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Advances in Computing and Network Communications Proceedings of CoCoNet 2020, Volume 1: Proceedings of CoCoNet 2020, Volume 1
Abstract
This book constitutes the thoroughly refereed post-conference proceedings of the 4th International Conference on Computing and Network Communications (CoCoNet'20), October 14–17, 2020, Chennai, India. The papers presented were carefully reviewed and selected from several initial submissions. The papers are organized in topical sections on Signal, Image and Speech Processing, Wireless and Mobile Communication, Internet of Things, Cloud and Edge Computing, Distributed Systems, Machine Intelligence, Data Analytics, Cybersecurity, Artificial Intelligence and Cognitive Computing and Circuits and Systems. The book is directed to the researchers and scientists engaged in various fields of computing and network communication domains.
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Discrete Wavelet Transform is the algorithm which can be used to increase the contrast of an image for better visual quality of an image. The histogram value for original image with highest bins is taken for embedding the data into an image to perform the histogram equalization for repeating the process simultaneously. Information can be embedded into the source image with some bit value, for recovering the original image without any loss of the pixels. DWT is the first algorithm which has achieved the image contrast enhancement accurately. This approach maintained the original visual quality of an image even though themessage bits are embedded into the contrast-enhanced images. The proposed work with an original watermarking scheme based on the least significant bit technique. As a substitute of embedding the data into a simple image as watermarking, least significant bitmethod by utilizing the three wavelets transform is applied in the proposed system in order to enhance the embedding technique using spatial domain. For security, the Huffman coding has used to secure the data embedded into a host image, which can convert the secret message sequence into bit sequence for least significant bit operation. DWT can analyze the signal at multiple resolutions and it can divide the image into two types of quadrants as high and low-frequency quadrants. Here dividing an image into low and high it makes the information to hide.
Being a good reflector, the human body can be recognized with the variation of radio signal. In this work we would show how this can be possible by a sequence of methods. First, we create a wireless sensor network (WSN) in a closed surface area and then we monitor the variation of the signal in that surface in presence of human being. In addition we monitor the radio signal also for the gesture movement. The variations are recorded by received signal strength indicator (RSSI). We then develop another human activity recognition system using radar and compare the accuracy for the two adopted systems. Some interesting results have reported in the paper.
Hybrid beamforming is a promising technique to reduce the complexity and cost of massive multiple-input multiple-output (MIMO) systems while providing high data rate. However, the hybrid precoder design is a challenging task requiring channel state information (CSI) feedback and solving a complex optimization problem. This paper proposes a novel RSSI-based unsupervised deep learning method to design the hybrid beamforming in massive MIMO systems. Furthermore, we propose i) a method to design the synchronization signal (SS) in initial access (IA); and ii) a method to design the codebook for the analog precoder. We also evaluate the system performance through a realistic channel model in various scenarios. We show that the proposed method not only greatly increases the spectral efficiency especially in frequency-division duplex (FDD) communication by using partial CSI feedback, but also has near-optimal sum-rate and outperforms other state-of-the-art full-CSI solutions.
Detecting a malicious activity like fingerprinting on wireless local area network is a challenging task. With cyber deception strategy, we can gather information about the malicious activity by placing honeypots that can act as a trap to lure the attacker. Cyber deception is a conventional method to cloak real-time environment into a virtual legitimate environment. Our analysis shows that deception is an existing strategy in a wired LAN environment. This paper provides a wider perspective of deception strategy on wireless LAN. We primarily focus on the evil twin access point which causes serious threat to the legitimate Wi-Fi access points. Here a novel approach has been suggested to detect and identify the malicious activity by deceiving the attackers in their evil twin access points using decoys which are honeypots. The paper also provides a reliable way to gather the attacker’s activity information. We can also detect SSL stripping and DNS spoofing attack using this approach.
Blockchain is an emerging technology for various business and industrial applications because of its characteristics like security, immutability, and transparency. But still, blockchain faces the main issue of scalability, which influences several factors such as throughput, which is defined as the number of transactions processed per second (tps), the limited size of the block, cost, and capacity (size of the blockchain) and latency. In this paper, a survey of literature on the various solutions for scalability issues, namely cashing, side chain, off chain, inter-chain, child chain, on chain, and also deep learning methods along with their pros and cons.
