Minimize Residual Energy of the 3-D Underwater Sensor Networks with Non-uniform Node Distribution to Prolong the Network Lifetime

2018 ◽  
pp. 647-659
Author(s):  
Gaotao Shi ◽  
Jia Zeng ◽  
Chunfeng Liu ◽  
Keqiu Li
Keyword(s):  
Sensor Networks ◽  
Network Lifetime ◽  
Residual Energy ◽  
Underwater Sensor Networks ◽  
Node Distribution

A DEPLOYMENT ALGORITHM FOR UNDERWATER SENSOR NETWORKS IN OCEAN ENVIRONMENT

2011 ◽  
Vol 20 (06) ◽  
pp. 1051-1066 ◽  
Author(s):  
LINFENG LIU
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Network Lifetime ◽  
Wireless Sensor ◽  
Underwater Sensor Networks ◽  
Cluster Shape ◽  
Simulation Results ◽  
Ocean Environment ◽  
Near Future

Underwater sensor networks will find many oceanic applications in near future, and the deployment problem in 3D sensor networks has not been paid enough attention at present. In order to maximize the network lifetime, a deployment algorithm (UDA) for underwater sensor networks in ocean environment is proposed. UDA can determine and select the best cluster shape, then partition the space into layers and clusters while maintaining full coverage and full connectivity. In addition, nodes closer to sinks are possible to bear a heavier data-relaying mission. UDA sets different node deployment densities at different layers in response to the potential relay discrepancy. The simulation results suggest UDA can choose the proper cluster shape to get the maximum underwater wireless sensor network lifetime approximately.

scholarly journals Implementation of Enhanced AODV based Self-Organized Tree for Energy Balanced Routing in Wireless Sensor Networks

2020 ◽  
Vol 9 (3) ◽  
pp. 1926-1933
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Data Transmission ◽  
Cluster Head ◽  
Residual Energy ◽  
Wireless Sensor ◽  
Self Organized ◽  
Energy Balanced Routing

Wireless sensor networks (WSN) are gaining attention in numerous fields with the advent of embedded systems and IoT. Wireless sensors are deployed in environmental conditions where human intervention is less or eliminated. Since these are not human monitored, powering and maintaining the energy of the node is a challenging issue. The main research hotspot in WSN is energy consumption. As energy drains faster, the network lifetime also decreases. Self-Organizing Networks (SON) are just the solution for the above-discussed problem. Self-organizing networks can automatically configure themselves, find an optimalsolution, diagnose and self-heal to some extent. In this work, “Implementation of Enhanced AODV based Self-Organized Tree for Energy Balanced Routing in Wireless Sensor Networks” is introduced which uses self-organization to balance energy and thus reduce energy consumption. This protocol uses combination of number of neighboring nodes and residual energy as the criteria for efficient cluster head election to form a tree-based cluster structure. Threshold for residual energy and distance are defined to decide the path of the data transmission which is energy efficient. The improvement made in choosing robust parameters for cluster head election and efficient data transmission results in lesser energy consumption. The implementation of the proposed protocol is carried out in NS2 environment. The experiment is conducted by varying the node density as 20, 40 and 60 nodes and with two pause times 5ms, 10ms. The analysis of the result indicates that the new system consumes 17.6% less energy than the existing system. The routing load, network lifetime metrics show better values than the existing system.

scholarly journals Trust model genetic node recovery based on cloud theory for underwater acoustic sensor network

2019 ◽  
Vol 9 (5) ◽  
pp. 3759
Author(s):  
Buddesab T ◽  
Thriveni J ◽  
Venugopal K R
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Total Energy ◽  
Network Lifetime ◽  
Trust Model ◽  
Residual Energy ◽  
Acoustic Sensor ◽  
Total Energy Consumption ◽  
Recovery Algorithm ◽  
Cloud Theory

<span>Underwater Acoustic Sensor Networks [UASNs] are becoming a very growing research topic in the field of WSNs. UASNs are harmful by many attacks such as Jamming attacks at the physical layer, Collision attacks at the data link layer and Dos attacks at the network layer. UASNs has a unique characteristic such as unreliable communication, mobility, and computation of underwater sensor network. Because of this the traditional security mechanism, e.g. cryptographic, encryption, authorization and authentications are not suitable for UASNs. Many trust mechanisms of TWSNs [Terrestrial Wireless Sensor Networks] had proposed to UASNs and failed to provide security for UASNs environment, due to dynamic network structure and weak link connection between sensors. In this paper, a novel Trust Model Genetic Algorithm based on Cloud Theory [TMC] for UASNs has been proposed. The TMC-GA suggested a genetic node recovery algorithm to improve the TMC network in terms of better network lifetime, residual energy and total energy consumption. Also ensures that sensor nodes are participating in the rerouting in the routing discovery and performs well in terms of successful packet delivery. Simulation result provides that the proposed TMC-Genetic node recovery algorithm outperforms compared to other related works in terms of the number of hops, end-to-end delay, total energy consumption, residual energy, routing overhead, throughput and network lifetime.</span>

scholarly journals Energy proficient clustering technique for lifetime enhancement of cognitive radio–based heterogeneous wireless sensor network

