Wogderes Semunigus

Bio: Wogderes Semunigus is an academic researcher. The author has contributed to research in topics: Bandwidth (computing) & Data compression. The author has an hindex of 1, co-authored 2 publications receiving 4 citations.

Lossless Compression Techniques for Low Bandwidth Io Ts

Abstract: In the recent years, Internet of things (IoT) has become an integral part of the modern digital ecosystem. It has the ability to handle the tasks smartly for many different situations. Therefore, it is one of the main technologies for autonomous systems. These IoTs deal with a lot of information. As the resources of the IoT are limited, data compression is an essential need. Some of the information transmitted over the IoTs cannot be compromised at all. Any loss of such sensitive data may cause serious consequences. Therefore, lossless data compression techniques are preferred for such data so that the integrity can be maintained. The low bandwidth IoTs are very popular in the recent times. They provide services over large coverage area with limited resources. These networks are known as low power wide area networks (LPWANs). In the 3GPP framework, there are some popular LPWANs such as narrowband IoT (NBIoT), and LTE machine-type communication (LTE-M). This article focuses on the lossless compression techniques employed in these popular LPWANs. This research work shows the reasons why lossless compression techniques are needed in NBIoT and LTE-M. It also goes through the challenges posed by the low bandwidth IoTs. Further, the recently used compression techniques for these low bandwidth IoTs are also discussed.

Lossless Compression Techniques for Low Bandwidth Networks

Abstract: Data compression is one of the fundamental processes in the modern communication and computing world. It provides essential reduction in the amount of data before it is sent to a remote destination. In the communication networks some data is very much redundant and it can be compressed by either lossless of lossy techniques. However, for some data any minor loss may result in serious short-comings. For that kind of data, lossy compression methods are not at all suitable. The modern communication networks are dealing with a significant amount of the desired data. In those cases, lossless compression techniques are the only methods to reduce the amount of data. More number of low bandwidth networks are observed these days. For instance, the early generation mobile cellular networks, IoT networks, signaling networks, and even some satellite networks are very much limited in bandwidth. In such cases, sensitive data is compressed using lossless compression techniques. The proposed research work focuses more on the common low bandwidth networks. Very commonly, it has been found that the low power wide area networks (LPWANs) have low bandwidth. However, in many applications the LPWANs deal with sensitive data in which any loss results in serious consequences. This article analyzes the compression techniques used in LPWANs and other low bandwidth networks.

Narrowband IoT Based Support Functions in Smart Cities

Abstract: This paper shows the main supporting roles of narrowband Internet of things (NBIoT) in the smart cities. This research work will consider the cases in which there already exists some infrastructure for the smart city operations. NBIoT is used to improve the overall functions and to ease the process of resource management. Smart city initiatives are extremely popular in the developed world. In the developing countries too, it gains popularity in the recent years. There are a lot of tasks in the smart city initiatives. It covers the basic services such as water and electricity distribution, maintenance of streetlights, city traffic management, garbage management, and support in effective policing. It also provides the advanced services such as the safety and surveillance monitoring, smart healthcare, location and tracking based services, and smart vehicular networking services. Several information and communication technology-based services are used to assist in these functions. It has been found that IoT is exemplary in these service provision operations. For large scale deployment of IoT a lot of resources are needed. Now, it is well known that NBIoT is one of the resource efficient versions of IoT. It is suitable for large scale deployment in projects like smart cities and smart grids. This article shows that, NBIoT can assist in the smart city functions to a large extent.

Adaptive Lossless Image Data Compression Method Inferring Data Entropy by Applying Deep Neural Network

Abstract: When we compress a large amount of data, we face the problem of the time it takes to compress it. Moreover, we cannot predict how effective the compression performance will be. Therefore, we are not able to choose the best algorithm to compress the data to its minimum size. According to the Kolmogorov complexity, the compression performances of the algorithms implemented in the available compression programs in the system differ. Thus, it is impossible to deliberately select the best compression program before we try the compression operation. From this background, this paper proposes a method with a principal component analysis (PCA) and a deep neural network (DNN) to predict the entropy of data to be compressed. The method infers an appropriate compression program in the system for each data block of the input data and achieves a good compression ratio without trying to compress the entire amount of data at once. This paper especially focuses on lossless compression for image data, focusing on the image blocks. Through experimental evaluation, this paper shows the reasonable compression performance when the proposed method is applied rather than when a compression program randomly selected is applied to the entire dataset.

Improving IoT data Security Using Compression and Lightweight Encryption Technique

Abstract: The Internet of Things (IoT) can be considered as a physical object that connects and exchanges data with other objects over networks. IoTs transmit different types of personal healthcare information, critical data, and other items, via the Internet. This data becomes a target of security issues. Therefore, the security of this data is vital to maintain the confidentiality and integrity of the data by using lightweight encryption. Due to the minimal resources available to these IoT devices, IoT requires other techniques to deal with these limitations, such as the compression methods to decrease data size and increase the speed of transmission. In this paper, the proposed system combines compression and lightweight encryption techniques to decrease the data size and protect it. The collected data from the IoT sensor is compressed by using the Zstandard algorithm, after which the compressed data is encrypted using the Tiny Encryption Algorithm (TEA) in the gateway and sent to the fog server. Besides, the Elliptic Curve Diffie-Hellman is used as the key exchange protocol among the IoT device layer to the Fog layer. The hardware used in the proposal is the Raspberry Pi model 4 and two types of sensors. The results show that the system reduces the size of the transmitted data in about 79% for 36.58 GB, and provides better security with the encryption and decryption time of about 0.36 msec for 100-byte data size, while its throughput is 277.7 byte/msec.