Abdullah Kadri

Wireless sensor network for real-time air pollution monitoring

This paper presents an ambient real-time air quality monitoring system. The system consists of several distributed monitoring stations that communicate wirelessly with a backend server using machine-to-machine communication. Each station is equipped with gaseous and meteorological sensors as well as data logging and wireless communication capabilities. The backend server collects real time data from the stations and converts it into information delivered to users through web portals and mobile applications. The system is implemented in pilot phase and four solar-powered stations are deployed over an area of 1 km2. Data over four months has been collected and performance analysis and assessment are performed. As the historical data bank becomes richer, more sophisticated operations can be performed.

UAV-Enabled Intelligent Transportation Systems for the Smart City: Applications and Challenges

There could be no smart city without a reliable and efficient transportation system. This necessity makes the ITS a key component of any smart city concept. While legacy ITS technologies are deployed worldwide in smart cities, enabling the next generation of ITS relies on effective integration of connected and autonomous vehicles, the two technologies that are under wide field testing in many cities around the world. Even though these two emerging technologies are crucial in enabling fully automated transportation systems, there is still a significant need to automate other road and transportation components. To this end, due to their mobility, autonomous operation, and communication/processing capabilities, UAVs are envisaged in many ITS application domains. This article describes the possible ITS applications that can use UAVs, and highlights the potential and challenges for UAV-enabled ITS for next-generation smart cities.

Visible Light Communication for Vehicular Networking: Performance Study of a V2V System Using a Measured Headlamp Beam Pattern Model

In this article, we discuss visible light communication (VLC) in the context of vehicular communication networks. With the omnipresence of light-emitting diodes (LEDs) in outdoor and automotive lightings, VLC emerges as a natural candidate for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. We first provide an overview of this emerging research area highlighting recent advances and identifying open problems for further research. Then, we present the performance evaluation of a typical V2V VLC system with realistic automative light sources. Our evaluation takes into account the measured headlamp beam pattern and the impact of road reflected light. We demonstrate that depending on the photodetector (PD) position above the ground level, a data rate of 50 Mb/s can be achieved at 70 m.

Performance Analysis of Passive UHF RFID Systems Under Cascaded Fading Channels and Interference Effects

In this paper, the performance of monostatic and bistatic passive ultra high frequency radio frequency identification (UHF RFID) systems under the effects of cascaded fading channels and interference is studied. The performance metric used is tag detection probability defined as the probability that the instantaneous received power is higher than the readers sensitivity. A closed-form expression of the detection probability is derived using cascaded forward and backscatter fading channels and the reader antennas orientation relative to the tag. Furthermore, the performance of passive UHF RFID systems under reader-to-tag interference caused by both the desired RFID signal and multiple RFID interferers is analyzed, and the effect of constructive and destructive interferences is examined. In addition, the maximum reading range in ideal, multipath fading, and interfering environments is presented. To the best of our knowledge, this is the first work that provides a 3-D performance analysis of the passive UHF RFID systems under cascaded fading channels. The obtained results are very useful for the design and optimization of passive UHF RFID systems from an RF physical channel point of view.

Low-Power Chirp Spread Spectrum Signals for Wireless Communication Within Nuclear Power Plants

Abstract There are two major barriers in deploying wireless communication systems in nuclear power plants (NPPs): (a) the electromagnetic compatibility (EMC) between the wireless devices and the existing plant instrumentation and control systems, and (b) the high levels of electromagnetic noise and interference from high-powered devices and ionizing radiation sources. In a typical NPP there exist strict regulations that limit transmission power levels to avoid interfering with the sensitive safety systems inside the containment such as ion chambers. This will result in performance degradation of wireless communication systems. This paper proposes a wireless communication scheme based on low-power chirp spread spectrum (CSS) signals, which meet with the EMC requirements of NPPs and also are capable of providing interference rejection. The advantage of such a scheme is that satisfactory performance can be obtained using low levels of transmission power. The structure of the optimal receiver for low-power binary CSS signals and a closed-form expression for asymptotic bit error rate of this receiver are derived. The electromagnetic environment within an NPP is modeled as a Gaussian-Gaussian mixture process, which is based on the measurement data published in a U.S. Nuclear Regulatory Commission Regulation (NUREG). The parameters in the model can be adjusted to suit a particular NPP site.

