A survey on data compression techniques: From the perspective of data quality, coding schemes, data type and applications

Wearable medical sensors (WMSs) are garnering ever-increasing attention from both the scientific community and the industry. Driven by technological advances in sensing, wireless communication, and machine learning, WMS-based systems have begun transforming our daily lives. Although WMSs were initially developed to enable low-cost solutions for continuous health monitoring, the applications of WMS-based systems now range far beyond health care. Several research efforts have proposed the use of such systems in diverse application domains, e.g., education, human-computer interaction, and security. Even though the number of such research studies has grown drastically in the last few years, the potential challenges associated with their design, development, and implementation are neither well-studied nor well-recognized. This article discusses various services, applications, and systems that have been developed based on WMSs and sheds light on their design goals and challenges. We first provide a brief history of WMSs and discuss how their market is growing. We then discuss the scope of applications of WMS-based systems. Next, we describe the architecture of a typical WMS-based system and the components that constitute such a system, and their limitations. Thereafter, we suggest a list of desirable design goals that WMS-based systems should satisfy. Finally, we discuss various research directions related to WMSs and how previous research studies have attempted to address the limitations of the components used in WMS-based systems and satisfy the desirable design goals.

Wearable Medical Sensor-Based System Design: A Survey

The use of wireless sensor network for industrial applications has attracted much attention from both academic and industrial sectors. It enables a continuous monitoring, controlling, and analyzing of the industrial processes, and contributes significantly to finding the best performance of operations. Sensors are typically deployed to gather data from the industrial environment and to transmit it periodically to the end user. Since the sensors are resource constrained, effective energy management should include new data collection techniques for an efficient utilization of the sensors. In this paper, we propose adaptive data collection mechanisms that allow each sensor node to adjust its sampling rate to the variation of its environment, while at the same time optimizing its energy consumption. We provide and compare three different data collection techniques. The first one uses the analysis of data variances via statistical tests to adapt the sampling rate, whereas the second one is based on the set-similarity functions, and the third one on the distance functions. Both simulation and real experimentations on telosB motes were performed in order to evaluate the performance of our techniques. The obtained results proved that our proposed adaptive data collection methods can reduce the number of acquired samples up to 80% with respect to a traditional fixed-rate technique. Furthermore, our experimental results showed significant energy savings and high accurate data collection compared to existing approaches.

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Energy-Efficient Sensor Data Collection Approach for Industrial Process Monitoring

Recently, the advancements in the field of wireless technologies and micro-electro-mechanical systems lead to the development of potential applications in wireless sensor networks (WSNs). The visual sensors in WSN create a significant impact on computer vision based applications such as pattern recognition and image restoration. generate a massive quantity of multimedia data. Since transmission of images consumes more computational resources, various image compression techniques have been proposed. But, most of the existing image compression techniques are not applicable for sensor nodes due to its limitations on energy, bandwidth, memory, and processing capabilities. In this article, we introduce a highly reliable and low-complexity image compression scheme using neighborhood correlation sequence (NCS) algorithm. The NCS algorithm performs the bit reduction operation and then encoded by a codec (such as PPM, Deflate, and Lempel Ziv Markov chain algorithm.) to further compress the image. The proposed NCS algorithm increases the compression performance and decreases the energy utilization of the sensor nodes with high fidelity. Moreover, it achieved a minimum end to end delay of 1074.46 ms at the average bit rate of 4.40 bpp and peak signal to noise ratio of 48.06 on the applied test images. On comparing with state-of-art methods, the proposed method maintains a better tradeoff between compression efficiency and reconstructed image quality.

Highly Reliable and Low-Complexity Image Compression Scheme Using Neighborhood Correlation Sequence Algorithm in WSN

The measurement of environmental variables has become a daily problem in recent years. However, the equipment commonly used for these measurements is expensive and bulky, and therefore, it is not possible to have enough spatial resolution. In addition, many of the measurement methods do not provide real-time information to deliver to citizens in a timely manner. In some works, these issues have been handled through the deployment of wireless sensor networks based on low cost technologies. The improvement of the spatial and temporal resolution implies the increase in the amount of information to be transmitted and stored. For this reason, this paper presented a method for data reduction through a dynamic subsampling of the measured variable, data fusion from several sensors for the same variable, and data scaling taking into account the variables range. The reduction of data is implemented to save energy, reduce the transmission time, keep the channel available, and save storage space. The method is validated using a low-cost monitoring station that combines environmental, particulate matter (PM), gas, electromagnetic radiation, and inertial sensors to be transmitted in a 50-byte reduced packet using an LoRa network. The subsampling adjustment was developed for the PM signal. The results show a reduction in the volume of stored data and the relevant information is not affected. The transmitted data packet can be reduced from 96 to 50 bytes, and sampling can be reduced to 4% of the original sampling without affecting the trend of the PM information.

Data Reduction in a Low-Cost Environmental Monitoring System Based on LoRa for WSN

Data compression techniques in Wireless Sensor Networks

Automatic Vehicle Location and Monitoring System Based on Data Distribution Service

The standard IEC 61850 is considered as a candidate for communication standard for smart grid applications. The delay performance is one of the critical issues that were specified by IEC 61850. However, a few works have been done to evaluate the delay performance under wireless networks. This paper presents the modeling and simulation of IEC 61850 substation automation system (SAS) under wireless and hybrid networks. OPNET is used to model and simulate the Intelligent Electronic Device (IEDs) for three types of messages: GOOSE, the Raw Data Sampled Values and the Interbay trip messages in Substation Automation System.

Performance Evaluation of IEC 61850 under Wireless Communication Networks

Septicemia in neonates refers to generalized bacterial infection documented by positive blood culture in the first four weeks of life and is one of the four leading causes of neonatal mortality and morbidity in India, so the study is carried out by taking 175 neonates blood samples which were proceed by blood culture and sepsis screening for early diagnosis by hematological test, total 103 samples were found to be blood culture positive and in the screening of proven cases abnormal values were seen in Total leukocyte count 78 (75.7%), Neutrophils count 79 (76.6%), Hemoglobin level 48 (46.6%), Platelet count 38 (36.8%) positive C‐reactive protein was seen in 92 (89.3%), rise ESR level 25 (34.7%). The results showed that abnormal TLC count, Neutrophil count and positive CRP were significant for early diagnosis of neonatal septicemia.

https://www.allresearchjournal.com/archives/2015/vol1issue11/PartM/1-11-47.pdf

Early Diagnosis of Neonatal Septicemia

With the emergence of wireless technology, and increase of mobile computing devices, the demand for the better network connectivity is the major interest of the users. Transmission Control Protocol (TCP) is the standard network protocol for communication in the Internet, but its performance drastically degrades over wireless networks, as TCP takes packet drop or collision as a sign of congestion. In this paper, we analyze TCP's performance over wireless networks using NS-2 Simulator. Unlike traditional performance evaluation methods for TCP over wireless networks, we consider different versions of TCP over different routing protocols. Moreover, we also consider the effectiveness of TCP both over infrastructure-based and infrastructure-less wireless networks. Simulation results show that TCP's performance varies from scenario to scenario both in infrastructure-based and ad-hoc wireless networks.

Muhammad Musaddiq

Papers

Data compression techniques in Wireless Sensor Networks

Automatic Vehicle Location and Monitoring System Based on Data Distribution Service

Performance Evaluation of IEC 61850 under Wireless Communication Networks

Early Diagnosis of Neonatal Septicemia

Analysis of TCP under Wireless Circumstances: A Performance Evaluation