A new scheme for evaluating energy efficiency of data compression in wireless sensor networks

Liu, Shaoqiang; Liu, Yanfang; Chen, Xiang; Fan, Xiaoping

doi:10.1177/1550147718776926

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…Based on the observation that large data packets sent at frequent intervals shorten the battery life of the sensors, data compression has been evaluated in the literature to reduce energy use [ 29 , 30 ]. In [ 31 ], the amount of energy lost during data transmission is reduced by decreasing the amount of data accepted or transmitted with the proposed compression algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

A Mathematical Programming Approach for IoT-Enabled, Energy-Efficient Heterogeneous Wireless Sensor Network Design and Implementation

Taparci,

Olcay,

Akmandor

et al. 2024

Sensors

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The Internet of Things (IoT) is playing a pivotal role in transforming various industries, and Wireless Sensor Networks (WSNs) are emerging as the key drivers of this innovation. This research explores the utilization of a heterogeneous network model to optimize the deployment of sensors in agricultural settings. The primary objective is to strategically position sensor nodes for efficient energy consumption, prolonged network lifetime, and dependable data transmission. The proposed strategy incorporates an offline model for placing sensor nodes within the target region, taking into account the coverage requirements and network connectivity. We propose a two-stage centralized control model that ensures cohesive decision making, grouping sensor nodes into protective boxes. This grouping facilitates shared resource utilization, including batteries and bandwidth, while minimizing box number for cost-effectiveness. Noteworthy contributions of this research encompass addressing connectivity and coverage challenges through an offline deployment model in the first stage, and resolving real-time adaptability concerns using an online energy optimization model in the second stage. Emphasis is placed on the energy efficiency, achieved through the sensor consolidation within boxes, minimizing data transmission hops, and considering energy expenditures in sensing, transmitting, and active/sleep modes. Our simulations on an agricultural farmland highlights its practicality, particularly focusing on the sensor placement for measuring soil temperature and humidity. Hardware tests validate the proposed model, incorporating parameters from the real-world implementation to enhance calculation accuracy. This study provides not only theoretical insights but also extends its relevance to smart farming practices, illustrating the potential of WSNs in revolutionizing sustainable agriculture.

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Section: Literature Reviewmentioning

confidence: 99%

A Mathematical Programming Approach for IoT-Enabled, Energy-Efficient Heterogeneous Wireless Sensor Network Design and Implementation

Taparci,

Olcay,

Akmandor

et al. 2024

Sensors

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“…Moreover, according to the energy saved by compressing data and the energy consumption for running data compression algorithms in edge devices, the net energy income obtained by running an algorithm to compress given data is further examined. Referring to the analysis in [45], we have (10) where E net is the net energy income obtained by running a data compression algorithm, which is the difference between E tx uncmp and E total cmp , E tx uncmp is the energy consumption for transmitting the raw data, E total cmp is the total energy consumption for compressing data E alg cmp and transmitting the compressed data E tx cmp , and CR is the compression rate as described in Equation (4). In the future, we plan to investigate the necessary condition of energy saving, considering the tradeoff among the compression rate CR, the energy consumption for communication and algorithm execution, such that energy conservation can be achieved.…”

Section: Energy Consumptionmentioning

confidence: 99%

Design of Edge-IoMT Network Architecture with Weight-Based Scheduling

Tseng,

Chen,

Wen

2023

Sensors

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Population health monitoring based on the Internet of Medical Things (IoMT) is becoming an important application trend healthcare improvement. This work aims to develop an autonomous network architecture, collecting sensor data with a cluster topology, forwarding information through relay nodes, and applying edge computing and transmission scheduling for network scalability and operational efficiency. The proposed distributed network architecture incorporates data compression technologies and effective scheduling algorithms for handling the transmission scheduling of various physiological signals. Compared to existing scheduling mechanisms, the experimental results depict the network performance and show that in analyzing the delay and jitter, the proposed WFQ-based algorithms have reduced the delay and jitter ratio by about 40% and 19.47% compared to LLQ with priority queueing scheme, respectively. The experimental results also demonstrate that the proposed network topology is more effective than the direct path transmission approach in terms of energy consumption, which suggests that the proposed network architecture may improve the development of medical applications with body area networks such that the goal of self-organizing population health monitoring can be achieved.

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“…For wireless communications and sensor networks, the most studied lossless data compression algorithms have been the Huffman and Lempel-Ziv Welch (LZW) algorithms [3][4][5][6]. In the present study, it has been implemented the Huffman algorithm for the compression of the spectrum of underwater noise data.…”

Section: Lossless Compressed Spectrummentioning

confidence: 99%

Compression Techniques of Underwater Acoustic Signals for Real-Time Underwater Noise Monitoring

Felis

Martı́nez

Ruíz

et al. 2019

The 6th International Electronic Conference on Sensors and Applications

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Monitoring of the marine environment results in large amounts of data that must be processed and transmitted effectively for efficient resource management. In particular, given its high sampling rate, underwater noise signal acquisition technologies deserve special attention. In this article, a comparative study of the efficiency of different information processing and compression techniques is carried out, depending on the characteristics that want to be transmitted from the original signal. The applications and experiments carried out are focused on responding to the Marine Strategies, a marine environment planning instrument created under Marine Strategy Framework Directive 2008/56/EC, of June 17, 2008 (MSFD), and more specifically to the Descriptor 1 that regards the noise levels (both continuous and impulsive), as well as part of the Descriptor 11, focused on the detection and abundance of cetaceans.

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A new scheme for evaluating energy efficiency of data compression in wireless sensor networks

Cited by 5 publications

References 24 publications

A Mathematical Programming Approach for IoT-Enabled, Energy-Efficient Heterogeneous Wireless Sensor Network Design and Implementation

A Mathematical Programming Approach for IoT-Enabled, Energy-Efficient Heterogeneous Wireless Sensor Network Design and Implementation

Design of Edge-IoMT Network Architecture with Weight-Based Scheduling

Compression Techniques of Underwater Acoustic Signals for Real-Time Underwater Noise Monitoring

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