Investigation of Energy Cost of Data Compression Algorithms in WSN for IoT Applications

Data compression techniques in IoT-enabled wireless body sensor networks: A systematic literature review and research trends for QoS improvement

As the technology progressing, WSN (Wireless sensor network) become a suitable choice for data gathering and monitoring applications. Power consumption is an important consideration for the design of sensor network. As the WSN is powered by small batteries and deployed in remote location where it is not possible to charge and change these batteries, so it is very important to reduce the energy consumption, which results in increase in life time of sensor network. The communication unit on a WSN is main energy consumer, so a data compression technique is required to reduce the data exchange among the various nodes in network and hence result in power saving Due to various constraints in WSN like low memory, less processing speed and limited capacity batteries, we require a lightweight compression scheme which full fills these constraints. In this paper we are presenting the lossless data compression scheme, which follows the methodology of Tunstall codes for data compression of text. The best part of above designed algorithm is that it uses the same length code for any stream of applied input in contrast to the variable length codes introduced previously. For the above algorithm we obtained desired compression ratio as listed below. Comparison is done with existing data compression algorithm in WSN to validate the result.

Medical applications create an enormous amount of data. Medical data transmission via networks necessitates a huge bandwidth rate. Also, digital medical data necessitate enormous storage and archive. With the evolution of the Internet and multimedia designs, medical data is required to be transmitted in a rapid manner. One of the practical solutions to this issue is medical data compression. Data compression (DC) and transmission is important in the medical field. DC is used to transmit a large amount of data for minimizing the cost. DC is introduced to minimize the image for focusing on the removal of redundant data. DC is classified into two categories, namely, lossy and lossless techniques. DC is designed to reduce storage, bandwidth, and time consumed for transmission. Coding is utilized to remove unwanted data. The different DC algorithm is used to enhance the compression rate. Some of the medical data compression techniques are outlined to lessen data redundancy via specialized data coding and, as a result, can significantly minimize the constructive amount of medical data. In other words, medical data compression involves the procedure of encoding medical data in such a manner that less storage is essential to archive them over a network. The contemporary prototype of medical data compression is split into two stages, namely, designing and entropy coding. Selecting the appropriate prototype is paramount due to the reason that the more consistencies we identify, the more are the probabilities to minimize the series scope. Next, based on the understanding acquired via designing, unwanted data are eliminated by applying coding. Here, encoding is performed to eliminate dispensable data. As several DC techniques have been progressed, a requirement comes to light to assess the techniques, and an endeavor is made to review and classify different DC techniques based on three classifications, namely, coding schemes, data quality specifications, and application appropriateness. Some of the coding schemes for lossless data compression, to name a few, are run-length encoding, Huffman encoding, and LZW encoding. Also with the expeditious rise in high-speed data acquisition, bandwidth acquisition and storage have become the focal restrictions concerning DC techniques. We observed that it is impracticable to outline an exclusive lossless compression technique for different data types without a certain understanding of the series. It is also unfeasible to develop a disparate lossless compression algorithm for every potential series. The intelligent alternative is to devise comprehensive DC and to utilize such an algorithm to the series that can be handled, with a higher amount of precision. Some of the analyzed algorithms are component analysis, partial matching, state-space transitions, and tree sequence.

Energy consumption has risen to be a bottleneck in wireless sensor networks. This is caused by the challenges faced by these networks due to their tiny sensor nodes that have limited memory storage, small battery capacity, limited processing capability, and bandwidth. Data compression has been used to reduce energy consumption and improve network lifetime, as it reduces data size before it can be forwarded from the sensing node to the sink node in the network. In this paper, a survey and comparison of currently available data compression techniques in wireless sensor networks are conducted. Suitable sets of criteria are defined to classify existing data compression algorithms. An adaptive lossless data compression algorithm (ALDC) is analyzed through MATLAB coding and simulation from the reviewed data compression techniques. The analysis aims to discover strategies that can be used to reduce the amount of data further before it is transmitted. From this analysis, it was discovered that encoding residue samples, rather than raw data samples, reduced the bitstream from 112 bits to a range of 30 to 36 bits depending on the sample block sizes. The average length of data samples to be passed to the encoder was minimized from the original 14 bits per symbol to 1.125 bits per symbol. This demonstrated a 0.875 code efficiency or redundancy. It resulted in an energy saving of 67.8% to 73.2%. This work further proposes a data compression algorithm that encodes the residue samples with fewer bits than the ALDC algorithm. The algorithm reduced the bitstream to 26 bits. The average length of the code is equal to the entropy of the data samples, demonstrating zero redundancy and an improved energy saving of 76.8% compared to ALDC . The proposed algorithm, therefore, shows improved energy efficiency through data compression.

