Prolonging the Lifetime of Wireless Sensor Networks using LPA-star Search Algorithm

2016 ◽  
Vol 1 (2) ◽  
pp. 390
Author(s):  
Ahmed A. Alkathmawee ◽  
Lusong Feng ◽  
Imad S. Alshawi
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Search Algorithm ◽  
Traffic Load ◽  
Wireless Sensor ◽  
Network Partition ◽  
Data Packets ◽  
Power Resources ◽  
New Energy

<p>Since sensors have limited power resources, energy consumption has become a critical challenge to Wireless Sensor Networks (WSNs). Most of the routing protocols proposed to transmit data packets through paths which consume low energy aim simply to reduce battery power consumption. This can lead to lead to network partition and reduce network lifetime.Therefore, to balance energy consumption and extend network lifetime while minimizing packet delivery delay; this paper proposes a new energy-routing protocol using the lifelong planning A-star (LPA-star) search algorithm. This algorithm is used to find an optimum forwarding path between the source node and the sink. The optimum path can be selected depending on highest residual sensor energy, the shortest distance to the sink and lowest traffic load. Simulation results indicate that the proposed protocol increased the lifetime of the network compared with the A-star routing (EERP) protocol.</p>

scholarly journals End to End Delay and Energy Consumption in a Two Tier Cluster Hierarchical Wireless Sensor Networks

Information ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 135 ◽  
Author(s):  
Vicente Casares-Giner ◽  
Tatiana Inés Navas ◽  
Dolly Smith Flórez ◽  
Tito R. Vargas H.
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Radial Distance ◽  
Energy Performance ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Data Packets ◽  
Transfer Delay ◽  
End To End

In this work it is considered a circular Wireless Sensor Networks (WSN) in a planar structure with uniform distribution of the sensors and with a two-level hierarchical topology. At the lower level, a cluster configuration is adopted in which the sensed information is transferred from sensor nodes to a cluster head (CH) using a random access protocol (RAP). At CH level, CHs transfer information, hop-by-hop, ring-by-ring, towards to the sink located at the center of the sensed area using TDMA as MAC protocol. A Markovian model to evaluate the end-to-end (E2E) transfer delay is formulated. In addition to other results such as the well know energy hole problem, the model reveals that for a given radial distance between the CH and the sink, the transfer delay depends on the angular orientation between them. For instance, when two rings of CHs are deployed in the WSN area, the E2E delay of data packets generated at ring 2 and at the “west” side of the sink, is 20% higher than the corresponding E2E delay of data packets generated at ring 2 and at the “east” side of the sink. This asymmetry can be alleviated by rotating from time to time the allocation of temporary slots to CHs in the TDMA communication. Also, the energy consumption is evaluated and the numerical results show that for a WSN with a small coverage area, say a radio of 100 m, the energy saving is more significant when a small number of rings are deployed, perhaps none (a single cluster in which the sink acts as a CH). Conversely, topologies with a large number of rings, say 4 or 5, offer a better energy performance when the service WSN covers a large area, say radial distances greater than 400 m.

scholarly journals Swarm Intelligence-based Partitioned Recovery in Wireless Sensor Networks

2018 ◽  
Vol 3 ◽  
pp. 70-81
Author(s):  
Gaurav Kumar ◽  
Virender Ranga
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Sensor Nodes ◽  
Traffic Load ◽  
Wireless Sensor ◽  
Grey Wolf ◽  
Relay Nodes ◽  
Network Partition ◽  
Heterogeneous Wireless Sensor Networks

The failure rate of sensor nodes in Heterogeneous Wireless Sensor Networks is high due to the use of low battery-powered sensor nodes in a hostile environment. Networks of this kind become non-operational and turn into disjoint segmented networks due to large-scale failures of sensor nodes. This may require the placement of additional highpower relay nodes. In this paper, we propose a network partition recovery solution called Grey Wolf, which is an optimizer algorithm for repairing segmented heterogeneous wireless sensor networks. The proposed solution provides not only strong bi-connectivity in the damaged area, but also distributes traffic load among the multiple deployed nodes to enhance the repaired network’s lifetime. The experiment results show that the Grey Wolf algorithm offers a considerable performance advantage over other state-of-the-art approaches.

scholarly journals Optimal Selection of the Cluster Head in Wireless Sensor Networks by Combining the Multiobjective Genetic Algorithm and the Gravitational Search Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Seyed Reza Nabavi ◽  
Vahid Ostovari Moghadam ◽  
Mohammad Yahyaei Feriz Hendi ◽  
Amirhossein Ghasemi
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Cluster Head ◽  
Wireless Sensor ◽  
Multiobjective Genetic Algorithm ◽  
Gravitational Search

