Polar Coordinate-Based Energy-Efficient-Chain Routing in Wireless Sensor Networks Using Random Projection

Qiao, Jianhua; Zhang, Xueying

doi:10.1109/access.2018.2824245

Cited by 18 publications

(12 citation statements)

References 27 publications

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“…Random projection based on compressed sensing might decrease the volume of data communicated in a WSN, and efficient routing could ease the network traffic. Jianhua Qiao [21] presented a Random projection-Polar coordinate-Chain routing (RPC) scheme to develop the time and energy efficient protocol. The review in [22] had presented the state of the art in the energy management schemes, and the available challenges in the area of WSN.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Intelligent Routing in WSN using Dominant Genetic Algorithm

Shanthi¹

2020

IJECE

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In the current era of wireless sensor network development, among the various challenging issues, the life enhancement has obtained the prime interest. Reason is clear and straight: the battery operated sensors do have limited period of life hence to keep the network active as much as possible, life of network should be larger. To enhance the life of the network, at different level different approaches has been applied, broadly defining the proper scheduling of sensors and defining the energy efficient communication. In this paper heuristic based energy efficient communication approch has applied. A new development in the Genetic algorithm has presented and called as Dominant Genetic algorithm to determine the optimum energy efficient routing path between sensor nodes and to define the optimal energy efficient trajectory for mobile data gathering node. Dominancy of high fitness solution has included in the Genetic algorithm because of its natural existence. The proposed solution has applied the connection oriented crossover and mutation operator to maintain the feasibility of generated solution. With various simulation experiments it has observed that proposed method not only has delivered the better solution but also very less number of iterations required as compared to conventional form of Genetic algorithm.

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Section: Related Workmentioning

confidence: 99%

Energy Efficient Intelligent Routing in WSN using Dominant Genetic Algorithm

Shanthi¹

2020

IJECE

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“…Each node considers its neighbors and if the energy level of the neighbor is smaller than its own energy level, it increases its point by one unit. The scoring point needed for each node to become a CH is updated after the computation given in Equation (6).…”

Section: Cluster Initializationmentioning

confidence: 99%

“…In order to prevent the network from hot spot issue, unequal clustering techniques can be utilized for load balancing between the CHs [18,19]. Wireless nodes typically have capabilities such as sensing, computing and self-organizing operations for routing and data transmission to a base station (BS) [6]. However, limitations of wireless nodes include short-range communications, low bandwidth, processing/storage limitation and, particularly, energy consumption [7].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Unequal Clustering Based on Density with Energy Conservation in Wireless Nodes

et al. 2019

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The Internet of things (IoT) provides the possibility of communication between smart devices and any object at any time. In this context, wireless nodes play an important role in reducing costs and simple use. Since these nodes are often used in less accessible locations, recharging their battery is hardly feasible and in some cases is practically impossible. Hence, energy conservation within each node is a challenging discussion. Clustering is an efficient solution to increase the lifetime of the network and reduce the energy consumption of the nodes. In this paper, a novel hybrid unequal multi-hop clustering based on density (HCD) is proposed to increase the network lifetime. In the proposed protocol, the cluster head (CH) selection is performed only by comparing the status of each node to its neighboring nodes. In this new technique, the parameters involving energy of nodes, the number of neighboring nodes, the distance to the base station (BS), and the layer where the node is placed in are considered in CH selection. So, in this new and simple technique considers energy consumption of the network and load balancing. Clustering is performed unequally so that cluster heads (CHs) close to BS have more energy for data relay. Also, a hybrid dynamic–static clustering was performed to decrease overhead. In the current protocol, a distributed clustering and multi-hop routing approach was applied between cluster members (CMs), to CHs, and CHs to BS. HCD is applied as a novel assistance to cluster heads (ACHs) mechanism, in a way that a CH accepts to use member nodes with suitable state to share traffic load. Furthermore, we performed simulation for two different scenarios. Simulation results showed the reliability of the proposed method as it was resulted in a significant increase in network stability and energy balance as well as network lifetime and efficiency.

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“…Qiao and Zhang [14] presented random projection-polar coordinate-chain routing (RPC) for improving the packet transmitting efficiency of the sensor network by alleviating data traffic. RPC is focused to locate nodes establish chained routing paths and random project-based data gathering for reliable transmission in WSN.…”

Section: Related Workmentioning

confidence: 99%

Energy-Centric Route Planning using Machine Learning Algorithm for Data Intensive Secure Multi-Sink Sensor Networks

2019

IJITEE

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Wireless sensor network (WSN) is energy operated self-disciplined ad-hoc technology capable of sensing and actuating environmental phenomenon. The sensed information is shared between communication sinks for access and processing. Energy conservation and route planning are prominent in determining the efficiency of the sensor network. In this paper, energy-centric route planning (ECRP) technique is introduced to address the inequalityin sensor node lifetime and routing along with security requirements. ECRP depends on the individual and co-operative energy expenses of the nodes to retain a balanced communication link. The expenses are monitored and a profitable route plan is designed using a machine learning algorithm that assists in identifying a non-deficient neighbor for routing. Both energy optimization and route refurbishing are controlled by the analysis and decisions of the learning algorithm. The Learning process is instigated with the node energy and current route neighbor information for discovering efficient communication paths to the sink. Security is administered using trust process in this path planning and routing for selecting reliable neighbors. This helps to retain security throughout the routing process.The impact of the proposed ECRP over sensor network is verified using the metrics: throughput, active nodes, transmitting energy, routing complexity and delay.

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Polar Coordinate-Based Energy-Efficient-Chain Routing in Wireless Sensor Networks Using Random Projection

Cited by 18 publications

References 27 publications

Energy Efficient Intelligent Routing in WSN using Dominant Genetic Algorithm

Energy Efficient Intelligent Routing in WSN using Dominant Genetic Algorithm

A Hybrid Unequal Clustering Based on Density with Energy Conservation in Wireless Nodes

Energy-Centric Route Planning using Machine Learning Algorithm for Data Intensive Secure Multi-Sink Sensor Networks

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