A novel cluster-based routing protocol wireless sensor networks using Spider Monkey Optimization

Gui, Tina; Ma, Chengbin; Wang, Feng; Li, Jinyang; Wilkins, Dawn

doi:10.1109/iecon.2016.7794106

Cited by 23 publications

(9 citation statements)

References 16 publications

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“…Spider monkey optimization has been applied in various fields like big data [19], image processing [20], cluster routing protocols for WSNs. In Reference [21], research was carried out to understand the mechanism of SMO in WSN route optimization by implementing a mathematical model of its behavioral patterns based on SMO-C. It transmits the data from the clusters to the base station via the optimal path.…”

Section: Literature Surveymentioning

confidence: 99%

BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones

Ganesan

Raajini

Nayyar

et al. 2020

Sensors

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Over the past few years, unmanned aerial vehicles (UAV) or drones have been used for many applications. In certain applications like surveillance and emergency rescue operations, multiple drones work as a network to achieve the target in which any one of the drones will act as the master or coordinator to communicate, monitor, and control other drones. Hence, drones are energy-constrained; there is a need for effective coordination among them in terms of decision making and communication between drones and base stations during these critical situations. This paper focuses on providing an efficient approach for the election of the cluster head dynamically, which heads the other drones in the network. The main objective of the paper is to provide an effective solution to elect the cluster head among multi drones at different periods based on the various physical constraints of drones. The elected cluster head acts as the decision-maker and assigns tasks to other drones. In a case where the cluster head fails, then the next eligible drone is re-elected as the leader. Hence, an optimally distributed solution proposed is called Bio-Inspired Optimized Leader Election for Multiple Drones (BOLD), which is based on two AI-based optimization techniques. The simulation results of BOLD compared with the existing Particle Swarm Optimization-Cluster head election (PSO-C) in terms of network lifetime and energy consumption, and from the results, it has been proven that the lifetime of drones with the BOLD algorithm is 15% higher than the drones with PSO-C algorithm.

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Section: Literature Surveymentioning

confidence: 99%

BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones

Ganesan

Raajini

Nayyar

et al. 2020

Sensors

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“…SMO-C [16] is a protocol that adopts SMO [15], and like PSO-C, optimally locates the CH that is assigned to the nearest node. The objective function consists of two fitness values, namely node–CH distance and the energy consumed by nodes and CHs.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, more calculations than in other protocols are required to obtain the fitness values, and the results have not shown a considerable improvement over similar protocols. In fact, the results in [16] showed that SMO-C is not much better than LEACH. Alternatively, SMOEC [17] has been shown to improve SMO-C by specifying the transmission protocol between CHs.…”

Section: Related Workmentioning

confidence: 99%

“…Another consideration is the node energy, which is widely used for CH selection [7,8,9,10,11,12,13,14,15,16,17,18,19]. The node energy is divided into the transmission, reception, residual, and data aggregation energy, all of which we combine back again into the reserve energy (i.e., the energy consumed and left when a node becomes a CH).…”

Section: Proposed Ch Selection Protocolmentioning

confidence: 99%

“…The recently proposed spider monkey optimization (SMO) mimics the behaviors of spider monkeys seeking food to quickly and accurately determine feasible solutions compared to other optimization algorithms based on swarm intelligence [15]. Therefore, various studies have used SMO for CH selection [16,17,18]. We modified the original SMO algorithm in this study to further improve CH selection.…”

Section: Introductionmentioning

confidence: 99%

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Energy-Efficient Cluster-Head Selection for Wireless Sensor Networks Using Sampling-Based Spider Monkey Optimization

Lee

Chim

Park

2019

Sensors

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Extending the lifetime and stability of wireless sensor networks (WSNs) through efficient energy consumption remains challenging. Though clustering has improved energy efficiency through cluster-head selection, its application is still complicated. In existing cluster-head selection methods, the locations where cluster-heads are desirable are first searched. Next, the nodes closest to these locations are selected as the cluster-heads. This location-based approach causes problems such as increased computation, poor selection accuracy, and the selection of duplicate nodes. To solve these problems, we propose the sampling-based spider monkey optimization (SMO) method. If the sampling population consists of nodes to select cluster-heads, the cluster-heads are selected among the nodes. Thus, the problems caused by different locations of nodes and cluster-heads are resolved. Consequently, we improve lifetime and stability of WSNs through sampling-based spider monkey optimization and energy-efficient cluster head selection (SSMOECHS). This study describes how the sampling method is used in basic SMO and how to select cluster-heads using sampling-based SMO. The experimental results are compared to similar protocols, namely low-energy adaptive clustering hierarchy centralized (LEACH-C), particle swarm optimization clustering protocol (PSO-C), and SMO based threshold-sensitive energy-efficient delay-aware routing protocol (SMOTECP), and the results are shown in both homogeneous and heterogeneous setups. In these setups, SSMOECHS improves network lifetime and stability periods by averages of 13.4%, 7.1%, 34.6%, and 1.8%, respectively.

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Genetic spider monkey‐based routing protocol to increase the lifetime of the network and energy management in WSN

Jothi

Chandrasekar

2020

Int J Communication

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SummaryWireless sensor networks (WSNs) include large distributed nodes in the sensing field. However, the sensor nodes may die due to energy deficiency as they are situated in a hostile environment. Therefore, an energy‐efficient WSN routing protocol is necessary in order to better accommodate the various environmental conditions. In this paper, we have proposed a new Energy‐Efficient Genetic Spider Monkey‐based Routing Protocol (EGSMRP) to improve the stability and lifetime of sensor nodes. The operation of EGSMRP is classified into two stages: (i) setup phase and (ii) steady‐state phase. In the setup phase, GSMO‐based cluster head selection procedure is done. In this phase, the base station utilizes the GSMO algorithm as a device to generate energy‐efficient clusters. Followed with this, the steady‐state phase solves the load balancing issue by utilizing the intracluster data broadcast and dual‐hop intercluster broadcasting algorithm. Thereby, the proposed EGSMRP protocol has shown the energy‐based opportunistic broadcasting with reduced control overhead. Simulation is performed in various conditions to evaluate the effectiveness of the proposed EGSMRP protocol using different metrics such as throughput, control overhead, energy consumption, end‐to‐end delay, and network lifetime. From the simulation results, it was evident that EGSMRP has achieved a higher performance compared to other traditional approaches such as EBAR, MCTRP, IEEMARP, HMCEER, and EFTETRP.

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A novel cluster-based routing protocol wireless sensor networks using Spider Monkey Optimization

Cited by 23 publications

References 16 publications

BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones

BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones

Energy-Efficient Cluster-Head Selection for Wireless Sensor Networks Using Sampling-Based Spider Monkey Optimization

Genetic spider monkey‐based routing protocol to increase the lifetime of the network and energy management in WSN

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