In this work we will develop an extension of one of existing routing algorithm in wireless sensor network. This new adaptation will permit the sensor node to save more energy and transmit images in wireless mode. This situation will be strategic and helpful especially in disaster scenario, where groups of rescuers must be on site to accomplish emergency tasks; therefore it's very important and necessary to establish a wireless communication in real time between individuals or groups. The nature of wireless video sensor network makes it suitable to be used in the context of emergencies because introducing a video give more information in precise time and this is very advantageous when the existing infrastructure is down or severely overloaded. In emergencies the network topology may change rapidly and randomly. The increasing mobility of terminals makes them progressively dependent on their autonomy from the power source. This is illustrated by introducing many mobility models and using many scenarios of mobility in emergency situation, where image transmission via sensor node is used. Low complexity algorithm in image processing in order to reduce time transfer of selected data by this way allows saving energy. Efficiency in emergency scenario is the main objective of this work, achieved by the combination of three strategies: low-power mode algorithm, a power-aware routing strategy and compression technique in image processing used in sensor node. A selected set of simulations studies and real test bed on sensor node platform (Telos-B) indicate a reduction in energy consumption and a significant increase in node lifetime whereas network performance is not affected significantly. This is the big interest of our work in emergency situation, by increasing life time of node, individual can communicate longer and give more chance to rescuers to find them.

Automatic localization is one of the major issues in Wireless Sensor Networks (WSN). DV-hop algorithm is a well-known localization algorithm in WSN but with limited localization accuracy. In this paper, an improved DV-hop localization algorithm in hybrid optical wireless sensor networks is proposed based on the optimization of the parameters in WSN. Various factors that affect the localization accuracy of the DV-hop algorithm in WSN are investigated, including the communication radius of the node, the number of beacon nodes and the number of the total nodes. As the DV-hop algorithm is applied into hybrid optical sensor and WSNs (O-WSN) with rectangular topology, different parameters have to be optimized accordingly. Simulation results show that the square topology outperforms the rectangle topology more than 45 % under the same network parameters using the improved DV-hop algorithm. Therefore another improved DV-hop called Sub-Square Weighted DV-hop (SSW DV-hop) is proposed for the rectangle topology. Both simulation and experiment results demonstrate that applying the SSW DV-hop algorithmin O-WSNs could significantly improve the localization accuracy.

Data compression has become more important than ever, due to the increasing demand for internet use and the exchange of a huge amount of images, videos, audio and documents as well as the growing demand for electronic archiving by government departments that produce thousands of documents per day. In this paper, the proposed technique for document compression will be presented.The proposed technique is a lossless and completed technique it is consists of two parts the compression part and decompression part. The compression part contains of some basic stages such as: pre-processing, blocks processing, run length encoding(RLE), replace maximum values by unused values, minimize levels, delta encoding, compression of ones values, encryption. After the encryption process is complete, the outputs are stored in two separate binary files with the extension of bmp;one of them is the header file and is considered a key for the second file which contains the compressed data. This technique applied on twenty documents and compared with other methods compression such as RLE, jpeg, tiff and png. The experimental results showed that the proposed technique gives a higher compression ratio than the rest of the methods.

In this paper, the author represents energy efficient data compression application based on LTC (Lightweight Temporal Compression) algorithm in wireless sensor networks (WSNs). WSNs are essentially constrained by motes' limited battery power and networks bandwidth. The author focuses on data compression algorithm which effectively supports data compression for data gathering in WSNs. Data reduction before transmission such as by compression will significantly decrease the resource usage. Therefore, the main idea of this paper is to show how a data compression application such as collection tree protocol (CTP) is used for data collection from different sensor nodes into the root node in order to increase the network lifetime. LTC algorithm is used to minimize the amount of error in each reading. In the context of the use of wireless sensor network technology for environmental monitoring, the two main elementary activities of wireless sensor network are data acquisition and transmission. However, transmitting/receiving data are power consuming task in order to reduce transmission associated power consumption; we explore data compression by processing information locally. The inception of sensor networks, in-network processing has been touted as enabling technology for long-lived deployments. Radio communication is the overriding consumer of energy in such networks. Therefore, data reduction before transmission, either by compression or feature extraction, will directly & significantly increase network lifetime. In many applications where all data must transport out of network, data may be compressed before transport, so chosen compression technique can operate under stringent resource constraints of low-power nodes and induces tolerable errors. This paper evaluates temporal compression scheme designed specially to be used by mica motes. By using LTC, it is possible to compress data up to −20 to −1. Furthermore this algorithm is simple and requires little storage as compared to other compression techniques. The proposed application is implemented on the tinyOS platform using the nesC programming language. To evaluate their work, the author conducts simulation via TOSSIM or a real-world testbed FlockLab. The result demonstrates the significance of the application.

