The staggering growth in smartphone and wearable device use has led to a massive scale generation of personal (user-specific) data. To explore, analyze, and extract useful information and knowledge from the deluge of personal data, one has to leverage these devices as the data-mining platforms in ubiquitous, pervasive, and big data environments. This study presents the personal ecosystem where all computational resources, communication facilities, storage and knowledge management systems are available in user proximity. An extensive review on recent literature has been conducted and a detailed taxonomy is presented. The performance evaluation metrics and their empirical evidences are sorted out in this paper. Finally, we have highlighted some future research directions and potentially emerging application areas for personal data mining using smartphones and wearable devices.

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Cloud computing represents the most recent enterprise trend in information technology and refers to the virtualization of computing resources that are available on demand. Cloud computing saves cost and time for businesses. Moreover, this computing process reflects a radical technological revolution in how companies develop, deploy, and manage enterprise applications over the Internet. Virtualized cloud computing mainly offers cloud‐computing delivery models such as software as a service, platform as a service, and infrastructure as a service. Security and privacy are presently considered critical factors in the adaptation of any cloud‐service delivery model. Cloud computing leverages several technologies; in the process, this model can inherit potential security threats. Thus far, security issues in cloud computing have rarely been addressed at the service delivery level. Key security concerns include Web application security, network security, data security, integration, vulnerabilities in the virtualized environment, and physical security. The aim of this research is to comprehensively present the security threats with respect to their cloud service deliver models. This study also determines how service delivery models differ from existing enterprise applications, classify these models, and investigate the inherent security challenges. This study primarily focuses on the security concerns of each layer of the cloud‐service delivery model, as well as on existing solutions and approaches. In addition, countermeasures to potential security threats are also presented for each cloud model. Copyright © 2016 John Wiley & Sons, Ltd.

Wireless networks are increasingly overwhelmed by Distributed Denial of Service (DDoS) attacks by generating flooding packets that exhaust critical computing and communication resources of a victim's mobile device within a very short period of time. This must be protected. Effective detection of DDoS attacks requires an adaptive learning classifier, with less computational complexity, and an accurate decision making to stunt such attacks. We propose a distributed intrusion detection system called Cooperative IDS to protect wireless nodes within the network and target nodes from DDoS attacks by using a Cooperative Fuzzy Q-learning (Co-FQL) optimization algorithmic technique to identify the attack patterns and take appropriate countermeasures. The Co-FQL algorithm was trained and tested to establish its performance by generating attacks from the NSL-KDD and "CAIDA DDoS Attack 2007" datasets during the simulation experiments. Experimental results show that the proposed Co-FQL IDS has a 90.58% higher accuracy of detection rate than Fuzzy Logic Controller or Q-learning algorithm or Fuzzy Q-learning alone.

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