Internet of Things (IoT) devices are widely used in many industries including smart cities, smart agriculture, smart medical, smart logistics, etc. However, Distributed Denial of Service (DDoS) attacks pose a serious threat to the security of IoT. Attackers can easily exploit the vulnerabilities of IoT devices and control them as part of botnets to launch DDoS attacks. This is because IoT devices are resource-constrained with limited memory and computing resources. As an emerging technology, Blockchain has the potential to solve the security issues in IoT. Therefore, it is important to analyse various Blockchain-based solutions to mitigate DDoS attacks in IoT. In this survey, a detailed survey of various Blockchain-based solutions to mitigate DDoS attacks in IoT is carried out. First, we discuss how the IoT networks are vulnerable to DDoS attacks, its impact over IoT networks and associated services, the use of Blockchain as a potential technology to address DDoS attacks, in addition to challenges of Blockchain implementation in IoT. We then discuss various existing Blockchain-based solutions to mitigate the DDoS attacks in the IoT environment. Then, we classify existing Blockchain-based solutions into four categories i.e., Distributed Architecture-based solutions, Access Management-based solutions, Traffic Control-based solutions and the Ethereum Platform-based solutions. All the solutions are critically evaluated in terms of their working principles, the DDoS defense mechanism (i.e., prevention, detection, reaction), strengths and weaknesses. Finally, we discuss future research directions that can be explored to design and develop better Blockchain-based solutions to mitigate DDoS attacks in IoT.

Reliable detection of the onset of epileptic seizures has seen renewed interest over the past few years, owing to several factors including, the global push towards digital health-care, the advancements in signal processing techniques, and the increased computational power of machines. A reliable automatic system could result in tremendous improvement in the quality of life of epilepsy patients. This paper presents dynamic mode decomposition (DMD), a data-driven dimensionality reduction technique, originally used in fluid mechanics, as an instrument for epileptic seizure detection from scalp electroencephalograph (EEG) data. DMD is employed in this work to measure power of signals in different frequency bands. These subband-powers, along with signal curve-lengths, are used as features for training Random Under-sampling Boost (RUSBoost) decision-tree classifier. Postprocessing measures ensure an acceptable balance between false positives and true positives. The proposed algorithm has been tested over a thousand hours of EEG data from two different datasets, the CHB-MIT dataset, and the KU Leuven dataset, giving sensitivity values of 0.87 and 0.88, respectively, and specificity values of 0.99 for both datasets.

Environmental factors such as fog and haze affect the image quality and make it unsuitable for automated systems, such as, intelligent vehicles, surveillance and outdoor object recognition, which require images with clear visibility for processing and decision making. In general, reconstruction of fog-free image from a single input image is quite challenging. Dark channel prior (DCP) method is used to estimate atmospheric light for the purpose of image defogging. This paper presents a DCP based image defogging method with improved transmission map to avoid blocking artifacts. The transmission maps are computed for RGB and YCbCr color spaces. Three transmission maps for the R, G, and B channels are utilized to compute a mean transmission map. In the YCbCr color space, Y channel is used to calculate the transmission map. The two transmission maps are refined by preserving edge information for constructing two intermediate images, which are assigned different weights to get the enhanced defogged output. The proposed method is evaluated against the current state-of-the-art approaches, and the experimental results based on structural similarity index, fog effect, anisotropic quality index and degradation score are calculated, which show that the defogged images reconstructed using the proposed method achieved better results with lower fog effect, similarity index, degradation score and higher quality index value. Reconstructed image has better contrast and luminance which is perceptually more appealing to the human visual system.

The Internet of Things (IoT) is aimed to provide efficient and seamless connectivity to a large number of low-power and low-cost embedded devices, consequently, the routing protocols play a fundamental role in achieving these goals. The IETF has recently standardized the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) for LLNs (i.e., Low-power and Lossy Networks) and is well-accepted among the Internet community. However, RPL was proposed for static IoT devices and suffers from many issues when IoT devices are mobile. In this paper, we first present various issues that are faced by the RPL when IoT devices are mobile. We then carry out a detailed survey of various solutions that are proposed in the current literature to mitigate the issues faced by RPL. We classify various solutions into five categories i.e., ‘Trickle-timer based solutions’, ‘ETX based solutions’, ‘RSSI based solutions’, ‘Position-based solutions’, and ‘Miscellaneous solutions’. For each category of these solutions, we illustrate their working principles, issues addressed and make a thorough assessment of their strengths and weaknesses. In addition, we found several flaws in the performance analysis done by the authors of each of the solutions, e.g., nodes mobility, time intervals, etc., and suggest further investigations for the performance evaluations of these solutions in order to assess their applicability in real-world environments. Moreover, we provide future research directions for RPL supporting various real-time applications, mobility support, energy-aware, and privacy-aware routing.

Image acquisition under bad weather conditions is prone to yield image with low contrast, faded color, and overall poor visibility. Different computer vision applications including surveillance, object classification, tracking, and recognition get effected due to degraded hazy images. Dehazing can significantly improve contrast, balance luminance, correct distortion, remove unwanted visual effects/ and therefore enhance the image quality. As a result, image defogging is imperative pre-processing step in computer vision applications. Previously, dark channel prior-based algorithms have proven promising results over the available techniques. In this paper, we have proposed a modified dark channel prior that uses fog density and guided image-filtering technique to estimate and refine transmission map, respectively. Guided image filter speeds up the refinement of transmission map, hence reduces the overall computational complexity of algorithm. We have also incorporated segmentation of the foggy image into sky and non-sky regions, after which, the modified dark channel prior and atmospheric light is computed for each segment. Then, the average value of atmospheric light for each segment is used to estimate transmission map. We have performed quantitative and subjective comparison for effective evaluation of our proposed algorithm against the current state-of-the-art algorithms on natural and synthetic images. Different quality metrics, such as saturation, mean square error, fog density, peak signal to noise ratio, structural similarity index metric, dehazing algorithm index (DHQI), full-reference image quality assessmen (FR-IQA), and naturalness of dehazed images have shown the proposed algorithm to be better than existing techniques.