scholarly journals Optimal Sensor Association and Data Collection in Power Materials Warehouse Based on Internet of Things

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7449
Author(s):  
Fangqiuzi He ◽  
Junfeng Xu ◽  
Jinglin Zhong ◽  
Guang Chen ◽  
Shixin Peng
Keyword(s):  
Internet Of Things ◽  
Optimization Problem ◽  
Ant Colony Algorithm ◽  
Cluster Head ◽  
Descent Method ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Upper And Lower Bounds ◽  
Battery Capacity ◽  
Intelligent Management

In order to realize the intelligent management of a power materials warehouse, the Internet of Things based on wireless sensor networks (WSNs) is a promising effective solution. Considering the limited battery capacity of sensor nodes, the optimization of the topology control and the determination of the amount of collected data are critical for prolonging the survival time of WSNs and increasing the satisfaction of the warehouse supplier. Therefore, in this paper, an optimization problem on sensor association and acquisition data satisfaction is proposed, and the subproblem of the sensor association is modeled as the knapsack problem. To cope with it, the block coordinate descent method is used to obtain the suboptimal solution. A sensor association scheme based on the ant colony algorithm (ACO) is proposed, and the upper and lower bounds of this optimization problem are also obtained. After this, a cluster head selection algorithm is given to find the optimal cluster head. Finally, the experimental simulations show that the algorithms proposed in this paper can effectively improve the energy utilization of WSNs to ensure the intelligent management of a power materials warehouse.

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.

scholarly journals Designing an Energy Efficient Clustering in Heterogeneous Wireless Sensor Network

2021 ◽  
Vol 13 (1) ◽  
pp. 75-92
Author(s):  
Lakshmi M ◽  
Prashanth C R
Keyword(s):  
Energy Efficient ◽  
Cluster Head ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Delivery Ratio ◽  
Mac Layer ◽  
Optimum Energy ◽  
Heterogeneous Wireless Sensor Network ◽  
Energy Efficient Clustering ◽  
Secured Data

Designing an energy-efficient scheme in a Heterogeneous Wireless Sensor Network (HWSN) is a critical issue that degrades the network performance. Recharging and providing security to the sensor devices is very difficult in an unattended environment once the energy is drained off. A Clustering scheme is an important and suitable approach to increase energy efficiency and transmitting secured data which in turn enhances the performance in the network. The proposed algorithm Energy Efficient Clustering (EEC) works for optimum energy utilization in sensor nodes. The algorithm is proposed by combining the rotation-based clustering and energy-saving mechanism for avoiding the node failure and prolonging the network lifetime. This shows MAC layer scheduling is based on optimum energy utilization depending on the residual energy. In the proposed work, a densely populated network is partitioned into clusters and all the cluster heads are formed at a time and selected on rotation based on considering the highest energy of the sensor nodes. Other cluster members are accommodated in a cluster based on Basic Cost Maximum flow (BCMF) to allow the cluster head for transmitting the secured data. Carrier Sense Multiple Access (CSMA), a contention window based protocol is used at the MAC layer for collision detection and to provide channel access prioritization to HWSN of different traffic classes with reduction in End to End delay, energy consumption, and improved throughput and Packet delivery ratio(PDR) and allowing the cluster head for transmission without depleting the energy. Simulation parameters of the proposed system such as Throughput, Energy, and Packet Delivery Ratio are obtained and compared with the existing system.

An Optimal Routing Algorithm for Internet of Things Enabling Technologies

2017 ◽  
Vol 4 (3) ◽  
pp. 1-16 ◽  
Author(s):  
Amol V. Dhumane ◽  
Rajesh S. Prasad ◽  
Jayashree R. Prasad
Keyword(s):  
Genetic Algorithm ◽  
Internet Of Things ◽  
Energy Cost ◽  
High Reliability ◽  
Routing Algorithm ◽  
Optimal Path ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Trust Level ◽  
Enabling Technologies

In Internet of things and its relevant technologies the routing of data plays one of the major roles. In this paper, a routing algorithm is presented for the networks consisting of smart objects, so that the Internet of Things and its enabling technologies can provide high reliability while the transmitting the data. The proposed technique executes in two stages. In first stage, the sensor nodes are clustered and an optimal cluster head is selected by using k-means clustering algorithm. The clustering is performed based on energy of sensor nodes. Then the energy cost of the cluster head and the trust level of the sensor nodes are determined. At second stage, an optimal path will be selected by using the Genetic Algorithm (GA). The genetic algorithm is based on the energy cost at cluster head, trust level at sensor nodes and path length. The resultant optimal path provides high reliability, better speed and more lifetimes.

scholarly journals Energy Efficiency Optimization of Cognitive UAV-Assisted Edge Communication for Semantic Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yilong Gu ◽  
Yangchao Huang ◽  
Hang Hu ◽  
Weiting Gao ◽  
Yu Pan
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
Energy Efficient ◽  
Optimization Problem ◽  
Optimization Scheme ◽  
Battery Capacity ◽  
Aerial Vehicle ◽  
Efficient Alternative ◽  
The Internet Of Things