With the development of various applications of wireless sensor networks, they have been widely used in different areas. These networks are established autonomously and easily in most environments without any infrastructure and collect information of environment phenomenon for proper performance and analysis of events and transmit them to the base stations. The wireless sensor networks are comprised of various sensor nodes that play the role of the sensor node and the relay node in relationship with each other. On the other hand, the lack of infrastructure in these networks constrains the sources such that the nodes are supplied by a battery of limited energy. Considering the establishment of the network in impassable areas, it is not possible to recharge or change the batteries. Thus, energy saving in these networks is an essential challenge. Considering that the energy consumption rate while sensing information and receiving information packets from another node is constant, the sensor nodes consume maximum energy while performing data transmission. Therefore, the routing methods try to reduce energy consumption based on organized approaches. One of the promising solutions for reducing energy consumption in wireless sensor networks is to cluster the nodes and select the cluster head based on the information transmission parameters such that the average energy consumption of the nodes is reduced and the network lifetime is increased. Thus, in this study, a novel optimization approach has been presented for clustering the wireless sensor networks using the multiobjective genetic algorithm and the gravitational search algorithm. The multiobjective genetic algorithm based on reducing the intracluster distances and reducing the energy consumption of the cluster nodes is used to select the cluster head, and the nearly optimal routing based on the gravitational search algorithm is used to transfer information between the cluster head nodes and the sink node. The implementation results show that considering the capabilities of the multiobjective genetic algorithm and the gravitational search algorithm, the proposed method has improved energy consumption, efficiency, data delivery rate, and information packet transmission rate compared to the previous methods.

scholarly journals CDARGA: Cluster-Based Data Aggregation with Genetic Routing Algorithm in Wireless Sensor Networks

2020 ◽  
Vol 8 (6) ◽  
pp. 2976-2982
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Search Algorithm ◽  
Fitness Function ◽  
Base Station ◽  
Wireless Sensor ◽  
Experimental Result ◽  
Genetic Search

In the wireless sensor networks (WSNs), the upholding the energy and routing formation at every sensor node is the major issues. The distance from base station and internal node mainly has imbalanced in the energy consumption during transformation of the data. To reduce the energy upholding and the data aggregation routing issues in Centralized Clustering-Task Scheduling for wireless sensor networks (WSNs), this paper focuses on a Cluster-Based Data Aggregation Routing with Genetic search Algorithm (CDARGA) , which reduces the energy consumption in a hyper round model. The proposed data aggregation routing protocol using the Genetic Algorithm (GA) estimates the fitness function using the three key parameters distance, energy, and Hyper round policy. The proposed methods were compared with RP-MAC and the experimental result shows that the proposed protocol is superior to RP-MAC protocol and the proposed algorithm improves the network lifetime which can used in real time application.

scholarly journals Monitoring square and circular fields with sensors using energy-efficient cluster-based routing for underwater wireless sensor networks

2017 ◽  
Vol 13 (7) ◽  
pp. 155014771771718 ◽  
Author(s):  
Arshad Sher ◽  
Nadeem Javaid ◽  
Irfan Azam ◽  
Hira Ahmad ◽  
Wadood Abdul ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Search Algorithm ◽  
Minimum Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Underwater Wireless Sensor Networks ◽  
Energy Efficient Clustering

In this article, to monitor the fields with square and circular geometries, three energy-efficient routing protocols are proposed for underwater wireless sensor networks. First one is sparsity-aware energy-efficient clustering, second one is circular sparsity-aware energy-efficient clustering, and the third one is circular depth–based sparsity-aware energy-efficient clustering routing protocol. All three protocols are proposed to minimize the energy consumption of sparse regions, whereas sparsity search algorithm is proposed to find sparse regions and density search algorithm is used to find dense regions of the network field. Moreover, clustering is performed in dense regions to minimize redundant transmissions of a data packet, while sink mobility is exploited to collect data from sensor nodes with an objective of minimum energy consumption. A depth threshold [Formula: see text] value is also used to minimize number of hops between source and destination for less energy consumption. Simulation results show that our schemes perform better than their counter-part schemes (depth-based routing and energy-efficient depth-based routing) in terms of energy efficiency.

scholarly journals Save Energy in Wireless Sensor Networks Routing Using Fuzzy Approach and Biogeography Based Optimization

2017 ◽  
Vol 12 (2) ◽  
pp. 3167-3178
Author(s):  
Yasser Kareem AlRikabi
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Search Algorithm ◽  
Wireless Sensor ◽  
Fuzzy Approach ◽  
Traffic Pattern ◽  
Balancing Energy ◽  
Save Energy

Extending the lifetime of the energy constrained wireless sensor networks is a crucial challenge in wireless sensor networks (WSNs) research. When designing a WSN infrastructure Resource limitations have to be taken into account. The inherent problem in WSNs is unbalanced energy consumption, characterized by multi hop routing and a many-to-one traffic pattern. This uneven energy dissipation can significantly reduce network lifetime. This paper proposes a new routing method for WSNs to extend network lifetime using a combination of a fuzzy approach and Biogeography Based Optimization (BBO) algorithm 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 the proposed method in terms of balancing energy consumption and maximization of network lifetime, we compare our approach with the BBO search algorithm and fuzzy approach using the same routing criteria. Simulation results demonstrate that the network lifetime achieved by the proposed method could be increased by nearly 25% more than that obtained by the BBO algorithm and by nearly 20% more than that obtained by the fuzzy approach.

