scholarly journals Identifying Faulty Nodes in Wireless Sensor Network to Enhance Reliability

2019 ◽  
Vol 8 (2) ◽  
pp. 1727-1731 ◽  
Keyword(s):  
Residual Energy ◽  
Initial Energy ◽  
Sensor Nodes ◽  
Reliable Communication ◽  
End To End Delay ◽  
Source To Sink ◽  
Secure Data Transmission ◽  
Energy Consumption Balancing ◽  
Transmission Energy Consumption ◽  
Transmission Energy

WSN having several major issues to deliver information from source to sink. Such that secure data transmission, energy consumption, balancing the load and fault tolerance will be the major tasks. In the WSN, one node communicates with its neighbour nodes with limited energy source. So if any node does not work properly or become faulty nodes, which have less initial energy level, bandwidth to communicate with faulty nodes. Hence there is a need of system which can overcome with fault and give the reliable communication. Fault may also occur due to the dislocation of sensor nodes, battery depletion or instability of transmission medium.This proposed work will identify the faulty nodes and calculate the end to end delay as well as residual energy. Hence a reliable communication for all nodes in the WSN can be attained.

Research on Energy Aware Routing for Wireless Sensor Networks

2013 ◽  
Vol 303-306 ◽  
pp. 191-196
Author(s):  
Wei Zhang ◽  
Ling Hua Zhang
Keyword(s):  
Energy Consumption ◽  
Optimal Path ◽  
Critical Issue ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Energy Aware ◽  
Energy Aware Routing ◽  
Transmission Energy Consumption ◽  
Transmission Energy ◽  
Improved Algorithm

Energy aware routing is a critical issue in WSN. Prior work in energy aware routing concerned about transmission energy consumption and residual energy, but often do not consider path hop length, which leads to unnecessary consumption of power at sensor nodes. Improved algorithm adds the control of routing hops. Simulation proof the improved algorithm is feasible, effectively reducing the network delay and the path of energy consumption. Taking into account the WSN is dynamic, in the end we put up dynamic hops control in order to adapt to WSN and select the optimal path.

Wireless Photoelectric Sensor Network Scheduling Algorithm Based on Divisible Load

2018 ◽  
Vol 13 (10) ◽  
pp. 1499-1504 ◽  
Author(s):  
Jiaqi Wu ◽  
Huahu Xu
Keyword(s):  
Sensor Networks ◽  
Sensor Node ◽  
Scheduling Algorithm ◽  
Residual Energy ◽  
Initial Energy ◽  
Sensor Nodes ◽  
Load Scheduling ◽  
Divisible Load ◽  
Photoelectric Sensor ◽  
Divisible Load Scheduling

To discuss the divisible load scheduling in wireless photoelectric sensor networks, a load scheduling algorithm called EDDLT based on residual energy landscape is proposed. In the algorithm, the constant of the time needed for the induction and reporting unit data is adjusted to the variable parameters based on the residual energy. In addition, the ratio of the initial energy to the residual energy is used to carry out the effective load scheduling. By using this algorithm, when the load is allocated to each sensor node, the remaining energy of nodes is considered, and a lighter load is allocated to sensor nodes with less residual energy. EDDLT, compared to the standard divisible load scheduling method SDLT, the number of execution rounds is greatly increased when the first sensor node is dead. The experimental results showed that EDDLT had a certain effect on prolonging the lifetime of wireless photoelectric sensor networks. To sum up, the scheduling algorithm has good performance in exploring divisible load.

scholarly journals Lightweight Secure and Reliable Authentication for Cluster Based WSN

2019 ◽  
Vol 9 (1) ◽  
pp. 7324-7327
Keyword(s):  
Data Transmission ◽  
Critical Infrastructure ◽  
Discrete Event ◽  
Sensor Nodes ◽  
Communication Overhead ◽  
Delivery Ratio ◽  
Event Simulation ◽  
Battery Power ◽  
End To End Delay ◽  
Secure Data Transmission

Wireless sensor networks (WSNs) are distributed all over the globe and are widely used for physical collection of data sensed in its surrounding. Tiny, affordable, constrained battery power, information processing capability devices called sensor nodes, plays a crucial role in agriculture, army, industry, intelligent grid, health care, critical infrastructure, etc. WSNs are often exposed to types of attacks. Once a sensor is affected by adversaries, the sensor's data materials become non- protective and intercepted by the enemy. In this paper we propose a lightweight polynomial secrete key (LWPK) sharing mechanism for secure hierarchal cluster based communication. LWPK is built on elliptical curve cryptography by exchanging symmetric keys to secure data transmission. Set of tests is carried on discrete event simulation tool and simulation results achieves better performance in terms of communication overhead, packet delivery ratio, end to end delay and network lifetime

scholarly journals An Adaptive Buffer tradeoff, energy-aware Congestion Control protocol in WSN

2021 ◽  
Vol 12 (3) ◽  
pp. 4880-4891
Author(s):  
M. Sri Lakshmi Et. al.
Keyword(s):  
Energy Consumption ◽  
Congestion Control ◽  
Residual Energy ◽  
Vital Role ◽  
Sensor Nodes ◽  
Data Loss ◽  
Energy Aware ◽  
End To End Delay ◽  
Adequate Communication ◽  
Control Management

In a Wireless sensor network, network lifetime plays a vital role, wherein regular communication and sensor nodes are positioned at different points. Nodes energy depletion may lead to communication interruption due to unlimited data flow from one point to another; for adequate communication, Nodes energy should be maximized by arranging cutting-edge techniques such as adaptive buffer switching and congestion control significant role. When the incoming data is more wide-ranging than available resources, a congestion situation arises. It results in energy consumption, loss of packets, buffer overflow, and raises end-to-end delay. In this paper, adaptive buffer switching and Congestion Control management are done effectively. Simultaneously, congestion detects based on residual energy, residual buffer space, and sensor nodes conviction level. This methodology shows based on the evaluation of cost, which selects main and spare buffers adaptively. Dynamic buffer switching and swapping are used to enhance the outcome of congestion. Result of the ABETCC approach is compared with the protocol like TCEER and TFCC compared to the data loss ratio and energy consumption

Distributed Entropy Energy-Efficient Clustering algorithm for cluster head selection (DEEEC)

2020 ◽  
Vol 39 (6) ◽  
pp. 8139-8147
Author(s):  
Ranganathan Arun ◽  
Rangaswamy Balamurugan
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Second Stage ◽  
Energy Efficient Clustering ◽  
Two Stages ◽  
Ch Selection

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.

Developing Multi-Tier Network Design for Effective Energy Consumption of Cluster Head Selection in WSN

2016 ◽  
Vol 13 (1) ◽  
pp. 116
Author(s):  
Wan Isni Sofiah Wan Din ◽  
Saadiah Yahya ◽  
Mohd Nasir Taib ◽  
Ahmad Ihsan Mohd Yassin ◽  
Razulaimi Razali
Keyword(s):  
Sensor Network ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Cluster Head Selection ◽  
Energy Usage ◽  
Network Cluster ◽  
Fuzzy Logic Approach ◽  
The Stability

Clustering in Wireless Sensor Network (WSN) is one of the methods to minimize the energy usage of sensor network. The design of sensor network itself can prolong the lifetime of network. Cluster head in each cluster is an important part in clustering to ensure the lifetime of each sensor node can be preserved as it acts as an intermediary node between the other sensors. Sensor nodes have the limitation of its battery where the battery is impossible to be replaced once it has been deployed. Thus, this paper presents an improvement of clustering algorithm for two-tier network as we named it as Multi-Tier Algorithm (MAP). For the cluster head selection, fuzzy logic approach has been used which it can minimize the energy usage of sensor nodes hence maximize the network lifetime. MAP clustering approach used in this paper covers the average of 100Mx100M network and involves three parameters that worked together in order to select the cluster head which are residual energy, communication cost and centrality. It is concluded that, MAP dominant the lifetime of WSN compared to LEACH and SEP protocols. For the future work, the stability of this algorithm can be verified in detailed via different data and energy. 

Security based on Received Signal Strength in Localization for Underwater Sensor Networks

2016 ◽  
Vol 1 (2) ◽  
pp. 1-7
Author(s):  
Karamjeet Kaur ◽  
Gianetan Singh Sekhon
Keyword(s):  
Sensor Networks ◽  
Threshold Value ◽  
Critical Issue ◽  
Sensor Nodes ◽  
Underwater Sensor Networks ◽  
Research Subjects ◽  
Malicious Nodes ◽  
Intermediate Node ◽  
End To End Delay ◽  
End To End

Underwater sensor networks are envisioned to enable a broad category of underwater applications such as pollution tracking, offshore exploration, and oil spilling. Such applications require precise location information as otherwise the sensed data might be meaningless. On the other hand, security critical issue as underwater sensor networks are typically deployed in harsh environments. Localization is one of the latest research subjects in UWSNs since many useful applying UWSNs, e.g., event detecting. Now day’s large number of localization methods arrived for UWSNs. However, few of them take place stability or security criteria. In purposed work taking up localization in underwater such that various wireless sensor nodes get localize to each other. RSS based localization technique used remove malicious nodes from the communication intermediate node list based on RSS threshold value. Purposed algorithm improves more throughput and less end to end delay without degrading energy dissipation at each node. The simulation is conducted in MATLAB and it suggests optimal result as comparison of end to end delay with and without malicious node.

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.

A Self-Adaptive Listen-Time Sleeping Algorithm Based on the Number of Neighbor Nodes for Wireless Mesh Sensor Networks

2012 ◽  
Vol 433-440 ◽  
pp. 5123-5128
Author(s):  
Jun Feng Hao ◽  
Jing Fan ◽  
Wen Yu Shi
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Heavy Traffic ◽  
Power Saving ◽  
Critical Issue ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Mesh ◽  
End To End Delay ◽  
Low Traffic

Power saving is a very critical issue in wireless sensor network. Many schemes for power saving can be found in the literature, but these schemes barely consider different topology of nodes. In this paper, based on S-MAC algorithm, NALS-MAC algorithm is designed and combined with the characters of application background of wireless mesh sensor networks. According to the number of neighbor nodes, sensor nodes self-adaptively generate the listen-time during a period respectively. The node with more neighbors will have longer listen-time, because more neighbors means higher probability of heavy traffic. The nodes need longer time to deal with information than nodes with low traffic. The results show that the sensor nodes adjusting the listen-time self-adaptively in proper way achieves the reduction of end-to-end delay and enhance throughout.

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