scholarly journals A Research Protocol for Detection and Optimization of Overlapping Coverage in Wireless Sensor Networks

2019 ◽  
Vol 8 (6S) ◽  
pp. 1-6
Keyword(s):  
Human Life ◽  
Data Gathering ◽  
Fire Detection ◽  
Life Time ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Coverage Area ◽  
Random Manner ◽  
The Given

Wireless sensor network (WSN) is an emerging area in which numbers of sensor nodes are deployed in two ways random and deterministic for data gathering. WSNs have expedited human life in diverse emerging fields: military, agriculture, structural health, perimeter access control, forest fire detection. A physical stimulus such as pressure, sound, light act as an environmental parameter on which the system is design to monitor and detect it for controlling the coverage area for assigned task. The nodes are deployed in the random manner in the given area for gathering the information and they may be overlapped so that the total area may not be covered. The proposed protocol uses radius and the residual energy as a function to increase the total coverage area so that the whole coverage area may be achieved. It also increases the life time of the network using Sleep and Wakeup protocol. Therefore the overall life time of the network increases.

scholarly journals Energy Efficient Routing Protocall By One Way Multi-Hope Sensor Nodes

2021 ◽  
Vol 12 (7) ◽  
pp. 3401-3410
Author(s):  
Heba Hussain Hadi, Et. al.
Keyword(s):  
Data Transmission ◽  
Sensor Node ◽  
Cluster Head ◽  
Geographic Location ◽  
Life Time ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Energy Efficient Routing ◽  
Coverage Area ◽  
Aggregated Data

Multi-hope is widely used for data aggregation and transmission in various applications. The resource availability determines the life time of a WSN. Sensor nodes are powered by a tiny battery that supplies the required energy for the sensor and transmitter. The residual energy available at any moment decides the fitness of the sensor node. The sensor node senses the environment and transmits the data to the sink. Efficient data transmission and aggregation with less energy consumption can prolong the lifetime of the sensor network. The sensor node that is inside the coverage area of the sink can directly transmit the data to sink in a single-hop transmission. The sensor node that is not inside the coverage area should transmit the data to the neighbor node which is in the coverage area of the sink. The data is then turn transmitted by the node close to it fall in multi-hop transmission involving a number of intermediate nodes to forward the data to the sink and consumes extra energy for the forwarding process. The formation clusters and data transmission of data by Cluster Heads (CH) can eliminate many nodes involved in the transmission of same data. Clusters are a group of self-organized nodes in a geographic location that can communicate among them. A node in a cluster with higher residual energy will be acting as CH and all other nodes in the cluster transmit the data to the CH. The CH transmits the aggregated data to the sink. The CH transmits the data to the sink either in single-hop transmission or multi-hop transmission. The cluster head consumes more energy than other nodes in the cluster as it is involved in aggregation and transmission process.

scholarly journals Data Clustering Method in Wireless Sensor Networks Based on Residual Energy Perception

2018 ◽  
Vol 14 (06) ◽  
pp. 85 ◽  
Author(s):  
Xudong Yang
Keyword(s):  
Energy Consumption ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Data Gathering ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Data Forwarding ◽  
Cluster Data ◽  
Head Node

<p class="0abstract"><span lang="EN-US">To prolong the survival time of wireless sensor network, an iterative scheme was proposed. First of all, spectrum clustering algorithm iteratively segmented the network into clusters, and cluster head nodes in each sub cluster were determined depending on the size of residual energy of sensor nodes. Then, a data forwarding balance tree was constructed in each sub cluster. Data forwarding path of each non-cluster head node was defined, and the moving path of a mobile data collector was determined, which used the residual energy as the basis for the network optimization. Finally, this scheme was simulated, and two traditional data gathering algorithms were compared. The results showed that the algorithm designed in this experiment could effectively balance energy consumption among all WSN nodes and had great performance improvement compared with the traditional data collection algorithm. To sum up, this algorithm can significantly reduce the energy consumption of the network and improve the lifetime of the network. </span></p>

An energy minimizing score based optimal data gathering in wireless sensor networks

2018 ◽  
Vol 7 (2.31) ◽  
pp. 161
Author(s):  
P Balamurugan ◽  
M Shyamala Devi ◽  
V Sharmila
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Gathering ◽  
Residual Energy ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Efficient Manner ◽  
High Level ◽  
The Impact

