scholarly journals Minimizing Energy Consumption in Large-Scale Sensor Networks Through Distributed Data Compression and Hierarchical Aggregation

2004 ◽  
Vol 22 (6) ◽  
pp. 1130-1140 ◽  
Author(s):  
S.J. Baek ◽  
G. deVeciana ◽  
X. Su
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Data Compression ◽  
Large Scale ◽  
Distributed Data ◽  
Hierarchical Aggregation

scholarly journals Energy management in sensor networks

2012 ◽  
Vol 370 (1958) ◽  
pp. 52-67 ◽  
Author(s):  
John A. Stankovic ◽  
Tian He
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Management ◽  
Large Scale ◽  
Energy Savings ◽  
Time Synchronization ◽  
Media Access Control ◽  
Media Access ◽  
Energy Aware ◽  
Holistic View

This paper presents a holistic view of energy management in sensor networks. We first discuss hardware designs that support the life cycle of energy, namely: (i) energy harvesting, (ii) energy storage and (iii) energy consumption and control. Then, we discuss individual software designs that manage energy consumption in sensor networks. These energy-aware designs include media access control, routing, localization and time-synchronization. At the end of this paper, we present a case study of the VigilNet system to explain how to integrate various types of energy management techniques to achieve collaborative energy savings in a large-scale deployed military surveillance system.

A Lossless Distributed Data Compression and Aggregation Approach for Low Resources Wireless Sensors Networks

2021 ◽  
Author(s):  
Elie TAGNE FUTE ◽  
Hugues Marie KAMDJOU ◽  
Adnen EL AMRAOUI ◽  
Armand NZEUKOU
Keyword(s):  
Energy Consumption ◽  
Data Compression ◽  
Signal To Noise Ratio ◽  
Communication Process ◽  
Experimental Simulation ◽  
Small Data ◽  
Distributed Data ◽  
Aggregated Data ◽  
Wireless Medium ◽  
Event Based

Abstract Wireless Sensor Networks (WSN) have been as useful and beneficial as resource-constrained distributed event-based system for several scenarios.Yet, in WSN, optimization oflimited resources (energy, computing memory, bandwidth and storage) during data collection and communication process is a major challenge. Most of energy consumption (as much as 80%) for standard WSN applications lies in the radio module where receiving and sending packets are necessary to communicate between stations.This paper proposes an approach to achieve optimal sensor resources by data compression and aggregation regarding integrity of raw data.Data aggregation discarded a certain sensing data packet, which leads to low data-rate communication and low likelihood of packet collisions on the wireless medium. Data compression reduces a redundancy in aggregated data, which leads to save storage and sending only one small data stream in the bandwidthof communication.The performance of the proposed approach is qualified using experimental simulation on OMNeT++/Castalia. Theperformance metricswere evaluated in terms of Compression Ratio (CR), data Aggregation Rate (AR), Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) and Energy Consumption (EC).The obtained resultshave significantly increased the network lifetime.Moreover, the integrity (quality) of the raw data is guaranteed.

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.

scholarly journals Designing Constrained Trajectory Based on Maximizing Energy Reduction in Large-Scale Wireless Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jia Xu ◽  
Chuan Ping Wang ◽  
Hua Dai ◽  
Da Qiang Zhang ◽  
Jing Jie Yu
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Collection ◽  
Large Scale ◽  
Mobile Sink ◽  
Energy Reduction ◽  
Wireless Sensor ◽  
Movement Trajectory ◽  
Computation Efficiency

TheMobile Sinkbased data collection in wireless sensor network can reduce energy consumption efficiently and has been a new data collection paradigm. In this paper, we focus on exploring polynomial algorithm to compute the constrained trajectory of theMobile Sinkfor data collection. We first present a universal system model for designing constrained trajectory in large-scale wireless sensor networks and formulate the problem as theMaximizing Energy Reduction for Constrained Trajectory(MERC) problem. We show that the MERC problem is NP-hard and design an approximation algorithm (CTMER), which follows the greedy approach to design the movement trajectory of theMobile Sinkby maximizing theeffective average energy reduction. Through both rigid theoretical analysis and extensive simulations, we demonstrate that our algorithm achieves high computation efficiency and is superior to otherMobile Sinkbased data collection methods in aspects of energy consumption and network lifetime.

scholarly journals Data Compression Based on Stacked RBM-AE Model for Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4273 ◽  
Author(s):  
Jianlin Liu ◽  
Fenxiong Chen ◽  
Dianhong Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Compression ◽  
Compression Ratio ◽  
Sensor Nodes ◽  
Learning Ability ◽  
Wireless Sensor ◽  
Compression Efficiency ◽  
Sensing Data

Data compression is very important in wireless sensor networks (WSNs) with the limited energy of sensor nodes. Data communication results in energy consumption most of the time; the lifetime of sensor nodes is usually prolonged by reducing data transmission and reception. In this paper, we propose a new Stacked RBM Auto-Encoder (Stacked RBM-AE) model to compress sensing data, which is composed of a encode layer and a decode layer. In the encode layer, the sensing data is compressed; and in the decode layer, the sensing data is reconstructed. The encode layer and the decode layer are composed of four standard Restricted Boltzmann Machines (RBMs). We also provide an energy optimization method that can further reduce the energy consumption of the model storage and calculation by pruning the parameters of the model. We test the performance of the model by using the environment data collected by Intel Lab. When the compression ratio of the model is 10, the average Percentage RMS Difference value is 10.04%, and the average temperature reconstruction error value is 0.2815 °C. The node communication energy consumption in WSNs can be reduced by 90%. Compared with the traditional method, the proposed model has better compression efficiency and reconstruction accuracy under the same compression ratio. Our experiment results show that the new neural network model can not only apply to data compression for WSNs, but also have high compression efficiency and good transfer learning ability.

An Energy-Efficient Data Dissemination Scheme in Large-Scale Wireless Sensor Networks

2013 ◽  
Vol 5 (3) ◽  
pp. 34-54
Author(s):  
Shiow-Fen Hwang ◽  
Han-Huei Lin ◽  
Chyi-Ren Dow
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Storage ◽  
Energy Efficient ◽  
Large Scale ◽  
Data Dissemination ◽  
Wireless Sensor ◽  
Event Data ◽  
Efficient Data

In wireless sensor networks, due to limited energy, how to disseminate the event data in an energy-efficient way to allow sinks quickly querying and receiving the needed event data is a practical and important issue. Many studies about data dissemination have been proposed. However, most of them are not energy-efficient, especially in large-scale networks. Hence, in this paper the authors proposed an energy-efficient data dissemination scheme in large-scale wireless sensor networks. First, the authors design a data storage method which disseminates only a few amount event data by dividing the network into regions and levels, and thus reducing the energy consumption. Then, the authors develop an efficient sink query forwarding strategy by probability analysis so that a sink can query events easily according to its location to reduce the delay time of querying event data, as well as energy consumption. In addition, a simple and efficient maintenance mechanism is also provided. The simulation results show that the proposed scheme outperforms TTDD and LBDD in terms of the energy consumption and control overhead.

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