Adaptive Linear Filtering Compression on Realtime Sensor Networks

Kiely, Aaron; Xu, Mingsen; Song, Wen-Zhan; Huang, Renjie; Shirazi, Behrooz

doi:10.1093/comjnl/bxp128

Cited by 12 publications

(11 citation statements)

References 27 publications

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“…The presented system design and deployment experience clears the doubts of domain scientists and proves that the low-cost sensor network system can work in extremely harsh environments. Our next plan is to integrate a localized TDMA MAC protocol [25] and light-weighted compression algorithm [16], and deploy a larger scale sensor network with multiple sinks and multiple channels for real-time volcano monitoring in summer/fall 2009.…”

Section: Conclusion and Future Plansmentioning

confidence: 99%

Air-dropped sensor network for real-time high-fidelity volcano monitoring

Song

Huang

et al. 2009

Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services

113

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This paper presents the design and deployment experience of an air-dropped wireless sensor network for volcano hazard monitoring. The deployment of five stations into the rugged crater of Mount St. Helens only took one hour with a helicopter. The stations communicate with each other through an amplified 802.15.4 radio and establish a self-forming and self-healing multi-hop wireless network. The distance between stations is up to 2 km. Each sensor station collects and delivers real-time continuous seismic, infrasonic, lightning, GPS raw data to a gateway. The main contribution of this paper is the design and evaluation of a robust sensor network to replace data loggers and provide real-time long-term volcano monitoring. The system supports UTCtime synchronized data acquisition with 1ms accuracy, and is online configurable. It has been tested in the lab environment, the outdoor campus and the volcano crater. Despite the heavy rain, snow, and ice as well as gusts exceeding 120 miles per hour, the sensor network has achieved a remarkable packet delivery ratio above 99% with an overall system uptime of about 93.8% over the 1.5 months evaluation period after deployment. Our initial deployment experiences with the system have alleviated the doubts of domain scientists and prove to them that a low-cost sensor network system can support real-time monitoring in extremely harsh environments.

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Section: Conclusion and Future Plansmentioning

confidence: 99%

Air-dropped sensor network for real-time high-fidelity volcano monitoring

Song

Huang

et al. 2009

Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services

113

View full text Add to dashboard Cite

show abstract

“…However, WSNs are generally deployed with the purpose of monitoring a particular phenomenon of interest [12]. Therefore, we show that, if the statistics of this phenomenon are known beforehand from general datasets, and if the data collected by the sensor nodes presents relatively low resolution, by employing simple Huffman encoding, it is possible to achieve compression ratios higher than those obtained by state-of-the-art algorithms such as those presented in [2–4]. More specifically, we show that by constructing a fixed Huffman dictionary to encode the differences between two consecutive samples from a large general dataset, the compression ratio obtained on test datasets of the same phenomenon at different locations and periods is very close to what would be achieved if a specific dictionary was constructed for each test dataset.…”

Section: Introductionmentioning

confidence: 93%

“…In this work, however, we consider approaches that attempt to achieve efficient lossless data compression by leveraging solely on the temporal correlation of the data collected by each sensor node and performing all the computations locally, without relying on information from other nodes. Two of the most recent and effective approaches in this category are Marcelloni and Vecchio's lossless entropy compression (LEC) [2, 3] and Kiely et al's adaptive linear filtering compression (ALFC) [4].…”

Section: Related Workmentioning

confidence: 99%

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Lightweight Data Compression in Wireless Sensor Networks Using Huffman Coding

Medeiros

Maciel

Souza

et al. 2014

International Journal of Distributed Sensor Networks

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This paper presents a lightweight data compression method for wireless sensor networks monitoring environmental parameters with low resolution sensors. Instead of attempting to devise novel ad hoc algorithms, we show that, given general knowledge of the parameters that must be monitored, it is possible to efficiently employ conventional Huffman coding to represent the same parameter when measured at different locations and time periods. When the data collected by the sensor nodes consists of integer measurements, the Huffman dictionary computed using statistics inferred from public datasets often approaches the entropy of the data. Results using temperature and relative humidity measurements show that even when the proposed method does not approach the theoretical limit, it outperforms popular compression mechanisms designed specifically for wireless sensor networks.

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“…Data compression is an important and efficient approach to maximize data return over the unreliable and low rate radio links [26][27][28]. We have developed a lightweight compression scheme for sensor networks, called Adaptive Linear Filtering Compression (ALFC) [12]. ALFC performs predictive compression, using adaptive linear filtering to predict sample values followed by entropy coding of prediction residuals, encoding a variable number of samples into fixed-length packets.…”

Section: Configurable Data Processing Tasksmentioning

confidence: 99%