3,042 research outputs found
SymbioCity: Smart Cities for Smarter Networks
The "Smart City" (SC) concept revolves around the idea of embodying
cutting-edge ICT solutions in the very fabric of future cities, in order to
offer new and better services to citizens while lowering the city management
costs, both in monetary, social, and environmental terms. In this framework,
communication technologies are perceived as subservient to the SC services,
providing the means to collect and process the data needed to make the services
function. In this paper, we propose a new vision in which technology and SC
services are designed to take advantage of each other in a symbiotic manner.
According to this new paradigm, which we call "SymbioCity", SC services can
indeed be exploited to improve the performance of the same communication
systems that provide them with data. Suggestive examples of this symbiotic
ecosystem are discussed in the paper. The dissertation is then substantiated in
a proof-of-concept case study, where we show how the traffic monitoring service
provided by the London Smart City initiative can be used to predict the density
of users in a certain zone and optimize the cellular service in that area.Comment: 14 pages, submitted for publication to ETT Transactions on Emerging
Telecommunications Technologie
A centralized localization algorithm for prolonging the lifetime of wireless sensor networks using particle swarm optimization in the existence of obstacles
The evolution in micro-electro-mechanical systems technology (MEMS) has
triggered the need for the development of wireless sensor network (WSN). These
wireless sensor nodes has been used in many applications at many areas. One of the
main issues in WSN is the energy availability, which is always a constraint. In a
previous research, a relocating algorithm for mobile sensor network had been
introduced and the goal was to save energy and prolong the lifetime of the sensor
networks using Particle Swarm Optimization (PSO) where both of sensing radius and
travelled distance had been optimized in order to save energy in long-term and shortterm.
Yet, the previous research did not take into account obstaclesâ existence in the
field and this will cause the sensor nodes to consume more power if obstacles are
exists in the sensing field. In this project, the same centralized relocating algorithm
from the previous research has been used where 15 mobile sensors deployed
randomly in a field of 100 meter by 100 meter where these sensors has been
deployed one time in a field that obstacles does not exist (case 1) and another time in
a field that obstacles existence has been taken into account (case 2), in which these
obstacles has been pre-defined positions, where these two cases applied into two
different algorithms, which are the original algorithm of a previous research and the
modified algorithm of this thesis. Particle Swarm Optimization has been used in the
proposed algorithm to minimize the fitness function. Voronoi diagram has also used
in order to ensure that the mobile sensors cover the whole sensing field. In this
project, the objectives will be mainly focus on the travelling distance, which is the
mobility module, of the mobile sensors in the network because the distance that the
sensor node travels, will consume too much power from this node and this will lead
to shortening the lifetime of the sensor network. So, the travelling distance, power
consumption and lifetime of the network will be calculated in both cases for original
algorithm and modified algorithm, which is a modified deployment algorithm, and compared between them. Moreover, the maximum sensing range is calculated, which
is 30 meter, by using the binary sensing model even though the sensing module does
not consume too much power compared to the mobility module. Finally, the
comparison of the results in the original method will show that this algorithm is not
suitable for an environment where obstacle exist because sensors will consume too
much power compared to the sensors that deployed in environment that free of
obstacles. While the results of the modified algorithm of this research will be more
suitable for both environments, that is environment where obstacles are not exist and
environment where obstacles are exist, because sensors in this algorithm .will
consume almost the same amount of power at both of these environments
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent âdevicesâ, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew âcognitive devicesâ are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
2011 Strategic roadmap for Australian research infrastructure
The 2011 Roadmap articulates the priority research infrastructure areas of a national scale (capability areas) to further develop Australiaâs research capacity and improve innovation and
research outcomes over the next five to ten years. The capability areas have been identified through considered analysis of input provided by stakeholders, in conjunction with specialist advice from Expert Working Groups
It is intended the Strategic Framework will provide a high-level policy framework, which will include principles to guide the development of policy advice and the design of programs related to the funding of research infrastructure by the Australian Government. Roadmapping has been identified in the Strategic Framework Discussion Paper as the most appropriate prioritisation mechanism for national, collaborative research infrastructure. The strategic identification of Capability areas through a consultative roadmapping process was also validated in the report of the 2010 NCRIS Evaluation.
The 2011 Roadmap is primarily concerned with medium to large-scale research infrastructure. However, any landmark infrastructure (typically involving an investment in excess of $100 million over five years from the Australian Government) requirements identified in this process will be noted. NRIC has also developed a âProcess to identify and prioritise Australian Government landmark research infrastructure investmentsâ which is currently under consideration by the government as part of broader deliberations relating to research infrastructure.
NRIC will have strategic oversight of the development of the 2011 Roadmap as part of its overall policy view of research infrastructure
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