3,298 research outputs found
Software Platforms for Smart Cities: Concepts, Requirements, Challenges, and a Unified Reference Architecture
Making cities smarter help improve city services and increase citizens'
quality of life. Information and communication technologies (ICT) are
fundamental for progressing towards smarter city environments. Smart City
software platforms potentially support the development and integration of Smart
City applications. However, the ICT community must overcome current significant
technological and scientific challenges before these platforms can be widely
used. This paper surveys the state-of-the-art in software platforms for Smart
Cities. We analyzed 23 projects with respect to the most used enabling
technologies, as well as functional and non-functional requirements,
classifying them into four categories: Cyber-Physical Systems, Internet of
Things, Big Data, and Cloud Computing. Based on these results, we derived a
reference architecture to guide the development of next-generation software
platforms for Smart Cities. Finally, we enumerated the most frequently cited
open research challenges, and discussed future opportunities. This survey gives
important references for helping application developers, city managers, system
operators, end-users, and Smart City researchers to make project, investment,
and research decisions.Comment: Accepted for publication in ACM Computing Survey
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
Analytics-as-a-Service in a Multi-Cloud Environment through Semantically-enabled Hierarchical Data Processing
yesA large number of cloud middleware platforms and tools are deployed to support a variety of Internet
of Things (IoT) data analytics tasks. It is a common practice that such cloud platforms are only used
by its owners to achieve their primary and predefined objectives, where raw and processed data are only
consumed by them. However, allowing third parties to access processed data to achieve their own objectives
significantly increases intergation, cooperation, and can also lead to innovative use of the data. Multicloud,
privacy-aware environments facilitate such data access, allowing different parties to share processed
data to reduce computation resource consumption collectively. However, there are interoperability issues in
such environments that involve heterogeneous data and analytics-as-a-service providers. There is a lack of
both - architectural blueprints that can support such diverse, multi-cloud environments, and corresponding
empirical studies that show feasibility of such architectures. In this paper, we have outlined an innovative
hierarchical data processing architecture that utilises semantics at all the levels of IoT stack in multicloud
environments. We demonstrate the feasibility of such architecture by building a system based on this
architecture using OpenIoT as a middleware, and Google Cloud and Microsoft Azure as cloud environments.
The evaluation shows that the system is scalable and has no significant limitations or overheads
- …