scholarly journals Multiobjective Coverage Control Strategy for Energy-Efficient Wireless Sensor Networks

2012 ◽  
Vol 8 (4) ◽  
pp. 720734 ◽  
Author(s):  
Hui Zhou ◽  
Tian Liang ◽  
Chen Xu ◽  
Jing Xie
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Control Strategy ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Coverage Control ◽  
Free Search

A multiobjective optimization coverage control strategy is proposed for solving the contradictory problem among energy consumption, equilibrium of energy, and network coverage in wireless sensor networks. A new evolutionary algorithm named Multiobjective free search algorithm (MOFS) is designed for WSN optimization problem based on fitness functions and binary coding schemes. The proposed strategy is used to estimate the number of active nodes because individual nodes cannot have their working state information readily. Simulation shows that MOFS is effective to solve the typical combinatorial optimization problem, and the coverage control strategy can obtain high network coverage and reduce energy consumption effectively by the reasonable selecting parameters, while equilibrium of energy consumption is also considered.

scholarly journals Spider monkey optimization routing protocol for wireless sensor networks

2021 ◽  
Vol 11 (3) ◽  
pp. 2432
Author(s):  
Ali H. Jabbar ◽  
Imad S. Alshawi
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Saving ◽  
Routing Protocol ◽  
Routing Protocols ◽  
Spider Monkey ◽  
Traffic Load ◽  
Wireless Sensor ◽  
Spider Monkey Optimization

Uneven energy consumption (UEC) is latent trouble in wireless sensor networks (WSNs) that feature a multiple motion pattern and a multi-hop routing. UEC often splits the network, reduces network life, and leads to performance degradation. Sometimes, improving energy consumption is more complicated because it does not reduce energy consumption only, but it also extends network life. This makes energy consumption balancing critical to WSN design calling for energy-efficient routing protocols that increase network life. Some energy-saving protocols have been applied to make the energy consumption among all nodes inside the network equilibrate in the expectancy and end power in almost all nodes simultaneously. This work has suggested a protocol of energy-saving routing named spider monkey optimization routing protocol (SMORP), which aims to probe the issue of network life in WSNs. The proposed protocol reduces excessive routing messages that may lead to the wastage of significant energy by recycling frequent information from the source node into the sink. This routing protocol can choose the optimal routing path. That is the preferable node can be chosen from nodes of the candidate in the sending ways by preferring the energy of maximum residual, the minimum traffic load, and the least distance to the sink. Simulation results have proved the effectiveness of the proposed protocol in terms of decreasing end-to-end delay, reducing energy consumption compared to well-known routing protocols.

EBH-DBR: energy-balanced hybrid depth-based routing protocol for underwater wireless sensor networks

2020 ◽  
pp. 2150061
Author(s):  
Rakesh Kumar ◽  
Diwakar Bhardwaj ◽  
Manas Kumar Mishra
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Transmission Loss ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Depth Information ◽  
Underwater Wireless Sensor Networks ◽  
Data Packets ◽  
High Attenuation

Recently, applications of underwater wireless sensor networks like environment monitoring, underwater life imaging, tactical surveillance, ocean floor monitoring demand a persistent network period. However, underwater wireless sensor networks face many design challenges like unreliable link, high packet drop rate, inadequate bandwidth, restricted battery power, high attenuation, etc. Therefore, to prolong the network lifespan, energy efficient as well as energy balanced both types of approach is equally demanded. An energy-balanced hybrid transmission approach is proposed in this article, which uses depth information in place of location to transmit data packets. It uses some parameters like depth of the sensor nodes, residual energy of the node, and reliability of the link to select the relay node to forward data packets. In the proposal network divided into the slices of the same width, to control the hop-count as well as to balance the energy consumption of the sensor nodes participating in data transmission, and also prolonging the network lifespan. The effectiveness of the proposal is validated through extensive simulation and results show that the EBH-DBR outperforms its counterpart techniques in terms of network lifespan, energy consumption, throughput, and transmission loss.

A Semantic-Based Routing Algorithm for Hierarchical WSNs

2014 ◽  
Vol 568-570 ◽  
pp. 514-518
Author(s):  
Zi Ping Du ◽  
Jian Feng Jiang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Routing Algorithm ◽  
Wireless Sensor ◽  
Data Packets ◽  
Redundant Data ◽  
Backbone Structure ◽  
Reduce Energy Consumption ◽  
Better Than

Wireless sensor networks have the characteristics of taking the data as the center, focusing on the functions and having no backbone structure. In this paper, a data-forwarding algorithm based on semantic-based routing is proposed, which is aiming at improving the routing efficiency and increasing the lifetime of the networks. First, we defined the semantics of networks, and constructed the expression architecture for semantics of wireless sensor networks. Then we used these semantics to compose data packets. Finally, we matched the semantics from the packets of networks, which determined the direction of packets forwarding. Experimental results show that the algorithm performed better than DD and LEACH. It can reduce energy consumption, redundant data and prolong the networks' lifetime.

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