scholarly journals An Energy Conserving and Transmission Radius Adaptive Scheme to Optimize Performance of Energy Harvesting Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2885 ◽  
Author(s):  
Xin Ju ◽  
Wei Liu ◽  
Chengyuan Zhang ◽  
Anfeng Liu ◽  
Tian Wang ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Network Performance ◽  
Maximum Energy ◽  
Energy Utilization ◽  
Utilization Rate ◽  
Cost Of Energy ◽  
Energy Conserving ◽  
The Cost

In energy harvesting wireless sensor networks (EHWSNs), the energy tension of the network can be relieved by obtaining the energy from the surrounding environment, but the cost on hardware cannot be ignored. Therefore, how to minimize the cost of energy harvesting hardware to reduce the network deployment cost, and further optimize the network performance, is still a challenging issue in EHWSNs. In this paper, an energy conserving and transmission radius adaptive (ECTRA) scheme is proposed to reduce the cost and optimize the performance of solar-based EHWSNs. There are two main innovations of the ECTRA scheme. Firstly, an energy conserving approach is proposed to conserve energy and avoid outage for the nodes in hotspots, which are the bottleneck of the whole network. The novelty of this scheme is adaptively rotating the transmission radius. In this way, the nodes with maximum energy consumption are rotated, balancing energy consumption between nodes and reducing the maximum energy consumption in the network. Therefore, the battery storage capacity of nodes and the cost on hardware. Secondly, the ECTRA scheme selects a larger transmission radius for rotation when the node can absorb enough energy from the surroundings. The advantages of using this method are: (a) reducing the energy consumption of nodes in near-sink areas, thereby reducing the maximum energy consumption and allowing the node of the hotspot area to conserve energy, in order to prevent the node from outage. Hence, the network deployment costs can be further reduced; (b) reducing the network delay. When a larger transmission radius is used to transmit data in the network, fewer hops are needed by data packet to the sink. After the theoretical analyses, the results show the following advantages compared with traditional method. Firstly, the ECTRA scheme can effectively reduce deployment costs by 29.58% without effecting the network performance as shown in experiment analysis; Secondly, the ECTRA scheme can effectively reduce network data transmission delay by 44–71%; Thirdly, the ECTRA scheme shows a better balance in energy consumption and the maximum energy consumption is reduced by 27.89%; And lastly, the energy utilization rate is effectively improved by 30.09–55.48%.

scholarly journals SIMULATION OF ENERGY CONSUMPTION IN MULTI CLUSTER WIRELESS SENSOR NETWORKS

2018 ◽  
pp. 22-26
Author(s):  
S. I. Pella
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Performance ◽  
Network Connectivity ◽  
Wireless Sensor ◽  
Cluster Network ◽  
Cluster Networks ◽  
Energy Conserving ◽  
Single Cluster

Energy conserving protocols in wireless sensor networks (WSNs), such as S-MAC, introduce multi-cluster network. The border nodes in multi cluster WSNs have more active time than the other nodes in the network; hence have more energy depletion rate. Since battery replacement in most networks is considered difficult, one or more nodes running out of energy prematurely will affect the network connectivity and decrease the overall network performance severely. This paper aims to (1) analyze the energy consumption in a multi-cluster sensor network and compare it to the single cluster scenario (2) investigate the merging time in a single cluster network. The result shows that, in average the energy needed to deliver a packet in the multi cluster networks is about 150% more than the energy needed in the single cluster networks. Moreover, the merging time in the single cluster network using schedule offset as the merging criteria in average is slightly smaller than one in the network using schedule ID as the merging criteria.

scholarly journals Analysis of total carbon emission of the logistics industry in Guangdong and solutions to emission reduction

2021 ◽  
Vol 245 ◽  
pp. 01020
Author(s):  
Aixia Xu ◽  
Xiaoyong Yang
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Utilization ◽  
Total Carbon ◽  
Utilization Rate ◽  
Low Carbon ◽  
Logistics Industry ◽  
Direct Energy ◽  
Reduce Emissions

The input-output method is employed in this study to measure the total carbon emission of the logistics industry in Guangdong. The findings revealed that the carbon emission of direct energy consumption of the logistics industry in Guangdong is far above the actual carbon emissions, the second and third industries play a significant role in carbon emission of indirect energy consumption in the logistics industry in Guangdong. To reduce energy consumption and carbon emissions in Guangdong, it is not only important to control the carbon emissions in the logistics industry, but strengthen carbon emission detection in relevant industries, improve the energy utilization rate and reduce emissions in other industries, and move towards low-carbon sustainable development.

