scholarly journals An Adaptive Lossless Data Compression Scheme for Wireless Sensor Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Jonathan Gana Kolo ◽  
S. Anandan Shanmugam ◽  
David Wee Gin Lim ◽  
Li-Minn Ang ◽  
Kah Phooi Seng
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Compression ◽  
Power Saving ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Node ◽  
Compression Scheme ◽  
Major Power ◽  
Lossless Data Compression

Energy is an important consideration in the design and deployment of wireless sensor networks (WSNs) since sensor nodes are typically powered by batteries with limited capacity. Since the communication unit on a wireless sensor node is the major power consumer, data compression is one of possible techniques that can help reduce the amount of data exchanged between wireless sensor nodes resulting in power saving. However, wireless sensor networks possess significant limitations in communication, processing, storage, bandwidth, and power. Thus, any data compression scheme proposed for WSNs must be lightweight. In this paper, we present an adaptive lossless data compression (ALDC) algorithm for wireless sensor networks. Our proposed ALDC scheme performs compression losslessly using multiple code options. Adaptive compression schemes allow compression to dynamically adjust to a changing source. The data sequence to be compressed is partitioned into blocks, and the optimal compression scheme is applied for each block. Using various real-world sensor datasets we demonstrate the merits of our proposed compression algorithm in comparison with other recently proposed lossless compression algorithms for WSNs.

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.

scholarly journals Concurrent Data Message Transmissions for Interference of Illegal Overhearing in Wireless Sensor Networks

2018 ◽  
Vol 210 ◽  
pp. 03011
Author(s):  
Masahiro Okuri ◽  
Hiroaki Higaki
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Node ◽  
Wireless Transmission ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Transmission Range ◽  
Wireless Sensor Node ◽  
Wireless Sensor Nodes ◽  
Wireless Nodes

In wireless sensor networks, data messages containing sensor data achieved by a sensor module in a wireless sensor node is transmitted to a stationary wireless sink node along a wireless multihop transmission route in which wireless sensor nodes themselves forward the data messages. Each intermediate wireless sensor node broadcast data messages in its wireless transmission range to forward them to its next-hop intermediate wireless sensor node. Hence, eavesdropper wireless nodes within the wireless transmission range easily overhear the data messages. In order to interfere with the eavesdropper wireless nodes illegally overhearing the data messages in transmission, wireless sensor nodes whose wireless transmission ranges overlap and their next-hop intermediate wireless sensor nodes are out of the wireless transmission ranges each other forward data messages in transmission concurrently and cause collisions between these two data messages at any possible eavesdropper wireless nodes intentionally. To enhance regions where concurrently forwarded data messages intentionally collide to prevent their overhearing and to realize concurrent forwarding of data messages, this paper designes an algorithm for TDMA transmission slot assignments for more opportunities to interfere the eavesdropper wireless nodes.

scholarly journals Energy Efficiency Analysis of LoRa and ZigBee Protocols in Wireless Sensor Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 2836-2849
Author(s):  
K. Raghava Rao ◽  
D. Sateesh Kumar ◽  
Mohiddin Shaw ◽  
V. Sitamahalakshmi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Power Consumption ◽  
Low Power ◽  
Sensor Node ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Node ◽  
Wireless Sensor Nodes ◽  
Iot Applications

Now a days IoT technologies are emerging technology with wide range of applications. Wireless sensor networks (WSNs) are plays vital role in IoT technologies. Construction of wireless sensor node with low-power radio link and high-speed processors is an interesting contribution for wireless sensor networks and IoT applications. Most of WSNs are furnished with battery source that has limited lifetime. The maximum operations of these networks require more power utility. Nevertheless, improving network efficiency and lifetime is a curtail issue in WSNs. Designing a low powered wireless sensor networks is a major challenges in recent years, it is essential to model its efficiency and power consumption for different applications. This paper describes power consumption model based on LoRa and Zigbee protocols, allows wireless sensor nodes to monitor and measure power consumption in a cyclic sleeping scenario. Experiential results reveals that the designed LoRa wireless sensor nodes have the potential for real-world IoT application with due consideration of communicating distance, data packets, transmitting speed, and consumes low power as compared with Zigbee sensor nodes. The measured sleep intervals achieved lower power consumption in LoRa as compared with Zigbee. The uniqueness of this research work lies in the review of wireless sensor node optimization and power consumption of these two wireless sensor networks for IoT applications.

scholarly journals Performance Analysis of Data Compression Algorithms for Energy Efficient Wireless Sensor Networks

2019 ◽  
Vol 9 (1S) ◽  
pp. 404-408
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Compression ◽  
Energy Saving ◽  
Life Time ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Piecewise Linear Approximation ◽  
Compression Algorithms

Wireless Sensor Networks (WSN), is an intensive area of research which is often used for monitoring, sensing and tracking various environmental conditions. It consists of a number of sensor nodes that are powered with fixed low powered batteries. These batteries cannot be changed often as most of the WSN will be in remote areas. Life time of WSN mainly depends on the energy consumed by the sensor nodes. In order to prolong the networks life time, the energy consumption has to be reduced. Different energy saving schemes has been proposed over the years. Data compression is one among the proposed schemes that can scale down the amount of data transferred between nodes and results in energy saving. In this paper, an attempt is made to analyze the performances of three different data compression algorithms viz. Light Weight Temporal Compression (LTC), Piecewise Linear Approximation with Minimum Number of Line Segments (PLAMLIS) and Univariate Least Absolute Selection and Shrinkage Operator (ULASSO). These algorithms are tested on standard univariate datasets and evaluated using assessment metrics like Mean Square Error (MSE), compression ratio and energy consumption. The results show that the ULASSO algorithm outperforms other algorithms in all three metrics and contributes more towards energy consumption

scholarly journals Raspberry Pi Based Intelligent Wireless Sensor Node for Localized Torrential Rain Monitoring

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Zhaozhuo Xu ◽  
Fangling Pu ◽  
Xin Fang ◽  
Jing Fu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Node ◽  
Heavy Rain ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Raspberry Pi ◽  
Support Vector ◽  
Wireless Sensor Node ◽  
Torrential Rain

Wireless sensor networks are proved to be effective in long-time localized torrential rain monitoring. However, the existing widely used architecture of wireless sensor networks for rain monitoring relies on network transportation and back-end calculation, which causes delay in response to heavy rain in localized areas. Our work improves the architecture by applying logistic regression and support vector machine classification to an intelligent wireless sensor node which is created by Raspberry Pi. The sensor nodes in front-end not only obtain data from sensors, but also can analyze the probabilities of upcoming heavy rain independently and give early warnings to local clients in time. When the sensor nodes send the probability to back-end server, the burdens of network transport are released. We demonstrate by simulation results that our sensor system architecture has potentiality to increase the local response to heavy rain. The monitoring capacity is also raised.

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