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

LOW BITRATE HYBRID SECURED IMAGE COMPRESSION FOR WIRELESS IMAGE SENSOR NETWORK

Wireless image sensor networks are capable of sensing, processing and transmitting the visual data along with the scalar data and have attainedwide attention in sensitive applications such as visual surveillance, habitat monitoring, and ubiquitous computing. The sensor nodes in the network are resource constrained in nature. Since the image data are huge always high computational cost and energy budget are levied on the sensor nodes. The compression standards JPEG and JPEG 2000 are not feasible as they involve complex computations. To stretch out the life span of these nodes,it is required to have low complex and low bitrate image compression techniques exclusively designed for this platform. The complicated scenarioof wireless sensor network in processing and transmitting image data has been addressed by a low complex hybrid secured image compression technique using discrete wavelet transform and Bin discrete cosine transformation. Â

The Hybrid Algorithm for Data Collection over a Tree Topology in WSN

Wireless sensor networks have wide range of application such as analysis of traffic, monitoring of environmental, industrial process monitoring, technical systems, civilian and military application. Data collection is a basic function of wireless sensor networks (WSN) where sensor nodes determine attributes about a phenomenon of concern and transmits their readings to a common base station(sink node). In this paper, we use contention-free Time Division Multiple Access (TDMA) support scheduling protocols for such data collection applications over tree-based routing topology. We represent a data gathering techniques to get the growing capacity, routing protocol all along with algorithms planned for remote wireless sensor networks. This paper describes about the model of sensor networks which has been made workable by the junction of micro-electro-mechanical systems technologies, digital electronics and wireless communications. Firstly the sensing tasks and the potential sensor network applications are explored, and assessment of factors influencing the design of sensor networks is provided. In our propose work using data compression and packet merging techniques; or taking advantage of the correlation in the sensor readings. Consider continuous monitoring applications where perfect aggregation is achievable, i.e., every node is capable of aggregate the entire packets expected from its children as well as that generate by itself into a particular packet before transmit in the direction of its sink node or base station or parent node. Keyword: Aggregation, Data Converge-cast, Data fusion, Energy Efficiency, Routing and TDMA

On the performance of emerging wireless mesh networks

Wireless networks are increasingly used within pervasive computing. The recent development of low-cost sensors coupled with the decline in prices of embedded hardware and improvements in low-power low-rate wireless networks has made them ubiquitous. The sensors are becoming smaller and smarter enabling them to be embedded inside tiny hardware. They are already being used in various areas such as health care, industrial automation and environment monitoring. Thus, the data to be communicated can include room temperature, heart beat, user’s activities or seismic events. Such networks have been deployed in wide range areas and various levels of scale. The deployment can include only a couple of sensors inside human body or hundreds of sensors monitoring the environment. The sensors are capable of generating a huge amount of information when data is sensed regularly. The information has to be communicated to a central node in the sensor network or to the Internet. The sensor may be connected directly to the central node but it may also be connected via other sensor nodes acting as intermediate routers/forwarders. The bandwidth of a typical wireless sensor network is already small and the use of forwarders to pass the data to the central node decreases the network capacity even further. Wireless networks consist of high packet loss ratio along with the low network bandwidth. The data transfer time from the sensor nodes to the central node increases with network size. Thus it becomes challenging to regularly communicate the sensed data especially when the network grows in size. Due to this problem, it is very difficult to create a scalable sensor network which can regularly communicate sensor data. The problem can be tackled either by improving the available network bandwidth or by reducing the amount of data communicated in the network. It is not possible to improve the network bandwidth as power limitation on the devices restricts the use of faster network standards. Also it is not acceptable to reduce the quality of the sensed data leading to loss of information before communication. However the data can be modified without losing any information using compression techniques and the processing power of embedded devices are improving to make it possible. In this research, the challenges and impacts of data compression on embedded devices is studied with an aim to improve the network performance and the scalability of sensor networks. In order to evaluate this, firstly messaging protocols which are suitable for embedded devices are studied and a messaging model to communicate sensor data is determined. Then data compression techniques which can be implemented on devices with limited resources and are suitable to compress typical sensor data are studied. Although compression can reduce the amount of data to be communicated over a wireless network, the time and energy costs of the process must be considered to justify the benefits. In other words, the combined compression and data transfer time must also be smaller than the uncompressed data transfer time. Also the compression and data transfer process must consume less energy than the uncompressed data transfer process. The network communication is known to be more expensive than the on-device computation in terms of energy consumption. A data sharing system is created to study the time and energy consumption trade-off of compression techniques. A mathematical model is also used to study the impact of compression on the overall network performance of various scale of sensor networks

(AMDC) Algorithm for wireless sensor networks in the marine environment

Abstract—Data compression is known today as one of the most important enabling technologies that form the foundation of the majority of data applications and networks as we know them, including wireless sensor networks and the popular world wide net (internet). Marine data networks are gaining increasing interest in the research community due to the increasing request for data services over the sea. There are a very narrow range of available solutions because of the absence of infrastructure over such vast water surfaces. We have previously proposed applying MANET networks in the marine environment using VHF technology available on the majority of ships and vessels in order to gather different sensor data such as sea depth, temperature, wind speed and direction, etc. and send it to a central server to produce a public information map. We also discusses the gains and drawbacks of our proposal including the problem of low rate data transmission offered by VHF radio limited to 9.6 Kbps. In this paper we investigate the application of appropriate data quantization and compression techniques to the marine sensor data collected in order to reduce the burden on the channel links and achieve better transmission efficiency. Keywords—Wireless sensor network, Mobile Ad hoc Network, Very High Frequency, Sensor

Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation