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.

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.

scholarly journals Fault Tolerance Mechanism for Internet of Things (IoT) by Solving Nature Inspired Computing Algorithm (NIC)

2021 ◽  
Vol 9 (2) ◽  
pp. 308-312
Author(s):  
A. Prasanth Rao, Et. al.
Keyword(s):  
Genetic Algorithm ◽  
Fault Tolerance ◽  
Internet Of Things ◽  
Research Work ◽  
Cluster Head ◽  
Life Time ◽  
Base Station ◽  
Sensor Nodes ◽  
Tolerance Mechanism ◽  
Network Efficiency

In clustering approach the sensor nodes are grouped to form a cluster. The nodes of a clustering network have low powered battery capability and limited processing capabilities. These nodes continuously exchange the data to cluster head which is in turn transforming the data to its base station. Few of these nodes in network may be faulty or may not support life time processing data due to its low power battery. All these sensor nodes measure the temperature, humidity, sound and pollution from environment and collected data is send to cloud for further processing. The fault tolerance mechanism of these nodes is solved by applying genetic algorithm by implementing chromosome technique to identify and avoid fault nodes in the network.  This proposed research work increases detection of fault nodes in a network, increase network efficiency, lifetime and reach energy optimization results in Internet of Things (IoT) concept. The performance evaluation shows that the data accuracy in Genetic Algorithm (GA) is higher when compared with Direct Diffusion (DD) Algorithm and Ad-hoc on demand Distance Vector (AODV) Algorithm.

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.

An Energy Aware Routing Algorithm for WSNs Based on Semi-Static Clustering

2015 ◽  
Vol 752-753 ◽  
pp. 1413-1418
Author(s):  
Tao Du ◽  
Qing Bei Guo ◽  
Kun Zhang ◽  
Kai Wang
Keyword(s):  
Energy Efficiency ◽  
Large Scale ◽  
Routing Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Routing Algorithms ◽  
Sensor Nodes ◽  
Energy Aware ◽  
Hierarchical Routing ◽  
Static Clustering

Energy efficiency is a key factor to improve WSNs’ performance, and hierarchical routing algorithms are fitter in large scale networks and have more reliability, so they are mostly used to improve the nodes’ energy efficiency now. In this paper, mainly existing hierarchical routing algorithms are introduced, and based on these researches, a new energy efficient hierarchical routing algorithm designed based on energy aware semi-static clustering method is proposed. In this algorithm named EASCA, the nodes’ residual energy and cost of communication would both be considered when clustering. And a special packet head is defined to update nodes’ energy information when transmitting message; to rotate cluster head automatically, a member management scheme is designed to complete this function; and a re-cluster mechanism is used to dynamic adjust the clusters to make sensor nodes organization more reasonable. At last, EASCA is compared with other typical hierarchical routing algorithms in a series of experiments, and the experiments’ result proves that EASCA has obviously improved WSNs’ energy efficiency.

scholarly journals Best Cluster Head Selection and Route Optimization for Cluster Based Sensor Network Using (M-pso) and Ga Algorithms

2021 ◽  
Author(s):  
Ved Prakash ◽  
Suman Pandey ◽  
deepti singh
Keyword(s):  
Sensor Network ◽  
Optimal Path ◽  
Cluster Head ◽  
Pso Algorithm ◽  
Vital Role ◽  
Base Station ◽  
Route Optimization ◽  
Sensor Nodes ◽  
Aggregated Data ◽  
Cluster Heads

Abstract Clustering plays a vital role in extending the lifespan and optimized direction of a wireless sensor network by integrating sensor nodes through clusters and choosing cluster heads (CHs) and non-cluster heads (NCHs). Cluster head aggregated data and non-cluster heads forward to the base station (BS). In this paper, we have introduced a new Dynamic Multipath Routing Protocol (DMPRP) for selections of cluster heads (CHs) and non-cluster heads (NCHs), which is ideally selected using M-PSO algorithm. After calculating the probabilities, the best selection of cluster heads has taken, and results have used to find the optimized shortest path using the Genetic Algorithm (GA). The GA algorithm uses an objective function consisting of a network to determine the optimal path.

scholarly journals Enhanced Quality of Service Strategy for Improving Network Coverage in IOT Applications

2021 ◽  
Vol 23 (05) ◽  
pp. 694-707
Author(s):  
Dr. D. I. George Amalarethinam ◽  
◽  
Ms. P. Mercy ◽  
Keyword(s):  
Quality Of Service ◽  
Large Scale ◽  
Routing Algorithm ◽  
Cluster Head ◽  
Threshold Value ◽  
Maximum Energy ◽  
Sensor Nodes ◽  
Network Coverage ◽  
Fuzzy C Means Clustering

The Internet of Things (IoT) is a network that includes physical things capable of aggregating and communicating electronic information. With the advancement in wireless sensor networks, IoT provides highly efficient communication for various real-time applications. IoT networks are large-scale networks where routing can be improved by focusing on the Quality of Service (QoS) Parameter. Network coverage can be enhanced by hierarchical clustering of the nodes which increases the network lifetime. The proposed algorithm Enhanced Fuzzy Based Clustering and Routing Algorithm (EFCRA) performs distance and energy-based cluster head selection to find a new path from source to destination. The algorithm uses Fuzzy c-means clustering to provide optimization in forming cluster centers. The cluster head (CH) is identified based on the minimum distance and maximum energy of the sensor node. The cluster head is updated when its energy is lesser than the threshold value. The distance between sensor nodes and its CH node and then to the destination is computed using Dijkstra’s algorithm. The proposed routing strategy provides improved network coverage and throughput which extends the lifetime of the IoT network.

scholarly journals Comprehensive Optimization of Energy Consumption and Delay Performance for Green Communication in Internet of Things

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Jiaze Wang ◽  
Chunhua Hu ◽  
Anfeng Liu
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Duty Cycle ◽  
Routing Algorithm ◽  
Operating Mode ◽  
Sensor Nodes ◽  
Green Communications ◽  
Green Communication ◽  
Long Lifetime

Energy efficiency as well as fast data transmission is vital to green communications-based applications for Internet of Things (IoT). Wireless sensors, which constitute one of the important parts of IoT, adopt duty cycle operating mode to save energy. Although duty cycle operating mode will decrease the energy consumption of sensor nodes, it leads to a larger communication delay. In this paper, a utility-based adaptive duty cycle (UADC) routing algorithm is proposed to increase energy efficiency, reduce transmission delay, and keep long lifetime at the same time. First, UADC routing algorithm adopts a comprehensive performance evaluation function to evaluate the utility of choosing different relay nodes. Then it selects the node which maximizes the utility of the system to perform data relay. The utility function synthesizes comprehensive indexes like the reliability, energy consumption, and delay of the node. UADC routing algorithm adopts a high-duty cycle operating mode in the areas which have more remaining energy to decrease the delay. And a low-duty cycle operating mode in the energy-strained areas is adopted to ensure a long lifetime. The simulation results also prove the significant performances of our proposed algorithms.

A New Clustering Routing Algorithm for WSN Based on PSO

2013 ◽  
Vol 850-851 ◽  
pp. 689-692
Author(s):  
Li Fu Wang ◽  
Jian Ding ◽  
Zhi Kong
Keyword(s):  
Energy Consumption ◽  
Clustering Algorithm ◽  
Routing Algorithm ◽  
Cluster Head ◽  
Pso Algorithm ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Long Distance ◽  
Wireless Sensor Nodes ◽  
Spatially Distributed

A wireless sensor network (WSN) consists of spatially distributed wireless sensor nodes. The node power constrains the development of WSN. Employing techniques of clustering can reduce energy consumption of wireless sensor nodes and prolong the network lifetime. Therefore, in the study a new clustering routing algorithm is presented. The clustering algorithm uses the double-layer sensor nodes to communicate. And in order to optimize power energy consumption for WSN node energy, PSO algorithm is employed to find cluster head in each layer. Simulation results show that the algorithm not only can equal power energy of node, but also can reduce consumption in the long distance data transmission.

scholarly journals EARPC – Energy Aware Routing Protocol for Cooperative MIMO Scheme in WSNs

2020 ◽  
Vol 3 ◽  
pp. 22-27
Author(s):  
Kummathi Chenna Reddy ◽  
Geetha D. Devanagavi ◽  
Thippeswamy M. N.
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Routing Protocol ◽  
Energy Levels ◽  
Routing Algorithm ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Energy Aware ◽  
Energy Efficient Routing ◽  
Energy Aware Routing

Wireless sensor networks are typically operated on batteries. Therefore, in order to prolong network lifetime, an energy efficient routing algorithm is required. In this paper, an energy-aware routing protocol for the co-operative MIMO scheme in WSNs (EARPC) is presented. It is based on an improved cluster head selection method that considers the remaining energy level of a node and recent energy consumption of all nodes. This means that sensor nodes with lower energy levels are less likely to be chosen as cluster heads. Next, based on the cooperative node selection in each cluster, a virtual MIMO array is created, reducing uneven distribution of clusters. Simulation results show that the proposed routing protocol may reduce energy consumption and improve network lifetime compared with the LEACH protocol

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.

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