In wireless sensor networks, Sensor nodes are arranged randomly in unkind physical surroundings to collect data and distribute the data to the remote base station. However the sensor nodes have to preserve the power source that has restricted estimation competence. The sensed information is difficult to be transmitted over the sensor network for a long period of time in an energy efficient manner.  In this paper, it finds the problem of communication data between sink nodes and remote data sources via intermediate nodes in sensor field. So this paper proposes a score based data gathering algorithm in wireless sensor networks. The high-level contribution of this study is the enhancement of a score- based data gathering algorithm and the impact of energy entity for Wireless Sensor Networks.  Then the energy and delay of data gathering are evaluated. Unlike PEGASIS and LEACH, the delay for every process of data gathering is considerably lower when SBDG is employed.  The energy consumed per round of data gathering for both SBDG and EE-SBDG is less than half of that incurred with PEGASIS and LEACH. Compared with LEACH and PEGASIS, SBDG and EE-SBDG are fair with node usage because of the scoring system and residual energy respectively.  Overall, the Score-based data gathering algorithm provides a significant solution to maximize the network lifetime as well as minimum delay per round of data gathering.

DATA GATHERING AND COMMUNICATION FOR WIRELESS SENSOR NETWORKS — A CENTRALIZED APPROACH

2005 ◽  
Vol 02 (04) ◽  
pp. 267-278 ◽  
Author(s):  
PETER X. LIU ◽  
NANCY DING
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Gathering ◽  
Life Time ◽  
Optimization Method ◽  
Chain Structure ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
The Social

This paper introduces a centralized approach to data gathering and communication for wireless sensor networks. Inspired by the social behaviors of natural ants, we clearly partition the task for the base station and sensor nodes in a wireless sensor network according to their different functions and capabilities. An ant colony optimization method is employed at the base station to form a near-optimal chain for sensor nodes to transmit collected data. Sensor nodes in the network then form a bi-direction chain structure, which is self-adaptive to any minor changes of the network topology. The simulation results show that the developed algorithm, which we call AntChain algorithm, performs much better than many other protocols in terms of energy efficiency, data integrity and life time when the base station is near where the sensor nodes are deployed.

scholarly journals An Adaptive Hybrid Routing Scheme For Energy Management In Wireless Sensor Networks

2021 ◽  
Author(s):  
Raviteja Kocherla ◽  
Chandra sekhar M ◽  
Ramesh Vatambeti
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Life Time ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Grey Wolf ◽  
Hybrid Routing ◽  
Huge Data ◽  
Ant Lion

Abstract In Wireless Sensor Network (WSN) the life time of nodes and energy management are important issues, because the nodes in WSN required more energy when it is used in different applications. On the other hand, unstable energy consumption among intermediate nodes tends to huge data loss. To address this problem the present research introduced a novel Hybrid Gossip Grey Wolf Ant lion (HGGW-AL) protocol to afford an efficient and better transmission channel. Here, the fitness of grey wolf and ant lion helps to categorize the energy drained node and also, to predict the malicious activities. Furthermore, the novel Rest Awake (RA) is initialized to process the clustering strategy to maintain the residual energy in WSN. Moreover, it enhances the energy level of sensor nodes by increasing its lifetime. Finally, the efficiency of the proposed strategy is compared with the existing works and achieved better performance by reducing the energy consumption of each sensor node.

scholarly journals HEAP: Hybrid Energy-efficient Aggregation Protocol for Large Scale Wireless Sensor Networks

2005 ◽  
Vol 4 (2) ◽  
pp. 713-721 ◽  
Author(s):  
Santhosh Simon ◽  
K Paulose Jacob
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Fire Detection ◽  
Temporal Correlation ◽  
Life Time ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Time Data ◽  
Cooperative Effort

Wireless sensor networks (WSNs) can be meritoriously used in several application areas like agriculture, military surveillance, environmental monitoring, forest fire detection etc. Since they are used to monitor large geographic areas numerous sensor nodes are to be deployed and their radio range is also very short. Hence they depend on the cooperative effort of these densely deployed sensor nodes for reporting the sensed data. Any changes in environment or an event of interest may be initially observed in a particular area. In other words, they are correlated in space domain. Many nodes in that area may detect the event and report the same event. This redundant information is of no use to system and also depletes the precious energy of the intermediate sensor nodes. Sensor nodes are having very limited energy and needs to be conserved for attaining maximum network life time. Data aggregation is an effective technique for conserving energy by reducing the packet transmissions. Many aggregation systems are available, but when employed for large scale wireless sensor networks they are less effective. In this paper we propose a scheme for large scale WSNs which effectively uses the spatial correlation and temporal correlation of the data for effective aggregation and thereby preserving precious energy.

