Internet of medical things (IoMT) has made it possible to collect applications and medical devices to improve healthcare information technology. Since the advent of the pandemic of coronavirus (COVID-19) in 2019, public health information has become more sensitive than ever. Moreover, different news items incorporated have resulted in differing public perceptions of COVID-19, especially on the social media platform and infrastructure. In addition, the unprecedented virality and changing nature of COVID-19 makes call centres to be likely overstressed, which is due to a lack of authentic and unregulated public media information. Furthermore, the lack of data privacy has restricted the sharing of COVID-19 information among health institutions. To resolve the above-mentioned limitations, this paper is proposing a privacy infrastructure based on federated learning and blockchain. The proposed infrastructure has the potentials to enhance the trust and authenticity of public media to disseminate COVID-19 information. Also, the proposed infrastructure can effectively provide a shared model while preserving the privacy of data owners. Furthermore, information security and privacy analyses show that the proposed infrastructure is robust against information security-related attacks.

Cost, performance, and penalties are the key factors to revenue generation and customer satisfaction. They have a complex correlation, that gets more complicated when missing a proper framework that unambiguously defines these factors. Service‐level agreement (SLA) is the initial document discussing selected parameters as a precondition to business initialisation. The clear definition and application of the SLA is of paramount importance as for modern as a Service online businesses no direct communication between provider and consumer is expected. For the proper implementation of SLA, there should be a satisfactory approach for measuring and monitoring quality of service metrics. This study investigated these issues and proposed performance‐based SLA (PerSLA) framework for cost, performance, penalties and revenue optimisation. PerSLA optimises these parameters and maximises both provider revenue and customers satisfaction. Simulation results confirm that the proposed framework is adequate in revenue generation and customers satisfaction. Customers and providers monitor the business with respect to agreed terms and conditions. On violation, the provider is penalised. This agreement increases the trust in relationship between provider and consumer.

A vital element of a cyberspace infrastructure is cybersecurity. Many protocols proposed for security issues, which leads to anomalies that affect the related infrastructure of cyberspace. Machine learning (ML) methods used to mitigate anomalies behavior in mobile devices. This paper aims to apply a High-Performance Extreme Learning Machine (HP-ELM) to detect possible anomalies in two malware datasets. Two widely used datasets (the CTU-13 and Malware) are used to test the effectiveness of HP-ELM. Extensive comparisons are carried out in order to validate the effectiveness of the HP-ELM learning method. The experiment results demonstrate that the HP-ELM was the highest accuracy of performance of0.9592 for the top 3 features with one activation function.

Summary Scheduling the tasks of a workflow to the cloud resources is a well‐known N‐P hard problem. The stakeholders involved in a cloud environment have different interests in scheduling problem. In addition to the traditional objectives like makespan, budget, and deadline, optimized in workflow scheduling, considering the green aspect of cloud, (ie, energy consumption) increase the problem complexity. Moreover, the interests of a cloud's stakeholders are conflicting, and satisfying all these interests simultaneously is a big problem. In this paper, we proposed a new Multi‐Objective Genetic Algorithm(MOGA) for workflow scheduling in a cloud environment. MOGA considered the conflicting interest of the cloud stakeholders for optimization and provided a solution, which not only minimizes the makespan under the budget and deadline constraints but also provided an energy efficient solution using the dynamic voltage frequency scaling. We provided a gap search algorithm in this paper, which is used to optimize the resource utilization of the cloud's resources. We compared our results with genetic algorithms considering the budget, deadline, and energy efficiency individually. We also compared the performance of MOGA with Multi‐objective Particle Swarm Optimization (MOPSO) with the same objectives as those of MOGA. To the best of our knowledge, there is no solution presented in the literature that considers the diverse objectives considered in this work. The results show that our proposed algorithm MOGA has significantly improved not only in terms of budget, deadline, and energy but also improved the utilization of cloud's resources as compared to the competitive algorithms of this work.

With limited resources, it is quite challenging for cloud providers to meet dynamic and massive customers' demands. Higher utilisation or refusing any service level agreement (SLA) may lead to penalties which play a crucial role in the cloud business. Overutilisation of resources, instead of maximizing the revenue, may lead to a decrease in revenue due to the SLA violations. Various studies have been conducted to investigate these issues; however, there is still room for improvement. In this study, the authors proposed a model to address the resource scalability and SLA violation issues by hiring external resources at low prices. However, in contrast to a federated cloud, the proposed model allows a provider to hire resources from any external provider with flexible terms and price. They designed algorithms to optimise providers' revenue by taking into account different parameters, including resource utilization, customer satisfaction, SLA violation, and prices. Simulation result shows that the proposed model is efficient in handling massive demands, and improves revenue generation and customer satisfaction. Offering joint pricing on customers' choice and outsourcing the extra workload to external resources leads to revenue maximization. Hiring external resources earns external revenue as well as it maximizes the total revenue.

Data collection and transmission are the fundamental operations of WSNs. The performance of WSNs relies upon these essential tasks because data gathering directly affects the efficiency and lifetime of WSNs. This paper presents a data volume based data collection technique using Mobile Data Collector (MDC). In this technique, the MDC uses data volume information to plan visits to the nodes. The MDC visits only those nodes which have generated data while the rests of the nodes are ignored. This scheme is validated with the help of simulations, and the results are compared with existing renowned techniques. The results show that the proposed scheme is energy efficient.

Summary In this paper, we present our implementation of the fast Fourier transforms on graphic processing unit (GPU) using OpenCL. This implementation of the FFT (ToPe‐FFT) is based on the Cooley‐Tukey set of algorithms with support for 1D and higher dimensional transforms using different radices. Factorization for mix‐radices enables our code to target FFTs of near arbitrary length. In systems with multiple graphic cards (GPUs), the library automatically balances the FFT computation thus achieving maximum resource utilization and higher speedup. Based on profiling and micro‐benchmarking of ToPe‐FFT, it is observed that the average speedup of our library for different sizes is 48× faster than the single CPU‐based code using FFTW and 3× faster than NVIDIA's GPU‐based cuFFT library.

Summary Recent graphic processing units (GPUs) have remarkable raw computing power, which can be used for very computationally challenging problems. Like in micromagnetic simulations, where the magnetostatic field computation to analyze the magnetic behavior at very small time and space scale demands a huge computation time. This paper presents a multidimensional FFT‐based parallel implementation of a magnetostatic field computation on GPUs. We have developed a specialized 3D FFT library for magnetostatic field calculation on GPUs. This made it possible to fully exploit the symmetries inherent in the field calculation and other optimizations specific to the GPUs architecture. We have compared our results with the widely used CPU‐based parallel OOMMF program and with an equivalent serial implementation on CPU. The results have shown a speedup of up to 95x and 8.7x for single and 66x and 4.6x for double precision floating point accuracy against equivalent serial implementation and OOMMF, respectively.

Robotic manipulators have very strong nonlinearities. Analytical modeling of the robotic gripper is very challenging task. Therefore in this paper soft computing methods is applied in order to estimate contact forces of the robotic finger. Support vector regression (SVR) with radial and polynomial basis functions and the soft computing methods were used. The primary purpose of this study are in clarification of kinetostatic examining of a new finger mechanism utilizing pseudo-unbending body model. The results show the better prediction accuracy with SVR methodology with radial basis function (RBF).

Vehicle routing is considered the basic issue in distribution management. In real-world problems, customer demand for some commodities increases on special situations. On the one hand, one of the factors that are very important for customers is the timely delivery of the demanded commodities. In this research, customers had several different kinds of demands. Therefore, a new routing model was introduced in the form of integer linear programming by combining the concepts of time windows and multiple demands and by considering the two contradictory goals of minimizing travel cost and maximizing demand coverage. Moreover, two approaches were designed for the problem-solving model based on the NSGA-II algorithm with diversification of the mutation operator structure. The two criteria of spread and coverage of non-dominated solutions were used to compare algorithms. Study of some typical created problems indicated the validity of the model and the computational efficiency of the proposed algorithm. The proposed algorithm could increase the criterion of solution spread by about 10%, and increased the number of obtained solutions on the Pareto border compared to other algorithms, which indicated its high efficiency.

The Multiple Traveling Salesmen Problem (mTSP) is of the famous and classical problems of research in operations and is accounted as one of the most famous and widely used problems of combinational optimization. Most of the complex problems can be modeled as the mTSP and then be solved. The mTSP is a NP-Complete one; therefore, it is not possible to use the exact algorithms for solving it instead the heuristics methods are often applied for solving such problems. In this paper, a new hybrid algorithm, called GELS-GA, has been presented for solving the mTSP. The utility of GELS-GA is compared with some related works such as GA and ACO and achieves optimality even in highly complex scenarios. Although, the proposed algorithm is simple, it includes an appropriate time of completion and the least traversed distance among existing algorithms.