Firefly Algorithm for Intelligent Context-Aware Sensor Deployment Problem in Wireless Sensor Network

2019 ◽  
Vol 28 (06) ◽  
pp. 1950094 ◽  
Author(s):  
Puri Vishal ◽  
A. Ramesh Babu
Keyword(s):  
Low Cost ◽  
Network Connectivity ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Sensor Deployment ◽  
Second Phase ◽  
Intelligent Sensor ◽  
Bee Colony ◽  
Improved Performance ◽  
Effective Network

Wireless sensor networks (WSNs) provide acceptable low cost and efficient deployable solutions to execute the target tracking, checking and identification of substantial measures. The primary step necessary for WSN is to organize all the sensor nodes in their positions to build up an effective network. In the sensor deployment model, Target COVerage (TCOV) and Network CONnectivity (NCON) are the basic issues in WSNs that have obtained significant consideration in sensor deployment. This paper intends to develop an intelligent context awareness algorithm for sensor deployment process in WSN. Accordingly, the process is divided into two phases. In the first phase, the TCOV process is performed, whereas the second phase of the algorithm establishes NCON among the sensors. An objective model to meet both TCOV and NCON is formulated as a minimization problem. The problem is solved using FireFly (FF) optimization to determine the optimal locations for sensors. It leads to an intelligent sensor deployment model that can determine the optimal locations for the sensors in the WSN. Further, the proposed FF-TCOV and FF-NCON models are compared against the conventional algorithms, namely, genetic algorithm, particle swarm optimization, artificial bee colony, differential evolution and evolutionary algorithm-based TCOV and NCON models. The results achieved from the simulation show the improved performance of the proposed technique.

Deployment of Context-Aware Sensor in Wireless Sensor Network Based on the Variants of Genetic Algorithm

2018 ◽  
Vol 8 (2) ◽  
pp. 1-24
Author(s):  
Puri Vishal ◽  
Ramesh Babu A.
Keyword(s):  
Genetic Algorithm ◽  
Network Connectivity ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Sensor Deployment ◽  
Adaptive Genetic Algorithm ◽  
Main Concern ◽  
Context Aware ◽  
Pollution Levels ◽  
Effective Network

Wireless sensor networks (WSNs) are generally a group of spatially scattered and devoted sensors to record and monitor the physical environmental condition, and the collected data is grouped at a central location. In fact, the environmental conditions such as sound, humidity, temperature, wind, pollution levels, etc., can be clearly determined by WSNs. The principal objective of WSNs is to organize the whole sensor nodes in their related positions, thereby developing an effective network. In WSNs, target COVerage (TCOV) and Network CONnectivity (NCON) are the main concern of the sensor deployment problem. Many research works aspire the evolvement of smart context awareness algorithm for sensor deployment issues in WSN. Here the TCOV and NCON process are deployed as the minimization problem. This article makes an analysis of different GA variations in attaining the objective. The GA variations are as follows: self-adaptive genetic algorithm (SAGA), deterministic-adaptive genetic algorithm (DAGA), Individual- Adaptive Genetic Algorithm (IAGA). Finally, the methods are compared to one another in terms of connectivity and coverage performance.

scholarly journals Detection of Coverage Hole Nodes in Wireless Sensor Network using Artificial Intelligence

2019 ◽  
Vol 8 (9S) ◽  
pp. 603-606
Keyword(s):  
Artificial Bee Colony ◽  
Fitness Function ◽  
Network Connectivity ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Physical Damage ◽  
Bee Colony ◽  
Coverage Hole ◽  
Adequate Coverage ◽  
Artificial Neural Network Ann

Adequate coverage of the sensing field in Wireless sensor networks (WSNs) is critical to many applications. However, when one or more sensor nodes stop working due to energy exhaustion or physical damage, the network may experience overlay vulnerability. This can disrupt network connectivity and hinder performance. Therefore, it must be fixed automatically. To resolve this problem, swarm inspired Artificial Bee Colony (ABC) scheme in addition to the Artificial Neural Network (ANN) approach is used. The aim of ABC is to optimize the shortest path by selecting an appropriate fitness function and then identify holes using ANN. Before the detection of holes, ANN is trained as per the optimized properties of nodes that are as per the genuine nodes and coverage hole repair properties. Therefore during the testing process, ANN compares these properties with the stored properties and then identify the hole repair node. From the experiment, it has been analyzed that the energy consumption up to 23.88% is saved.

