Energy-efficient routing sensing technology of wireless sensor networks based on Internet of Things

2021 ◽  
Vol 27 (3) ◽  
pp. 225-235
Author(s):  
Xiaotao Ju

This research was conducted to enhance the energy performance of wireless sensor networks (WSN) and improve the performance of end-to-end delay and packet receiving rate of network operation. In this study, the low-energy data collection routing algorithm and adaptive environment sensing method in WSN were mainly examined. The node centrality, energy surplus, and node temperature were calculated for cluster head selection to reduce the energy consumption of nodes and improve the reliability of network data. The research results have shown that the parameter setting guided by the theoretical analysis makes each node selfishly achieve the maximum expected benefit while the whole network runs reliably, and the energy consumption is reduced by the selfishness of the node. As a result, the proposed algorithm can effectively reduce the network energy consumption and increase the network life cycle of wireless sensor networks. It can be seen that the machine learning methods such as support vector machine are used to model and analyze the state of the sensing node, and to obtain more accurate wireless channel availability judgment based on the historical state data, thereby adaptively adjusting the working duty ratio and reducing the invalidity data sent.

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1835 ◽  
Author(s):  
Ruan ◽  
Huang

Since wireless sensor networks (WSNs) are powered by energy-constrained batteries, many energy-efficient routing protocols have been proposed to extend the network lifetime. However, most of the protocols do not well balance the energy consumption of the WSNs. The hotspot problem caused by unbalanced energy consumption in the WSNs reduces the network lifetime. To solve the problem, this paper proposes a PSO (Particle Swarm Optimization)-based uneven dynamic clustering multi-hop routing protocol (PUDCRP). In the PUDCRP protocol, the distribution of the clusters will change dynamically when some nodes fail. The PSO algorithm is used to determine the area where the candidate CH (cluster head) nodes are located. The adaptive clustering method based on node distribution makes the cluster distribution more reasonable, which balances the energy consumption of the network more effectively. In order to improve the energy efficiency of multi-hop transmission between the BS (Base Station) and CH nodes, we also propose a connecting line aided route construction method to determine the most appropriate next hop. Compared with UCCGRA, multi-hop EEBCDA, EEMRP, CAMP, PSO-ECHS and PSO-SD, PUDCRP prolongs the network lifetime by between 7.36% and 74.21%. The protocol significantly balances the energy consumption of the network and has better scalability for various sizes of network.


2014 ◽  
Vol 626 ◽  
pp. 20-25
Author(s):  
K. Kalaiselvi ◽  
G.R. Suresh

In wireless sensor networks Energy-efficient routing is an important issue due to the limited battery power within the network, Energy consumption is one of the important performance factors. Specifically for the election of cluster head selection and distance between the cluster head node and base station. The main objective of this proposed system is to reduce the energy consumption and prolong the network lifetime. This paper introduces a new clustering algorithm for energy efficient routing based on a cluster head selection


Wireless Sensor Networks (WSN) consists of a large amount of nodes connected in a self-directed manner. The most important problems in WSN are Energy, Routing, Security, etc., price of the sensor nodes and renovation of these networks is reasonable. The sensor node tools included a radio transceiver with an antenna and an energy source, usually a battery. WSN compute the environmental conditions such as temperature, sound, pollution levels, etc., WSN built the network with the help of nodes. A sensor community consists of many detection stations known as sensor nodes, every of which is small, light-weight and portable. Nodes are linked separately. Each node is linked into the sensors. In recent years WSN has grow to be an essential function in real world. The data’s are sent from end to end multiple nodes and gateways, the data’s are connected to other networks such as wireless Ethernet. MGEAR is the existing mechanism. It works with the routing and energy consumption. The principal problem of this work is choosing cluster head, and the selection is based on base station, so the manner is consumes energy. In this paper, develop the novel based hybrid protocol Low Energy Aware Gateway (LEAG). We used Zigbee techniques to reduce energy consumption and routing. Gateway is used to minimize the energy consumption and data is send to the base station. Nodes are used to transmit the data into the cluster head, it transmit the data into gateway and gateway compress and aggregate the data then sent to the base station. Simulation result shows our proposed mechanism consumes less energy, increased throughput, packet delivery ration and secure routing when compared to existing mechanism (MGEAR).


Author(s):  
Amarasimha T. ◽  
V. Srinivasa Rao

Wireless sensor networks are used in machine learning for data communication and classification. Sensor nodes in network suffer from low battery power, so it is necessary to reduce energy consumption. One way of decreasing energy utilization is reducing the information transmitted by an advanced machine learning process called support vector machine. Further, nodes in WSN malfunction upon the occurrence of malicious activities. To overcome these issues, energy conserving and faulty node detection WSN is proposed. SVM optimizes data to be transmitted via one-hop transmission. It sends only the extreme points of data instead of transmitting whole information. This will reduce transmitting energy and accumulate excess energy for future purpose. Moreover, malfunction nodes are identified to overcome difficulties on data processing. Since each node transmits data to nearby nodes, the misbehaving nodes are detected based on transmission speed. The experimental results show that proposed algorithm provides better results in terms of reduced energy consumption and faulty node detection.


2013 ◽  
Vol 706-708 ◽  
pp. 635-638
Author(s):  
Yong Lv

Wireless Sensor Networks consisting of nodes with limited power are deployed to collect and distribute useful information from the field to the other sensor nodes. Energy consumption is a key issue in the sensor’s communications since many use battery power, which is limited. In this paper, we describe a novel energy efficient routing approach which combines swarm intelligence, especially the ant colony based meta-heuristic, with a novel variation of reinforcement learning for sensor networks (ARNet). The main goal of our study was to maintain network lifetime at a maximum, while discovering the shortest paths from the source nodes to the sink node using an improved swarm intelligence. ARNet balances the energy consumption of nodes in the network and extends the network lifetime. Simulation results show that compared with the traditional EEABR algorithm can obviously improve adaptability and reduce the average energy consumption effectively.


