scholarly journals GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 43
Author(s):  
Muhammad K. Shahzad ◽  
S. M. Riazul Islam ◽  
Mahmud Hossain ◽  
Mohammad Abdullah-Al-Wadud ◽  
Atif Alamri ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Smart Cities ◽  
Hot Spot ◽  
Time Instant ◽  
Wireless Sensor ◽  
Injection Attacks ◽  
Fixed Path

In recent years, the deployment of wireless sensor networks has become an imperative requisite for revolutionary areas such as environment monitoring and smart cities. The en-route filtering schemes primarily focus on energy saving by filtering false report injection attacks while network lifetime is usually ignored. These schemes also suffer from fixed path routing and fixed response to these attacks. Furthermore, the hot-spot is considered as one of the most crucial challenges in extending network lifetime. In this paper, we have proposed a genetic algorithm based fuzzy optimized re-clustering scheme to overcome the said limitations and thereby minimize the effect of the hot-spot problem. The fuzzy logic is applied to capture the underlying network conditions. In re-clustering, an important question is when to perform next clustering. To determine the time instant of the next re-clustering (i.e., number of nodes depleted—energy drained to zero), associated fuzzy membership functions are optimized using genetic algorithm. Simulation experiments validate the proposed scheme. It shows network lifetime extension of up to 3.64 fold while preserving detection capacity and energy-efficiency.

scholarly journals Energy-Efficient Unequal Chain Length Clustering for Wireless Sensor Networks in Smart Cities

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Mohammad Baniata ◽  
Jiman Hong
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Chain Length ◽  
Network Lifetime ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Smart Cities ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Wireless Sensor

The recent advances in sensing and communication technologies such as wireless sensor networks (WSN) have enabled low-priced distributed monitoring systems that are the foundation of smart cities. These advances are also helping to monitor smart cities and making our living environments workable. However, sensor nodes are constrained in energy supply if they have no constant power supply. Moreover, communication links can be easily failed because of unequal node energy depletion. The energy constraints and link failures affect the performance and quality of the sensor network. Therefore, designing a routing protocol that minimizes energy consumption and maximizes the network lifetime should be considered in the design of the routing protocol for WSN. In this paper, we propose an Energy-Efficient Unequal Chain Length Clustering (EEUCLC) protocol which has a suboptimal multihop routing algorithm to reduce the burden on the cluster head and a probability-based cluster head selection algorithm to prolong the network lifetime. Simulation results show that the EEUCLC mechanism enhanced the energy balance and prolonged the network lifetime compared to other related protocols.

Lifetime Maximization Routing Based on Genetic Algorithm for Wireless Sensor Networks

2011 ◽  
Vol 230-232 ◽  
pp. 283-287
Author(s):  
You Rong Chen ◽  
Tiao Juan Ren ◽  
Zhang Quan Wang ◽  
Yi Feng Ping
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Data Gathering ◽  
Transmission Probability ◽  
Wireless Sensor ◽  
Total Energy Consumption ◽  
Lifetime Maximization

To prolong network lifetime, lifetime maximization routing based on genetic algorithm (GALMR) for wireless sensor networks is proposed. Energy consumption model and node transmission probability are used to calculate the total energy consumption of nodes in a data gathering cycle. Then, lifetime maximization routing is formulated as maximization optimization problem. The select, crosss, and mutation operations in genetic algorithm are used to find the optimal network lifetime and node transmission probability. Simulation results show that GALMR algorithm are convergence and can prolong network lifetime. Under certain conditions, GALMR outperforms PEDAP-PA, LET, Sum-w and Ratio-w algorithms.

Genetic optimization of hybrid clustering algorithm in mobile wireless sensor networks

Sensor Review ◽  
2018 ◽  
Vol 38 (4) ◽  
pp. 526-533 ◽  
Author(s):  
Sangeetha M. ◽  
Sabari A.
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Clustering Algorithm ◽  
Wireless Sensor ◽  
Mobile Wireless ◽  
Content Type ◽  
Mobile Wireless Sensor

