8,275 research outputs found
Smart Grid Technologies in Europe: An Overview
The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity networkāthe smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Unio
MOSDEN: An Internet of Things Middleware for Resource Constrained Mobile Devices
The Internet of Things (IoT) is part of Future Internet and will comprise
many billions of Internet Connected Objects (ICO) or `things' where things can
sense, communicate, compute and potentially actuate as well as have
intelligence, multi-modal interfaces, physical/ virtual identities and
attributes. Collecting data from these objects is an important task as it
allows software systems to understand the environment better. Many different
hardware devices may involve in the process of collecting and uploading sensor
data to the cloud where complex processing can occur. Further, we cannot expect
all these objects to be connected to the computers due to technical and
economical reasons. Therefore, we should be able to utilize resource
constrained devices to collect data from these ICOs. On the other hand, it is
critical to process the collected sensor data before sending them to the cloud
to make sure the sustainability of the infrastructure due to energy
constraints. This requires to move the sensor data processing tasks towards the
resource constrained computational devices (e.g. mobile phones). In this paper,
we propose Mobile Sensor Data Processing Engine (MOSDEN), an plug-in-based IoT
middleware for mobile devices, that allows to collect and process sensor data
without programming efforts. Our architecture also supports sensing as a
service model. We present the results of the evaluations that demonstrate its
suitability towards real world deployments. Our proposed middleware is built on
Android platform
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges
Today's mobile phones are far from mere communication devices they were ten
years ago. Equipped with sophisticated sensors and advanced computing hardware,
phones can be used to infer users' location, activity, social setting and more.
As devices become increasingly intelligent, their capabilities evolve beyond
inferring context to predicting it, and then reasoning and acting upon the
predicted context. This article provides an overview of the current state of
the art in mobile sensing and context prediction paving the way for
full-fledged anticipatory mobile computing. We present a survey of phenomena
that mobile phones can infer and predict, and offer a description of machine
learning techniques used for such predictions. We then discuss proactive
decision making and decision delivery via the user-device feedback loop.
Finally, we discuss the challenges and opportunities of anticipatory mobile
computing.Comment: 29 pages, 5 figure
Video analytics on the MLK Smart Corridor testbed
With the predicted boom of urban environment populations in the next 30 years, many new challenges in urban transportation will surface. In an effort to mitigate these, the Center for Urban Informatics and Progress (CUIP) has been introduced along with its testbed. One opportunity this testbed provides is the ability to utilize computer vision and video analytics to anonymously gather data on how citizens traverse the city. This thesis shall discuss an approach to real-time object tracking that serves as a basis for further analytics such as traffic flow data collection and near-miss detection. The proposed video analytics platform will aid citizens with their day-to-day commute through the corridor by deriving real-time data based on actual behavior seen in the citizens\u27 commute. Furthermore, since the testbed is ever-expanding in both hardware and size the algorithms and software proposed in this thesis are designed to prioritize scalability
Navigating the IoT landscape: Unraveling forensics, security issues, applications, research challenges, and future
Given the exponential expansion of the internet, the possibilities of
security attacks and cybercrimes have increased accordingly. However, poorly
implemented security mechanisms in the Internet of Things (IoT) devices make
them susceptible to cyberattacks, which can directly affect users. IoT
forensics is thus needed for investigating and mitigating such attacks. While
many works have examined IoT applications and challenges, only a few have
focused on both the forensic and security issues in IoT. Therefore, this paper
reviews forensic and security issues associated with IoT in different fields.
Future prospects and challenges in IoT research and development are also
highlighted. As demonstrated in the literature, most IoT devices are vulnerable
to attacks due to a lack of standardized security measures. Unauthorized users
could get access, compromise data, and even benefit from control of critical
infrastructure. To fulfil the security-conscious needs of consumers, IoT can be
used to develop a smart home system by designing a FLIP-based system that is
highly scalable and adaptable. Utilizing a blockchain-based authentication
mechanism with a multi-chain structure can provide additional security
protection between different trust domains. Deep learning can be utilized to
develop a network forensics framework with a high-performing system for
detecting and tracking cyberattack incidents. Moreover, researchers should
consider limiting the amount of data created and delivered when using big data
to develop IoT-based smart systems. The findings of this review will stimulate
academics to seek potential solutions for the identified issues, thereby
advancing the IoT field.Comment: 77 pages, 5 figures, 5 table
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