2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Jianpo Li ◽  
Xue Jiang ◽  
I-Tai Lu

Wireless sensor networks are usually energy limited and therefore an energy-efficient routing algorithm is desired for prolonging the network lifetime. In this paper, we propose a new energy balance routing algorithm which has the following three improvements over the conventional LEACH algorithm. Firstly, we propose a new cluster head selection scheme by taking into consideration the remaining energy and the most recent energy consumption of the nodes and the entire network. In this way, the sensor nodes with smaller remaining energy or larger energy consumption will be much less likely to be chosen as cluster heads. Secondly, according to the ratio of remaining energy to distance, cooperative nodes are selected to form virtual MIMO structures. It mitigates the uneven distribution of clusters and the unbalanced energy consumption of the whole network. Thirdly, we construct a comprehensive energy consumption model, which can reflect more realistically the practical energy consumption. Numerical simulations analyze the influences of cooperative node numbers and cluster head node numbers on the network lifetime. It is shown that the energy consumption of the proposed routing algorithm is lower than the conventional LEACH algorithm and for the simulation example the network lifetime is prolonged about 25%.


2015 ◽  
Vol 785 ◽  
pp. 744-750
Author(s):  
Lei Gao ◽  
Qun Chen

In order to solve the energy limited problem of sensor nodes in the wireless sensor networks (WSN), a fast clustering algorithm based on energy efficiency for wire1ess sensor networks is presented in this paper. In the system initialization phase, the deployment region is divided into several clusters rapidly. The energy consumption ratio and degree of the node are chosen as the selection criterion for the cluster head. Re-election of the cluster head node at this time became a local trigger behavior. Because of the range of the re-election is within the cluster, which greatly reduces the complexity and computational load to re-elect the cluster head node. Theoretical analysis indicates that the timing complexity of the clustering algorithm is O(1), which shows that the algorithm overhead is small and has nothing to do with the network size n. Simulation results show that clustering algorithm based on energy efficiency can provide better load balancing of cluster heads and less protocol overhead. Clustering algorithm based on energy efficiency can reduce energy consumption and prolong the network lifetime compared with LEACH protocol.


Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jun Wang ◽  
Zhuangzhuang Du ◽  
Zhengkun He ◽  
Xunyang Wang

Balancing energy consumption using the clustering routing algorithms is one of the most practical solutions for prolonging the lifetime of resource-limited wireless sensor networks (WSNs). However, existing protocols cannot adequately minimize and balance the total network energy dissipation due to the additional tasks of data acquisition and transmission of cluster heads. In this paper, a cluster-head rotating election routing protocol is proposed to alleviate the problem. We discovered that the regular hierarchical clustering method and the scheme of cluster-head election area division had positive effects on reducing the energy consumption of cluster head election and intracluster communication. The election criterion composed of location and residual energy factor was proved to lower the probability of premature death of cluster heads. The chain multihop path of intercluster communication was performed to save the energy of data aggregation to the base station. The simulation results showed that the network lifetime can be efficiently extended by regulating the adjustment parameters of the protocol. Compared with LEACH, I-LEACH, EEUC, and DDEEC, the algorithm demonstrated significant performance advantages by using the number of active nodes and residual energy of nodes as the evaluation indicators. On the basis of these results, the proposed routing protocols can be utilized to increase the capability of WSNs against energy constraints.


Author(s):  
VENKATESH S

In Wireless Sensor Networks (WSNs), Efficient clustering is key for optimal use of available nodes. Fault tolerance to any failure on the network or node level is an essential requirement in this context. Hence, a novel approach towards clustering and multiple object tracking in WSNs is being explored. The Proposed method employs judicious mix of burdening all available nodes including GH (Group Head) to earn energy efficiency and fault tolerance. Initially, node with the maximum residual energy in a cluster becomes group head and node with the second maximum residual energy becomes altruist node, but not mandatory. Later on, selection of cluster head will be based on available residual energy. We use Matlab software as simulation platform to check energy consumption at cluster by evaluation of proposed algorithm. Eventually we evaluated and compare this proposed method against previous method and we demonstrate our model is better optimization than other method such as Traditional clustering in energy consumption rate.


Wireless sensor networks (WSN) are gaining attention in numerous fields with the advent of embedded systems and IoT. Wireless sensors are deployed in environmental conditions where human intervention is less or eliminated. Since these are not human monitored, powering and maintaining the energy of the node is a challenging issue. The main research hotspot in WSN is energy consumption. As energy drains faster, the network lifetime also decreases. Self-Organizing Networks (SON) are just the solution for the above-discussed problem. Self-organizing networks can automatically configure themselves, find an optimalsolution, diagnose and self-heal to some extent. In this work, “Implementation of Enhanced AODV based Self-Organized Tree for Energy Balanced Routing in Wireless Sensor Networks” is introduced which uses self-organization to balance energy and thus reduce energy consumption. This protocol uses combination of number of neighboring nodes and residual energy as the criteria for efficient cluster head election to form a tree-based cluster structure. Threshold for residual energy and distance are defined to decide the path of the data transmission which is energy efficient. The improvement made in choosing robust parameters for cluster head election and efficient data transmission results in lesser energy consumption. The implementation of the proposed protocol is carried out in NS2 environment. The experiment is conducted by varying the node density as 20, 40 and 60 nodes and with two pause times 5ms, 10ms. The analysis of the result indicates that the new system consumes 17.6% less energy than the existing system. The routing load, network lifetime metrics show better values than the existing system.


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