scispace - formally typeset
Search or ask a question
Journal ArticleDOI

A survey on bio inspired meta heuristic based clustering protocols for wireless sensor networks

A Datta, S. Nandakumar1
01 Nov 2017-Vol. 263, Iss: 5, pp 052026
TL;DR: The author presents a brief survey on various bio inspired swarm intelligence based protocols used in routing data in wireless sensor networks while outlining their general principle and operation.
Abstract: Recent studies have shown that utilizing a mobile sink to harvest and carry data from a Wireless Sensor Network (WSN) can improve network operational efficiency as well as maintain uniform energy consumption by the sensor nodes in the network. Due to Sink mobility, the path between two sensor nodes continuously changes and this has a profound effect on the operational longevity of the network and a need arises for a protocol which utilizes minimal resources in maintaining routes between the mobile sink and the sensor nodes. Swarm Intelligence based techniques inspired by the foraging behavior of ants, termites and honey bees can be artificially simulated and utilized to solve real wireless network problems. The author presents a brief survey on various bio inspired swarm intelligence based protocols used in routing data in wireless sensor networks while outlining their general principle and operation.
Citations
More filters
Journal ArticleDOI
01 May 2022-Heliyon
TL;DR: A thorough review of state-of-the-art and classical strategies for PID controller parameters tuning using metaheuristic algorithms can be found in this article , where the primary objectives of PID control parameters are to achieve minimal overshoot in steady state response and lesser settling time.

62 citations

Journal ArticleDOI
TL;DR: This paper reviews the application of several methodologies under the CI umbrella to the WSAN field and describes and categorizes existing works leaning on fuzzy systems, neural networks, evolutionary computation, swarm intelligence, learning systems, and their hybridizations to well-known or emerging WSAN problems along five major axes.
Abstract: Wireless sensor and actuator networks (WSANs) are heterogeneous networks composed of many different nodes that can cooperatively sense the environment, determine an appropriate action to take, then change the environment’s state after acting on it. As a natural extension of wireless sensor networks (WSNs), WSANs inherit from them a variety of research challenges and bring forth many new ones. These challenges are related to dealing with imprecise and vague information, solving complicated optimization problems or collecting and processing data from multiple sources. Computational intelligence (CI) is an overarching term denoting a conglomerate of biologically and linguistically inspired techniques that provide robust solutions to NP-hard problems, reason in imprecise terms and yield high-quality yet computationally tractable approximate solutions to real-world problems. Many researchers have consequently turned to CI in hope of finding answers to a plethora of WSAN-related challenges. This paper reviews the application of several methodologies under the CI umbrella to the WSAN field. We describe and categorize existing works leaning on fuzzy systems , neural networks , evolutionary computation , swarm intelligence , learning systems , and their hybridizations to well-known or emerging WSAN problems along five major axes: 1) actuation; 2) communication; 3) sink mobility; 4) topology control; and 5) localization. The survey offers informative discussions to help reason through all the studies under consideration. Finally, we point to future research avenues by: 1) suggesting suitable CI techniques to specific problems; 2) borrowing concepts from WSNs that have yet to be applied to WSANs; or 3) describing the shortcomings of current methods in order to spark interest on the development of more refined models.

54 citations


Cites background from "A survey on bio inspired meta heuri..."

  • ...reported on the applications of a certain CI technique to numerous problems revolving around WSNs [27]–[34]....

    [...]

Journal ArticleDOI
TL;DR: The objective, key features, shortcomings, and benefits of different clustering techniques are examined in this survey to provide a deeper insight of the clustering area to the researchers, which can help them in their new journey of research in this domain.

37 citations

Journal ArticleDOI
TL;DR: A particle swarm optimization-based energy efficient clustering protocol (PSO-EEC) is proposed to enhance the network lifetime and performance and is compared with the various existing approaches in terms of different performance parameters such as energy expenditure, network lifetime, and throughput to evaluate its effectiveness.
Abstract: The nodes in the wireless sensor network are furnished with restricted and irreplaceable battery power. The continuous sensing, computation, and communication drain out the energy of sensors very quickly. The optimal utilization of the sensor energy has always been a key issue for all the applications in the wireless sensor network. To manage the energy issue of nodes, various approaches were proposed in the past which focused on designing the proper energy management methods. The clustering of sensors is one of the most popular techniques used to manage the energy-related concerns of networks. In this paper, a particle swarm optimization-based energy efficient clustering protocol (PSO-EEC) is proposed to enhance the network lifetime and performance. The proposed protocol uses the particle swarm optimization technique to select the cluster head and relay nodes for the network. The cluster head is selected by employing the particle swarm optimization based fitness function which considers the energy ratio (initial energy and residual energy) of nodes, distance between nodes and cluster head, and node degree to appoint the most optimal node for the cluster head job. For the data transfer to base station, the proposed scheme uses the fitness value based on residual energy of cluster head and distance to base station parameters to nominate the relay nodes for the multi-hop data transfer to the base station. The performance of the proposed protocol is compared with the various existing approaches in terms of different performance parameters such as energy expenditure, network lifetime, and throughput to evaluate its effectiveness. The proposed scheme has improved the lifetime of the network by 238%, 136%, 106%, and 71% as compared to the existing MDCH-PSO, MCHEOR, MOPSO, and HSA-PSO techniques used in the simulation results for the comparison purpose. The stability period of the network in proposed scheme is approximately 396%, 321%, 246%, and 126% more than the existing MDCH-PSO, MCHEOR, MOPSO, and HSA-PSO protocol .

