scholarly journals LoRa-Based Sensor Node Energy Consumption with Data Compression

2021 ◽  
Author(s):  
Olli Vaananen ◽  
Timo Hamalainen
Keyword(s):  
Energy Consumption ◽  
Data Compression ◽  
Sensor Node ◽  
Node Energy

Energy-Saving Technologies of WSN

2012 ◽  
Vol 605-607 ◽  
pp. 566-569
Author(s):  
Rong Mao Zheng
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Power Supply ◽  
Sensor Node ◽  
Large Scale ◽  
Working Life ◽  
Environmental Data ◽  
Wireless Sensor ◽  
Wireless Sensor Node ◽  
Node Energy

In order to layout convenient the wireless sensor node generally used battery for power supply, the node require working up to several months or even years but battery replacement was difficult or impossible. In this paper, research does not affect the function of WSN how to save the node energy consumption, which can work more time in large-scale collection, processing and communication of complex environmental data. Results show that the energy-saving technologies can be to reduce the energy consumption of 55.6%, which can greatly extend the working life of the wireless sensor node battery.

Energy-Saving Technologies of WSN in Water Quality Monitoring

2012 ◽  
Vol 241-244 ◽  
pp. 876-880
Author(s):  
Rong Mao Zheng
Keyword(s):  
Water Quality ◽  
Energy Consumption ◽  
Energy Saving ◽  
Sensor Node ◽  
Large Scale ◽  
Water Quality Monitoring ◽  
Environmental Data ◽  
Wireless Sensor ◽  
Wireless Sensor Node ◽  
Node Energy

The quality of overall national surface water has been slightly polluted, large number of cheap and convenient wireless sensor were used by environmental protection departments to 24 hours water quality monitor, in order to layout convenient the wireless sensor node generally used battery for power supply, the node require working up to several months or even years but battery replacement was difficult or impossible. In this paper, research does not affect the function of WSN how to save the node energy consumption, which can work more time in large-scale collection, processing and communication of complex environmental data. Results show that the node energy-saving technologies and network integrated energy-saving technologies can be to reduce the energy consumption of nodes 31.8% and 55.6%, which can greatly extend the working life of the wireless sensor node battery.

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.

scholarly journals Maximization of Energy Efficiency in Wireless ad hoc and Sensor Networks With SERENA

2009 ◽  
Vol 5 (1) ◽  
pp. 33-52 ◽  
Author(s):  
Saoucene Mahfoudh ◽  
Pascale Minet
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Ad Hoc ◽  
Idle State ◽  
Time Simulation ◽  
Simulation Results ◽  
Remaining Time ◽  
Slot Reuse ◽  
Node Energy

In wireless ad hoc and sensor networks, an analysis of the node energy consumption distribution shows that the largest part is due to the time spent in the idle state. This result is at the origin of SERENA, an algorithm to SchEdule RoutEr Nodes Activity. SERENA allows router nodes to sleep, while ensuring end-to-end communication in the wireless network. It is a localized and decentralized algorithm assigning time slots to nodes. Any node stays awake only during its slot and the slots assigned to its neighbors, it sleeps the remaining time. Simulation results show that SERENA enables us to maximize network lifetime while increasing the number of user messages delivered. SERENA is based on a two-hop coloring algorithm, whose complexity in terms of colors and rounds is evaluated. We then quantify the slot reuse. Finally, we show how SERENA improves the node energy consumption distribution and maximizes the energy efficiency of wireless ad hoc and sensor networks. We compare SERENA with classical TDMA and optimized variants such as USAP in wireless ad hoc and sensor networks.

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.

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.

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