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.

Nature-Inspired Algorithms in Wireless Sensor Networks

2019 ◽  
pp. 246-275
Author(s):  
Ajay Kaushik ◽  
S. Indu ◽  
Daya Gupta
Keyword(s):  
Wireless Sensor Networks ◽  
Big Data ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Sensor Node ◽  
Optimization Problem ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Nature Inspired Algorithms

Wireless sensor networks (WSNs) are becoming increasingly popular due to their applications in a wide variety of areas. Sensor nodes in a WSN are battery operated which outlines the need of some novel protocols that allows the limited sensor node battery to be used in an efficient way. The authors propose the use of nature-inspired algorithms to achieve energy efficient and long-lasting WSN. Multiple nature-inspired techniques like BBO, EBBO, and PSO are proposed in this chapter to minimize the energy consumption in a WSN. A large amount of data is generated from WSNs in the form of sensed information which encourage the use of big data tools in WSN domain. WSN and big data are closely connected since the large amount of data emerging from sensors can only be handled using big data tools. The authors describe how the big data can be framed as an optimization problem and the optimization problem can be effectively solved using nature-inspired algorithms.

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 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 Balancing Energy Consumption in Wireless Sensor Networks Using Biogeography Based Optimization Algorithm and Fuzzy Approach

2016 ◽  
Vol 15 (3) ◽  
pp. 6596-6607
Author(s):  
Basim Abood Yasir ◽  
Yu Li ◽  
Aliaa Hussien ◽  
Desheng Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Search Algorithm ◽  
Wireless Sensor ◽  
Fuzzy Approach ◽  
Traffic Pattern ◽  
Balancing Energy ◽  
Routing Method

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.  Determine an optimal routing path from the source to the destination by favoring the highest remaining battery power, and the lowest distance (minimum number of hops) to the sink. 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 is significantly increased by employing the proposed routing method.

QoS Aware Multi-hop Multi-path Routing Approach in Wireless Sensor Networks

2019 ◽  
Vol 9 (1) ◽  
pp. 43-52
Author(s):  
Omkar Singh ◽  
Vinay Rishiwal
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Life Time ◽  
Base Station ◽  
Environmental Data ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
End To End

Background & Objective: Wireless Sensor Network (WSN) consist of huge number of tiny senor nodes. WSN collects environmental data and sends to the base station through multi-hop wireless communication. QoS is the salient aspect in wireless sensor networks that satisfies end-to-end QoS requirement on different parameters such as energy, network lifetime, packets delivery ratio and delay. Among them Energy consumption is the most important and challenging factor in WSN, since the senor nodes are made by battery reserved that tends towards life time of sensor networks. Methods: In this work an Improve-Energy Aware Multi-hop Multi-path Hierarchy (I-EAMMH) QoS based routing approach has been proposed and evaluated that reduces energy consumption and delivers data packets within time by selecting optimum cost path among discovered routes which extends network life time. Results and Conclusion: Simulation has been done in MATLAB on varying number of rounds 400- 2000 to checked the performance of proposed approach. I-EAMMH is compared with existing routing protocols namely EAMMH and LEACH and performs better in terms of end-to-end-delay, packet delivery ratio, as well as reduces the energy consumption 13%-19% and prolongs network lifetime 9%- 14%.

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