scholarly journals A Hybrid Artificial Bee Colony and Bacterial Foraging Algorithm for Optimized Clustering in Wireless Sensor Network

2019 ◽  
Vol 8 (10) ◽  
pp. 2186-2190
Keyword(s):  
Sensor Networks ◽  
Network Lifetime ◽  
Artificial Bee Colony ◽  
Weather Forecasting ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Bee Colony ◽  
Bacterial Foraging Algorithm

The emerging ubiquitous nature of wireless sensor networks has made it suitable and applicable to a diversified number of vital applications that include environment surveillance, health monitoring using implantable sensors, weather forecasting and other plethora of contexts. The critical issues such as computation time, limited memory and energy are more common due to the tiny sized hundred and thousands of sensor nodes existing in the networks. In this context, the network lifetime completely depends on the potential use of available resources. The process of organizing closely located sensor nodes into clusters is convenient for effective management of cluster and improving the lifetime of the complete network. At this juncture, swarm intelligent and evolutionary algorithms the pertains to the problem of NP-complete is determined to achieve a superior optimal solution. In this paper, a Hybrid Artificial Bee Colony and Bacterial Foraging Algorithm-based Optimized Clustering (HABC-BFA-OC) is proposed for achieving enhanced network lifetime in sensor networks. In this proposed HABC-BFA-OC technique, the benefits of Bacterial Foraging Optimization is included for improving the local search potential of ABC algorithm in order to attain maximum exploitation and exploration over the parameters considered for cluster head selection. The simulation experiments of the proposed HABC-BFA-OC technique confirmed an enhanced network lifetime with minimized energy consumptions during its investigation with a different number of sensor nodes.

scholarly journals Balancing Energy Consumption in Wireless Sensor Networks Using Fuzzy Artificial Bee Colony Routing Protocol

2013 ◽  
Vol 7 (2) ◽  
pp. 1018-1032
Author(s):  
Imad S. Alshawi
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Artificial Bee Colony ◽  
Wireless Sensor ◽  
Bee Colony ◽  
Balancing Energy

Energy is an extremely critical resource for battery-powered Wireless Sensor Networks (WSNs), thus making energy-efficient protocol design a key challenging problem. Most of the existing routing protocols always forward packets along the minimum energy paths to merely minimize energy consumption, which causes an Uneven Energy Consumption (UEC) problem and eventually results in a network partition. Due to the limited energy resources of sensor nodes, selecting an appropriate routing protocol can be significantly improve overall performance especially energy awareness in WSNs. Therefore, this paper proposes an energy-efficient routing protocol called Fuzzy Artificial Bee Colony Routing Protocol (FABCRP) which is capable of finding the optimal routing path form the source to the destination by favoring some of routing criteria and balancing among them to prolong the network lifetime. To demonstrate the effectiveness of FABCRP in terms of balancing energy consumption and maximization of network lifetime, we compare it with Fuzzy approach, ABC algorithm and Fuzzy_A-star approach using the same criteria in two different topographical areas. Simulation results show that the network lifetime achieved by FABCRP could be increased by nearly 35%, 30%, and 15% more than that obtained by Fuzzy, ABC and Fuzzy_A-star respectively.

Dynamic Deployment of Wireless Sensor Networks by an Improved Artificial Bee Colony Algorithm

2014 ◽  
Vol 511-512 ◽  
pp. 862-866
Author(s):  
Pei Yu He ◽  
Ming Yan Jiang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Coverage Rate ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Selection Probability ◽  
Bee Colony ◽  
Dynamic Deployment

The main objective of dynamic deployment in Wireless Sensor Networks is to use the mobility of sensor nodes, trying to increase the coverage area of the networks through dynamically adjusting the node positions. In this paper, with the characteristics of the optimal sensor deployment in theory, we improve the standard artificial bee colony algorithm by introducing the distance factor to improve the onlooker bees selection probability formula, changing the working mode of the scout bee to avoid worse coverage rate and changing the limit l for scout bee to increase the chance to get better solution. Results show that the improved artificial bee colony algorithm has faster convergent speed and higher coverage rate compared with the standard artificial bee colony algorithm.

Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jasleen Kaur ◽  
Punam Rani ◽  
Brahm Prakash Dahiya
Keyword(s):  
Artificial Bee Colony ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cluster Head Selection ◽  
Swarm Optimization ◽  
Content Type ◽  
Bee Colony ◽  
Glowworm Swarm Optimization ◽  
Optimal Cluster

Purpose This paper aim to find optimal cluster head and minimize energy wastage in WSNs. Wireless sensor networks (WSNs) have low power sensor nodes that quickly lose energy. Energy efficiency is most important factor in WSNs, as they incorporate limited sized batteries that would not be recharged or replaced. The energy possessed by the sensor nodes must be optimally used so as to increase the lifespan. The research is proposing hybrid artificial bee colony and glowworm swarm optimization [Hybrid artificial bee colony and glowworm swarm optimization (HABC-GSO)] algorithm to select the cluster heads. Previous research has considered fitness-based glowworm swarm with Fruitfly (FGF) algorithm, but existing research was limited to maximizing network lifetime and energy efficiency. Design/methodology/approach The proposed HABC-GSO algorithm selects global optima and improves convergence ratio. It also performs optimal cluster head selection by balancing between exploitation and exploration phases. The simulation is performed in MATLAB. Findings The HABC-GSO performance is evaluated with existing algorithms such as particle swarm optimization, GSO, Cuckoo Search, Group Search Ant Lion with Levy Flight, Fruitfly Optimization algorithm and grasshopper optimization algorithm, a new FGF in the terms of alive nodes, normalized energy, cluster head distance and delay. Originality/value This research work is original.

scholarly journals Particle Swarm and Bacterial Foraging Inspired Hybrid Artificial Bee Colony Algorithm for Numerical Function Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Li Mao ◽  
Yu Mao ◽  
Changxi Zhou ◽  
Chaofeng Li ◽  
Xiao Wei ◽  
...  
Keyword(s):  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Function Optimization ◽  
Local Optimum ◽  
Search Range ◽  
Bacterial Foraging Optimization ◽  
Numerical Function ◽  
Bacterial Foraging ◽  
Bee Colony

Artificial bee colony (ABC) algorithm has good performance in discovering the optimal solutions to difficult optimization problems, but it has weak local search ability and easily plunges into local optimum. In this paper, we introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC) algorithm. To obtain a global optimal solution efficiently, we make HABC algorithm converge rapidly in the early stages of the search process, and the search range contracts dynamically during the late stages. Our experimental results on 16 benchmark functions of CEC 2014 show that HABC achieves significant improvement at accuracy and convergence rate, compared with the standard ABC, best-so-far ABC, directed ABC, Gaussian ABC, improved ABC, and memetic ABC algorithms.

Event Based Data Gathering in Wireless Sensor Networks

2012 ◽  
pp. 445-462 ◽  
Author(s):  
Asfandyar Khan ◽  
Azween Abdullah ◽  
Nurul Hasan
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Energy Savings ◽  
Detection System ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Detection Model ◽  
Physical Environments ◽  
Event Based

Wireless sensor networks (WSANs) are increasingly being used and deployed to monitor the surrounding physical environments and detect events of interest. In wireless sensor networks, energy is one of the primary issues and requires the conservation of energy of the sensor nodes, so that network lifetime can be maximized. It is not recommended as a way to transmit or store all data of the sensor nodes for analysis to the end user. The purpose of this “Event Based Detection” Model is to simulate the results in terms of energy savings during field activities like a fire detection system in a remote area or habitat monitoring, and it is also used in security concerned issues. The model is designed to detect events (when occurring) of significant changes and save the data for further processing and transmission. In this way, the amount of transmitted data is reduced, and the network lifetime is increased. The main goal of this model is to meet the needs of critical condition monitoring applications and increase the network lifetime by saving more energy. This is useful where the size of the network increases. Matlab software is used for simulation.