Urban Air Pollution Monitoring System With Forecasting Models

A system for monitoring and forecasting urban air pollution is presented in this paper. The system uses low-cost air-quality monitoring motes that are equipped with an array of gaseous and meteorological sensors. These motes wirelessly communicate to an intelligent sensing platform that consists of several modules. The modules are responsible for receiving and storing the data, preprocessing and converting the data into useful information, forecasting the pollutants based on historical information, and finally presenting the acquired information through different channels, such as mobile application, Web portal, and short message service. The focus of this paper is on the monitoring system and its forecasting module. Three machine learning (ML) algorithms are investigated to build accurate forecasting models for one-step and multi-step ahead of concentrations of ground-level ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2). These ML algorithms are support vector machines, M5P model trees, and artificial neural networks (ANN). Two types of modeling are pursued: 1) univariate and 2) multivariate. The performance evaluation measures used are prediction trend accuracy and root mean square error (RMSE). The results show that using different features in multivariate modeling with M5P algorithm yields the best forecasting performances. For example, using M5P, RMSE is at its lowest, reaching 31.4, when hydrogen sulfide (H2S) is used to predict SO2. Contrarily, the worst performance, i.e., RMSE of 62.4, for SO2 is when using ANN in univariate modeling. The outcome of this paper can be significantly useful for alarming applications in areas with high air pollution levels.

A Hybrid Energy Sharing Framework for Green Cellular Networks

Cellular operators are increasingly turning toward renewable energy (RE) as an alternative to using traditional electricity in order to reduce operational expenditure and carbon footprint. Due to the randomness in both RE generation and mobile traffic at each base station (BS), a surplus or shortfall of energy may occur at any given time. To increase energy self-reliance and minimize the network’s energy cost, the operator needs to efficiently exploit the RE generated across all BSs. In this paper, a hybrid energy sharing framework for cellular network is proposed, where a combination of physical power lines and energy trading with other BSs using smart grid is used. Algorithms for physical power lines deployment between BSs, based on average and complete statistics of the net RE available, are developed. Afterward, an energy management framework is formulated to optimally determine the quantities of electricity and RE to be procured and exchanged among BSs, respectively, while considering battery capacities and real-time energy pricing. Three cases are investigated, where RE generation is unknown, perfectly known, and partially known ahead of time. Results investigate the time varying energy management of BSs and demonstrate considerable reduction in average energy cost thanks to the hybrid energy sharing scheme.

Cooperative wireless sensor networks for green internet of things

Green internet of things are investigated by studying energy-efficiency in wireless sensor networks. A cooperative multihop data transmission approach is presented and analyzed. Significant energy savings are achieved with the proposed approach compared to the non cooperative scenario, in addition to better delay results.

Front-End Intelligence for Large-Scale Application-Oriented Internet-of-Things

The Internet-of-things (IoT) refer to the massive integration of electronic devices, vehicles, buildings, and other objects to collect and exchange data. It is the enabling technology for a plethora of applications touching various aspects of our lives, such as healthcare, wearables, surveillance, home automation, smart manufacturing, and intelligent automotive systems. Existing IoT architectures are highly centralized and heavily rely on a back-end core network for all decision-making processes. This may lead to inefficiencies in terms of latency, network traffic management, computational processing, and power consumption. In this paper, we advocate the empowerment of front-end IoT devices to support the back-end network in fulfilling end-user applications requirements mainly by means of improved connectivity and efficient network management. A novel conceptual framework is presented for a new generation of IoT devices that will enable multiple new features for both the IoT administrators as well as end users. Exploiting the recent emergence of software-defined architecture, these smart IoT devices will allow fast, reliable, and intelligent management of diverse IoT-based applications. After highlighting relevant shortcomings of the existing IoT architectures, we outline some key design perspectives to enable front-end intelligence while shedding light on promising future research directions.

Air quality monitoring and analysis in Qatar using a wireless sensor network deployment

In this paper, an actual deployment of a wireless sensor network is described. The purpose of the sensor network is to monitor and analyze air quality in Doha. Small scale wireless sensor stations communicate with a backend server to relay their measurements in real-time. Data stored on the server is subjected to intelligent processing and analysis in order to present it in different formats for different categories of end users. This paper describes a user friendly computation of an air quality index to disseminate the data to the general public. In addition, it describes data presentation for environmental experts using dedicated software tools, e.g. the R software system and its OpenAir package. Analysis and assessment of real measurement data is also performed in the paper.