Walid Balid

Evaluating Bluetooth Low Energy in realistic wireless environments

The 2.4 GHz ISM band is crowded with a wide variety of wireless devices operating under various access protocols, Bluetooth Low Energy (BLE) among them. Low power requirements, low cost, and ease of integration have promoted BLEs rapidly growing popularity. BLE applications range from providing wireless interface for monitoring household equipment status to reporting critical information from medical devices that might have less tolerance for transmission errors to function properly. In this paper, we identify risks in a real world wireless environment that adversely affect BLE system functionality. We also propose a methodology utilizing spectrum surveys to quantify probability of transmission failure relative to the systems interference detection threshold. Spectrum surveys were conducted in a basketball sport facility, a university student union and a hospital intensive care unit (ICU). Results demonstrate how a BLE system selects data transmission channels in the presence of interference. Moreover, findings of this study confirm that a BLE system is able to maintain a low probability of failed transmission while operating in the presence of high interference unless the environment noise floor is close to the employed interference detection threshold.

Intelligent Vehicle Counting and Classification Sensor for Real-Time Traffic Surveillance

Real-time traffic surveillance is essential in today’s intelligent transportation systems and will surely play a vital role in tomorrow’s smart cities. The work detailed in this paper reports on the development and implementation of a novel smart wireless sensor for traffic monitoring. Computationally efficient and reliable algorithms for vehicle detection, speed and length estimation, classification, and time-synchronization were fully developed, integrated, and evaluated. Comprehensive system evaluation and extensive data analysis were performed to tune and validate the system for a reliable and robust operation. Several field studies conducted on highway and urban roads for different scenarios and under various traffic conditions resulted in 99.98% detection accuracy, 97.11% speed estimation accuracy, and 97% length-based vehicle classification accuracy. The developed system is portable, reliable, and cost-effective. The system can also be used for short-term or long-term installment on surface of highway, roadway, and roadside. Implementation cost of a single node including enclosure is US

Versatile real-time traffic monitoring system using wireless smart sensors networks

50.

Development of Portable Wireless Sensor Network System for Real-Time Traffic Surveillance

Real-time traffic surveillance is essential in todays intelligent transportation systems. This paper reports on the design of a novel, cost-effective, and intelligent real-time traffic monitoring systems using wireless smart sensor networks. Reliable and computationally efficient algorithms were developed for vehicle counting and speed estimation. The integration of system components and distinctive algorithms is discussed in detail. A preliminary experimental study composed of various scenarios conducted under highway conditions resulted in 99.95% counting accuracy and 96.11% speed estimation accuracy. Estimated cost of a single sensor node is less than

Comprehensive study of spectrum occupancy for 802.11b/g/n homogeneous networks

40.

Development of measurement techniques and tools for coexistence testing of wireless medical devices

Wireless sensor network (WSN) technology is rapidly maturing and gaining momentum as an enabling technology for Internet-of-things, and Intelligent Transportation Systems (ITSs). This paper introduces the design of a novel, cost-effective intelligent vehicle counting and classification sensor system. Distinctive algorithms for vehicles detection, counting, and signature re-identification were developed and integrated. The systems integration of wireless communication, smart sensors, and intelligent algorithms is discussed in detail. Various experimental analysis scenarios are reported. A preliminary experimental study resulted in 98% accuracy for counting and detection. After system improvement in detecting class 9 vehicles by analyzing the inter-arrival time, detection accuracy reached 100%. Estimated cost of the proposed system is less than

Energy detection and machine learning for the identification of wireless MAC technologies

40.

Comprehensive study of spectrum utilization for 802.11b/g/n networks

This paper presents a scheme for comprehensive experimental measurements of Wi-Fi networks spectrum occupancy in the ISM band. The work presented herein provides duty cycle measurements for single and multiple communicating Wi-Fi pairs. Duty cycle results are provided for 802.11b, g and n Wi-Fi standards at different throughput levels. Lower values were observed for 802.11b and g networks. Spectrum occupancy measurements are essential for wireless networks planning and deployment. This comes as a consequence of the ever increasing demand for spectrum to accommodate newly deployed wireless systems [1]. Duty cycle serves as a measure for spectrum busyness. Higher duty cycle levels directly impact other wireless links, which either refrain from transmission or suffer from increased errors. Such measurements assist other technologies in mitigating interference effects suffered from Wi-Fi by varying transmission parameters to accommodate measured duty cycle. Measurements also enable improved spectrum planning for the overcrowded 2.4GHz Industrial Scientific and Medical ISM band. Measurements were carried and validated using developed tools in both time domain and frequency domain. Results have shown that duty cycle is able to reach up to 98.9% with 802.11n three-communicating pairs.

Cost effective Vehicle Classification using a single wireless magnetometer

The 2.4 GHz ISM band is crowded with a wide variety of wireless devices operating under various protocols. For many reasons, medical device manufacturers are increasingly incorporating wireless technologies into their devices, many of which operate in the ISM band. Monitoring and characterizing wireless spectrum utilization is vital to better plan wireless network deployments and to assess the risk of interference. This work introduces measurement techniques and tools to aid in providing reliable spectrum utilization characterization for coexistence testing of wireless medical devices. Measurements obtained from developed tools can help the US Food and Drug Administration (FDA) and medical device manufacturers to gain a better understanding of expected interference factors. Wireless medical device testing with these tools could ensure a reliable device that will enhance patient safety and accelerate introducing innovative wireless medical devices to market.

Real-Time Magnetic Length-Based Vehicle Classification: Case Study for Inductive Loops and Wireless Magnetometer Sensors in Oklahoma State

ISM spectrum is becoming increasingly populated with various wireless technologies, rendering it a scarce resource. Consequently, wireless coexistence is increasingly vulnerable to new wireless devices attempting to access the same spectrum. This paper presents a novel method for identifying wireless technologies through the use of simple energy detection techniques. Energy detection is used to measure the channel statistical temporal characteristics including activity and inactivity probability distributions. Features uniquely belonging to specific wireless technologies are extracted from the probability distributions and fed into a machine-learning algorithm to identify the technologies under evaluation. Wireless technology identification enables situational awareness to improve coexistence and reduce interference among the devices. An intelligent wireless device is capable of detecting wireless technologies operating within same vicinity. This can be performed by scanning energy levels without the need for signal demodulation and decoding. In this work, a wireless technology identification algorithm was assessed experimentally. Temporal traffic pattern for 802.11b/g/n homogeneous and heterogeneous networks were measured and used as algorithm input. Identification accuracies of up to 96.83% and 85.9% were achieved for homogeneous and heterogeneous networks, respectively.