3 research outputs found
Unleashing the Power of Participatory IoT with Blockchains for Increased Safety and Situation Awareness of Smart Cities
IoT emerges as an unprecedented paradigm with great potential for changing
how people interact, think and live. It is making existing Internet services
feasible in ways that were previously impossible, as well as paving the way for
new situation-awareness applications suitable for smart cities, such as
realtime video surveillance, traffic control, and emergency management. These
applications will typically rely on large numbers of IoT devices to collect and
collaboratively process streamed data to enable real-time decision making. In
this paper, we introduce the concept of Semantic Virtual Space (SVS), an
abstraction for virtualized cloud-enabled IoT infrastructure that is
commensurate with the goals and needs of these emerging smart city
applications, and propose and discuss scalable architectures and mechanisms
that enable and automate the deployment and management of multiple SVS
instances on top of the cloud-enabled IoT infrastructure
QoS-aware Dynamic Fog Service Provisioning
Recent advances in the areas of Internet of Things (IoT), Big Data, and
Machine Learning have contributed to the rise of a growing number of complex
applications. These applications will be data-intensive, delay-sensitive, and
real-time as smart devices prevail more in our daily life. Ensuring Quality of
Service (QoS) for delay-sensitive applications is a must, and fog computing is
seen as one of the primary enablers for satisfying such tight QoS requirements,
as it puts compute, storage, and networking resources closer to the user. In
this paper, we first introduce FogPlan, a framework for QoS-aware Dynamic Fog
Service Provisioning (QDFSP). QDFSP concerns the dynamic deployment of
application services on fog nodes, or the release of application services that
have previously been deployed on fog nodes, in order to meet low latency and
QoS requirements of applications while minimizing cost. FogPlan framework is
practical and operates with no assumptions and minimal information about IoT
nodes. Next, we present a possible formulation (as an optimization problem) and
two efficient greedy algorithms for addressing the QDFSP at one instance of
time. Finally, the FogPlan framework is evaluated using a simulation based on
real-world traffic traces.Comment: Accepted for publication in IEEE Internet of Things Journal, 201
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey
With the Internet of Things (IoT) becoming part of our daily life and our
environment, we expect rapid growth in the number of connected devices. IoT is
expected to connect billions of devices and humans to bring promising
advantages for us. With this growth, fog computing, along with its related edge
computing paradigms, such as multi-access edge computing (MEC) and cloudlet,
are seen as promising solutions for handling the large volume of
security-critical and time-sensitive data that is being produced by the IoT. In
this paper, we first provide a tutorial on fog computing and its related
computing paradigms, including their similarities and differences. Next, we
provide a taxonomy of research topics in fog computing, and through a
comprehensive survey, we summarize and categorize the efforts on fog computing
and its related computing paradigms. Finally, we provide challenges and future
directions for research in fog computing.Comment: 48 pages, 7 tables, 11 figures, 450 references. The data (categories
and features/objectives of the papers) of this survey are now available
publicly. Accepted by Elsevier Journal of Systems Architectur