The advancement of the Internet of Things (IoT) and the availability of wide cloud services have led to the horizon of edge computing paradigm which demands for processing the data at the edge of the network. The development of 5G technology has led to the increased usage of IoT-based devices and the generation of a large volume of data followed by increased data traffic, which is difficult to process by the mobile edge computing (MEC) platform. The latest inventions related to unmanned aerial vehicles (UAVs) helps to assist and replace the edge servers used for MEC. In the present work, the objective is to develop self-adaptive trajectory optimization algorithm (STO) which is a multi-objective optimization algorithm used to solve the vital objectives associated with the above scenario of a UAV-assisted MEC system. The objectives identified are minimizing the energy consumed by the MEC and minimizing the process emergency indicator, where the process emergency indicator implies the urgency level of a particular process. Finding the optimal values for these conflicting objectives will help to further efficiently apply UAV for MEC systems. A self-adaptive multi-objective differential evolution-based trajectory optimization algorithm (STO) is proposed, where a pool of trial vector generation strategies is extended. The strategies and the crossover rate associated with a differential evolution (DE) algorithm are self-adapted using fuzzy systems to improve the population diversity. The experimentation is planned to be conducted on hundreds of IoT device instances considered to be fixed on the ground level and to evaluate the performance of the proposed algorithm for a single unmanned aerial vehicle-assisted mobile edge computing system.

Nowadays, Chronic Kidney Disease (CKD) is one of the vigorous public health diseases. Hence, early detection of the disease may reduce the severity of its consequences. Besides, medical databases of any disease diagnosis may be collected from the blood test, urine test, and patient history. Nevertheless, medical information retrieved from various sources is diverse. Therefore, it is unadaptable to evaluate numerical and nominal features using the same feature selection algorithm, which may lead to fallacious analysis. Applying machine learning techniques over the medical database is a common way to help feature identification for CKD prediction. In this paper, a novel Mixed Data Feature Selection (MDFS) model is proposed to select and filter preeminent features from the medical dataset for earlier CKD prediction, where CKD clinical data with 12 numerical and 12 nominal features are fed to the MDFS model. For each feature in the mixed dataset, the model applies feature selection methods according to the data type of the feature. Point Biserial correlation and a Chi-square filter are applied to filter the numerical features and nominal features, respectively. Meanwhile, an SVM algorithm is employed to evaluate and select the best feature subset. In our experimental results, the proposed MDFS model performs superior to existing works in terms of accuracy and the number of reduced features. The identified feature subset is also demonstrated to preserve its original properties without discretization during feature selection.

Technology advancements have enabled the capture of Renewable Energy Sources (RES) on a massive scale. Smart Grids (SGs) that combine conventional and RES are predicted as a sustainable method of power generation. Moreover, environmental conditions impact all RES, causing changes in the amount of electricity produced by these sources. Furthermore, availability is dependent on daily or annual cycles. Although smart meters allow real-time demand prediction, precise models that predict the electricity produced by RES are also required. Prediction Models (PMs) accurately guarantee grid stability, efficient scheduling, and energy management. For example, the SG must be smoothly transformed into the conventional energy source for that time and guarantee that the electricity generated meets the predicted demand if the model predicts a period of Renewable Energy (RE) loss. The literature also suggests scheduling methods for demand-supply matching and different learning-based PMs for sources of RE using open data sources. This paper developed a model that accurately replicates a microgrid, predicts demand and supply, seamlessly schedules power delivery to meet demand, and gives actionable insights into the SG system’s operation. Furthermore, this work develops the Demand Response Program (DRP) using improved incentive-based payment as cost suggestion packages. The test results are valued in different cases for optimizing operating costs through the multi-objective ant colony optimization algorithm (MOACO) with and without the input of the DRP.

The new conceivable rule of the future is going to be anything can be connected and will be connected over the internet. Technically, internet of things, is defined as the computing concept of connecting the devices over the internet. This adds a level of digital intelligence to the devices, enabling them to communicate without the human being involved. This chapter will discuss about the business aspects, models, and opportunities involved in IoT. The internet of things or IoT is basically about the interconnection of uniquely identifiable and programmable embedded devices within its infrastructure with the help of the internet.