Energy-efficient Data Compression Research Articles

Energy Efficient Data Compression in Cloud Based IoT

In this study, an adaptive data compression scheme (ADCS) is proposed for efficiently controlling the IoT device compression rate and energy consumption in the cloud based IoT network. The ADCS consists of two data compression schemes, the sensor Lempel-Ziv-Welch (S-LZW) scheme and the sequential lossless entropy compression (S-LEC) scheme. In Auto state, the ADCS can select the appropriate energy efficient data compression scheme for each IoT device, while taking into consideration the IoT device's processing capability, the available energy in each IoT device battery, and the amount of compression power. Our proposed scheme has been developed using mixed integer linear programming. The result verifies that the proposed ADCS scheme saves power by an average of 40% compared to the non-compression scheme (NCS) due to reducing the traffic load and the number of hops in the network, which leads to an ability to handle higher traffic demands and increasing the lifetime of IoT devices by 50% compared to NCS systems.

Energy efficient data compression and aggregation technique for wireless sensor networks [TELOSB MOTES

Energy efficient data compression and aggregation technique for wireless sensor networks [TELOSB MOTES

Energy efficient data compression and aggregation technique for wireless sensor networks [TELOSB MOTES

This paper present and analyse an energy efficient data compression and data aggregation algorithm which results in the whole network lifetime prolonged by about 24%. In this paper, a new idea is proposed for sensor values compression based on a technique that involves feedback mechanism. In this technique, the base node in the sensor network generates Huffman code for the sensor data that needs to be compressed and broadcast the Huffman code in to the sensor network. All nodes in the sensor network receive Huffman code, compress the sensor data and transmit to base node. For data aggregation, secure data aggregation algorithm is used which does not necessitate additional phase for data integrity verification and also it eludes extra transmissions and computational overhead on the sensor nodes to reduce the amount of energy used up by the network. The whole idea was tested on TelosB sensor network platform, programmed in nesC language and also analyses the performance of the algorithm in the Contiki OS-simulator COOJA. A comparison is also done with existing compression algorithms in terms of lifetime of the sensor network.

Distributed data aggregation algorithm based on lifting wavelet compression in wireless sensor networks

Compressive sensing (CS) is one of the most promising recoverable data aggregation schemes, which can considerably reduce the amount of data transmitted. However, CS technique brings heavy aggregation burden on sensor nodes, which challenges their restricted available energy and computation capacity. In this paper, we focus on the energy-efficient data compression with the objective of recovering the original dataset. We first propose a dynamic clustering algorithm based on data spatial correlation (CDSC) to balance aggregation load. Furthermore, we propose a faster data compression approach based on eliminable lifting wavelet, which can eliminate spatial and temporal data redundancy. Also, it offers high fidelity recovery for the raw data. Extensive experiments demonstrate that our CDSC algorithm outperforms other methods on prolonging network lifetime and reducing the amount of data transmitted.

Performance Analysis of Threshold Based Compressive Sensing Algorithm in Wireless Sensor Network

Wireless Sensor Networks (WSN) are comprised of spatially distributed sensor nodes, where each node contains sensors, processors and transceivers for communicating data. Regardless of the application in which the sensor network is serving, the data generated in the network eventually must be delivered to the sink. However the limited network bandwidth, frequent node/link failure along with the unreliable communication medium poses great challenges for node to node communication in WSN. Hence, energy efficient data compression algorithms are necessary for sensor nodes as they enhance the transmission efficiency in WSN. Compressive sensing is a new compression algorithm in which the input signal is converted into sparse signal and the sparse signal is further converted into a signal of reduded dimension than original signal. The dimensionality reduction improves the transmission efficiency. This new concept is recently applied in WSN, however suitable threshold selection to sparsify the one dimensional sensor reading and suitable sparifying basis for image input data are not considered in literature. Hence, in this paper analysis of compressive sensing algorithm with a suitable threshold selection is performed in order to increase the level of sparsity for one dimensional data and a suitable sparsifying basis selection is performed for image data. Results indicate that compressive sensing with suitable threshold selection improves transmission and bandwidth efficiency in case of low correlated one dimensional sensor data and a suitable basis improves the quality of transmission for image sensor data and hence the overall lifetime of sensor network can be increased.

