Impact of data compression on energy consumption of wireless-networked handheld devices

2004 ◽  
Author(s):  
Rong Xu ◽  
Zhiyuan Li ◽  
Cheng Wang ◽  
Peifeng Ni
Keyword(s):  
Energy Consumption ◽  
Data Compression ◽  
Handheld Devices

Design and Implementation of Low Energy Wireless Network Nodes based on Hardware Compression Acceleration

2019 ◽  
Vol 12 ◽  
Author(s):  
Hui Yang ◽  
Anand Nayyar
Keyword(s):  
Energy Consumption ◽  
Data Compression ◽  
Energy Saving ◽  
Optimization Design ◽  
Hardware Acceleration ◽  
Transmission Efficiency ◽  
General Purpose ◽  
Storage Space ◽  
General Purpose Processor ◽  
Compression Time

: In the fast development of information, the information data is increasing in geometric multiples, and the speed of information transmission and storage space are required to be higher. In order to reduce the use of storage space and further improve the transmission efficiency of data, data need to be compressed. processing. In the process of data compression, it is very important to ensure the lossless nature of data, and lossless data compression algorithms appear. The gradual optimization design of the algorithm can often achieve the energy-saving optimization of data compression. Similarly, The effect of energy saving can also be obtained by improving the hardware structure of node. In this paper, a new structure is designed for sensor node, which adopts hardware acceleration, and the data compression module is separated from the node microprocessor.On the basis of the ASIC design of the algorithm, by introducing hardware acceleration, the energy consumption of the compressed data was successfully reduced, and the proportion of energy consumption and compression time saved by the general-purpose processor was as high as 98.4 % and 95.8 %, respectively. It greatly reduces the compression time and energy consumption.

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.

Networked Wireless Sensors, Active RFID, and Handheld Devices for Modern Car Park Management

2015 ◽  
Vol 6 (3) ◽  
pp. 33-45 ◽  
Author(s):  
Djamel Djenouri ◽  
ElMouatezbillah Karbab ◽  
Sahar Boulkaboul ◽  
Antoine Bagula
Keyword(s):  
Energy Consumption ◽  
Wireless Sensors ◽  
Handheld Computers ◽  
Management Systems ◽  
Handheld Devices ◽  
Park Management ◽  
Proof Of Concept ◽  
Active Rfid ◽  
Car Park ◽  
The Cost

Networked wireless sensors, actuators, RFID, and mobile computing technologies are explored in this paper on the quest for modern car park management systems with sophisticated services over the emerging internet of things (IoT), where things such as ubiquitous handheld computers, smart ubiquitous sensors, RFID readers and tags are expected to be interconnected to virtually form networks that enable a variety of services. After an overview of the literature, the authors propose a scalable and lowcost car parking framework (CPF) based on the integration of aforementioned technologies. A preliminary prototype implementation has been performed, as well as experimentation of some modules of the proposed CPF. The results demonstrate proof of concept, and particularly reveal that the proposed approach for WSN deployment considerably reduces the cost and energy consumption compared to existing solutions.

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 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.

Networked Wireless Sensors, Active RFID, and Handheld Devices for Modern Car Park Management

2020 ◽  
pp. 1012-1024
Author(s):  
Djamel Djenouri ◽  
ElMouatezbillah Karbab ◽  
Sahar Boulkaboul ◽  
Antoine Bagula
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Wireless Sensors ◽  
Handheld Computers ◽  
Handheld Devices ◽  
Park Management ◽  
Prototype Implementation ◽  
Active Rfid ◽  
Car Park ◽  
The Cost

Networked wireless sensors, actuators, RFID, and mobile computing technologies are explored in this paper on the quest for modern car park management systems with sophisticated services over the emerging internet of things (IoT), where things such as ubiquitous handheld computers, smart ubiquitous sensors, RFID readers and tags are expected to be interconnected to virtually form networks that enable a variety of services. After an overview of the literature, the authors propose a scalable and lowcost car parking framework (CPF) based on the integration of aforementioned technologies. A preliminary prototype implementation has been performed, as well as experimentation of some modules of the proposed CPF. The results demonstrate proof of concept, and particularly reveal that the proposed approach for WSN deployment considerably reduces the cost and energy consumption compared to existing solutions.

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