On the Optimal Calculation of the Rice Coding Parameter

Solano, Fernando

doi:10.3390/a13080181

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…Thanks to this driver and the CoAP [41] and 6LoWPAN [42] libraries of RIOT-OS, the three Micromole IoT devices and its gateway establish an autonomous 6LoWPAN Wireless Sensor and Actuator Network (WSAN). In order to reduce energy consumption, sensor measurements are compressed using the algorithm described in [43].…”

Section: Sensors Telecommunications and Data Processing Modulementioning

confidence: 99%

An Internet-of-Things Enabled Smart System for Wastewater Monitoring

2022

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We present and evaluate an IoT-enabled sensing and actuating system for localizing illegal industrial harsh discharges of polluting wastewater in sewer networks. The special conditions of the sewer environment bring special challenges for the design of an IoT system and of its real-time algorithm for anomaly detection and localization in wastewater networks. The proposed design fulfills these requirements by using a new IoT architecture pattern, which we generalize and name Hop-by-hop Anomaly Detection and Actuation (HADA). The distributed anomaly detection and localization algorithm makes predictions over previous sensor measurements, while taking into account seasonality effects of wastewater and noise of the sensors. Based on simulations in a large network with three common illegal industrial wastewater pollutants, the advantages and limitations of the proposed wastewater anomaly localization system are discussed. The IoT system, including its anomaly detection and localization algorithm, was implemented using in a low-power microcontroller and tested in flowing wastewater with different harsh industrial waste.

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Section: Sensors Telecommunications and Data Processing Modulementioning

confidence: 99%

An Internet-of-Things Enabled Smart System for Wastewater Monitoring

2022

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“…By investigating the best log-likelihood distribution of the residual, we found that the two most likely estimators are a negative binomial and geometric distribution. The likelihoods of both distributions are similar; thus, we consider the rice coding [24] a suitable entropy encoder for the residuals. See Eq.…”

Section: A Lossless Compression For Pressure Datamentioning

confidence: 99%

Aerosense: A Self-Sustainable And Long-Range Bluetooth Wireless Sensor Node for Aerodynamic and Aeroacoustic Monitoring on Wind Turbines

Polonelli¹,

Müller²,

Kong³

et al. 2022

Preprint

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This paper presents a low-power, self-sustainable, and modular wireless sensor node for aerodynamic and acoustic measurements on wind turbines and other industrial structures. It includes 40 high-accuracy barometers, 10 microphones, 5 differential pressure sensors, and implements a lossy and a lossless on-board data compression algorithm to decrease the transmission energy cost. The wireless transmitter is based on Bluetooth Low Energy 5.1 tuned for long-range and high throughput while maintaining adequate per-bit energy efficiency (80 nJ). Moreover, we field-assessed the node capability to collect precise and accurate aerodynamic data. Outdoor experimental tests revealed that the system can acquire and sustain a data rate of 850 kbps over 438 m. The power consumption while collecting and streaming all measured data is 120 mW, enabling selfsustainability and long-term in-situ monitoring with a 111 cm 2 photovoltaic panel.

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“…Encoding a number x works by computing

so that

, with q being the integer part and p the remainder, and then by coding q with a unary coding (i.e., q bits set to 1 followed by a single bit set to 0) and the remainder p of the division using r bits in standard binary. Of course, storing large q is way less efficient than storing large p , so the efficiency of the compression strongly depends on the parameter r which sets the trade-off between the magnitudes of q and p , and that can be optimally computed based on the statistics of the data [ 26 ]. To partially overcome the inefficiency of storing large q , the exponential-Golomb methods uses a variable length binary encoding method, whereas

is simply written down in binary using the minimum possible number of bits, i.e.,

, and the actual number of bits used (minus 1) is indicated by preceding it with the same number of 0 bits.…”

Section: Related Workmentioning

confidence: 99%

Energy and Performance Analysis of Lossless Compression Algorithms for Wireless EMG Sensors

Biagetti

Crippa

Falaschetti

et al. 2021

Sensors

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Electromyography (EMG) sensors produce a stream of data at rates that can easily saturate a low-energy wireless link such as Bluetooth Low Energy (BLE), especially if more than a few EMG channels are being transmitted simultaneously. Compressing data can thus be seen as a nice feature that could allow both longer battery life and more simultaneous channels at the same time. A lot of research has been done in lossy compression algorithms for EMG data, but being lossy, artifacts are inevitably introduced in the signal. Some artifacts can usually be tolerable for current applications. Nevertheless, for some research purposes and to enable future research on the collected data, that might need to exploit various and currently unforseen features that had been discarded by lossy algorithms, lossless compression of data may be very important, as it guarantees no extra artifacts are introduced on the digitized signal. The present paper aims at demonstrating the effectiveness of such approaches, investigating the performance of several algorithms and their implementation on a real EMG BLE wireless sensor node. It is demonstrated that the required bandwidth can be more than halved, even reduced to 1/4 on an average case, and if the complexity of the compressor is kept low, it also ensures significant power savings.

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On the Optimal Calculation of the Rice Coding Parameter

Cited by 5 publications

References 28 publications

An Internet-of-Things Enabled Smart System for Wastewater Monitoring

An Internet-of-Things Enabled Smart System for Wastewater Monitoring

Aerosense: A Self-Sustainable And Long-Range Bluetooth Wireless Sensor Node for Aerodynamic and Aeroacoustic Monitoring on Wind Turbines

Energy and Performance Analysis of Lossless Compression Algorithms for Wireless EMG Sensors

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