12,113 research outputs found
How 5G wireless (and concomitant technologies) will revolutionize healthcare?
The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution
Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.
Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions
Traditional power grids are being transformed into Smart Grids (SGs) to
address the issues in existing power system due to uni-directional information
flow, energy wastage, growing energy demand, reliability and security. SGs
offer bi-directional energy flow between service providers and consumers,
involving power generation, transmission, distribution and utilization systems.
SGs employ various devices for the monitoring, analysis and control of the
grid, deployed at power plants, distribution centers and in consumers' premises
in a very large number. Hence, an SG requires connectivity, automation and the
tracking of such devices. This is achieved with the help of Internet of Things
(IoT). IoT helps SG systems to support various network functions throughout the
generation, transmission, distribution and consumption of energy by
incorporating IoT devices (such as sensors, actuators and smart meters), as
well as by providing the connectivity, automation and tracking for such
devices. In this paper, we provide a comprehensive survey on IoT-aided SG
systems, which includes the existing architectures, applications and prototypes
of IoT-aided SG systems. This survey also highlights the open issues,
challenges and future research directions for IoT-aided SG systems
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
IoT-Enabled Smart Cities: A Review of Concepts, Frameworks and Key Technologies
In recent years, smart cities have been significantly developed and have greatly expanded their potential. In fact, novel advancements to the Internet of things (IoT) have paved the way for new possibilities, representing a set of key enabling technologies for smart cities and allowing the production and automation of innovative services and advanced applications for the different city stakeholders. This paper presents a review of the research literature on IoT-enabled smart cities, with the aim of highlighting the main trends and open challenges of adopting IoT technologies for the development of sustainable and efficient smart cities. This work first provides a survey on the key technologies proposed in the literature for the implementation of IoT frameworks, and then a review of the main smart city approaches and frameworks, based on classification into eight domains, which extends the traditional six domain classification that is typically adopted in most of the related works
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