Dynamic relocation of mobile base station in wireless sensor networks using a cluster-based harmony search algorithm

Alia, Osama Moh’d

doi:10.1016/j.ins.2016.12.046

Cited by 54 publications

(23 citation statements)

References 24 publications

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“…In [169] the authors proposed an improved ACO algorithm approach for WSNs that use mobile sinks by considering the cluster heads distances. In [170] the authors proposed an energy-efficient network model that dynamically relocates a mobile BS within a cluster-based network infrastructure using a harmony search algorithm. Kumar and Kumar [171] proposed an energy efficient clustering mechanism, based on artificial bee colony algorithm and factional calculus to maximize the network energy and life time of nodes by optimally selecting cluster-head.…”

Section: ) Network-based Routing Protocolmentioning

confidence: 99%

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

2019

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Network performance optimization has always been one of the important research subjects in mobile wireless sensor networks. With the expansion of the application field of MWSNs and the complexity of the working environment, traditional network performance optimization algorithms have become difficult to meet people's requirements due to their own limitations. The traditional swarm intelligence algorithms have some shortcomings in solving complex practical multi-objective optimization problems. In recent years, scholars have proposed many novel swarm intelligence optimization algorithms, which have strong applicability and achieved good experimental results in solving complex practical problems. These algorithms, like their natural systems of inspiration, show the desirable properties of being adaptive, scalable, and robust. Therefore, the swarm intelligent algorithms (PSO, ACO, ASFA, ABC, SFLA) are widely used in the performance optimization of mobile wireless sensor networks due to its cluster intelligence and biological preference characteristics. In this paper, the main contributions is to comprehensively analyze and summarize the current swarm intelligence optimization algorithm and key technologies of mobile wireless sensor networks, as well as the application of swarm intelligence algorithm in MWSNs. Then, the concept, classification and architecture of Internet of things and MWSNs are described in detail. Meanwhile, the latest research results of the swarm intelligence algorithms in performance optimization of MWSNs are systematically described. The problems and solutions in the performance optimization process of MWSNs are summarized, and the performance of the algorithms in the performance optimization of MWSNs is compared and analyzed. Finally, combined with the current research status in this field, the issues that need to be paid attention to in the research of swarm intelligence algorithm optimization for MWSNs are put forward, and the development trend and prospect of this research direction in the future are prospected.

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Section: ) Network-based Routing Protocolmentioning

confidence: 99%

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

2019

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“…The optimal path of the mobile sink is derived by the evolutionary method of the particles. Alia 23 proposed an efficient harmony search algorithm for dynamically relocating the mobile sink in a clustering network. First, the network is dynamically clustered according to the number of live nodes and the location of nodes to achieve the load balance, and all nodes are divided into the most appropriate clusters.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary game–based trajectory design algorithm for mobile sink in wireless sensor networks

Gong

Panpan

Chen

et al. 2020

International Journal of Distributed Sensor Networks

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In wireless sensor networks, if sink node is stationary, the nodes close to sink will deplete energy faster than those in other areas. The unbalanced energy consumption among the nodes will lead to the energy hole problem, which shortens the network lifetime. To solve the problem, evolutionary game–based trajectory design algorithm for mobile sink in wireless sensor networks is proposed in this article. In evolutionary game model, the average residual energy of each cluster, average intra-cluster energy consumption, and average inter-cluster energy consumption are used to design utility function. The sink will select the cluster with more utility value as its new location and move to the cluster head which has the largest residual energy and the shortest distance to other cluster heads. The simulation results show that the algorithm can effectively balance network load, reduce network energy consumption, prolong the network lifetime, and increase the number of data packets that sink receives.

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“…In a WSN, the sensor nodes typically route the sensed data wirelessly to a single BS or multiple BSs. Moreover, a substantial amount of energy by the sensor nodes is consumed during the wireless transmission of the sensed data as is reported in Kulkarni and Venayagamoorthy (2011) and Alia (2017). Due to the fact that these nodes are severely power-constrained and the wireless communication is mainly dependent on the distance, the location of BS(s) and the physical distance(s) from the nodes are significant.…”

Section: Related Workmentioning

confidence: 99%

Average distance estimation in randomly deployed wireless sensor networks (WSNs): an analytical study

Sevgi

2019

IJSNET

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A Wireless Sensor Network (WSN) is an energy-scarce network in which the energy is primarily dissipated by the sensor nodes during data transmission to the base station (BS). The location of the BS in a WSN dramatically affects the energy dissipation, the throughput, and the lifetime. While in a number of studies the optimal positioning of a BS is considered, the system parameters are optimized when the BS location is known in advance in many others. In this paper, we provide a general-purpose mathematical framework to find the expected distance value between every point within any n-sided simple polygon shaped sensing field and an arbitrarily located BS. Having the knowledge of this value is very imperative particularly in random deployment as it is used for energy-efficient clustering. Although similar derivations appear in the related literature, to the best of our knowledge, this study departs from them, since our derivations do not depend on the shape of the sensing field and the orientation of BS relative to it.In a deterministic deployment scenario, as there is prior knowledge about the locations of the deployed nodes, the average distance between each node and its neighbours, and similarly the average distance between deployed nodes and the BS, can easily be calculated. However, in the random deployment scenarios, the above mentioned distances, which indeed affect the energy consumption and thus the lifetime of an application, are not known before the deployment. Therefore, a probabilistic approach is required to estimate this average distance which would provide a beneficial tool for the network designers. For example, for a given number of sensor nodes that fulfil the required system parameters such as connected coverage, the network designer might need to estimate, if possible, the average distance between each sensor and a BS to adopt either multi-hop communication or single-hop communication before the deployment.In a number of RDWSN applications, each sensor node is assumed to reach the BS within a single-hop. However, in these applications which exploit direct communication, it is observed that a set of sensor nodes, which is distant from the BS, consumes a considerable amount of energy because it needs to perform long-haul direct transmissions. Therefore, these nodes, which are distant from the BS, tend to die early and thus shorten the lifetime of the network. To tackle this problem, multi-hop communication is usually taken into consideration because it is more energy-efficient than direct communication in such dense environments. In multi-hop WSNs, however, the nodes close to the BS have to forward data for the rest of the nodes that are away from the BS. Thus, they are again more likely to die earlier, causing the network to become partitioned and the network lifetime to get shortened. This is known as the energy-hole problem as is defined in Gong et al. (2013) and Du et al. (2015). Even before attempting to tackle an energyhole problem, the use of this estimated average distance to BS is imper...

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Dynamic relocation of mobile base station in wireless sensor networks using a cluster-based harmony search algorithm

Cited by 54 publications

References 24 publications

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

Evolutionary game–based trajectory design algorithm for mobile sink in wireless sensor networks

Average distance estimation in randomly deployed wireless sensor networks (WSNs): an analytical study

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