Optimal use of scarce radio spectrum is essential in the proliferation of beyond 5G networks, and promising blockchain technology offers various benefits for the spectrum management. However, existing blockchain-based solutions are expensive, nonoptimized, and lack spectrum fraud detection. This article proposes a novel consensus mechanism for a blockchain-based dynamic spectrum access (DSA) system. The proposed “ Proof-of-Sense ” consensus mechanism operates based on spectrum sensing procedures rather than cryptographic calculations. It is specially designed to address fraudulent/unauthorized access to the spectrum by analyzing the sensed spectrum data. The core of the consensus mechanism is a cryptographic key sharing mechanism inspired by Shamir’s secret sharing scheme. Moreover, the proposed DSA system can enable different microservices, such as automated spectrum auctions, payment and penalty handling, and spectrum fraud detection. A proof of concept based on experimental approaches coupled with Matlab simulations is presented to analyze the performance of the proposed consensus mechanism.

Diabetic Retinopathy (DR) is a major cause of blindness which is caused by damage to the small blood vessels of the retina. Early detection of Diabetic Retinopathy is important to prevent vision loss. Main two stages of diabetic retinopathy are non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). Microaneurysms, hemorrhages, cotton-wool spots and exudates are symptoms of non proliferative diabetic retinopathy which is the early stage of diabetic retinopathy. Of these symptoms exudates detection is more important for the early detection of diabetic retinopathy. In this paper, we are proposing a method for the automated detection of exudates by using retinal images. We show that different morphological operations could be incorporated in order to detect exudates quite accurately. In the process of exudates detection optic disk detection is an important part. Otherwise, optic disk could be wrongly identified as exudates. We have used different morphological operations for the exudates detection from the retinal images and the developed algorithm has a sensitivity and specificity 94.59% and 88.46% respectively. This is considerably a higher success rate with the existing systems.

The exponential growth in connected devices with Internet-of-Things (IoT) and next-generation wireless networks requires more advanced and dynamic spectrum access mechanisms. Blockchain-based approaches to Dynamic Spectrum Access (DSA) seem efficient and robust due to their inherited characteristics such as decentralization, immutability, and transparency. However, conventional consensus mechanisms used in blockchain networks are expensive to be used due to the cost, processing, and energy constraints. Moreover, addressing spectrum violations (i.e., unauthorized access to the spectrum) is not well-discussed in most blockchain-based DSA systems in the literature. In this work, we propose a newly tailored energy-efficient consensus mechanism called “Distributed-Proof-of-Sense (DPoS)” that is specially designed to enable DSA and detect spectrum violations. The proposed consensus algorithm motivates blockchain miners to perform spectrum sensing, which leads to the collection of a full spectrum of sensing data. An elliptic curve cryptography-based zero-knowledge proof is used as the core of the proposed mechanism. We use MATLAB simulations to analyze the performance of the consensus mechanism and implement several consensus algorithms in a microprocessor to highlight the benefits of adopting the proposed system.

Typical wearable devices use a dedicated mobile phone as relay node to transfer the collected sensor data to a server. However, such relay nodes can be faulty or inactive due to various reasons, leading to interruptions of the communication link. To mitigate this challenge, we propose a novel security-enhanced emergency situation detection system, where 3 ^rd party unknown mobile relays are used instead of dedicated gateways as opposed to many existing solutions for IoT healthcare applications. The proposed underlying key agreement and authentication scheme ensures anonymity and untraceability for both sensors (wearable devices) and relay nodes, and relies on symmetric key-based operations to function under resource-constrained environments. We have also developed a prototype of the system using commercial off-the-shelf devices to verify the proposed method's validity and evaluate the performance advantage over existing approaches. Bluetooth Low Energy (BLE) communication technology is used to connect sensor nodes (wearable devices) and mobile relays. After sending medical data to the cloud server, the relay node is responsible for emergency detection and alert generation.

Vehicle tracking and surveillance is an area which is having a considerable attention in the context of security and safety. The detection and tacking of moving vehicles through multiple cameras is considered as a method of vehicle surveillance. This work addresses a problem of detecting and matching vehicles through multiple cameras. The power of GPUs are used to increase the number of video streams which can be processed using a single computer. In the detection process the Gabor filter is used as a directional filter and the SURF is used by the matcher to uniquely represent the vehicle.

The massive volume of video and image data, compels them to be stored in a distributed file system. To process the data stored in the distributed file system, Google proposed a programming model named MapReduce. Existing methods of processing images held in such a distributed file system, requires whole image or a substantial portion of the image to be streamed every time a filter is applied. In this work an image filtering technique using MapReduce programming model is proposed, which only requires the image to be streamed only once. The proposed technique extends for a cascade of image filters with the constrain of a fixed kernel size. To verify the proposed technique for a single filter a median filter is applied on an image with salt and pepper noise. In addition a corner detection algorithm is implemented with the use of a filter cascade. Comparison of the results of noise filtering and corner detection with the corresponding CPU version show the accuracy of the method.