Samer A. Rajab

Experimental assessment of wireless coexistence for 802.15.4 in the presence of 802.11g/n

Wireless coexistence is a growing concern, given the ubiquity of wireless technology. Although IEEE Standards have started to address this problem in an analytical framework, a standard experimental setup and process to evaluate wireless coexistence is lacking. Literature that reports experimental assessment of wireless coexistence places little emphasis on separation distance of wireless nodes under test or the spectrum occupancy of the interfering network, making comparisons difficult. This paper provides an extensive literature survey of 802.15.4 and 802.11 b/g/n wireless coexistence and demonstrates that in a higher wireless channel occupancy environment, ZigBee coexists with 802.11n better than with 802.11g. A reproducible, versatile, and practical test setup is presented to serve as a starting point toward establishing standard practice for wireless coexistence testing of wireless systems in general and wireless medical devices in particular. Experimental evaluations demonstrated consistency with results reported in the literature.

Comparison of duty cycle measurement techniques of 802.11b/g in the frequency and time domain

Medical device manufacturers are evermore including wireless communication in their medical devices. Because most utilize common wireless standards such as ZigBee and Bluetooth that operate in the unlicensed 2.4 GHz industrial, scientific, and medical (ISM) band, the Food and Drug Administration (FDA) is asking medical device manufacturers to test for wireless coexistence. Coexistence among wireless devices is dependent on three factors, namely frequency, space, and time. Specific to time, the probability of coexistence in the 2.4 GHz ISM depends on channel occupancy (or duty cycle) of the interfering wireless network-the main source being WiFi (802.11b/g) for ZigBee and Bluetooth. This paper reports on the implementation of two dutycycle measurement systems for 802.11b/g utilizing a National Instruments 5663E vector signal analyzer. Measurement techniques were designed in the frequency and time domain, and then compared. Simulations of 802.11b/g duty cycles were also executed and compared with experimental measurements.

Novel vehicle and motorcycle classification using single element piezoelectric sensor

Accurate data reporting ensures suitable roadway design for safety and capacity. Currently, vehicle classifier devices employ inductive loops, piezoelectric sensors, or some combination of both to identify 13 different FHWA vehicle classifications. Systems using inductive loops have failed to accurately classify motorcycles and record relative pertinent data. Previous investigations have focused on classification techniques utilizing inductive loop signal output, magnetic sensor output with neural networks, or the fusion of several sensor outputs. This paper presents a novel vehicle classification setup that uses a single piezoelectric sensor placed diagonally across the traffic lane to accurately identify motorcycles from among other vehicles by detecting the number of vehicle tires. A vehicle classification algorithm based on number of tires detected and axle/tire spacing was formulated and deployed in an embedded system for field testing.

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

Vehicle classification and accurate speed calculation using multi-element piezoelectric sensor

Vehicle monitoring and classification is a necessary Intelligent Transportation System ITS activity, as nationwide departments of transportation (DOT) use the information to effectively design safe and durable roadways. Because over 70% of the weight of goods shipped in the U.S. are trucked, substantial pavement damage is becoming more and more problematic [1]. Thus, an accurate classification system for estimating vehicle parameters is sorely needed. Currently, the most widely used classification solution consists of a combination of inductive loops and piezoelectric sensors. Installing these systems causes pavement damage. Even more challenging is that current systems greatly under-classify class 1 motorcycle vehicles. In this paper we present a novel system for classifying vehicles and determining track width and speed. The system employs a multi-element piezoelectric sensor positioned diagonally across a single traffic lane; a data acquisition unit; and a processing and classification algorithm operating on a computing device. Vehicle front axle tires distinctively impact different element sensors, which aids in calculating track width, speed and axle spacing. Given these factors, a classification decision can be made using vehicle axle spacing. The developed system was tested on highway conditions. Classification accuracy was 86.9% overall and even better for class 1 motorcycles (100%) and passenger vehicles (98.9%).

Vehicle classification accuracy of AVC and WIM sites in Oklahoma

Vehicle classification is a vital measure used to ensure appropriate roadway design as it affects both capacity and pavement endurance. Given that, departments of transportation across the US collect vehicle miles travelled (VMT) for their highways using automatic vehicle classifiers (AVC), and then use these figures for future highway design. Accuracy assessment of AVCs is thus important to ensure proper VMT reporting. Studying the accuracy of AVC devices is therefore essential. Previous studies employed either manual counting or a “play and pause” method of traffic video recording to verify the accuracy of AVC devices. This paper details a custom vision-aided software developed to aid in extracting accurate vehicle count and classification information used as ground truth data. Authors discuss the methodology used to study vehicle classification accuracy of AVC and weigh-in-motion sites tasked with vehicle classification. Several indicators introduced to investigate the accuracy of each site are highlighted. Results of a year-long 2013 study indicate a good performance of AVC devices and that the main source of error was the misclassification of class 2 and 3 vehicles as class 5.

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

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.

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

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.

802.11g channel characterization utilizing labview and NI-USRP

Medical device manufacturers have recently begun to incorporate wireless communication, such as ZigBee and Bluetooth operating in the unlicensed 2.4 GHz industrial, scientific, and medical (ISM) band, into their medical devices. Wi-Fi, however, is a major source of interference in the ISM band. With patient safety in mind, the FDA has mandated coexistence testing for wireless medical devices [1]. An initial step toward supporting this mandate is to be able to accurately characterize the interfering network in a typical environment. In this paper, a software defined radio (SDR) is employed to serve as a platform for measuring channel duty cycle, packet arrival rate, node distribution, and packet inter-arrival time distribution of 802.11g networks. Theoretical and technical concerns are discussed, and tests performed to assess system integrity are described. Experimental tests examining channel characteristics of an 802.11g network are also reported.

Creating an automated and emulated 802.11g wireless interfering network for wireless coexistence testing

Although the proliferation of wireless medical devices is mounting-partially due to benefits of wireless technology-associated risks must be evaluated. The Food and Drug Administration (FDA) is asking medical device manufacturers to quantify these by testing their wireless medical devices for coexistence. This can be a tedious and complicated chore. To streamline the process and to disseminate information about wireless coexistence testing, we are undertaking the task of automating the process. One of the most difficult steps in coexistence testing is setting up an interfering network. A major source of interference in the 2.4 GHz ISM band is Wi-Fi (802.11 b/g/n). This paper informs about tools developed to accurately characterize 802.11g and then emulate an 802.11g access point. Our previous work has shown that by employing a similar period and duty cycle, a signal generator can emulate an interfering 802.11g wireless network during wireless coexistence; however, the outcome performance of the wireless network under test is drastically different. An emulated interfering network must mimic channel characteristics of an actual network, as well as its influence on the wireless network under test. In response to previous findings, we performed wireless coexistence testing and compared the influence of an actual 802.11g wireless network with an emulated interfering 802.11g wireless network. A ZigBee network acted as the wireless network under test.