scholarly journals Energy Efficient Trust Node Based Routing Protocol (EETRP) to Maximize the Lifetime of Wireless Sensor Networks in Plateaus

2019 ◽  
Vol 15 (06) ◽  
pp. 113 ◽  
Author(s):  
Nandoori Srikanth ◽  
Muktyala Sivaganga Prasad
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Mobile Nodes ◽  
Energy Limitation

<p>Wireless Sensor Networks (WSNs) can extant the individual profits and suppleness with regard to low-power and economical quick deployment for numerous applications. WSNs are widely utilized in medical health care, environmental monitoring, emergencies and remote control areas. Introducing of mobile nodes in clusters is a traditional approach, to assemble the data from sensor nodes and forward to the Base station. Energy efficiency and lifetime improvements are key research areas from past few decades. In this research, to solve the energy limitation to upsurge the network lifetime, Energy efficient trust node based routing protocol is proposed. An experimental validation of framework is focused on Packet Delivery Ratio, network lifetime, throughput, energy consumption and network loss among all other challenges. This protocol assigns some high energy nodes as trusted nodes, and it decides the mobility of data collector.  The energy of mobile nodes, and sensor nodes can save up to a great extent by collecting data from trusted nodes based on their trustworthiness and energy efficiency.  The simulation outcome of our evaluation shows an improvement in all these parameters than existing clustering and Routing algorithms.<strong></strong></p>

scholarly journals Energy-Efficient Unequal Chain Length Clustering for Wireless Sensor Networks in Smart Cities

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Mohammad Baniata ◽  
Jiman Hong
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Chain Length ◽  
Network Lifetime ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Smart Cities ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor

The recent advances in sensing and communication technologies such as wireless sensor networks (WSN) have enabled low-priced distributed monitoring systems that are the foundation of smart cities. These advances are also helping to monitor smart cities and making our living environments workable. However, sensor nodes are constrained in energy supply if they have no constant power supply. Moreover, communication links can be easily failed because of unequal node energy depletion. The energy constraints and link failures affect the performance and quality of the sensor network. Therefore, designing a routing protocol that minimizes energy consumption and maximizes the network lifetime should be considered in the design of the routing protocol for WSN. In this paper, we propose an Energy-Efficient Unequal Chain Length Clustering (EEUCLC) protocol which has a suboptimal multihop routing algorithm to reduce the burden on the cluster head and a probability-based cluster head selection algorithm to prolong the network lifetime. Simulation results show that the EEUCLC mechanism enhanced the energy balance and prolonged the network lifetime compared to other related protocols.

An Energy Aware Routing Based on Swarm Intelligence for Wireless Sensor Networks

2013 ◽  
Vol 706-708 ◽  
pp. 635-638
Author(s):  
Yong Lv
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Swarm Intelligence ◽  
Network Lifetime ◽  
Average Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Aware ◽  
Energy Efficient Routing

Wireless Sensor Networks consisting of nodes with limited power are deployed to collect and distribute useful information from the field to the other sensor nodes. Energy consumption is a key issue in the sensor’s communications since many use battery power, which is limited. In this paper, we describe a novel energy efficient routing approach which combines swarm intelligence, especially the ant colony based meta-heuristic, with a novel variation of reinforcement learning for sensor networks (ARNet). The main goal of our study was to maintain network lifetime at a maximum, while discovering the shortest paths from the source nodes to the sink node using an improved swarm intelligence. ARNet balances the energy consumption of nodes in the network and extends the network lifetime. Simulation results show that compared with the traditional EEABR algorithm can obviously improve adaptability and reduce the average energy consumption effectively.

Sign in / Sign up

Export Citation Format

Share Document