scholarly journals Intelligent On-Demand Connectivity Restoration for Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Khalid Mahmood ◽  
Muhammad Amir Khan ◽  
Mahmood ul Hassan ◽  
Ansar Munir Shah ◽  
Shahzad Ali ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Connectivity ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
On Demand ◽  
Connectivity Restoration ◽  
Restoration Technique ◽  
Near Future

Wireless sensor networks are envisioned to play a very important role in the Internet of Things in near future and therefore the challenges associated with wireless sensor networks have attracted researchers from all around the globe. A common issue which is well studied is how to restore network connectivity in case of failure of single or multiple nodes. Energy being a scarce resource in sensor networks drives all the proposed solutions to connectivity restoration to be energy efficient. In this paper we introduce an intelligent on-demand connectivity restoration technique for wireless sensor networks to address the connectivity restoration problem, where nodes utilize their transmission range to ensure the connectivity and the replacement of failed nodes with their redundant nodes. The proposed technique helps us to keep track of system topology and can respond to node failures effectively. Thus our system can better handle the issue of node failure by introducing less overhead on sensor node, more efficient energy utilization, better coverage, and connectivity without moving the sensor nodes.

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 DOS Attacks on TCP/IP Layers in WSN

2013 ◽  
Vol 1 (2013) ◽  
pp. 40-45
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Low Cost ◽  
Denial Of Service ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Renewable Energy Resources ◽  
Dos Attack ◽  
Dos Attacks

The emergence of sensor networks as one of the dominant technology trends in the coming decades has posed numerous unique challenges on their security to researchers. These networks are likely to be composed of thousands of tiny sensor nodes, which are low-cost devices equipped with limited memory, processing, radio, and in many cases, without access to renewable energy resources. While the set of challenges in sensor networks are diverse, we focus on security of Wireless Sensor Network in this paper. First, we propose some of the security goal for Wireless Sensor Network. To perform any task in WSN, the goal is to ensure the best possible utilization of sensor resources so that the network could be kept functional as long as possible. In contrast to this crucial objective of sensor network management, a Denial of Service (DoS) attack targets to degrade the efficient use of network resources and disrupts the essential services in the network. DoS attack could be considered as one of th

Optimisation of Radiation Beam in Linear Nodes Array of Wireless Sensor Network for Improved Performance

2012 ◽  
pp. 215-225
Author(s):  
N. N. N. Abd. Malik ◽  
M. Esa ◽  
S. K. S. Yusof ◽  
S. A. Hamzah ◽  
M. K. H. Ismail
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Linear Array ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Communication Link ◽  
Radiation Beam ◽  
Wireless Sensor Nodes ◽  
Position Errors ◽  
Improved Performance

This chapter presents an intelligent method of optimising the radiation beam of wireless sensor nodes in Wireless Sensor Network (WSN). Each node has the feature of a monopole antenna. The optimisation involves selection of nodes to be organised as close as possible to a uniform linear array (ULA) in order to minimise the position errors, which will improve the radiation beam reconfiguring performance. Instead of utilising random beamforming, which needs a large number of sensor nodes to interact with each other and form a narrow radiation beam, the developed optimisation algorithm is emphasized to only a selected number of sensor nodes which can construct a linear array. Thus, the method utilises radiation beam reconfiguration technique to intelligently establish a communication link in a WSN.

Basic Concepts of Wireless Sensor Networks

2011 ◽  
pp. 57-64
Author(s):  
Lina M. Pestana Leão de Brito ◽  
Laura M. Rodríguez Peralta
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Low Cost ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Distributed Sensor Networks ◽  
Distributed Sensor ◽  
Technological Advances ◽  
The Cost ◽  
Communication Equipment

As with many technologies, defense applications have been a driver for research in sensor networks, which started around 1980 due to two important programs of the Defense Advanced Research Projects Agency (DARPA): the distributed sensor networks (DSN) and the sensor information technology (SensIT) (Chong & Kumar, 2003). However, the development of sensor networks requires advances in several areas: sensing, communication, and computing. The explosive growth of the personal communications market has driven the cost of radio devices down and has increased the quality. At the same time, technological advances in wireless communications and electronic devices (such as low-cost, low-power, small, simple yet efficient wireless communication equipment) have enabled the manufacturing of sensor nodes and, consequently, the development of wireless sensor networks (WSNs).

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