Purpose This paper aims to provide a prolonging network lifetime and optimizing energy consumption in mobile wireless sensor networks (MWSNs). MWSNs have characteristics of dynamic topology due to the factors such as energy consumption and node movement that lead to create a problem in lifetime of the sensor network. Node clustering in wireless sensor networks (WSNs) helps in extending the network life time by reducing the nodes’ communication energy and balancing their remaining energy. It is necessary to have an effective clustering algorithm for adapting the topology changes and improve the network lifetime. Design/methodology/approach This work consists of two centralized dynamic genetic algorithm-constructed algorithms for achieving the objective in MWSNs. The first algorithm is based on improved Unequal Clustering-Genetic Algorithm, and the second algorithm is Hybrid K-means Clustering-Genetic Algorithm. Findings Simulation results show that improved genetic centralized clustering algorithm helps to find the good cluster configuration and number of cluster heads to limit the node energy consumption and enhance network lifetime. Research limitations/implications In this work, each node transmits and receives packets at the same energy level throughout the solution. The proposed approach was implemented in centralized clustering only. Practical implications The main reason for the research efforts and rapid development of MWSNs occupies a broad range of circumstances in military operations. Social implications The research highly gains impacts toward mobile-based applications. Originality/value A new fitness function is proposed to improve the network lifetime, energy consumption and packet transmissions of MWSNs.

scholarly journals Improving the Network Lifetime in Wireless Sensor Network for Internet of Thing Applications

2019 ◽  
Vol 15 (4) ◽  
pp. 79-90
Author(s):  
Ahmed A. Salman ◽  
Zainab T. Alisa
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Clustering Algorithm ◽  
Smart Cities ◽  
Base Station ◽  
Packet Transmission ◽  
Low Energy ◽  
Wireless Sensor ◽  
Clustering Model

Mobile Wireless sensor networks have acquired a great interest recently due to their capability to provide good solutions and low-priced in multiple fields. Internet of Things (IoT) connects different technologies such as sensing, communication, networking, and cloud computing. It can be used in monitoring, health care and smart cities. The most suitable infrastructure for IoT application is wireless sensor networks. One of the main defiance of WSNs is the power limitation of the sensor node. Clustering model is an actual way to eliminate the inspired power during the transmission of the sensed data to a central point called a Base Station (BS). In this paper, efficient clustering protocols are offered to prolong network lifetime. A kernel-based fuzzy C-means clustering algorithm (KFCM) is adopted to cluster sensor nodes, while a cluster head (CH) is selected for each cluster based on a fuzzy logic system. Results depicts that the new work performs better than the existing algorithms (as Low Energy Adaptive Cluster Hierarchy-Mobile (LEACH-M) and Low Energy Adaptive Cluster Hierarchy-Mobile Enhancement (LEACH-ME)) in terms of network lifetime, energy consumption, packet transmission and stability period.  

scholarly journals Efficient Node Deployment of Large-Scale Heterogeneous Wireless Sensor Networks

2021 ◽  
Vol 11 (22) ◽  
pp. 10924
Author(s):  
Fatma H. Elfouly ◽  
Rabie A. Ramadan ◽  
Ahmed Y. Khedr ◽  
Ahmad Taher Azar ◽  
Kusum Yadav ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Large Scale ◽  
Smart Cities ◽  
Minimum Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
High Coverage ◽  
Heterogeneous Wireless Sensor Networks

 Wireless Sensor Networks (WSNs) became essential in developing many applications, including smart cities and Internet of Things (IoT) applications. WSN has been used in many critical applications such as healthcare, military, and transportation. Such applications depend mainly on the performance of the deployed sensor nodes. Therefore, the deployment process has to be perfectly arranged. However, the deployment process for a WSN is challenging due to many of the constraints to be taken into consideration. For instance, mobile nodes are already utilized in many applications, and their localization needs to be considered during the deployment process. Besides, heterogeneous nodes are employed in many recent applications due to their efficiency and cost-effectiveness. Moreover, the development areas might have different properties due to their importance. Those parameters increase the deployment complexity and make it hard to reach the best deployment scheme. This work, therefore, seeks to discover the best deployment plan for a WSN, considering these limitations throughout the deployment process. First, the deployment problem is defined as an optimization problem and mathematically formulated using Integer Linear Programming (ILP) to understand the problem better. The main objective function is to maximize the coverage of a given field with a network lifetime constraint. Nodes’ mobility and heterogeneity are added to the deployment constraints. The importance of the monitored field subareas is also introduced in this paper, where some subareas could have more importance than others. The paper utilizes Swarm Intelligence as a heuristic algorithm for the large-scale deployment problem. Simulation experiments show that the proposed algorithm produces efficient deployment schemes with a high coverage rate and minimum energy consumption compared to some recent algorithms. The proposed algorithm shows more than a 30% improvement in coverage and network lifetime. 

scholarly journals proposing a new clustering algorithm to Reduction of Energy Consumption using Fuzzy Logic and Genetic Algorithm for Wireless Sensor Networks.

