5 research outputs found
Mobile-Edge Computing Come Home! Connecting things in future smart homes using LTE device-to-device communications
Future 4G/5G networks are expected to play a major
role in supporting the future Internet of Things (IoT), due to
their widespread, ubiquitous coverage, plug-and-play configuration
and embedded security. Besides connectivity, however, future
IoT will need computation and storage in proximity of sensors
and actuators to support time-critical and opportunistic applications.
To this aim, the introduction of Mobile Edge Computing
(MEC) is currently under standardization as a novel paradigm
to enable deployment of applications straight into the network.
In this work we analyze solutions to bring MEC computation
and storage as close as possible to end users and smart objects.
First, a smart-home system is designed as a reference smallscale
IoT system to derive network requirements; then alternative
network configurations to support such requirements are
analyzed to highlight their pros and cons. In particular, we show
how LTE Device-to-Device (D2D) operation mode can be exploited
to guarantee proximity communication with reduced costs.
Finally, the expected benefits for operators are assessed via simulation
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
Fog based intelligent transportation big data analytics in the internet of vehicles environment: motivations, architecture, challenges, and critical issues
The intelligent transportation system (ITS) concept was introduced to increase road safety, manage traffic efficiently, and preserve our green environment. Nowadays, ITS applications are becoming more data-intensive and their data are described using the '5Vs of Big Data'. Thus, to fully utilize such data, big data analytics need to be applied. The Internet of vehicles (IoV) connects the ITS devices to cloud computing centres, where data processing is performed. However, transferring huge amount of data from geographically distributed devices creates network overhead and bottlenecks, and it consumes the network resources. In addition, following the centralized approach to process the ITS big data results in high latency which cannot be tolerated by the delay-sensitive ITS applications. Fog computing is considered a promising technology for real-time big data analytics. Basically, the fog technology complements the role of cloud computing and distributes the data processing at the edge of the network, which provides faster responses to ITS application queries and saves the network resources. However, implementing fog computing and the lambda architecture for real-time big data processing is challenging in the IoV dynamic environment. In this regard, a novel architecture for real-time ITS big data analytics in the IoV environment is proposed in this paper. The proposed architecture merges three dimensions including intelligent computing (i.e. cloud and fog computing) dimension, real-time big data analytics dimension, and IoV dimension. Moreover, this paper gives a comprehensive description of the IoV environment, the ITS big data characteristics, the lambda architecture for real-time big data analytics, several intelligent computing technologies. More importantly, this paper discusses the opportunities and challenges that face the implementation of fog computing and real-time big data analytics in the IoV environment. Finally, the critical issues and future research directions section discusses some issues that should be considered in order to efficiently implement the proposed architecture