Guest Editorial Computational and smart cameras

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Application and support for high-performance simulation

types: Editorial CommentHigh performance simulation that supports sophisticated simulation experimentation and optimization can require non-trivial amounts of computing power. Advanced distributed computing techniques and systems found in areas such as High Performance Computing (HPC), High Throughput Computing (HTC), grid computing, cloud computing and e-Infrastructures are needed to provide effectively the computing power needed for the high performance simulation of large and complex models. In simulation there has been a long tradition of translating and adopting advances in distributed computing as shown by contributions from the parallel and distributed simulation community. This special issue brings together a contemporary collection of work showcasing original research in the advancement of simulation theory and practice with distributed computing. This special issue is divided into two parts. This first part focuses on research pertaining to high performance simulation that support a range of applications including the study of epidemics, social networks, urban mobility and real-time embedded and cyber-physical systems. Compared to other simulation techniques agent-based modeling and simulation is relatively new; however, it is increasingly being used to study large-scale problems. Agent-based simulations present challenges for high performance simulation as they can be complex and computationally demanding, and it is therefore not surprising that this special issue includes several articles on the high performance simulation of such systems.Research Councils U

Performance evaluation of two-fuzzy based cluster head selection systems for wireless sensor networks

Sensor networks supported by recent technological advances in low power wireless communications along with silicon integration of various functionalities are emerging as a critically important computer class that enable novel and low cost applications. There are many fundamental problems that sensor networks research will have to address in order to ensure a reasonable degree of cost and system quality. Cluster formation and cluster head selection are important problems in sensor network applications and can drastically affect the network’s communication energy dissipation. However, selecting of the cluster head is not easy in different environments which may have different characteristics. In this paper, in order to deal with this problem, we propose two fuzzy-based systems for cluster head selection in sensor networks. We call these systems: FCHS System1 and FCHS System2. We evaluate the proposed systems by simulations and have shown that FCHS System2 make a good selection of the cluster head compared with FCHS System1 and another previous system.Peer ReviewedPostprint (published version

Green Wireless Internet Technology

YesIET Editorial: In the future communications will be pervasive in nature, allowing users access at the “touch of button” to attain any service, at any time, on any device. The future device design process requires both a reconfigurable RF front end and back end with high tuning speed, energy efficiency, excellent linearity and intelligence to maximise the “greenness” of the network. But energy efficiency and excellent linearity are the main topics that are driving the designs of future transceivers, including their efforts to minimise network contributions to climate changes such as the effect of CO2 emissions: the minimisation of these is a requirement for information and communication technology (ICT) as much as for other technologies. Recently, information and communication technologies were shown to account for 3% of global power consumption and 2% of global CO2 emissions, and hence far from insignificant. The approach towards energy conservation and CO2 reduction in future communications will require a gret deal of effort which should be targeted both at the design of energy efficient, low-complexity physical, MAC and network layers, while maintaining the required Quality of Service (QoS). There is also a need, in infrastructures, networks and user terminals, to take a more holistic approach to improving or achieving green communications, from radio operation, through functionality, up to implementation. The increasing demand for data and voice services is not the only cause for concern since energy management and conservation are now at the forefront of the political agenda. The vision of Europe 2020 is to become a smart, sustainable and inclusive economy, and as part of these priorities the EU have set forth the 20:20:20 targets, whereby greenhouse gas emissions and energy consumption should be reduced by 20% while energy from renewables should be increased by 20%

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture