Parallel data compression

Data compression schemes remove data redundancy in communicated and stored data and increase the effective capacities of communication and storage devices. Parallel algorithms and implementations for textual data compression are surveyed. Related concepts from parallel computation and information theory are briefly discussed. Static and dynamic methods for codeword construction and transmission on various models of parallel computation are described. Included are parallel methods which boost system speed by coding data concurrently, and approaches which employ multiple compression techniques to improve compression ratios. Theoretical and empirical comparisons are reported and areas for future research are suggested

Data compression for full motion video transmission

Clearly transmission of visual information will be a major, if not dominant, factor in determining the requirements for, and assessing the performance of the Space Exploration Initiative (SEI) communications systems. Projected image/video requirements which are currently anticipated for SEI mission scenarios are presented. Based on this information and projected link performance figures, the image/video data compression requirements which would allow link closure are identified. Finally several approaches which could satisfy some of the compression requirements are presented and possible future approaches which show promise for more substantial compression performance improvement are discussed

Wireless sensor network as a distribute database

Wireless sensor networks (WSN) have played a role in various fields. In-network data processing is one of the most important and challenging techniques as it affects the key features of WSNs, which are energy consumption, nodes life circles and network performance. In the form of in-network processing, an intermediate node or aggregator will fuse or aggregate sensor data, which are collected from a group of sensors before transferring to the base station. The advantage of this approach is to minimize the amount of information transferred due to lack of computational resources. This thesis introduces the development of a hybrid in-network data processing for WSNs to fulfil the WSNs constraints. An architecture for in-network data processing were proposed in clustering level, data compression level and data mining level. The Neighbour-aware Multipath Cluster Aggregation (NMCA) is designed in the clustering level, which combines cluster-based and multipath approaches to process different packet loss rates. The data compression schemes and Optimal Dynamic Huffman (ODH) algorithm compressed data in the cluster head for the compressed level. A semantic data mining for fire detection was designed for extracting information from the raw data by the semantic data-mining model is developed to improve data accuracy and extract the fire event in the simulation. A demo in-door location system with in-network data processing approach is built to test the performance of the energy reduction of our designed strategy. In conclusion, the added benefits that the technical work can provide for in-network data processing is discussed and specific contributions and future work are highlighted

Data and resource management in wireless networks via data compression, GPS-free dissemination, and learning

“This research proposes several innovative approaches to collect data efficiently from large scale WSNs. First, a Z-compression algorithm has been proposed which exploits the temporal locality of the multi-dimensional sensing data and adapts the Z-order encoding algorithm to map multi-dimensional data to a one-dimensional data stream. The extended version of Z-compression adapts itself to working in low power WSNs running under low power listening (LPL) mode, and comprehensively analyzes its performance compressing both real-world and synthetic datasets. Second, it proposed an efficient geospatial based data collection scheme for IoTs that reduces redundant rebroadcast of up to 95% by only collecting the data of interest. As most of the low-cost wireless sensors won’t be equipped with a GPS module, the virtual coordinates are used to estimate the locations. The proposed work utilizes the anchor-based virtual coordinate system and DV-Hop (Distance vector of hops to anchors) to estimate the relative location of nodes to anchors. Also, it uses circle and hyperbola constraints to encode the position of interest (POI) and any user-defined trajectory into a data request message which allows only the sensors in the POI and routing trajectory to collect and route. It also provides location anonymity by avoiding using and transmitting GPS location information. This has been extended also for heterogeneous WSNs and refined the encoding algorithm by replacing the circle constraints with the ellipse constraints. Last, it proposes a framework that predicts the trajectory of the moving object using a Sequence-to-Sequence learning (Seq2Seq) model and only wakes-up the sensors that fall within the predicted trajectory of the moving object with a specially designed control packet. It reduces the computation time of encoding geospatial trajectory by more than 90% and preserves the location anonymity for the local edge servers”--Abstract, page iv

An enhanced lossless compression with cryptography hybrid mechanism for ECG biomedical signal monitoring

