16,180 research outputs found
Middleware Technologies for Cloud of Things - a survey
The next wave of communication and applications rely on the new services
provided by Internet of Things which is becoming an important aspect in human
and machines future. The IoT services are a key solution for providing smart
environments in homes, buildings and cities. In the era of a massive number of
connected things and objects with a high grow rate, several challenges have
been raised such as management, aggregation and storage for big produced data.
In order to tackle some of these issues, cloud computing emerged to IoT as
Cloud of Things (CoT) which provides virtually unlimited cloud services to
enhance the large scale IoT platforms. There are several factors to be
considered in design and implementation of a CoT platform. One of the most
important and challenging problems is the heterogeneity of different objects.
This problem can be addressed by deploying suitable "Middleware". Middleware
sits between things and applications that make a reliable platform for
communication among things with different interfaces, operating systems, and
architectures. The main aim of this paper is to study the middleware
technologies for CoT. Toward this end, we first present the main features and
characteristics of middlewares. Next we study different architecture styles and
service domains. Then we presents several middlewares that are suitable for CoT
based platforms and lastly a list of current challenges and issues in design of
CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268,
Digital Communications and Networks, Elsevier (2017
Middleware Technologies for Cloud of Things - a survey
The next wave of communication and applications rely on the new services
provided by Internet of Things which is becoming an important aspect in human
and machines future. The IoT services are a key solution for providing smart
environments in homes, buildings and cities. In the era of a massive number of
connected things and objects with a high grow rate, several challenges have
been raised such as management, aggregation and storage for big produced data.
In order to tackle some of these issues, cloud computing emerged to IoT as
Cloud of Things (CoT) which provides virtually unlimited cloud services to
enhance the large scale IoT platforms. There are several factors to be
considered in design and implementation of a CoT platform. One of the most
important and challenging problems is the heterogeneity of different objects.
This problem can be addressed by deploying suitable "Middleware". Middleware
sits between things and applications that make a reliable platform for
communication among things with different interfaces, operating systems, and
architectures. The main aim of this paper is to study the middleware
technologies for CoT. Toward this end, we first present the main features and
characteristics of middlewares. Next we study different architecture styles and
service domains. Then we presents several middlewares that are suitable for CoT
based platforms and lastly a list of current challenges and issues in design of
CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268,
Digital Communications and Networks, Elsevier (2017
Microservices: Granularity vs. Performance
Microservice Architectures (MA) have the potential to increase the agility of
software development. In an era where businesses require software applications
to evolve to support software emerging requirements, particularly for Internet
of Things (IoT) applications, we examine the issue of microservice granularity
and explore its effect upon application latency. Two approaches to microservice
deployment are simulated; the first with microservices in a single container,
and the second with microservices partitioned across separate containers. We
observed a neglibible increase in service latency for the multiple container
deployment over a single container.Comment: 6 pages, conferenc
Applying Lessons from Cyber Attacks on Ukrainian Infrastructures to Secure Gateways onto the Industrial Internet of Things
Previous generations of safety-related industrial control systems were ‘air gapped’. In other words, process control
components including Programmable Logic Controllers (PLCs) and smart sensor/actuators were disconnected and
isolated from local or wide area networks. This provided a degree of protection; attackers needed physical access to
compromise control systems components. Over time this ‘air gap’ has gradually been eroded. Switches and
gateways have subsequently interfaced industrial protocols, including Profibus and Modbus, so that data can be
drawn from safety-related Operational Technology into enterprise information systems using TCP/IP. Senior
management uses these links to monitor production processes and inform strategic planning. The Industrial Internet
of Things represents another step in this evolution – enabling the coordination of physically distributed resources
from a centralized location. The growing range and sophistication of these interconnections create additional
security concerns for the operation and management of safety-critical systems. This paper uses lessons learned
from recent attacks on Ukrainian critical infrastructures to guide a forensic analysis of an IIoT switch. The intention
is to identify and mitigate vulnerabilities that would enable similar attacks to be replicated across Europe and North
America
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
Requirements traceability in model-driven development: Applying model and transformation conformance
The variety of design artifacts (models) produced in a model-driven design process results in an intricate relationship between requirements and the various models. This paper proposes a methodological framework that simplifies management of this relationship, which helps in assessing the quality of models, realizations and transformation specifications. Our framework is a basis for understanding requirements traceability in model-driven development, as well as for the design of tools that support requirements traceability in model-driven development processes. We propose a notion of conformance between application models which reduces the effort needed for assessment activities. We discuss how this notion of conformance can be integrated with model transformations
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