An Adaptive Lossless Data Compression Scheme for Wireless Sensor Networks

Energy is an important consideration in the design and deployment of wireless sensor networks (WSNs) since sensor nodes are typically powered by batteries with limited capacity. Since the communication unit on a wireless sensor node is the major power consumer, data compression is one of possible techniques that can help reduce the amount of data exchanged between wireless sensor nodes resulting in power saving. However, wireless sensor networks possess significant limitations in communication, processing, storage, bandwidth, and power. Thus, any data compression scheme proposed for WSNs must be lightweight. In this paper, we present an adaptive lossless data compression (ALDC) algorithm for wireless sensor networks. Our proposed ALDC scheme performs compression losslessly using multiple code options. Adaptive compression schemes allow compression to dynamically adjust to a changing source. The data sequence to be compressed is partitioned into blocks, and the optimal compression scheme is applied for each block. Using various real-world sensor datasets we demonstrate the merits of our proposed compression algorithm in comparison with other recently proposed lossless compression algorithms for WSNs

Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

Compressing Inertial Motion Data in Wireless Sensing Systems – An Initial Experiment

The use of wireless inertial motion sensors, such as accelerometers, for supporting medical care and sport’s training, has been under investigation in recent years. As the number of sensors (or their sampling rates) increases, compressing data at source(s) (i.e. at the sensors), i.e. reducing the quantity of data that needs to be transmitted between the on-body sensors and the remote repository, would be essential especially in a bandwidth-limited wireless environment. This paper presents a set of compression experiment results on a set of inertial motion data collected during running exercises. As a starting point, we selected a set of common compression algorithms to experiment with. Our results show that, conventional lossy compression algorithms would achieve a desirable compression ratio with an acceptable time delay. The results also show that the quality of the decompressed data is within acceptable range

Antioxidants: nanotechnology and biotechnology fusion for medicine in overall

Antioxidant is a chemical substance that is naturally found in our food. It can prevent or reduce the oxidative stress of the physiological system. Due to the regular usage of oxygen, the body continuously produces free radicals. Excessive number of free radicals could cause cellular damage in the human body that could lead to various diseases like cancer, muscular degeneration and diabetes. The presence of antioxidants helps to counterattack the effect of these free radicals. The antioxidant can be found in abundance in plants and most of the time there are problems with the delivery. The solution is by using nanotechnology that has multitude potential for advanced medical science. Nano devices and nanoparticles have significant impact as they can interact with the subcellular level of the body with a high degree of specificity. Thus, the treatment can be in maximum efficacy with little side effect

Efficient Data Compression with Error Bound Guarantee in Wireless Sensor Networks

We present a data compression and dimensionality reduction scheme for data fusion and aggregation applications to prevent data congestion and reduce energy consumption at network connecting points such as cluster heads and gateways. Our in-network approach can be easily tuned to analyze the data temporal or spatial correlation using an unsupervised neural network scheme, namely the autoencoders. In particular, our algorithm extracts intrinsic data features from previously collected historical samples to transform the raw data into a low dimensional representation. Moreover, the proposed framework provides an error bound guarantee mechanism. We evaluate the proposed solution using real-world data sets and compare it with traditional methods for temporal and spatial data compression. The experimental validation reveals that our approach outperforms several existing wireless sensor network's data compression methods in terms of compression efficiency and signal reconstruction.Comment: ACM MSWiM 201

An efficient technique for lossless address data compression using adaptive SPIHT Algorithm in WSN

The computer is becoming more and more powerful day by day. Data compression is a popular approach to reducing data volumes and hence lowering disk I/O and network data transfer times. While several lossy data compression techniques have demonstrated excellent compression ratios, lossless data compression techniques are still among the most popular ones. Sensor networks represent a non-traditional source of information, as readings generated by sensors flow continuously, leading to an infinite stream of data. Sensors are non-reactive elements which are used to monitor real life phenomena, such as live weather conditions, network traffic, etc. They are usually organized into networks where their readings are transmitted using low level protocols