Due to their use in daily life situation, demand for remote health applications and e-health monitoring equipment is growing quickly. In this phase, for fast diagnosis and therapy, information can be transferred from the patient to the distant clinic. Nowadays, the most chronic disease is cardiovascular diseases (CVDs). However, the storage and transmission of the ECG signal, consumes more energy, bandwidth and data security which is faced many challenges. Hence, in this work, we present a combined approach for ECG data compression and cryptography. The compression is performed using adaptive Huffman encoding and encrypting is done using AES (CBC) scheme with a 256-bit key. To increase the security, we include Diffie-Hellman Key exchange to authenticate the receiver, RSA key generation for encrypting and decrypting the data. Experimental results show that the proposed approach achieves better performance in terms of compression and encryption on MIT-BIH ECG dataset

Study of on-board compression of earth resources data

The current literature on image bandwidth compression was surveyed and those methods relevant to compression of multispectral imagery were selected. Typical satellite multispectral data was then analyzed statistically and the results used to select a smaller set of candidate bandwidth compression techniques particularly relevant to earth resources data. These were compared using both theoretical analysis and simulation, under various criteria of optimality such as mean square error (MSE), signal-to-noise ratio, classification accuracy, and computational complexity. By concatenating some of the most promising techniques, three multispectral data compression systems were synthesized which appear well suited to current and future NASA earth resources applications. The performance of these three recommended systems was then examined in detail by all of the above criteria. Finally, merits and deficiencies were summarized and a number of recommendations for future NASA activities in data compression proposed

Efficient ECG Compression and QRS Detection for E-Health Applications

Current medical screening and diagnostic procedures have shifted toward recording longer electrocardiogram (ECG) signals, which have traditionally been processed on personal computers (PCs) with high-speed multi-core processors and efficient memory processing. Battery-driven devices are now more commonly used for the same purpose and thus exploring highly efficient, low-power alternatives for local ECG signal collection and processing is essential for efficient and convenient clinical use. Several ECG compression methods have been reported in the current literature with limited discussion on the performance of the compressed and the reconstructed ECG signals in terms of the QRS complex detection accuracy. This paper proposes and evaluates different compression methods based not only on the compression ratio (CR) and percentage root-mean-square difference (PRD), but also based on the accuracy of QRS detection. In this paper, we have developed a lossy method (Methods III) and compared them to the most current lossless and lossy ECG compression methods (Method I and Method II, respectively). The proposed lossy compression method (Method III) achieves CR of 4.5×, PRD of 0.53, as well as an overall sensitivity of 99.78% and positive predictivity of 99.92% are achieved (when coupled with an existing QRS detection algorithm) on the MIT-BIH Arrhythmia database and an overall sensitivity of 99.90% and positive predictivity of 99.84% on the QT database.This work was made possible by NPRP grant #7-684-1-127 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Enabling Compression in Tiny Wireless Sensor Nodes

A Wireless Sensor Network (WSN) is a network composed of sensor nodes communicating among themselves and deployed in large scale (from tens to thousands) for applications such as environmental, habitat and structural monitoring, disaster management, equipment diagnostic, alarm detection, and target classification. In WSNs, typically, sensor nodes are randomly distributed over the area under observation with very high density. Each node is a small device able to collect information from the surrounding environment through one or more sensors, to elaborate this information locally and to communicate it to a data collection centre called sink or base station. WSNs are currently an active research area mainly due to the potential of their applications. However, the deployment of a large scale WSN still requires solutions to a number of technical challenges that stem primarily from the features of the sensor nodes such as limited computational power, reduced communication bandwidth and small storage capacity. Further, since sensor nodes are typically powered by batteries with a limited capacity, energy is a primary constraint in the design and deployment of WSNs. Datasheets of commercial sensor nodes show that data communication is very expensive in terms of energy consumption, whereas data processing consumes significantly less: the energy cost of receiving or transmitting a single bit of information is approximately the same as that required by the processing unit for executing a thousand operations. On the other hand, the energy consumption of the sensing unit depends on the specific sensor type. In several cases, however, it is negligible with respect to the energy consumed by the communication unit and sometimes also by the processing unit. Thus, to extend the lifetime of a WSN, most of the energy conservation schemes proposed in the literature aim to minimize the energy consumption of the communication unit (Croce et al., 2008). To achieve this objective, two main approaches have been followed: power saving through duty cycling and in-network processing. Duty cycling schemes define coordinated sleep/wakeup schedules among nodes in the network. A detailed description of these techniques applied to WSNs can be found in (Anastasi et al., 2009). On the other hand, in-network processing consists in reducing the amount of information to be transmitted by means of aggregation (Boulis et al., 2003) (Croce et al., 2008) (Di Bacco et al., 2004) (Fan et al., 2007)