2018 ◽  
Vol 14 (3) ◽  
pp. 155014771876759 ◽  
Author(s):  
Venkatasubramanian Srividhya ◽  
Thangavelu Shankar
Keyword(s):  
Wireless Sensor Networks ◽  
Cognitive Radio ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Routing Algorithm ◽  
Residual Energy ◽  
Base Station ◽  
Wireless Sensor ◽  
Heterogeneous Wireless Sensor Network ◽  
Communication Approach

Utilizing the available spectrum in a more optimized manner and selecting a proper routing technique for transferring the data, without any data collision, from the sensor node to the base station play a major role in any network for increasing their network lifetime. Cognitive radio techniques play a major role to achieve the same, and when combined with wireless sensor networks the above-said requirements can be greatly accomplished. In this article, a novel energy-efficient distance-based clustering and routing algorithm using multi-hop communication approach is proposed. Based on distance, the given heterogeneous cognitive radio–based wireless sensor networks are divided into regions and are allocated with a unique spectrum. Dynamic clustering through distance calculation and routing of data through multi-hop communication is done. The simulation results illustrate that the proposed algorithm has improved energy efficiency and is more stable. The first node death and 80% node death illustrate the improved scalability. Also, the increased throughput aids in maintaining the residual energy of the network, which further solves the problem of load balancing among nodes. All the above results combined with half node death analysis show that the proposed algorithm also has an improved network lifetime.

EOLSR: AN ENERGY EFFICIENT ROUTING PROTOCOL IN WIRELESS AD HOC AND SENSOR NETWORKS

2008 ◽  
Vol 09 (04) ◽  
pp. 389-408 ◽  
Author(s):  
SAOUCENE MAHFOUDH ◽  
PASCALE MINET
Keyword(s):  
Sensor Networks ◽  
Network Lifetime ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Ad Hoc ◽  
Residual Energy ◽  
Energy Efficient Routing ◽  
Routing Strategy ◽  
Minimum Residual ◽  
Selection Algorithms

Energy efficiency is a key issue in wireless ad hoc and sensor networks. In order to maximize network lifetime, several directions have been explored, among them energy efficient routing. In this paper, we show how to extend the standardized OLSR routing protocol, in order to make it energy efficient. This extension selects the path minimizing the energy consumed in the end-to-end transmission of a flow packet and avoids nodes with low residual energy. As it has been shown that two-path routing is energy efficient, we compare this extension with a two-path source routing strategy (with different links or different nodes). Moreover, to take into account residual node energy, the native selection of multipoint relays of OLSR is changed. Three selection algorithms based on the minimum residual energy are evaluated. An extensive performance evaluation allows us to choose for EOLSR (Energy efficient OLSR) the best variant maximizing both network lifetime and delivery rate.

Distributed Lifetime Optimized Routing Algorithm for Wireless Sensor Networks

2010 ◽  
Vol 40-41 ◽  
pp. 448-452 ◽  
Author(s):  
You Rong Chen ◽  
Li Yu ◽  
Qi Fen Dong ◽  
Zhen Hong
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Routing Algorithm ◽  
Cost Effective ◽  
Residual Energy ◽  
Wireless Sensor ◽  
Sink Node ◽  
Simulation Results ◽  
Routing Tree

The network hub nodes consumed excessive energy and failed prematurely, thus it reduced the network lifetime. In order to solve the problem, distributed lifetime optimized routing algorithm (DLOR) for wireless sensor networks was proposed. Energy for transmitting data and neighbor node residual energy were considered comprehensively. Then new weight function was introduced and distributed asynchronous Bellman-Ford algorithm was also used to construct the shortest routing tree. Finally, data were gathered along the shortest routing tree to sink node. Simulation results show that DLOR algorithm can extend network lifetime and enable cost-effective energy consumption. Under certain conditions, DLOR algorithm outperforms PEDAP, GreedyDijkstra, LET, Ratio-w and Sum-w algorithms.

scholarly journals Path unaware layered routing protocol (PULRP) with non-uniform node distribution for underwater sensor networks

2008 ◽  
Vol 8 (8) ◽  
pp. 1045-1060 ◽  
Author(s):  
Sarath Gopi ◽  
G. Kannan ◽  
Deepthi Chander ◽  
U. B. Desai ◽  
S. N. Merchant
Keyword(s):  
Sensor Networks ◽  
Routing Protocol ◽  
Underwater Sensor Networks ◽  
Node Distribution
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