With the consolidation of the Internet of Things (IoT), the unmanned aerial vehicle- (UAV-) based IoT has attracted much attention in recent years. In the IoT, cognitive UAV can not only overcome the problem of spectrum scarcity but also improve the communication quality of the edge nodes. However, due to the generation of massive and redundant IoT data, it is difficult to realize the mutual understanding between UAV and ground nodes. At the same time, the performance of the UAV is severely limited by its battery capacity. In order to form an autonomous and energy-efficient IoT system, we investigate semantically driven cognitive UAV networks to maximize the energy efficiency (EE). The semantic device model for cognitive UAV-assisted IoT communication is constructed. And the sensing time, the flight speed of UAV, and the coverage range of UAV communication are jointly optimized to maximize the EE. Then, an efficient alternative algorithm is proposed to solve the optimization problem. Finally, we provide computer simulations to validate the proposed algorithm. The performance of the joint optimization scheme based on the proposed algorithm is compared to some benchmark schemes. And the simulation results show that the proposed scheme can obtain the optimal system parameters and can significantly improve the EE.

Enhanced Auxiliary Cluster Head Selection Routing Algorithm in Wireless Sensor Networks

2019 ◽  
Vol 12 ◽  
Author(s):  
Gaurav Kumar Nigam ◽  
Chetna Dabas
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Cluster Head ◽  
Base Station ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cluster Head Selection

Background & Objective: Wireless sensor networks are made up of huge amount of less powered small sensor nodes that can audit the surroundings, collect meaningful data, and send it base station. Various energy management plans that pursue to lengthen the endurance of overall network has been proposed over the years, but energy conservation remains the major challenge as the sensor nodes have finite battery and low computational capabilities. Cluster based routing is the most fitting system to help for burden adjusting, adaptation to internal failure, and solid correspondence to draw out execution parameters of wireless sensor network. Low energy adaptive clustering hierarchy is an efficient clustering based hierarchical protocol that is used to enhance the lifetime of sensor nodes in wireless sensor network. It has some basic flaws that need to be overwhelmed in order to reduce the energy utilization and inflating the nodes lifetime. Methods : In this paper, an effective auxiliary cluster head selection is used to propose a new enhanced GC-LEACH algorithm in order to minimize the energy utilization and prolonged the lifespan of wireless sensor network. Results & Conclusion: Simulation is performed in NS-2 and the outcomes show that the GC-LEACH outperforms conventional LEACH and its existing versions in the context of frequent cluster head rotation in various rounds, number of data packets collected at base station, as well as reduces the energy consumption 14% - 19% and prolongs the system lifetime 8% - 15%.

An Optimal Routing Algorithm for Internet of Things Enabling Technologies

2020 ◽  
pp. 522-538
Author(s):  
Amol V. Dhumane ◽  
Rajesh S. Prasad ◽  
Jayashree R. Prasad
Keyword(s):  
Genetic Algorithm ◽  
Internet Of Things ◽  
Energy Cost ◽  
High Reliability ◽  
Routing Algorithm ◽  
Optimal Path ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Trust Level ◽  
Enabling Technologies

In Internet of things and its relevant technologies the routing of data plays one of the major roles. In this paper, a routing algorithm is presented for the networks consisting of smart objects, so that the Internet of Things and its enabling technologies can provide high reliability while the transmitting the data. The proposed technique executes in two stages. In first stage, the sensor nodes are clustered and an optimal cluster head is selected by using k-means clustering algorithm. The clustering is performed based on energy of sensor nodes. Then the energy cost of the cluster head and the trust level of the sensor nodes are determined. At second stage, an optimal path will be selected by using the Genetic Algorithm (GA). The genetic algorithm is based on the energy cost at cluster head, trust level at sensor nodes and path length. The resultant optimal path provides high reliability, better speed and more lifetimes.

Optimization of Energy Conservation in Wireless Sensor Networks

2020 ◽  
Vol 17 (6) ◽  
pp. 2658-2663
Author(s):  
Anju Rani ◽  
Amit Kumar Bindal
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Cluster Head ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Adaptive Clustering ◽  
Cluster Heads ◽  
Heterogeneous Wireless Sensor Network ◽  
Stable Election Protocol