A Big Data Application for Energy Consumption Management of Commercial Buildings

2021 ◽  
Vol 2 (1) ◽  
pp. 0-0
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Data Streams ◽  
Design And Implementation ◽  
Available Energy ◽  
Cost Of Energy ◽  
Data Application ◽  
Consumption Data ◽  
Big Data Application ◽  
The Cost

The demand for energy is increasing rapidly and, after a few years, it may surpass the available energy, which may lead the energy providers to increase the cost of energy consumption to compensate the cost for the production. This paper provides design and implementation details of a prototype big data application developed to help large buildings to automatically manage their energy consumption by setting energy consumption targets, collecting periodic energy consumption data, storing the data streams, displaying the energy consumption graphically in real-time, analyzing the consumption patterns, and generating energy consumption graphs and reports. The application is connected to Mongo NoSQL backend database to handle the large and continuously changing data. This big data energy consumption management system is expected to help the users in managing energy consumption by analyzing the patterns to see if it is within or above the desired consumption targets and displaying the data graphically.

Efficient Energy Conservation and Faulty Node Detection on Machine Learning-Based Wireless Sensor Networks

2021 ◽  
Vol 13 (2) ◽  
pp. 1-20
Author(s):  
Amarasimha T. ◽  
V. Srinivasa Rao
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Communication ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Support Vector ◽  
Faulty Node

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.

scholarly journals EOTS: An Energy-consumption Optimization-oriented Task Scheduling Algorithm for Wireless Sensor Networks

2016 ◽  
Vol 12 (07) ◽  
pp. 59
Author(s):  
Zeyu Sun ◽  
Yuanbo Li ◽  
Chuanfeng Li ◽  
Yalin Nie
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Task Scheduling ◽  
Energy Cost ◽  
Iterative Refinement ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Utilization Rate ◽  
Energy Consumption Optimization

<p><span style="font-family: Times New Roman;"><strong>The mismatch of task scheduling results in rapid network energy consumption during data transmission in wireless sensor networks. To address this issue, the paper proposed an </strong><strong>E</strong><strong>nergy-consumption </strong><strong>O</strong><strong>ptimization-oriented </strong><strong>T</strong><strong>ask </strong><strong>S</strong><strong>cheduling </strong><strong>A</strong><strong>lgorithm (EOTS algorithm) which formally described the overall power dissipation in the network system. On this basis, a network model was built up such that both the idle energy consumption in sensor nodes and energy consumption during the execution of tasks were taken into account, with which the whole task was effectively decomposed into sub-task sequences. They underwent simulated annealing and iterative refinement, with the intention of improving sensor nodes’ utilization rate, reducing local idle energy cost, as well as cutting down the overall energy consumption accordingly. The experiment result shows that under the environment of multi-task operation, from the perspective of energy cost optimization, the proposed scheduling strategy recorded an increase of 21.24% compared with the FIFO algorithm, and an increase of 16.77% in comparison to the EMRSA algorithm; while in light of network lifetimes, the EOTS algorithm surpassed the ECTA algorithm by a gain of 19.21%. Therefore, the effectiveness of the proposed EOTS algorithm is verified.</strong></span></p>

Two-Level Data Collection for an Energy-Efficient Solution in Wireless Sensor Networks

2016 ◽  
Vol 7 (4) ◽  
pp. 50-67 ◽  
Author(s):  
Besma Benaziz ◽  
Okba Kazar ◽  
Laid Kahloul ◽  
Ilham Kitouni ◽  
Samir Bourekkache
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Data Collection ◽  
Timed Automata ◽  
High Energy ◽  
Wireless Sensor ◽  
Data Collection Strategy ◽  
Level Data ◽  
Consumption Data ◽  
The Cost

Density in sensor networks often causes data redundancy, which is often the origin of high energy consumption. Data collection techniques are proposed to avoid retransmission of the same data by several sensors. In this paper, the authors propose a new data collection strategy based on static agents and clustering nodes in wireless sensor network (WSN) for an efficient energy consumption called: Two-Level Data Collection Strategy (TLDC). It consists in two-level hierarchy of nodes grouping. The technique is based on an experience building to perform a reorganization of the groups. Cooperation between agents can be used to reduce the communication cost significantly, by managing the data collection smartly. In order to validate the proposed scheme, the authors use the timed automata (TA) model and UPPAAL engine to validate the proposed strategy; the results after and before reorganization are compared. They establish that the proposed approach reduces the cost of communication in the group and thus minimizes the consumed energy.

scholarly journals Increasing network lifetime using data compression in wireless sensor networks with energy harvesting

2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668968 ◽  
Author(s):  
Sunyong Kim ◽  
Chiwoo Cho ◽  
Kyung-Joon Park ◽  
Hyuk Lim
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Data Compression ◽  
Network Lifetime ◽  
Average Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Entire Network

In wireless sensor networks powered by battery-limited energy harvesting, sensor nodes that have relatively more energy can help other sensor nodes reduce their energy consumption by compressing the sensing data packets in order to consequently extend the network lifetime. In this article, we consider a data compression technique that can shorten the data packet itself to reduce the energies consumed for packet transmission and reception and to eventually increase the entire network lifetime. First, we present an energy consumption model, in which the energy consumption at each sensor node is derived. We then propose a data compression algorithm that determines the compression level at each sensor node to decrease the total energy consumption depending on the average energy level of neighboring sensor nodes while maximizing the lifetime of multihop wireless sensor networks with energy harvesting. Numerical simulations show that the proposed algorithm achieves a reduced average energy consumption while extending the entire network lifetime.

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