26 citations

Journal ArticleDOI
19 Oct 2021
TL;DR: The modern age metaheuristics that gained popular use in mostly cited combinatorial optimization problems such as vehicle routing problems, traveling salesman problems, and supply chain network design problems are analyzed and discussed.
Abstract: Combinatorial optimization problems are often considered NP-hard problems in the field of decision science and the industrial revolution. As a successful transformation to tackle complex dimensional problems, metaheuristic algorithms have been implemented in a wide area of combinatorial optimization problems. Metaheuristic algorithms have been evolved and modified with respect to the problem nature since it was recommended for the first time. As there is a growing interest in incorporating necessary methods to develop metaheuristics, there is a need to rediscover the recent advancement of metaheuristics in combinatorial optimization. From the authors’ point of view, there is still a lack of comprehensive surveys on current research directions. Therefore, a substantial part of this paper is devoted to analyzing and discussing the modern age metaheuristic algorithms that gained popular use in mostly cited combinatorial optimization problems such as vehicle routing problems, traveling salesman problems, and supply chain network design problems. A survey of seven different metaheuristic algorithms (which are proposed after 2000) for combinatorial optimization problems is carried out in this study, apart from conventional metaheuristics like simulated annealing, particle swarm optimization, and tabu search. These metaheuristics have been filtered through some key factors like easy parameter handling, the scope of hybridization as well as performance efficiency. In this study, a concise description of the framework of the selected algorithm is included. Finally, a technical analysis of the recent trends of implementation is discussed, along with the impacts of algorithm modification on performance, constraint handling strategy, the handling of multi-objective situations using hybridization, and future research opportunities.

16 citations

References
More filters
Journal ArticleDOI
TL;DR: A new distributed energy-efficient clustering scheme for heterogeneous wireless sensor networks, which is called DEEC, is proposed and evaluated, which achieves longer lifetime and more effective messages than current important clustering protocols in heterogeneous environments.

1,131 citations

Journal ArticleDOI
01 Sep 2005
TL;DR: AntHocNet is a hybrid algorithm, which combines reactive path setup with proactive path probing, maintenance and improvement, based on the nature-inspired ant colony optimisation framework, and its performance advantage is visible over a broad range of possible network scenarios.
Abstract: In this paper, we describe AntHocNet, an algorithm for routing in mobile ad hoc networks. It is a hybrid algorithm, which combines reactive path setup with proactive path probing, maintenance and improvement. The algorithm is based on the nature-inspired ant colony optimisation framework. Paths are learned by guided Monte Carlo sampling using ant-like agents communicating in a stigmergic way. In an extensive set of simulation experiments, we compare AntHocNet with AODV, a reference algorithm in the field. We show that our algorithm can outperform AODV on different evaluation criteria. AntHocNet's performance advantage is visible over a broad range of possible network scenarios, and increases for larger, sparser and more mobile networks. Copyright © 2005 AEIT.

596 citations

Journal ArticleDOI
TL;DR: An extensive survey of protocols developed according to the principles of swarm intelligence, taking inspiration from the foraging behaviors of ant and bee colonies, and introduces a novel taxonomy for routing protocols in wireless sensor networks.

370 citations

Journal ArticleDOI
TL;DR: A novel energy efficient clustering mechanism, based on artificial bee colony algorithm, is presented to prolong the network life-time and can successfully be applied to WSN routing protocols.
Abstract: Due to recent advances in wireless communication technologies, there has been a rapid growth in wireless sensor networks research during the past few decades. Many novel architectures, protocols, algorithms, and applications have been proposed and implemented. The efficiency of these networks is highly dependent on routing protocols directly affecting the network life-time. Clustering is one of the most popular techniques preferred in routing operations. In this paper, a novel energy efficient clustering mechanism, based on artificial bee colony algorithm, is presented to prolong the network life-time. Artificial bee colony algorithm, simulating the intelligent foraging behavior of honey bee swarms, has been successfully used in clustering techniques. The performance of the proposed approach is compared with protocols based on LEACH and particle swarm optimization, which are studied in several routing applications. The results of the experiments show that the artificial bee colony algorithm based clustering can successfully be applied to WSN routing protocols.

283 citations

Posted Content
Chi Lin1, Guowei Wu1, Feng Xia1, Mingchu Li1, Lin Yao1, Zhongyi Pei1 
TL;DR: In this article, a family of ant colony algorithms called DAACA for data aggregation has been presented which contains three phases: the initialization, packet transmission and operations on pheromones.
Abstract: In this paper, a family of ant colony algorithms called DAACA for data aggregation has been presented which contains three phases: the initialization, packet transmission and operations on pheromones. After initialization, each node estimates the remaining energy and the amount of pheromones to compute the probabilities used for dynamically selecting the next hop. After certain rounds of transmissions, the pheromones adjustment is performed periodically, which combines the advantages of both global and local pheromones adjustment for evaporating or depositing pheromones. Four different pheromones adjustment strategies are designed to achieve the global optimal network lifetime, namely Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data aggregation algorithms, DAACA shows higher superiority on average degree of nodes, energy efficiency, prolonging the network lifetime, computation complexity and success ratio of one hop transmission. At last we analyze the characteristic of DAACA in the aspects of robustness, fault tolerance and scalability.

85 citations