Lossless Compression Techniques in Wireless Sensor Networks: Monitoring Microacoustic Emissions

Alippi, Cesare; Camplani, Romolo; Galperti, C.

doi:10.1109/rose.2007.4373963

Cited by 16 publications

(9 citation statements)

References 5 publications

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“…The event to be looked for refers to local micro acoustic/seismic activity, a phenomenon associated with the hierarchical organization of fractures inside the rock [47] or concrete-based structures. Micro acoustic bursts are generated in correspondence with the fracture evolution and cover a bandwidth up to 10kHz [48]. An example of micro acoustic burst is presented in Figure 5.…”

Section: B Rock Collapse Forecastingmentioning

confidence: 99%

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Effective design of WSNs: From the lab to the real world

Alippi

Camplani

Galperti

et al. 2008

2008 3rd International Conference on Sensing Technology

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Abstract-Distributed environmental monitoring with wireless sensor networks (WSNs) is one of the most challenging research activities faced by the embedded system community in the last decade. Here, the need for pervasive, reliable and accurate monitoring systems has pushed the research towards the realization of credible deployments able to survive in harsh environments for long time. Design an effective WSN requires a good piece of engineer work, not to mention the research contribution needed to provide a credible deployment. As a matter of fact, to solve our application, we are looking for a monitoring framework scalable, adaptive with respect to topological changes in the network, power-aware in its middleware components and endowed with energy harvesting mechanisms to grant a long lifetime for the network. The paper addresses all main aspects related to the design of a WSN ranging from the -possible-need of an ad-hoc embedded system, to sensing, local and remote transmission, data storage and visualization; particular attention will be devoted to energy harvesting and management aspects at the unit and network level. Two applications, namely monitoring the marine environment and forecasting the collapse of rock faces in mountaineering areas will be the experimental leitmotiv of the presentation.

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Section: B Rock Collapse Forecastingmentioning

confidence: 99%

“…This required the use of a detection algorithm [48] able to identify bursts presence directly on the SPUs. To save energy, even the transmission between BC-WSN and GW is performed only to signal an event to the Control Room.…”

Section: B Rock Collapse Forecastingmentioning

confidence: 99%

Effective design of WSNs: From the lab to the real world

Alippi

Camplani

Galperti

et al. 2008

2008 3rd International Conference on Sensing Technology

Self Cite

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“…The binary representation of the array index containing the data is transmitted as suffix i.e., 11 corresponding to 3 which is the array index containing the data 0.3 is transmitted. So the code is transmitted is 111 [11].…”

Section: Binary Tree Constructionmentioning

confidence: 99%

“…Compression ratio is defined as compressed size to the original size [5] . The formula used for compression ratio analysis is given as follows: Compression ratio = 100(compressed size/original size)…”

Section: Compression Ratiomentioning

confidence: 99%

Power Efficient Adaptive Compression Technique for Wireless Sensor Networks

2013

IOSR-JVSP

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“…In [2] a distributed way of continuously exploiting existing correlations in sensor data based on adaptive signal processing and distributed source coding principles is discussed. In [6,8] hardware architecture for data compression using adaptive Huffman algorithm for data compression is proposed. In [3,4,7] also the spatial correlation in sensor data is exploited for the data compression.…”

Section: Introductionmentioning

confidence: 99%

Design of Modified Adaptive Huffman Data Compression Algorithm for Wireless Sensor Network

Tharini¹,

Ranjan²

2009

J. of Computer Science

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Problem statement: Efficient utilization of energy has been a core area of research in wireless sensor networks. Sensor nodes deployed in a network are battery operated. As batteries cannot be recharged frequently in the field setting, energy optimization becomes paramount in prolonging the battery-life and, consequently, the network lifetime. The communication module utilizes a major part of the energy expenditure of a sensor node. Hence data compression methods to reduce the number of bits to be transmitted by the communication module will significantly reduce the energy requirement and increase the lifetime of the sensor node. The present objective of the study contracted with the designing of efficient data compression algorithm, specifically suited to wireless sensor network. Approach: In this investigation, the natural correlation in a typical wireless sensor network data was exploited and a modified Huffman algorithm suited to wireless sensor network was designed. Results: The performance of the modified adaptive Huffman algorithm was analyzed and compared with the static and adaptive Huffman algorithm. The results indicated better compression ratio. Conclusion: Hence the proposed algorithm outperformed both static and adaptive Huffman algorithms, in terms of compression ratio and was well suited to embedding in sensor nodes for compressed data communication.

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Lossless Compression Techniques in Wireless Sensor Networks: Monitoring Microacoustic Emissions

Cited by 16 publications

References 5 publications

Effective design of WSNs: From the lab to the real world

Effective design of WSNs: From the lab to the real world

Power Efficient Adaptive Compression Technique for Wireless Sensor Networks

Design of Modified Adaptive Huffman Data Compression Algorithm for Wireless Sensor Network

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