scholarly journals CRCM: A New Combined Data Gathering and Energy Charging Model for WRSN

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 319 ◽  
Author(s):  
Yuhou Wang ◽  
Ying Dong ◽  
Shiyuan Li ◽  
Hao Wu ◽  
Mengyao Cui
Keyword(s):  
Energy Consumption ◽  
Sensor Node ◽  
Data Gathering ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Mobile Charger ◽  
Earliest Deadline ◽  
No Sensor ◽  
Combined Data ◽  
Better Than

With the development of wireless sensor networks (WSNs), the problem about how to increase the lifecycle of the WSNs is always a hot discussion point, and some researchers have devoted to the ‘energy saving’ to decrease the energy consumption of the sensor nodes by different algorithms. However, the fundamental technique is ‘energy acquiring’ for the battery which can solve the limited capacity problem. In this paper, we study the data gathering and energy charging by a mobile charger (MC) at the same time that most energy consumption can be saved by short communication distance. We have named this as the recharging model-combined recharging and collecting data model on-demand (CRCM). Firstly, the hexagon-based (HB) algorithm is proposed to sort all sensor nodes in the region to make data collecting and energy charging work at the same time. Then we consider both residual energy and geographic position (REGP) of the sensor node to calculate the priority of each cluster. Thirdly, the dynamic mobile charger (DMC) algorithm is proposed to calculate the number of MCs to make sure no sensor node will die in each charging queue. Finally, the simulations show that our REGP algorithm is better than Earliest Deadline First (EDF) and Nearest-Job-Next with Preemption (NJNP), and the DMC plays well when the number of sensor nodes increase.

scholarly journals Greedy Knapsack Based Energy Efficient Routing in WMSNS

2019 ◽  
Vol 8 (10) ◽  
pp. 1455-1461
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Sensor Node ◽  
Life Time ◽  
Residual Energy ◽  
Multimedia Data ◽  
Sensor Nodes ◽  
Wireless Multimedia ◽  
Energy Efficient Routing ◽  
Critical Situation

Managing the energy is very challenging in wireless multimedia sensor networks because of heavy consumption of energy by the sensor nodes. Multimedia data transmission contains heavy energy consumption operations such as sensing, aggregating, compressing and transferring the data from one sensor node to neighbour sensor node. Many routing techniques considers residual energy of a neighbour node to forward the data to that node. But, in reality a critical situation occurs where required energy is greater than individual neighbour node’s residual energy. In this situation it is not possible to select any neighbour node as a data forwarder. The proposed greedy knapsack based energy efficient routing algorithm (GKEERA) can address this critical situation very efficiently. And also a Two-in-One Mobile Sink (TIOMS) is used to provide the power supply and to collect the data from a battery drained sensor node. GKEERA improves the life time of a network by balancing the energy consumption between the neighbour nodes.

scholarly journals Data Clustering Method in Wireless Sensor Networks Based on Residual Energy Perception

2018 ◽  
Vol 14 (06) ◽  
pp. 85 ◽  
Author(s):  
Xudong Yang
Keyword(s):  
Energy Consumption ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Data Gathering ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Data Forwarding ◽  
Cluster Data ◽  
Head Node

<p class="0abstract"><span lang="EN-US">To prolong the survival time of wireless sensor network, an iterative scheme was proposed. First of all, spectrum clustering algorithm iteratively segmented the network into clusters, and cluster head nodes in each sub cluster were determined depending on the size of residual energy of sensor nodes. Then, a data forwarding balance tree was constructed in each sub cluster. Data forwarding path of each non-cluster head node was defined, and the moving path of a mobile data collector was determined, which used the residual energy as the basis for the network optimization. Finally, this scheme was simulated, and two traditional data gathering algorithms were compared. The results showed that the algorithm designed in this experiment could effectively balance energy consumption among all WSN nodes and had great performance improvement compared with the traditional data collection algorithm. To sum up, this algorithm can significantly reduce the energy consumption of the network and improve the lifetime of the network. </span></p>

Research on Energy Aware Routing for Wireless Sensor Networks

2013 ◽  
Vol 303-306 ◽  
pp. 191-196
Author(s):  
Wei Zhang ◽  
Ling Hua Zhang
Keyword(s):  
Energy Consumption ◽  
Optimal Path ◽  
Critical Issue ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Energy Aware ◽  
Energy Aware Routing ◽  
Transmission Energy Consumption ◽  
Transmission Energy ◽  
Improved Algorithm

Energy aware routing is a critical issue in WSN. Prior work in energy aware routing concerned about transmission energy consumption and residual energy, but often do not consider path hop length, which leads to unnecessary consumption of power at sensor nodes. Improved algorithm adds the control of routing hops. Simulation proof the improved algorithm is feasible, effectively reducing the network delay and the path of energy consumption. Taking into account the WSN is dynamic, in the end we put up dynamic hops control in order to adapt to WSN and select the optimal path.

