The Internet of Things (IoT) has seen a surge in mobile devices with the market and technical expansion. IoT networks provide end-to-end connectivity while keeping minimal latency. To reduce delays, efficient data delivery schemes are required for dispersed fog-IoT network orchestrations. We use a Spark-based big data processing scheme (BDPS) to accelerate the distributed database (RDD) delay efficient technique in the fogs for a decentralized heterogeneous network architecture to reinforce suitable data allocations via IoTs. We propose BDPS based on Spark-RDD in fog-IoT overlay architecture to address the performance issues across the network orchestration. We evaluate data processing delays from fog-IoT integrated parts using a depth-first-search-based shortest path node finding configuration, which outperforms the existing shortest path algorithms in terms of algorithmic (i.e., depth-first search) efficiency, including the Bellman–Ford (BF) algorithm, Floyd–Warshall (FW) algorithm, Dijkstra algorithm (DA), and Apache Hadoop (AH) algorithm. The BDPS exhibits low latency in packet deliveries as well as low network overhead uplink activity through a map-reduced resilient data distribution mechanism, better than in BF, DA, FW, and AH. The overall BDPS scheme supports efficient data delivery across the fog-IoT orchestration, outperforming faster node execution while proving effective results, compared to DA, BF, FW and AH, respectively.

Insecure and portable devices in the smart city’s Internet of Things (IoT) network are increasing at an incredible rate. Various distributed and centralized platforms against cyber-attacks have been implemented in recent years, but these platforms are inefficient due to their constrained levels of storage, high energy consumption, the central point of failure, underutilized resources, high latency, and etc. In addition, the current architecture confronts the problems of scalability, flexibility, complexity, monitoring, managing & collecting of IoT data, and defend against cyber-threats. To address these issues, the author presents distributed and decentralized Blockchain-Software Defined Networking (SDN) based energy-optimized architecture for IoT in smart cities. Thus, SDN continuous observing, controlling, managing IoT devices activities and detect possible attacks in the network; Blockchain provides adequate security & privacy against cyber-attacks, reduces the central point of failure issues; Network Function Virtualization (NFV) are used to saving energy, load balancing, as well as increasing the lifetime of the entire network. Also, we introduce a Cluster Head Selection (CHS) algorithm to reduce the energy consumption in the presented model. Finally, we analyze the performance using various parameters (e.g. throughput, response time, gas consumption, communication overhead) and demonstrating the result that provides higher throughput, lower response time, lower gas consumption than existing works for smart cities.

Imbalance class problem in data mining occurs where one class known as the minority class has the significantly lower number of samples than the other classes known as the majority class(es). It affects the performance of machine learning algorithms by allowing them to show bias towards the majority class. This occurs because of the sub-concepts from the minority class. Recent studies has further divided the minority class into four sub-concepts: Safe, Borderline, Rare and Outlier using the majority-minority proportion at the neighborhood of every minority sample. Among the sub-concepts safe are easy to identify and classifiers are increasing inaccurate while classifying other subsequent sub-categories (Boarderline, Rare, Outlier). In some recent studies, heterogeneous value difference metric is used as the distance calculation mechanism for categorizing data. However, there are numerous other distance metrics whose effects on determining the sub-concepts are not explored yet. This research aimed at evaluating the effects of different distance metrics in the calculation of different sub-concepts within the minority class data. We have considered ten datasets and five distance metrics for the calculation. The datasets are divided into three categories: all categorical, mixed and fully numeric data. For the datasets with more categorical data outputs hugely differs between the distance functions. In those cases, relatively safer examples are calculated by the Euclidean and the Manhattan distance function. Our study shows that for categorizing minority data distance metrics should be chosen dataset wise.

Currently, thousands of people are suffering from paralysis. They have difficulties with speaking and walking. So we’ve developed a new kind of robot that can help those people who can’t walk or speak. By utilizing this robot (hand gloves or wheelchair handle) and gesture-based regulators, people with physical disabilities will improve their quality of life. The robot of the proposal has two components, one is the controller of the motion, and the other is the Robotic Wheelchair (RW). Where one can easily interact with the robotic-base wheelchair-using sensor-based hand gesture. With this human-robot interaction, a patient can quite easily control the robot and can move freely. In addition, the required patients may use gestures (hand gloves or wheelchair handle) to express their needs. Furthermore, we will reduce the effort to regulate the RW and hand movements with this device, that’s really difficult for disabled or dumb people. Our device can run with approximately 94% accuracy and very minimal delay. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 7, Dec 2020 P 85-93