Ansar Yasar

Bio: Ansar Yasar is an academic researcher from University of Hasselt. The author has contributed to research in topics: Cloud computing & Node (networking). The author has an hindex of 8, co-authored 34 publications receiving 410 citations.

UAV-enabled intelligent traffic policing and emergency response handling system for the smart city

Abstract: As modern cities expand and develop, the resultant increase in population density gives rise to the need for smart solutions to cope with the demands applied to the infrastructure of the city. In this paper, we investigate the shortcomings of traffic policing and emergency response handling systems; propose an intelligent, autonomous UAV-enabled solution; and describe the system in a simulated environment. Several scenarios of traffic monitoring and policing system are considered in the simulation: traffic light violations and accident detection, mobile speeding traps and automated notification, congestion detection and traffic rerouting, flagged stolen vehicles/pending arrest warrants and vehicle tracking using UAVs, and autonomous emergency response handling systems. Furthermore, smart city infrastructure enable intelligent handling of emergencies by providing traffic light prioritization for ground emergency response units to reduce delay for patient care, automated physical bollard on routes with congested points due to accidents or hazards, first responder support UAV units—medical supplies UAV, fire fighting UAV to combat or control small fires, and numerous other benefits. Lastly, we present the results of the simulated system and discuss our findings.

An agent-based model to evaluate carpooling at large manufacturing plants

Abstract: Carpooling is thought to be part of the solution to resolve traffic congestion in regions where large companies dominate the traffic situation because coordination and matching between commuters is more likely to be feasible in cases where most people work for a single employer. Moreover, carpooling is not very popular for commuting. In order for carpooling to be successful, an online service for matching commuter profiles is indispensable due to the large community involved. Such service is necessary but not sufficient because carpooling requires rerouting and activity rescheduling along with candidate matching. We advise to introduce services of this kind using a two step process: (1) an agentbased simulation is used to investigate opportunities and inhibitors and (2) online matching is made available. This paper describes the challenges to build the model and in particular investigates possibilities to derive the data required for commuter behavior modeling from big data (such as GSM, GPS and/or Bluetooth).

Evaluation of a low-cost optical particle counter (Alphasense OPC-N2) for ambient air monitoring

Abstract: . A fast-growing area of research is the development of low-cost sensors for measuring air pollutants. The affordability and size of low-cost particle sensors makes them an attractive option for use in experiments requiring a number of instruments such as high-density spatial mapping. However, for these low-cost sensors to be useful for these types of studies their accuracy and precision need to be quantified. We evaluated the Alphasense OPC-N2, a promising low-cost miniature optical particle counter, for monitoring ambient airborne particles at typical urban background sites in the UK. The precision of the OPC-N2 was assessed by co-locating 14 instruments at a site to investigate the variation in measured concentrations. Comparison to two different reference optical particle counters as well as a TEOM-FDMS enabled the accuracy of the OPC-N2 to be evaluated. Comparison of the OPC-N2 to the reference optical instruments shows some limitations for measuring mass concentrations of PM 1 , PM 2.5 and PM 10 . The OPC-N2 demonstrated a significant positive artefact in measured particle mass during times of high ambient RH (> 85 %) and a calibration factor was developed based upon κ -Kohler theory, using average bulk particle aerosol hygroscopicity. Application of this RH correction factor resulted in the OPC-N2 measurements being within 33 % of the TEOM-FDMS, comparable to the agreement between a reference optical particle counter and the TEOM-FDMS (20 %). Inter-unit precision for the 14 OPC-N2 sensors of 22 ± 13 % for PM 10 mass concentrations was observed. Overall, the OPC-N2 was found to accurately measure ambient airborne particle mass concentration provided they are (i) correctly calibrated and (ii) corrected for ambient RH. The level of precision demonstrated between multiple OPC-N2s suggests that they would be suitable devices for applications where the spatial variability in particle concentration was to be determined.

Underwater Wireless Sensor Networks: A Review of Recent Issues and Challenges

Abstract: Underwater Wireless Sensor Networks (UWSNs) contain several components such as vehicles and sensors that are deployed in a specific acoustic area to perform collaborative monitoring and data collection tasks. These networks are used interactively between different nodes and ground-based stations. Presently, UWSNs face issues and challenges regarding limited bandwidth, high propagation delay, 3D topology, media access control, routing, resource utilization, and power constraints. In the last few decades, research community provided different methodologies to overcome these issues and challenges; however, some of them are still open for research due to variable characteristics of underwater environment. In this paper, a survey of UWSN regarding underwater communication channel, environmental factors, localization, media access control, routing protocols, and effect of packet size on communication is conducted. We compared presently available methodologies and discussed their pros and cons to highlight new directions of research for further improvement in underwater sensor networks.

The influence of humidity on the performance of a low-cost air particle mass sensor and the effect of atmospheric fog

Abstract: . While low-cost particle sensors are increasingly being used in numerous applications, most of them have no heater or dryer at the inlet to remove water from the sample before measurement. Deliquescent growth of particles and the formation of fog droplets in the atmosphere can lead to significant increases in particle number concentration (PNC) and mass concentrations reported by such sensors. We carried out a detailed study using a Plantower PMS1003 low-cost particle sensor, both in the laboratory and under actual ambient field conditions, to investigate its response to increasing humidity and the presence of fog in the air. We found significant increases in particle number and mass concentrations at relative humidity above about 75 %. During a period of fog, the total PNC increased by 28 %, while the PNC larger than 2.5 µ m increased by over 50 %. The PM 10 concentration reported by the PMS1003 was 46 % greater than that on the standard monitor with a charcoal dryer at the inlet. While there is a causal link between particle pollution and adverse health effects, the presence of water on the particles is not harmful to humans. Therefore, air quality standards for particles are specifically limited to solid particles and standard particle monitoring instruments are fitted with a heater or dryer at the inlet to remove all liquid material from the sample before the concentrations are measured. This study shows that it is important to understand that the results provided by low-cost particle sensors, such as the PMS1003, cannot be used to ascertain if air quality standards are being met.

A Survey on Sensor Calibration in Air Pollution Monitoring Deployments

Abstract: Air pollution is a major concern for public health and urban environments Conventional air pollution monitoring systems install a few highly accurate, expensive stations at representative locations Their sparse coverage and low spatial resolution are insufficient to quantify urban air pollution and its impacts on human health and environment Advances in low-cost portable air pollution sensors have enabled air pollution monitoring deployments at scale to measure air pollution at high spatiotemporal resolution However, it is challenging to ensure the accuracy of these low-cost sensor deployments because the sensors are more error-prone than high-end sensing infrastructures and they are often deployed in harsh environments Sensor calibration has proven to be effective to improve the data quality of low-cost sensors and maintain the reliability of long-term, distributed sensor deployments In this paper, we review the state-of-the-art low-cost air pollution sensors, identify their major error sources, and comprehensively survey calibration models as well as network recalibration strategies suited for different sensor deployments We also discuss limitations of exiting methods and conclude with open issues for future sensor calibration research