With the advancement of wireless technology, Wireless Body Sensor Networks, such as Electrocardiograms (ECGs) will serve as state-of-the-art method for electronic-healthcare systems and applications. Like other digital communications, however, ECGs highlight power consumption as the main design constraint and bottleneck as it affects device lifespan and data accuracy. Hence, power reduction and power management techniques and schemes have been developed to eliminate this constraint such as hardware optimization, source and channel coding, signal conditioning, and resolution control. This paper proposes a lossless ECG encoder that combines existing data compression techniques specifically the adaptive fuzzy predictor based on fuzzy decision making and an enhanced entropy encoding that utilizes algorithms in both run length encoding (RLE) and incremental prefix encoding. Simulation results that the proposed scheme outperforms the entropy encoding using Huffman, RLE, and predictive encoding schemes in compression ratio (CR), with the enhanced entropy encoding leading the pre-existing compression techniques by 24.0907 on RLE and 23.6580 on Huffman.

14 - Introduction to video compression

As a typical hierarchical routing algorithm in wireless sensor network, LEACH protocol can reduce the routing overhead greatly. And it makes the network load balance relatively and have a good scalability. But there are still many deficiencies, such as the uneven clustering, unreasonable cluster head-selection, the single hop communication inter clusters and unsuitable for the large-scale network. Therefore, this paper proposes a new energy balancing routing algorithm, which determines the optimal number of cluster head based on the energy consumption of the network and selects the optimal cluster head based on network load balancing. The simulation results show that the algorithm can balance network load, prolong the network lifetime effectively.

Technology is developing very quickly and will continue to increase, so it plays an important role in the process of sending information or data from one device to another. The speed of transmission depends on the size of the data to be sent. Data with a larger size requires a longer delivery time. The amount of storage space required increases as more files are stored. This has led to the development of file shrinking techniques, also known as data compression techniques, with the aim of minimizing the loss of data quality after transmission and reducing the amount of storage space required. Compression techniques have several algorithms that can be used to reduce file size. As in this research, the compression process is done with the run length encoding algorithm and the fixed length binary encoding algorithm. Both algorithms have different compression results, so it is necessary to make a comparison. To make the comparison, 6 grayscale image files with *.jpg extension are used with different resolutions and compare their performance according to predetermined parameters. The compression comparison results of one image data resolution of 300 x 300 in the Run Length Encoding algorithm has a Ratio of Compression (RC) 1.038792, Compression Ratio (CR) 96.266%, Redundancy (Rd) 3.734%, Compression time 399ms, and Decompression time 297ms. While the Fixed Length Binary Encoding algorithm has a Ratio of Compression (RC) of 1.37, Compression Ratio (CR) of 73.248%, Redundancy (Rd) of 26.752%, Compression time of 3258ms, and Decompression time of 1047ms. So from these results it can be said that the better performance in compressing images is the Fixed Length Binary Encoding algorithm compared to Run Length Encoding.

Data reduction based on machine learning algorithms for fog computing in IoT smart agriculture

Wireless Sensor Network (WSN) has limited resources such as energy, computation and transmission capacity. These resources are not sufficient for transmitting large amount of data collected by the sensor nodes. Wireless Multimedia Sensor Network (WMSN) generates large amount of data that requires more energy and transmission capacity as compared to scalar data. So it is desired to perform in-network data compression in WMSN. In this paper the authors have used Principal Component Analysis (PCA) technique for data compression. PCA can be efficiently used in wireless multimedia sensor network to reduce the energy consumption, reduce the network load and prolong the network lifetime. Simulation results show that PCA based compression conserves energy of sensor nodes and prolongs the lifetime of WMSN.

In order to improve the power supply problem of network nodes, the energy efficient routing algorithm of wireless sensor network is studied and improved. A new energy efficient routing algorithm and protocol is proposed to prolong the lifetime of the network. First, the energy efficient (3PEC-MBCR) routing algorithm based on partial energy level is proposed in this paper. Secondly, according to the PEC-AODV routing protocol, the energy consumption of the network and the balance of energy consumption of each node are taken into account, so as to prolong the network lifetime. Finally, the PEC-AODV protocol module is added to the NS-2 simulation platform, and the simulation and performance evaluation of the PEC-AODV protocol are made by NS-2. The simulation results show that the algorithm not only reduces the total energy consumption of the network, but also balances the energy consumption between nodes, which maximizes the lifetime of each node. It is concluded that the PEC-AODV routing protocol is more effective in energy utilization than the existing AODV routing protocol or other energy-efficient routing protocols, which results in a certain role in prolonging the lifetime of the entire network.