Multimedia streaming applications with stringent QoS requirements in 60 GHz mmWave wireless personal area networks (WPANs) demand high rate and low latency data transfer as well as little service disruption. In this paper, we consider the problem of robust relay placement in 60 GHz WPANs with directional antenna. Relays forward traffic from transmitter devices to receiver devices facilitating i) the primary communication path for non-line-of-sight (NLOS) transceiver pairs, and ii) secondary (backup) communication path for line-of-sight (LOS) or NLOS transceiver pairs. By incorporating a classic directional antenna model and characterizing the link contention, we formulate the robust minimum relay placement problem and the robust maximum utility relay placement problem with the objective to minimize the number of relays deployed and maximize the network utility, respectively. Efficient algorithms are developed to solve both problems and have been shown to incur less service disruption in presence of moving subjects that may block the LOS paths in the environment.

ZigBee and other wireless technologies operating in the (2.4GHz) ISM band are being applied in Wireless Personal Area Networks (WPAN) for many medical applications. However, these low duty cycle, low power, and low data rate medical WPANs suffer from WiFi co-channel interferences. WiFi interference can lead to longer latency and higher packet losses in WPANs, which can be particularly harmful to safety-critical applications with stringent temporal requirements, such as ElectroCardioGraphy (ECG). This paper exploits the Clear Channel Assessment (CCA) mechanism in WiFi devices and proposes a novel policing framework, WiCop, that can effectively control the temporal white-spaces between WiFi transmissions. Such temporal white-spaces can be utilized for delivering low duty cycle WPAN traffic. We have implemented and validated WiCop on SORA, a software-defined radio platform. Experimental results show that with the assistance of the proposed WiCop policing schemes, the packet reception rate of a ZigBee-based WPAN can increase by up to 116% in the presence of a heavy WiFi interferer. A case study on the medical application of WPAN ECG monitoring demonstrates that WiCop can bound ECG signal distortion within 2% even under heavy WiFi interference. An analytical framework is devised to model the CCA behavior of WiFi interferers and the performance of WPANs under WiFi interference with or without WiCop protection. The analytical results are corroborated by experiments.

Spectrum sensing problem, which focuses on detecting the presence of primary users (PUs) in the cognitive radio (CR) network receives much attention recently. In this paper, we introduce the PU separation problem, which concerns with the issue of distinguishing and characterizing the activities of PUs in the context of collaborative spectrum sensing and monitor selection. Observations of secondary users (SUs) are modeled as boolean OR mixtures of underlying binary PU sources. We devise a binary inference algorithm for PU separation. With binary inference, not only PU-SU relationship are revealed, but PUs' transmission statistics and activities at each time slot can also be inferred. Simulation results show that without any prior knowledge regarding PUs' activities, the algorithm achieves high inference accuracy even in the presence of noisy measurements.

Spectrum monitoring is important to ensure the safe operation of mission critical systems as well as the satisfactory performance of non-critical applications over wireless. In this paper, we present a novel blind technology identification (BTI) approach that utilizes only binary representation of spectrum activities to identify transmission technologies that are present in the radio spectrum. Spectrum observations are modeled as Boolean OR mixtures on the underlying signal sources, and the binary independent component analysis technique is applied. Not only can we reveal the latent independent wireless technologies, but their transmission statistics and activities at each time slot can also be inferred. Evaluation results on both synthetic and real spectrum traces show that without any high-level features, the proposed methodology achieves high inference accuracy in noisy measurements.

Accurate estimation of PHY conditions would allow for better cross-layer resource management and provisioning. Unfortunately, most commercial-of-the-shelf (COTS) devices typically provide very limited information. For example, IEEE 802.15.4 Zigbee radios only provide received signal strength indicator (RSSI), link quality indicator (LQI) and noise floor readings, which are stored in frame check sequence (FCS) of MAC frames. In this paper, we revisit the issue of link quality prediction in IEEE 802.15.4 low rate wireless personal area networks (LR-WPAN) analytically and experimentally. By deciphering LQI readings available in Zigbee radios with CC2420 chipset, we demonstrate for the first time that LQI truly reflects the signal-to-noise ratio (SNR) at receiver. We also investigate the chip correlation (CORR) defined in CC2420 data sheet, and verify its relationship with LQI readings through measurement studies. Furthermore, in order to predict the instantaneous link quality for commodity Zigbee radios, we develop an inference model under different channel environments that uses instantaneous LQI readings as input. The proposed model is validated using extensive simulation and experimental study. We believe it will lead to more informed resource management decisions in WPANs.

Each wireless device has its unique fingerprint, which can be utilized for device identification and intrusion detection. Most existing literature employs supervised learning techniques and assumes the number of devices is known. In this paper, based on device-dependent channel-invariant radio-metrics, we propose a non-parametric Bayesian method to detect the number of devices as well as classify multiple devices in a unsupervised passive manner. Specifically, the infinite Gaussian mixture model is used and a modified collapsed Gibbs sampling method is proposed. Sybil attacks and Masquerade attacks are investigated. We have proven the effectiveness of the proposed method by both simulation data and experimental measurements obtained by USRP2 and Zigbee devices.

ZigBee and other wireless technologies operating in the (2.4GHz) ISM band are being applied in Wireless Body Area Networks (WBAN) for many medical applications. However, these low duty cycle, low power, and low data rate medical WBANs suffer from WiFi co-channel interferences. WiFi interference can lead to longer latency and higher packet losses in WBANs, which can be particularly harmful to safety-critical applications with stringent temporal requirements. Existing solutions to WiFi-WBAN coexistence either require modifications to WiFi or WBAN devices, or have limited applicability. In this paper, by exploiting the Clear Channel Assessment (CCA) mechanisms in WiFi devices, we propose a novel policing framework, WiCop, that can effectively control the temporal white-spaces between WiFi transmissions. Specifically, the WiCop Fake-PHY-Header policing strategy uses a fake WiFi PHY preamble-header broadcast to mute other WiFi interferers for the duration of WBAN active interval, while the WiCop DSSS-Nulling policing strategy uses repeated WiFi PHY preamble (with its spectrum side lobe nulled by a band-pass filter) to mute other WiFi interferers throughout the duration of WBAN active interval. The resulted WiFi temporal white-spaces can be utilized for delivering low duty cycle WBAN traffic. We have implemented and validated WiCop on SORA, a software defined radio platform. Experiments show that with the assistance of the proposed WiCop policing schemes, the packet reception rate of a ZigBee-based WBAN can increase by up to 43.8% in presence of a busy WiFi interferer.