Sociology Paradigms for Dynamic Integration of Devices into a Context-Aware System

Ubiquitous and m obile context - aware computing is an essential component of the smart cities infrastructure. Widely available wireless networks, the maturity level of distributed computing and the increasing number of mobile devices have significantly influenced the human experience with computing. In the present paper, we discuss the need for a model that will be able to represent a formal structure of a context - aware system in a device . The core functionality of the model is expected to expose context - aware behaviour and support dynamic integration of mobile devices and context - aware behaviour. The major contribution of this work is to identify deficiencies of the existing model which is using the notions from sociology such as Role, Ownership and Responsibility.The authors gratefully acknowledge funding from the European Commission through the GEO-C project (H2020-MSCA-ITN-2014, Grant Agreement Number 642332, http://www.geo-c.eu/)

Big Data Caching for Networking: Moving from Cloud to Edge

In order to cope with the relentless data tsunami in $5G$ wireless networks, current approaches such as acquiring new spectrum, deploying more base stations (BSs) and increasing nodes in mobile packet core networks are becoming ineffective in terms of scalability, cost and flexibility. In this regard, context-aware $5$G networks with edge/cloud computing and exploitation of \emph{big data} analytics can yield significant gains to mobile operators. In this article, proactive content caching in $5$G wireless networks is investigated in which a big data-enabled architecture is proposed. In this practical architecture, vast amount of data is harnessed for content popularity estimation and strategic contents are cached at the BSs to achieve higher users' satisfaction and backhaul offloading. To validate the proposed solution, we consider a real-world case study where several hours of mobile data traffic is collected from a major telecom operator in Turkey and a big data-enabled analysis is carried out leveraging tools from machine learning. Based on the available information and storage capacity, numerical studies show that several gains are achieved both in terms of users' satisfaction and backhaul offloading. For example, in the case of $16$ BSs with $30\%$ of content ratings and $13$ Gbyte of storage size ($78\%$ of total library size), proactive caching yields $100\%$ of users' satisfaction and offloads $98\%$ of the backhaul.Comment: accepted for publication in IEEE Communications Magazine, Special Issue on Communications, Caching, and Computing for Content-Centric Mobile Network

Quality of service optimization in IoT driven intelligent transportation system

High mobility in ITS, especially V2V communication networks, allows increasing coverage and quick assistance to users and neighboring networks, but also degrades the performance of the entire system due to fluctuation in the wireless channel. How to obtain better QoS during multimedia transmission in V2V over future generation networks (i.e., edge computing platforms) is very challenging due to the high mobility of vehicles and heterogeneity of future IoT-based edge computing networks. In this context, this article contributes in three distinct ways: to develop a QoS-aware, green, sustainable, reliable, and available (QGSRA) algorithm to support multimedia transmission in V2V over future IoT-driven edge computing networks; to implement a novel QoS optimization strategy in V2V during multimedia transmission over IoT-based edge computing platforms; to propose QoS metrics such as greenness (i.e., energy efficiency), sustainability (i.e., less battery charge consumption), reliability (i.e., less packet loss ratio), and availability (i.e., more coverage) to analyze the performance of V2V networks. Finally, the proposed QGSRA algorithm has been validated through extensive real-time datasets of vehicles to demonstrate how it outperforms conventional techniques, making it a potential candidate for multimedia transmission in V2V over self-adaptive edge computing platforms

Multi-dimensional networking and distributed computing services

Various types of wired and wireless networks and their applications can be seen in many places now-a-days. In the past decade, we have witnessed a significant increase in the number of Internet users and technology hunters. With the aid of wireless communications and demand of flexible anytime, anywhere networking, many types of wireless self-organizing networks have already gained huge popularity among users. For wider support of wireless connectivity and developing easy-to-use technologies, substantial efforts are underway to reduce human intervention in the configuration, formation, and maintenance processes of these networks. Furthermore, different types of distributed computing technologies are widening the scope of our thinking and research issues in networking. The multi-dimensional research issues include wireless and mobility problems, routing protocols and algorithms, resource and service location protocols, performance evaluations of networking systems, ubiquitous systems, context aware ubiquitous environment, mechanisms to improve throughput over wireless links, network management and network security, and other related areas. To meet the vast demand of networking and distributed computing knowledge in the current times, IJIDCS was launched in the year 2010. It is my pleasure to see that the second volume is coming out with some important and solid contributions. The first issue of the second volume includes some of the papers chosen from the 4th International Workshop on Internet and Distributed Computing Systems (IDCS 2011) in conjunction with ICA3PP 2011 conference, held from October 24 to October 26, 2011 in Melbourne, Australia. The selected papers have been considerably extended and revised. Other regular papers are also included to put multi-dimensional fields in one single issue. The topics range from failure detection to secure transactions, processing online documents to complex wireless system and signaling

Towards Optimising WLANs Power Saving: Novel Context-aware Network Traffic Classification Based on a Machine Learning Approach

Energy is a vital resource in wireless computing systems. Despite the increasing popularity of Wireless Local Area Networks (WLANs), one of the most important outstanding issues remains the power consumption caused by Wireless Network Interface Controller (WNIC). To save this energy and reduce the overall power consumption of wireless devices, most approaches proposed to-date are focused on static and adaptive power saving modes. Existing literature has highlighted several issues and limitations in regards to their power consumption and performance degradation, warranting the need for further enhancements. In this paper, we propose a novel context-aware network traffic classification approach based on Machine Learning (ML) classifiers for optimizing WLAN power saving. The levels of traffic interaction in the background are contextually exploited for application of ML classifiers. Finally, the classified output traffic is used to optimize our proposed, Context-aware Listen Interval (CALI) power saving modes. A real-world dataset is recorded, based on nine smartphone applications’ network traffic, reflecting different types of network behaviour and interaction. This is used to evaluate the performance of eight ML classifiers in this initial study. Comparative results show that more than 99% of accuracy can be achieved. Our study indicates that ML classifiers are suited for classifying smartphone applications’ network traffic based on levels of interaction in the background