfolio optimization problem to MOEAs when it comes to optimization accuracy as well as computation robustness.

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An Effective Multi-Objective Optimization Algorithm for Spectrum Allocations in the Cognitive-Radio-Based Internet of Things

Cited by 66 publications

References 25 publications

Elite Immune Ant Colony Optimization-Based Task Allocation for Maximizing Task Execution Efficiency in Agricultural Wireless Sensor Networks

Elite Immune Ant Colony Optimization-Based Task Allocation for Maximizing Task Execution Efficiency in Agricultural Wireless Sensor Networks

Fair Resource Allocation in Cooperative Cognitive Radio Iot Networks

Multi‐Swarm Multi‐Objective Optimizer Based on p‐Optimality Criteria for Multi‐Objective Portfolio Management

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