Adaptive lossless compression in wireless body sensor networks

Abstract

In most wireless body sensor network (BSN) applications, the vast majority of the total energy is consumed by the wireless transmission of sensed data. Transmitting one bit using a typical wireless communication system can consume as much energy as 1000 cycles of an embedded processor. Reducing this transmission energy -even at the expense of increasing another component's energy -is essential to meeting the battery life and form factor (i.e. small battery) requirements of many BSN applications. While improved wireless communication and networking techniques can help do just that, simply compressing the sensed data to reduce the number of transmitted bits can provide significant savings. However, BSN platforms and applications impose many constraints on compression techniques, including fidelity (focus on lossless techniques, as required for many medical BSN applications), programmability (enable ease of code development and deployment), adaptability (achieve high compression ratio regardless of location, subject, activity, etc.), and implementability (require low processing and memory resources). This paper analyzes variations of two known real-time lossless compression algorithms, Huffman encoding and delta encoding, within the context of these BSN constraints. Experimental results on a multi-node accelerometer-based BSN show the strengths and weaknesses of each algorithm and ultimately reveal the superiority of dynamic delta encoding for BSNs, including an average 35% energy savings across a range of activities, sensor locations, and sensor axes.

A novel source coding technique for wireless sensor networks based on Benford's law

“…When no a priori knowledge of the signal source is available, and if off-line statistical tests are either unreliable or unfeasible, adaptive compression schemes might be employed [6]. The compression step is periodically preceded by a learning process, which adapts the parameters according to the varying signal characteristics.…”

“…Beside the accuracy of the received data, other desirable features for a compression algorithm are its adaptability to different location, parameters, or resolutions, and its implementation complexity, fulfilling the processing and memory constraints imposed by typical WSN platforms [6].…”

Using DWT Lifting Scheme for Lossless Data Compression in Wireless Body Sensor Networks

“…Energy consumption is one of the key considerations for WBSNs since it not only determines the size of the battery required but also the duration that a biosensor can be left in situ [1]. One of the main techniques to reduce the energy consumption of a biosensor node is by decreasing the energy consumption related to wireless transmission of the collected data since it is the likeliest cause of energy consumption [8] [9]. Data reduction can be considered as a direct way to reduce the energy consumption due to wireless transmission.…”

SPARTAN: A framework for Smart Phone Assisted Real-Time Health care Network design

“…However, a certain level energy in crease would be observed because of inappropriate trade-off between computation and communication [7]. Two families of data compression algorithms, Huffman encoding and delta encoding are evaluated in [6]. Dynamic delta encoding is chosen to adaptively change the size of delta bits, which represent the difference between the current reading and its predecessor.…”