2,383 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
A Comprehensive and Effective Framework for Traffic Congestion Problem Based on the Integration of IoT and Data Analytics
This research was funded by the Deanship of Scientific Research, Islamic University of Madinah, Saudi Arabia, under Tamayuz research grant number 2/710.Traffic congestion is still a challenge faced by most countries of the world. However, it can
be solved most effectively by integrating modern technologies such as Internet of Things (IoT), fog
computing, cloud computing, data analytics, and so on, into a framework that exploits the strengths of
these technologies to address specific problems faced in traffic management. Unfortunately, no such
framework that addresses the reliability, flexibility, and efficiency issues of smart-traffic management
exists. Therefore, this paper proposes a comprehensive framework to achieve a reliable, flexible, and
efficient solution for the problem of traffic congestion. The proposed framework has four layers.
The first layer, namely, the sensing layer, uses multiple data sources to ensure a reliable and accurate
measurement of the traffic status of the streets, and forwards these data to the second layer. The
second layer, namely, the fog layer, consumes these data to make efficient decisions and also forwards
them to the third layer. The third layer, the cloud layer, permanently stores these data for analytics
and knowledge discoveries. Finally, the fourth layer, the services layer, provides assistant services for
traffic management. We also discuss the functional model of the framework and the technologies
that can be used at each level of the model. We propose a smart-traffic light algorithm at level 1 for
the efficient management of congestion at intersections, tweet-classification and image-processing
algorithms at level 2 for reliable and accurate decision-making, and support services at level 4 of the
functional model. We also evaluated the proposed smart-traffic light algorithm for its efficiency, and
the tweet classification and image-processing algorithms for their accuracy.Deanship of Scientific Research, Islamic University of Madinah, Saudi Arabia 2/71
End to End Performance Analysis of Relay Cooperative Communication Based on Parked Cars
Parking lots (PLs) are usually full with cars. If these cars are formed into
a self-organizing vehicular network, they can be new kind of road side units
(RSUs) in urban area to provide communication data forwarding between mobile
terminals nearby and a base station. However cars in PLs can leave at any time,
which is neglected in the existing studies. In this paper, we investigate relay
cooperative communication based on parked cars in PLs. Taking the impact of the
car's leaving behavior into consideration, we derive the expressions of outage
probability in a two-hop cooperative communication and its link capacity.
Finally, the numerical results show that the impact of a car's arriving time is
greater than the impact of the duration the car has parked on outage
probability.Comment: 7 pages, 7 figures, accepted by ICACT201
Edge computing and iot analytics for agile optimization in intelligent transportation systems
[EN] With the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens' mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing. These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated.This work was partially supported by the Spanish Ministry of Science (PID2019111100RB-C21/AEI/10.13039/501100011033, RED2018-102642-T), and the Erasmus+ program (2019I-ES01-KA103-062602).Peyman, M.; Copado, PJ.; Tordecilla, RD.; Do C. Martins, L.; Xhafa, F.; Juan-Pérez, ÁA. (2021). Edge computing and iot analytics for agile optimization in intelligent transportation systems. Energies. 14(19):1-26. https://doi.org/10.3390/en14196309126141
A Study on Recent Developments and Issues with Obstacle Detection Systems for Automated Vehicles
This paper reviews current developments and discusses some critical issues with obstacle detection systems for automated vehicles. The concept of autonomous driving is the driver towards future mobility. Obstacle detection systems play a crucial role in implementing and deploying autonomous driving on our roads and city streets. The current review looks at technology and existing systems for obstacle detection. Specifically, we look at the performance of LIDAR, RADAR, vision cameras, ultrasonic sensors, and IR and review their capabilities and behaviour in a number of different situations: during daytime, at night, in extreme weather conditions, in urban areas, in the presence of smooths surfaces, in situations where emergency service vehicles need to be detected and recognised, and in situations where potholes need to be observed and measured. It is suggested that combining different technologies for obstacle detection gives a more accurate representation of the driving environment. In particular, when looking at technological solutions for obstacle detection in extreme weather conditions (rain, snow, fog), and in some specific situations in urban areas (shadows, reflections, potholes, insufficient illumination), although already quite advanced, the current developments appear to be not sophisticated enough to guarantee 100% precision and accuracy, hence further valiant effort is needed
Modeling the Internet of Things: a simulation perspective
This paper deals with the problem of properly simulating the Internet of
Things (IoT). Simulating an IoT allows evaluating strategies that can be
employed to deploy smart services over different kinds of territories. However,
the heterogeneity of scenarios seriously complicates this task. This imposes
the use of sophisticated modeling and simulation techniques. We discuss novel
approaches for the provision of scalable simulation scenarios, that enable the
real-time execution of massively populated IoT environments. Attention is given
to novel hybrid and multi-level simulation techniques that, when combined with
agent-based, adaptive Parallel and Distributed Simulation (PADS) approaches,
can provide means to perform highly detailed simulations on demand. To support
this claim, we detail a use case concerned with the simulation of vehicular
transportation systems.Comment: Proceedings of the IEEE 2017 International Conference on High
Performance Computing and Simulation (HPCS 2017
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