Deterministic Deployment for Wireless Image Sensor Nodes

2014 ◽  
Vol 8 (1) ◽  
pp. 668-674
Author(s):  
Junguo Zhang ◽  
Yutong Lei ◽  
Fantao Lin ◽  
Chen Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Visual Information ◽  
Image Sensor ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Coverage Area ◽  
Effective Coverage ◽  
Area Of Interest ◽  
Key Issues

Wireless sensor networks composed of camera enabled source nodes can provide visual information of an area of interest, potentially enriching monitoring applications. The node deployment is one of the key issues in the application of wireless sensor networks. In this paper, we take the effective coverage and connectivity as the evaluation indices to analyze the effect of the perceivable angle and the ratio of communication radius and sensing radius for the deterministic circular deployment. Experimental results demonstrate that the effective coverage area of the triangle deployment is the largest when using the same number of nodes. When the nodes are deployed in the same monitoring area in the premise of ensuring connectivity, rhombus deployment is optimal when √2 < rc / rs < √3 . The research results of this paper provide an important reference for the deployment of the image sensor networks with the given parameters.

Quasi Oppositional Glowworm Swarm Optimization Algorithm for Energy Efficient Clustering in Wireless Sensor Networks

2020 ◽  
Vol 17 (12) ◽  
pp. 5447-5456
Author(s):  
R. M. Alamelu ◽  
K. Prabu
Keyword(s):  
Energy Efficient ◽  
Cluster Head ◽  
Residual Energy ◽  
Link Quality ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Node Degree ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Energy Efficient Clustering

Wireless sensor network (WSN) becomes popular due to its applicability in distinct application areas like healthcare, military, search and rescue operations, etc. In WSN, the sensor nodes undergo deployment in massive number which operates autonomously in harsh environment. Because of limited resources and battery operated sensor nodes, energy efficiency is considered as a main design issue. To achieve, clustering is one of the effective technique which organizes the set of nodes into clusters and cluster head (CH) selection takes place. This paper presents a new Quasi Oppositional Glowworm Swarm Optimization (QOGSO) algorithm for energy efficient clustering in WSN. The proposed QOGSO algorithm is intended to elect the CHs among the sensor nodes using a set of parameters namely residual energy, communication cost, link quality, node degree and node marginality. The QOGSO algorithm incorporates quasi oppositional based learning (QOBL) concept to improvise the convergence rate of GSO technique. The QOGSO algorithm effectively selects the CHs and organizes clusters for minimized energy dissipation and maximum network lifetime. The performance of the QOGSO algorithm has been evaluated and the results are assessed interms of distinct evaluation parameters.

scholarly journals Performance Analysis of Q-Leach Algorithm in WSN

2020 ◽  
Vol 5 (10) ◽  
pp. 1-4
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Data Transmission ◽  
Short Range ◽  
Sensor Node ◽  
Life Time ◽  
Time Span ◽  
Sensor Nodes ◽  
Wireless Sensor

Sensor nodes are exceedingly energy compelled instrument, since it is battery operated instruments. In wsn network, every node is liable to the data transmission through the wireless mode [1]. Wireless sensor networks (WSN) is made of a huge no. of small nodes with confined functionality. The essential theme of the wireless sensor network is energy helpless and the WSN is collection of sensor. Every sensor terminal is liable to sensing, store and information clan and send it forwards into sink. The communication within the node is done via wireless network [3].Energy efficiency is the main concentration of a desining the better routing protocol. LEACH is a protocol. This is appropriate for short range network, since imagine that whole sensor node is capable of communication with inter alia and efficient to access sink node, which is not always correct for a big network. Hence, coverage is a problem which we attempt to resolve [6]. The main focus within wireless sensor networks is to increase the network life-time span as much as possible, so that resources can be utilizes efficiently and optimally. Various approaches which are based on the clustering are very much optimal in functionality. Life-time of the network is always connected with sensor node’s energy implemented at distant regions for stable and defect bearable observation [10].

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