5,453 research outputs found
Reconfigurable Security: Edge Computing-based Framework for IoT
In various scenarios, achieving security between IoT devices is challenging
since the devices may have different dedicated communication standards,
resource constraints as well as various applications. In this article, we first
provide requirements and existing solutions for IoT security. We then introduce
a new reconfigurable security framework based on edge computing, which utilizes
a near-user edge device, i.e., security agent, to simplify key management and
offload the computational costs of security algorithms at IoT devices. This
framework is designed to overcome the challenges including high computation
costs, low flexibility in key management, and low compatibility in deploying
new security algorithms in IoT, especially when adopting advanced cryptographic
primitives. We also provide the design principles of the reconfigurable
security framework, the exemplary security protocols for anonymous
authentication and secure data access control, and the performance analysis in
terms of feasibility and usability. The reconfigurable security framework paves
a new way to strength IoT security by edge computing.Comment: under submission to possible journal publication
Electric Vehicles Charging Control based on Future Internet Generic Enablers
In this paper a rationale for the deployment of Future Internet based
applications in the field of Electric Vehicles (EVs) smart charging is
presented. The focus is on the Connected Device Interface (CDI) Generic Enabler
(GE) and the Network Information and Controller (NetIC) GE, which are
recognized to have a potential impact on the charging control problem and the
configuration of communications networks within reconfigurable clusters of
charging points. The CDI GE can be used for capturing the driver feedback in
terms of Quality of Experience (QoE) in those situations where the charging
power is abruptly limited as a consequence of short term grid needs, like the
shedding action asked by the Transmission System Operator to the Distribution
System Operator aimed at clearing networks contingencies due to the loss of a
transmission line or large wind power fluctuations. The NetIC GE can be used
when a master Electric Vehicle Supply Equipment (EVSE) hosts the Load Area
Controller, responsible for managing simultaneous charging sessions within a
given Load Area (LA); the reconfiguration of distribution grid topology results
in shift of EVSEs among LAs, then reallocation of slave EVSEs is needed.
Involved actors, equipment, communications and processes are identified through
the standardized framework provided by the Smart Grid Architecture Model
(SGAM).Comment: To appear in IEEE International Electric Vehicle Conference (IEEE
IEVC 2014
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A classification of emerging and traditional grid systems
The grid has evolved in numerous distinct phases. It started in the early ’90s as a model of metacomputing in which supercomputers share resources; subsequently, researchers added the ability to share data. This is usually referred to as the first-generation grid. By the late ’90s, researchers had outlined the framework for second-generation grids, characterized by their use of grid middleware systems to “glue” different grid technologies together. Third-generation grids originated in the early millennium when Web technology was combined with second-generation grids. As a result, the invisible grid, in which grid complexity is fully hidden through resource virtualization, started receiving attention. Subsequently, grid researchers identified the requirement for semantically rich knowledge grids, in which middleware technologies are more intelligent and autonomic. Recently, the necessity for grids to support and extend the ambient intelligence vision has emerged. In AmI, humans are surrounded by computing technologies that are unobtrusively embedded in their surroundings.
However, third-generation grids’ current architecture doesn’t meet the requirements of next-generation grids (NGG) and service-oriented knowledge utility (SOKU).4 A few years ago, a group of independent experts, arranged by the European Commission, identified these shortcomings as a way to identify potential European grid research priorities for 2010 and beyond. The experts envision grid systems’ information, knowledge, and processing capabilities as a set of utility services.3 Consequently, new grid systems are emerging to materialize these visions. Here, we review emerging grids and classify them to motivate further research and help establish a solid foundation in this rapidly evolving area
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