Cloud federation enhances cloud services by enabling resource sharing and cooperation among multiple Cloud Service Providers (CSPs) for improved performance. However, one of the significant challenges for CSPs within the federation is maintaining coordination among heterogeneous CSPs. Additionally, reducing the presence of pernicious CSPs within the federation will allow the federation to deliver services with committed Quality of Service (QoS). Moreover, minimising the energy while migrating virtual machines within the federation will allow an increase in the revenue of CSPs within the federation. This paper proposed a Bayesian game-based model for detecting pernicious CSPs within the federation and developed a greedy double-auction-based resource-sharing mechanism. Here, the proposed mechanism ensures that CSPs achieve high satisfaction levels and guarantee a fair, efficient, and energy-aware resource-sharing environment among CSPs in a federation. Simulated results of the proposed Energy, Pernicious, and QoS Greedy Double Auction model (EPQ-GDA) are extensively compared with other competing models. The results demonstrate the effectiveness and superiority of our proposed method over the nearest competitor, with an average improvement in satisfaction of 2.61% for buyer CSPs and 3.67% for seller CSPs, 3.77% over energy, and 14.90% over perniciousness. Further simulated results also show that the EPQ-GDA satisfies important auction properties such as truthfulness, individual rationality, and budget balance.

Summary Nowadays, the technological advancements of low power electronic and sensing devices, wearable systems, communication technologies, and cloud computing have encouraged the provision of ubiquitous health monitoring, medical diagnosis, and treatment consultations. The data collected from the patients are transmitted to mHealth server for real‐time and self‐reliant activity detection, behavior analysis, ambient assisted living, elderly care, activity of daily living, rehabilitations, entertainments, and surveillance in smart home environments. This helps in building sensor analyst systems that analyze the data on mHealth server for disseminating it to the respective end users. This is of utmost importance as it can provide real time feedback to patients, family members, caregivers, and so forth about the behavioral changes of elderly people and people with special needs. In this article, we have proposed knowledge based data dissemination system using machine learning techniques that analyses the data collected on mHealth server in three steps. First, the data are analyzed using support vector machine, k‐nearest neighbor, neural network, and logistic regression. Second, it finds hidden patterns from the data using hidden Markov model (HMM). Third, it defines fuzzy rules to disseminate the data to the end users. The system has been compared against conventional approaches. The uniqueness of the system lies in the fact that it uses temporal nature of data and provides with real‐time feedback as well as predicts outcomes and detects hidden patterns. The results have shown that the techniques NN, HMM, and fuzzy logic when used in conjunction with each other for disease prediction, hidden pattern detection, and data dissemination gives an accuracy of 98%. Thus, increasing effectiveness of sensor analyst system.

Abstract Smart‐phones have become a necessity for users due to their abundance of services such as global positioning system, Wi‐Fi, voice/video calls, SMS, camera, and so forth. It contains personal information of users including photos, documents, messages, and videos. Android‐based smart‐phones enriched with many applications (commonly known as apps) fascinates users to use this ubiquitous technology up to a full extent. With open architecture and 73% of market share, Android is the most popular mobile operating system (OS) among developers. At the same time, the increasing popularity of Android OS woos attackers or cyber‐criminals to exploit its vulnerabilities. The attackers write malicious code to harm the device and grab users' sensitive information. For example, ransomware (a form of malware) demands ransom from victims to liberate the ceased material for illegal financial gain. The existing survey papers cover the analysis and detection of generic Android malware. The focus of this survey paper is to present an in‐depth threat scenario of Android ransomware. This article not only provides a comprehensive survey on analysis and detection methods for Android ransomware since its beginning (2015) till date (2020); but also presents observations and suggestions for researchers and practitioners to carry out further research.