TiNA

Sharaf, Mohamed A.; Beaver, Jonathan; Labrinidis, Alexandros; Chrysanthis, Panos K.

doi:10.1145/940923.940937

Cited by 156 publications

(2 citation statements)

References 12 publications

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“…Hong Luo et al [ 27 ] proposed the MFST (minimum fusion Steiner tree) algorithm for energy-efficient data collection in data fusion mode in WSNs. Based on the spatiotemporal correlation, the TiNA model (temporal coherency aware in network aggregation) was proposed [ 28 ]. Its basic idea is that the node sends the data only when the difference between the currently collected data and the last collected data is greater than the tolerance limit specified by a certain user.…”

Section: Related Workmentioning

confidence: 99%

Research on Energy Harvesting Mechanism and Low Power Technology in Wireless Sensor Networks

Chen,

Tang,

Cui

et al. 2023

Sensors

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Wireless sensor networks (WSNs) are widely used in various fields such as military, industrial, and transportation for real-time monitoring, sensing, and data collection of different environments or objects. However, the development of WSNs is hindered by several limitations, including energy, storage space, computing power, and data transmission rate. Among these, the availability of power energy plays a crucial role as it directly determines the lifespan of WSN. To extend the life cycle of WSN, two key approaches are power supply improvement and energy conservation. Therefore, we propose an energy harvesting system and a low-energy-consumption mechanism for WSNs. Firstly, we delved into the energy harvesting technology of WSNs, explored the utilization of solar energy and mechanical vibration energy to ensure a continuous and dependable power supply to the sensor nodes, and analyzed the voltage output characteristics of bistable piezoelectric cantilever. Secondly, we proposed a neighbor discovery mechanism that utilizes a separation beacon, is based on reply to ACK, and can facilitate the identification of neighboring nodes. This mechanism operates at a certain duty cycle ratio, significantly reduces idle listening time and results in substantial energy savings. In comparison to the Disco and U-connect protocols, our proposed mechanism achieved a remarkable reduction of 66.67% and 75% in the worst discovery delay, respectively. Furthermore, we introduced a data fusion mechanism based on integer wavelet transform. This mechanism effectively eliminates data redundancy caused by spatiotemporal correlation, resulting in a data compression rate of 5.42. Additionally, it significantly reduces energy consumption associated with data transmission by the nodes.

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Section: Related Workmentioning

confidence: 99%

Research on Energy Harvesting Mechanism and Low Power Technology in Wireless Sensor Networks

Chen,

Tang,

Cui

et al. 2023

Sensors

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“…Notable WSN application examples include environmental monitoring [Szewczyk et al 2004], geology [Werner-Allen et al 2006], structural monitoring [Xu et al 2004], and smart grid and household energy metering [Kappler and Riegel 2004;Benzi et al 2011]. These applications often require the collection and the subsequent analysis of large amounts of data, which are to be sent through suitable routing protocols to some data collection point(s).…”

Section: Introductionmentioning

confidence: 99%

On the Performance of Lossy Compression Schemes for Energy Constrained Sensor Networking

Zordan

Martínez

Vilajosana

et al. 2014

ACM Trans. Sen. Netw.

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Lossy temporal compression is key for energy-constrained wireless sensor networks (WSNs), where the imperfect reconstruction of the signal is often acceptable at the data collector, subject to some maximum error tolerance. In this article, we evaluate a number of selected lossy compression methods from the literature and extensively analyze their performance in terms of compression efficiency, computational complexity, and energy consumption. Specifically, we first carry out a performance evaluation of existing and new compression schemes, considering linear, autoregressive, FFT-/DCT- and wavelet-based models, by looking at their performance as a function of relevant signal statistics. Second, we obtain formulas through numerical fittings to gauge their overall energy consumption and signal representation accuracy. Third, we evaluate the benefits that lossy compression methods bring about in interference-limited multihop networks, where the channel access is a source of inefficiency due to collisions and transmission scheduling. Our results reveal that the DCT-based schemes are the best option in terms of compression efficiency but are inefficient in terms of energy consumption. Instead, linear methods lead to substantial savings in terms of energy expenditure by, at the same time, leading to satisfactory compression ratios, reduced network delay, and increased reliability performance

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