Resource allocation solution for sensor networks using improved chaotic firefly algorithm in IoT environment

Wang, Zhiyong; Liu, Dong; Jolfaei, Alireza

doi:10.1016/j.comcom.2020.03.039

Cited by 22 publications

(9 citation statements)

References 9 publications

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“…. n) is determined by the below equation and put in matrix form [34]- [38]. The sensor node agent starts with random values of Q(s, a).…”

Section: A Q-reinforced Learningmentioning

confidence: 99%

“…The next action is to initialize the number of agents, their states and actions, and set Q-values to zero. The states are Sleep state, Beacon window state, active state etc., are the operating states of every sensor node reported in [34]- [38]. Repeat this action until a global optimum has been achieved for each sensor node agent at time t. Assume that the rewards for coverage and connectivity maintenance where the updated Q-values are using global reward r. The purpose of Q value in Q-learning is to learn an optimum policy for an agent to choose its best action that can maximize the overall reward value.…”

Section: B Artificial Intelligence Reinforced Learningmentioning

confidence: 99%

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An Artificial Intelligence-Based Quorum System for the Improvement of the Lifespan of Sensor Networks

et al. 2021

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Artificial I ntelligence-based Q uorum s ystems a re used to solve the energy crisis in real-time wireless sensor networks. They tend to improve the coverage, connectivity, latency, and lifespan of the networks where millions of sensor nodes need to be deployed in a smart grid system. The reality is that sensors may consume more power and reduce the lifetime of the network. This paper proposes a quorum-based grid system where the number of sensors in the quorum is increased without actually increasing quorums themselves, leading to improvements in throughput and latency by 14.23%. The proposed artificial intelligence scheme reduces the network latency due to an increase in time slots over conventional algorithms previously proposed. Secondly, energy consumption is reduced by weighted load balancing, improving the network's actual lifespan. Our experimental results show that the coverage rate is increased on an average of 11% over the conventional Coverage Contribution Area (CCA), Partial Coverage with Learning Automata (PCLA), and Probabilistic Coverage Protocol (PCP) protocols respectively.

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“…. n) is determined by the below equation and put in matrix form [34]- [38]. The sensor node agent starts with random values of Q(s, a).…”

Section: A Q-reinforced Learningmentioning

confidence: 99%

Section: B Artificial Intelligence Reinforced Learningmentioning

confidence: 99%

An Artificial Intelligence-Based Quorum System for the Improvement of the Lifespan of Sensor Networks

et al. 2021

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“…Wang et al applied FA to some difficult MOPs with large‐scale decision variables 31 . A chaotic FA to optimize the resource allocation in Internet of Things (IoT) environment 32 . To accelerate the convergence, fireflies in the swarm are perturbed.…”

Section: Recent Work On Famentioning

confidence: 99%

“…31 A chaotic FA to optimize the resource allocation in Internet of Things (IoT) environment. 32 To accelerate the convergence, fireflies in the swarm are perturbed. The literature reported that the proposed method could reduce the complexity of the problem.…”

Section: Recent Work On Famentioning

confidence: 99%

An efficient firefly algorithm based on modified search strategy and neighborhood attraction

Wang

Zhou

et al. 2021

Int J Intell Syst

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Firefly algorithm (FA) is a popular swarm intelligence optimization algorithm. Though FA was employed to solve various optimization problems, it still has some deficiencies, such as high complexity, slow convergence rate, and low precision of solutions. To tackle these issues, this paper proposes an efficient FA based on modified search strategy and neighborhood attraction (namely MSSNaFA). In MSSNaFA, there are four main modifications. First, a novel search strategy based on dimension differences is designed. The attractiveness in the original FA is related to the Euclidean distance, while our new method uses the differences of each dimension for two fireflies to compute the attractiveness. Then, a modified neighborhood attraction mechanism is utilized to reduce the computational complexity. When the current solution is selected, it will move to the global best solution based on the new movement strategy. Third, for each firefly, three neighborhood search operations are carried out based on a preset probability. Lastly, the step factor is adaptively adjusted in the search process. Performance validation between MSSNaFA and four other FA variants show the effectiveness of our approach.

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“…Priority-based stable matching algorithm is used for the allocation of resources for the corresponding activities. 11 Wang et al 12 distributed a resource allocation solution for IoT which using an improved chaotic firefly algorithm that obtains the optimal location and working channel of secondary information gathering stations (SIGSs) to manage interference and resource allocation based on cognitive radio. Connectivity of nodes is critical for the IoT, as data collected need to be sent to the base station (BS).…”

Section: Introductionmentioning

confidence: 99%

Resource scheduling based on routing tree and detection matrix for Internet of things

Bai

Zhang

Liu

et al. 2021

International Journal of Distributed Sensor Networks

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Effective scheduling of limited communication resources is one of the critical methods for data transmission in the Internet of things. However, the time slot utilization rate of many existing resource scheduling methods of Internet of things is not high. This article proposes a new efficient resource scheduling based on routing tree and detection matrix for Internet of things. In heterogeneous Internet of things, according to the different working modes and functions, the nodes are divided into Internet of things devices, routing nodes, and base station. We use time slot multiplexing to improve the time slot utilization of continuous transmission in Internet of things. First, the time slot allocation table in a round is obtained by the time slot scheduling based on the routing tree. Then, the collision matrix and the transmission matrix are established based on the time slot allocation table in a round. Finally, the minimum time slot scheduling in continuous rounds is determined based on the routing tree and the detection matrix. The experimental results show that the resource scheduling based on routing tree and detection matrix effectively improves the utilization of time slots and improves the throughput of the Internet of things.

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Resource allocation solution for sensor networks using improved chaotic firefly algorithm in IoT environment

Cited by 22 publications

References 9 publications

An Artificial Intelligence-Based Quorum System for the Improvement of the Lifespan of Sensor Networks

An Artificial Intelligence-Based Quorum System for the Improvement of the Lifespan of Sensor Networks

An efficient firefly algorithm based on modified search strategy and neighborhood attraction

Resource scheduling based on routing tree and detection matrix for Internet of things

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