2016 ◽  
Vol 11 (2) ◽  
pp. 2702-2719
Author(s):  
Sayyed Hedayat Tarighi Nejad ◽  
Reza Alinaghian ◽  
Mehdi Sadeghzadeh
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Sensor Network ◽  
Network Lifetime ◽  
Fuzzy System ◽  
Cluster Head ◽  
Wireless Sensor ◽  
Reduction Of Energy Consumption

The large-scale deployment of wireless sensor networks  and the need for data aggregation necessitate efficient organization of the network topology for the purpose of balancing the load and prolonging the network lifetime. Clustering is one of the important methods for prolonging the network lifetime in wireless sensor networks. It involves grouping of sensor nodes into clusters and electing cluster heads for all the clusters. Clustering has proven to be an effective approach for organizing the network into a connected hierarchy.In this paper, using fuzzy system design and system optimization by genetic algorithm and colony of ants is presented approach to select the best cluster head in sensor networks. Using design and simulation a sensor network has been addressed to evaluation the presented fuzzy system in this paper, and finally the amount of energy consumption using proposed fuzzy system in comparison with LEACH method is calculated in select the cluster head. The result of evaluations is representative of a reduction of energy consumption in the proposed method in comparison with LEATCH method for select the cluster head. The reduction of energy consumption directly is effective on lifetime of wireless sensor network and can cause increase the lifetime these networks.

scholarly journals A Genetic Algorithm Based and Subtree Restricted Routing Algorithm in Wireless Sensor Networks

2018 ◽  
Vol 14 (03) ◽  
pp. 52
Author(s):  
Zhi Huang
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Genetic Algorithms ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Lower Layer ◽  
Routing Algorithm ◽  
Search Space ◽  
Wireless Sensor ◽  
Optimal Routing

<span lang="EN-US">The GA(genetic algorithms) was applied to find routing trees with minimal maximal sensor load</span><span lang="EN-US"> thus extend network lifetime in wireless sensor networks. However,  t</span><span lang="EN-US">he existing algorithm</span><span lang="EN-US">limits search space of GA to avoid generating cycles by dividing sensors into layers and stipulating that a sensor only can select a sensor in its lower layer as its new parent.</span><span lang="EN-US"> In this paper, w</span><span lang="EN-US">e have found that how <a name="OLE_LINK26"></a><a name="OLE_LINK25"></a>cycles are generated during GA operations</span><span lang="EN-US">, and </span><span lang="EN-US">proposed a solution based on subtree to avoid generating cycles during GA operations.</span><span lang="EN-US">The solution only requires a sensor not to change its parent to a sensor in the subtree with it as the root. And layers are no longer considered. As a result, GA has more search space and GA operations can be performed more freely. Thus more optimal routing trees are obtained and network lifetime is extended.</span><span lang="EN-US"> The experiment shows that our proposed algorithm extend network lifetime notably.</span>

A new approach to solving target coverage problem in wireless sensor networks using an effective hybrid genetic algorithm and tabu search

2021 ◽  
pp. 1-11
Author(s):  
Leila Ajam ◽  
Ali Nodehi ◽  
Hosein Mohamadi
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Tabu Search ◽  
Network Lifetime ◽  
Wireless Sensor ◽  
Target Coverage ◽  
Coverage Problem ◽  
Multiple Power ◽  
Target Coverage Problem

Literature in recent years has introduced several studies conducted to solve the target coverage problem in wireless sensor networks (WSNs). Sensors are conventionally assumed as devices with only a single power level. However, real applications may involve sensors with multiple power levels (i.e., multiple sensing ranges each of which possesses a unique power consumption). Consequently, one of the key problems in WSNs is how to provide a full coverage on all targets distributed in a network containing sensors with multiple power levels and simultaneously prolong the network lifetime as much as possible. This problem is known as Maximum Network Lifetime With Adjustable Ranges (MNLAR) and its NP-completeness has been already proved. To solve this problem, we proposed an efficient hybrid algorithm containing Genetic Algorithm (GA) and Tabu Search (TS) aiming at constructing cover sets that consist of sensors with appropriate sensing ranges to provide a desirable coverage for all the targets in the network. In our hybrid model, GA as a robust global searching algorithm is used for exploration purposes, while TS with its already-proved local searching ability is utilized for exploitation purposes. As a result, the proposed algorithm is capable of creating a balance between intensification and diversification. To solve the MNLR problem in an efficient way, the proposed model was also enriched with an effective encoding method, genetic operators, and neighboring structure. In the present paper, different experiments were performed for the purpose of evaluating how the proposed algorithm performs the tasks defined. The results clearly confirmed the superiority of the proposed algorithm over the greedy-based algorithm and learning automata-based algorithm in terms of extending the network lifetime. Moreover, it was found that the use of multiple power levels altogether caused the extension of the network lifetime.

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