No abstract

Today's mobile devices offer a variety of computational, memory, storage, communication and sensing resources. In addition, mobile communication technologies are continuously evolving and mobile networks are becoming more and more complex. Modern mobile devices are capable of supporting a wide range of new innovative applications from real-time location-based tracking to mobile gaming. However, the usage of power-hungry applications, sensors and their demand for 24/7 Internet connectivity requires an efficient energy management mechanism in mobile devices. With the increasing energy limitations, there has been a corresponding rise of energy management solutions proposed by researchers. However, this research area is still immature and existing literature lacks the critical review of recent self organization based energy management techniques. This paper aims to provide a structured overview of the research developments on self organization based energy management techniques used in mobile complex networks. This review paper surveys the state-of-the-art self organization based energy management techniques that have been proposed over the period of 2010-2015. Based on the proposed optimization, we have grouped the existing approaches in different categories, which are further classified at different levels, from energy-efficient operating systems to computation off-loading. With this classification we aim to provide an easy and summarized view of the latest self organization based energy management techniques that can be implemented in mobile devices.

Complex self-organizing cognitive radio (CR) networks serve as a framework for accessing the spectrum allocation dynamically where the vacant channels can be used by CR nodes opportunistically. CR devices must be capable of exploiting spectrum opportunities and exchanging control information over a control channel. Moreover, CR nodes should intelligently coordinate their access between different cognitive radios to avoid collisions on the available spectrum channels and to vacate the channel for the licensed user in timely manner. Since inception of CR technology, several MAC protocols have been designed and developed. This paper surveys the state of the art on tools, technologies and taxonomy of complex self-organizing CR networks. A detailed analysis on CR MAC protocols form part of this paper. We group existing approaches for development of CR MAC protocols and classify them into different categories and provide performance analysis and comparison of different protocols. With our categorization, an easy and concise view of underlying models for development of a CR MAC protocol is provided.

Purpose The purpose of this paper is to critically analyze the state-of-the-art session identification techniques used in web usage mining (WUM) process in terms of their limitations, features, and methodologies. Design/methodology/approach In this research, systematic literature review has been conducted using review protocol approach. The methodology consisted of a comprehensive search for relevant literature over the period of 2005-2015, using four online database repositories (i.e. IEEE, Springer, ACM Digital Library, and ScienceDirect). Findings The findings revealed that this research area is still immature and existing literature lacks the critical review of recent session identification techniques used in WUM process. Originality/value The contribution of this study is to provide a structured overview of the research developments, to critically review the existing session identification techniques, highlight their limitations and associated challenges and identify areas where further improvements are required so as to complement the performance of existing techniques.

Abstract-Pedestrian detection is an important aspect of autonomous vehicle driving as recognizing pedestrians helps in reducing accidents between the vehicles and the pedestrians. In literature, feature based approaches have been mostly used for pedestrian detection. Features from different body portions are extracted and analyzed for interpreting the presence or absence of a person in a particular region in front of car. But these approaches alone are not enough to differentiate humans from non-humans in dynamic environments, where background is continuously changing. We present an automated pedestrian detection system by finding pedestrians' motion patterns and combing them with HOG features. The proposed scheme achieved 17.7% and 14.22% average miss rate on ETHZ and Caltech datasets, respectively.