Presently, Wireless Sensor Networks (WSNs) is quickest developing technology which broadly embracing for different application services including; climate observing, traffic expectation, reconnaissance, research and scholastic fields and so on. As the sensor nodes are haphazardly conveyed in remote condition, security measurements turns out to be most encouraging test where correspondence wirelesses systems confronting today. The Stable Election Protocol (SEP) is an enhanced algorithm of Adaptive Clustering Hierarchy (LEACH) with low energy in heterogeneous Wireless Sensor Network (WSN) for improving the life cycle. Be that as it may, the unequal energy circulation of cluster heads and nodes would diminish the lifetime. From one perspective, adding node vitality to cluster head selection to decrease the energy utilization of cluster heads; on the contrary, decline the energy utilization of nodes in cluster through not directly transmitted by interlude nodes. SEP, a protocol of heterogeneous-aware to drag out the time interim before the passing of the first node (we allude to as steady period), which is essential for some applications where the input from the sensor arrange must be solid. SEP depends on weighted election decision probabilities of every node to turn into cluster head as indicated by the rest of the energy in every node. The outcomes show that the E-SEP protocol functions admirably in adjusting the vitality utilization for improving the lifetime looking at LEACH and SEP protocol with enhanced SEP along with proposed E-SEP algorithm using MATLAB.

scholarly journals A clustering algorithm based on nonuniform partition for WSNs

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 1154-1160
Author(s):  
Chunling Tang
Keyword(s):  
Energy Consumption ◽  
Clustering Algorithm ◽  
Routing Algorithm ◽  
Cluster Head ◽  
Fire Detection ◽  
Base Station ◽  
Sensor Nodes ◽  
Energy Efficient Routing ◽  
Distance Information ◽  
Battery Capacity

Abstract Wireless sensor networks (WSNs) have great application potential in partition parameter observation, such as forest fire detection. Due to the limited battery capacity of sensor nodes, how to reduce energy consumption is an important technical challenge. In this paper, we propose an energy efficient routing algorithm of adaptive double cluster head (CH) based on nonuniform partition for WSN. Firstly, according to the distance information from the base station (BS) to every sensor node, the network is divided into several uneven partitions. Secondly, CH is selected for each partition as the primary cluster head (PCH). Because of the cluster-level routing, the CHs close to the BS need to forward more data than the CHs in other areas, which consumes more energy. Therefore, an adaptive double CH method can be used to generate a secondary cluster head (SCH) in the cluster near the BS according to the parameters. Finally, the PCH is responsible for data collection, data integration, and data transmission. while the SCH is in charge of data routing. Simulation results show that the proposed algorithm can reduce the energy consumption and extend the life of the WSNs, compared with LEACH protocol and the HEED protocol.

scholarly journals FLCH: Intuitionistic Fuzzy Logic based Cluster Head Selection in Wireless Sensor Networks

2019 ◽  
Vol 8 (9S2) ◽  
pp. 78-83
Keyword(s):  
Fuzzy Logic ◽  
Cluster Head ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Cluster Head Selection ◽  
Intuitionistic Fuzzy ◽  
Energy Distance ◽  
Intuitionistic Fuzzy Logic

A large number of tiny sensor nodes are grouped together to form Wireless Sensor Network (WSN). In Industry and other areas using of sensors are increasing every day. Therefore, the energy utilization of sensor nodes becomes a vital problem due to non-rechargeable battery. To improve the vital resources, the energy efficient clustering models are to be improved. This paper presents a novel idea IFLCH: Intuitionistic Fuzzy Logic based Cluster Head Selection for WSNs for electing Cluster Head (CH) based on the energy efficiency parameters such as residual energy, distance between neighbors. The proposed scheme also elects Super CH (SCH) based on the above-mentioned parameters along with number of neighbors. The simulation results compared the proposed model with the existing schemes and it receives better performance by selecting efficient CH and SCH.

scholarly journals An Efficient Proposed Approach of Dynamic Clustering for Target Tracing in Wireless Sensor Networks

2018 ◽  
Vol 7 (2) ◽  
pp. 48-51
Author(s):  
Manas Kumar Ray ◽  
Gitanjali Roy
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Mean Value ◽  
Fixed Number ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Dynamic Clustering ◽  
Dynamic Cluster ◽  
Movable Object

This paper proposes and evaluates decentralized dynamic clustering algorithm for tracing a movable target. Here firstly we proposed dynamic K mean clustering algorithm. In this algorithm a fixed number of sensor nodes is choose and then cluster is created. When the cluster is created then a cluster head (CH) is active. This active CH sensor nodes will create new cluster and that new cluster is also formed a new mean value of cluster head. But, the newly created cluster is only active when a moving objected is trace. According from the position of cluster head, few sensor nodes is active, where as few sensor nodes are inactive. According from the CH nodes newly cluster is created. So, creation of dynamic cluster is less energy efficient and stability of cluster will more than static cluster with sensor nodes. On the other hand, movable object tracing sensor nodes are familiar with energy utilization of sensor nodes. Here we proposed an energy efficient target tracing approach which follow network stability as well as energy saving. As we use dynamic clustering technique, so optimization of energy each sensor nodes with cluster head is maximum. So all the sensors with cluster head sensor nodes will continue more time for object tracing. In simulation result we show that our proposed dynamic K mean clustering algorithm is more accurate and more stable.

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