A data acquisition protocol for a reactive wireless sensor network monitoring application.

Limiting energy consumption is one of the primary aims for most real-world deployments of wireless sensor networks. Unfortunately, attempts to optimize energy efficiency are often in conflict with the demand for network reactiveness to transmit urgent messages. In this article, we propose SWIFTNET: a reactive data acquisition scheme. It is built on the synergies arising from a combination of the data reduction methods and energy-efficient data compression schemes. Particularly, it combines compressed sensing, data prediction and adaptive sampling strategies. We show how this approach dramatically reduces the amount of unnecessary data transmission in the deployment for environmental monitoring and surveillance networks. SWIFTNET targets any monitoring applications that require high reactiveness with aggressive data collection and transmission. To test the performance of this method, we present a real-world testbed for a wildfire monitoring as a use-case. The results from our in-house deployment testbed of 15 nodes have proven to be favorable. On average, over 50% communication reduction when compared with a default adaptive prediction method is achieved without any loss in accuracy. In addition, SWIFTNET is able to guarantee reactiveness by adjusting the sampling interval from 5 min up to 15 s in our application domain.

Spatiotemporal sparse Bayesian learning with applications to compressed sensing of multichannel physiological signals.

Energy consumption is an important issue in continuous wireless telemonitoring of physiological signals. Compressed sensing (CS) is a promising framework to address it, due to its energy-efficient data compression procedure. However, most CS algorithms have difficulty in data recovery due to nonsparsity characteristic of many physiological signals. Block sparse Bayesian learning (BSBL) is an effective approach to recover such signals with satisfactory recovery quality. However, it is time-consuming in recovering multichannel signals, since its computational load almost linearly increases with the number of channels. This work proposes a spatiotemporal sparse Bayesian learning algorithm to recover multichannel signals simultaneously. It not only exploits temporal correlation within each channel signal, but also exploits inter-channel correlation among different channel signals. Furthermore, its computational load is not significantly affected by the number of channels. The proposed algorithm was applied to brain computer interface (BCI) and EEG-based driver's drowsiness estimation. Results showed that the algorithm had both better recovery performance and much higher speed than BSBL. Particularly, the proposed algorithm ensured that the BCI classification and the drowsiness estimation had little degradation even when data were compressed by 80%, making it very suitable for continuous wireless telemonitoring of multichannel signals.

Compressed data-stream protocol: an energy-efficient compressed data-stream protocol for wireless sensor networks

In this study, the authors present an energy-efficient data compression protocol for data collection in wireless sensor networks (WSNs). WSNs are essentially constrained by motes’ limited battery power and networks bandwidth. The authors focus on data compression algorithms and protocol development to effectively support data compression for data gathering in WSNs. Their design of compressed data-stream protocol (CDP) is generic in the sense that other lossless or lossy compression algorithms can be easily ‘plugged’ into the proposed protocol system without any changes to the rest of the CDP. This design intends to support various different WSN applications where users may prefer more specific compression algorithms, tailored to the sensing data characteristics in question, to their general algorithm. CDP is not only able to significantly reduce energy consumptions of data gathering in multi-hop WSNs, but also able to reduce sensor network traffic and thus avoid congestion accordingly. The proposed CDP is implemented on the tinyOS platform using the nesC programming language. To evaluate their work, the authors conduct simulations via TOSSIM and PowerTOSSIM-z with real-world sensor data. The results demonstrate the significance of CDP.

Energy-Efficient Data Compression in Clustered Wireless Sensor Networksusing Adaptive Arithmetic Coding with Low Updating Cost

Energy-Efficient Data Compression in Clustered Wireless Sensor Networksusing Adaptive Arithmetic Coding with Low Updating Cost