Nature-Inspired Algorithms in Wireless Sensor Networks

2019 ◽  
pp. 246-275
Author(s):  
Ajay Kaushik ◽  
S. Indu ◽  
Daya Gupta
Keyword(s):  
Wireless Sensor Networks ◽  
Big Data ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Sensor Node ◽  
Optimization Problem ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Nature Inspired Algorithms

Wireless sensor networks (WSNs) are becoming increasingly popular due to their applications in a wide variety of areas. Sensor nodes in a WSN are battery operated which outlines the need of some novel protocols that allows the limited sensor node battery to be used in an efficient way. The authors propose the use of nature-inspired algorithms to achieve energy efficient and long-lasting WSN. Multiple nature-inspired techniques like BBO, EBBO, and PSO are proposed in this chapter to minimize the energy consumption in a WSN. A large amount of data is generated from WSNs in the form of sensed information which encourage the use of big data tools in WSN domain. WSN and big data are closely connected since the large amount of data emerging from sensors can only be handled using big data tools. The authors describe how the big data can be framed as an optimization problem and the optimization problem can be effectively solved using nature-inspired algorithms.

scholarly journals Green Data Gathering under Delay Differentiated Services Constraint for Internet of Things

2018 ◽  
Vol 2018 ◽  
pp. 1-23 ◽  
Author(s):  
Mingfeng Huang ◽  
Anfeng Liu ◽  
Tian Wang ◽  
Changqin Huang
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Energy Efficient ◽  
Data Gathering ◽  
Differentiated Services ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensitive Data ◽  
Delay Sensitive ◽  
Delay Tolerant

Energy-efficient data gathering techniques play a crucial role in promoting the development of smart portable devices as well as smart sensor devices based Internet of Things (IoT). For data gathering, different applications require different delay constraints; therefore, a delay Differentiated Services based Data Routing (DSDR) scheme is creatively proposed to improve the delay differentiated services constraint that is missed from previous data gathering studies. The DSDR scheme has three advantages: first, DSDR greatly reduces transmission delay by establishing energy-efficient routing paths (E2RPs). Multiple E2RPs are established in different locations of the network to forward data, and the duty cycles of nodes on E2RPs are increased to 1, so the data is forwarded by E2RPs without the existence of sleeping delay, which greatly reduces transmission latency. Secondly, DSDR intelligently chooses transmission method according to data urgency: the direct-forwarding strategy is adopted for delay-sensitive data to ensure minimum end-to-end delay, while wait-forwarding method is adopted for delay-tolerant data to perform data fusion for reducing energy consumption. Finally, DSDR make full use of the residual energy and improve the effective energy utilization. The E2RPs are built in the region with adequate residual energy and they are periodically rotated to equalize the energy consumption of the network. A comprehensive performance analysis demonstrates that the DSDR scheme has obvious advantages in improving network performance compared to previous studies: it reduces transmission latency of delay-sensitive data by 44.31%, reduces transmission latency of delay-tolerant data by 25.65%, and improves network energy utilization by 30.61%, while also guaranteeing the network lifetime is not lower than previous studies.

Wireless Photoelectric Sensor Network Scheduling Algorithm Based on Divisible Load

2018 ◽  
Vol 13 (10) ◽  
pp. 1499-1504 ◽  
Author(s):  
Jiaqi Wu ◽  
Huahu Xu
Keyword(s):  
Sensor Networks ◽  
Sensor Node ◽  
Scheduling Algorithm ◽  
Residual Energy ◽  
Initial Energy ◽  
Sensor Nodes ◽  
Load Scheduling ◽  
Divisible Load ◽  
Photoelectric Sensor ◽  
Divisible Load Scheduling

To discuss the divisible load scheduling in wireless photoelectric sensor networks, a load scheduling algorithm called EDDLT based on residual energy landscape is proposed. In the algorithm, the constant of the time needed for the induction and reporting unit data is adjusted to the variable parameters based on the residual energy. In addition, the ratio of the initial energy to the residual energy is used to carry out the effective load scheduling. By using this algorithm, when the load is allocated to each sensor node, the remaining energy of nodes is considered, and a lighter load is allocated to sensor nodes with less residual energy. EDDLT, compared to the standard divisible load scheduling method SDLT, the number of execution rounds is greatly increased when the first sensor node is dead. The experimental results showed that EDDLT had a certain effect on prolonging the lifetime of wireless photoelectric sensor networks. To sum up, the scheduling algorithm has good performance in exploring divisible load.

scholarly journals Qualification of Cluster Header and Cluster Member in Wireless Sensor Networks

2019 ◽  
Vol 8 (2S6) ◽  
pp. 101-105
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Transmission ◽  
Sensor Node ◽  
Data Transfer ◽  
Cluster Head ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Sink Node

Many researches have been proposed for efficiency of data transmission from sensor nodes to sink node for energy efficiency in wireless sensor networks. Among them, cluster-based methods have been preferred In this study, we used the angle formed with the sink node and the distance of the cluster members to calculate the probability of cluster head. Each sensor node sends measurement values to header candidates, and the header candidate node measures the probability value of the header with the value received from its candidate member nodes. To construct the cluster members, the data transfer direction is considered. We consider angle, distance, and direction as cluster header possibility value. Experimental results show that data transmission is proceeding in the direction of going to the sink node. We calculated and displayed the header possibility value of the neighbor nodes of the sensor node and confirmed the candidates of the cluster header for data transfer as the value. In this study, residual energy amount of each sensor node is not considered. In the next study, we calculate the value considering the residual energy amount of the node when measuring the header possibility value of the cluster.

scholarly journals An efficient hybrid model for secure transmission of data by using efficient data collection and dissemination (EDCD) algorithm based WSN

2020 ◽  
Vol 20 (1) ◽  
pp. 545
Author(s):  
Mustafa Mahmood Akawee ◽  
Mohanad Ali Meteab Al-Obaidi ◽  
Haider Mohammed Turki Al-Hilfi ◽  
Sabbar Insaif Jassim ◽  
Tole Sutikno
Keyword(s):  
Energy Consumption ◽  
Data Collection ◽  
Hybrid Model ◽  
Sensor Node ◽  
Sensor Nodes ◽  
Security Issue ◽  
Proposed Model ◽  
Efficient Data ◽  
Secure Transmission ◽  
Efficient Data Collection

<span>Wireless Sensor Network (WSN) is one of the most important elements of the Internet of Things paradigm. Energy consumption is a vital issue in IoT and WSN.  Security primitives in the IoT are energy consuming. Addressed the security issue for transmitted data by IoT sensor node add another challenge in term of energy consumption. finding the satisfactory solutions that reduce power consumption at the same time as making sure the required security services is not always an easy undertaking. Therefore, in this article, we proposed an efficient hybrid model for secure transmission of data from sensor nodes to receivers in WSN applications.  The proposed model includes two algorithms Rivest–Shamir–Adleman (RSA) and efficient data collection and dissemination (EDCD). The key idea behind the proposed model is to prevent to secure sensed data if no significant change between the current data and the last transmitted data by the apply EDCD1 algorithm, which that will help in saving the sensor node energy. The reason for that the size of cipher data is so large compared to the sensed data, which that will increase the energy consumption.  The outcome results shown that the proposed model has a high performance compared to RSA in term of energy consumption.</span>

scholarly journals An Unequal Cluster-based Routing Protocol Based on Data Controlling for Wireless Sensor Network

2016 ◽  
Vol 6 (5) ◽  
pp. 2403
Author(s):  
Slaheddine Chelbi ◽  
Majed Abdouli ◽  
Mourad Kaddes ◽  
Claude Duvallet ◽  
Rafik Bouaziz
Keyword(s):  
Energy Consumption ◽  
Communication Networks ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Sensitive Data ◽  
Link Cost ◽  
Data Controller ◽  
Routing Tree ◽  
Multiple Metrics

<p>Wireless Sensor Networks (WSN) differ from traditional wireless communication networks in several characteristics. One of these characteristics is power awarness, due to the fact that the batteries of sensor nodes have a restricted lifetime and are difficult to be replaced. Therefore, all protocols must be designed to minimize energy consumption and preserve the longevity of the network. In this paper, we propose (i) to fairly balance the load among nodes. For this, we generate an unequal clusters size where the cluster heads (CH) election is based on energy availability, (ii) to reduce the energy consumption due to the transmission by using multiple metrics in the CH jointure process and taking into account the link cost, residual energy and number of cluster members to construct the routing tree and (iii) to minimize the number of transmissions by avoiding the unnecessary updates using sensitive data controller. Simulation results show that our Advanced Energy-Efficient Unequal Clustering (AEEUC) mechanism improves the fairness energy consumption among all sensor nodes and achieves an obvious improvement on the network lifetime.</p>

scholarly journals Energy Efficient Model for Maximizing the Network Coverage Time in Non-Deterministic WSN Model

2019 ◽  
Vol 9 (2) ◽  
pp. 27-32
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Sensor Node ◽  
Deterministic Model ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Network Coverage ◽  
Node Placement ◽  
Node Location ◽  
Death And Loss

WSN are the group nodes and these nodes are grouped into several clusters, each cluster has its own CH (Cluster Head). Moreover, each cluster Head collects the data and sends either through the corresponding CH or through the CH. Moreover, the clustering plays one of the eminent role in WSN, since Clustering reduces the energy consumption in the cluster Head and improvises the lifetime and scalability of WSN. However, this maximizes the burden on the CH and certainly, it causes the coverage loss. Hence, in this paper we design a model named as EE-NCT (Energy Efficient model for maximizing the network coverage time) which helps in increasing the Network Coverage time for the non-deterministic model, i.e. Sensor nodes location are not known. Non –deterministic model makes hard to maximize, as the node placement is not known. Moreover, this is achieved through monitoring the sensor node location and applying the routing based clustering scheme. Our model is evaluated by considering the various constraint such as first sensor node death, 75% of node death and loss of connectivity by considering the parameter as energy consumption and average number of failed nodes

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