This paper presents VINCE - a novel visible light sensing design for smartphone-based Near Field Communication (NFC) systems. VINCE encodes information as different brightness levels of smartphone screens, while receivers capture the light signal via light sensors. In contrast to RF technologies, the direction and distance of such a Visible Light Communication (VLC) link can be easily controlled, preserving communication privacy and security. As a result, VINCE can be used in a wide range of NFC applications such as contactless payments and device pairing. We experimentally profile the impact of screen brightness levels and refresh rates of smartphones, and then use the results to guide the design of light intensity encoding scheme of VINCE. We adopt several signal processing techniques and empirically derive a model to deal with the significant variation of received light intensity caused by noises and low screen refresh rates. To improve the communication reliability, VINCE adopts a feedback-based retransmission scheme, and dynamically adjusts the number of encoding brightness levels based on the current light channel condition. We also derive an analytical model that characterizes the relation among the distance, SNR (Signal to Noise Ratio), and BER (Bit Error Rate) of VINCE. Our design and theoretical model are validated via extensive evaluations using a hardware implementation of VINCE on Android smartphones and the Arduino platform.

Clock synchronization is critical for many WSNs due to the need of inter-node coordination and collaborative information processing. Existing protocols based on message passing achieve satisfactory clock synchronization accuracy, however, incur prohibitively high overhead especially in large-scale networks. In this paper, we propose a new clock synchronization approach called ROCS which exploits the radio data system (RDS) from FM radio stations. First, we design a new hardware FM receiver that can extract a periodic pulse from FM broadcasts, referred to as RDS clock. We then conduct a large-scale measurement study of RDS clock in our lab for a period of six days and on a vehicle driving through a metropolitan area of over 40km2. Our results show that RDS clock is highly stable and hence is a viable means to calibrate the clocks of large-scale city-wide sensor networks. To reduce the high power consumption of FM receiver, ROCS adaptively calibrates the native clock via the RDS clock. We implement ROCS in TinyOS on our hardware FM receiver and a TelosB-compatible WSN platform. Our extensive experiments using a 12-node testbed and our driving measurement traces show that ROCS achieves accurate and precise clock synchronization with low power consumption.

The Near Field Communication (NFC) technology is gaining increasing popularity among mobile users. However, as a relatively new and developing technology, NFC may also introduce security threats that make mobile devices vulnerable to various malicious attacks. This work presents the first system study on the feasibility of and defense again passive NFC eavesdropping. Our experiments show that commodity NFC-enabled mobile devices can be eavesdropped from up to 240 cm away, which is at least an order of magnitude of the intended NFC communication distance. This finding challenges the general perception that NFC is largely immune to eavesdropping because of its short working range. We then present the design of a hardware security system called nShield. With a small form factor, nShield can be attached to the back of mobile devices to attenuate the signal strength against passive eavesdropping. At the same time, the absorbed RF energy is scavenged by nShield for its perpetual operation. nShield intelligently determines the right attenuation level that is just enough to sustain reliable data communication. We implement a prototype of nShield, and evaluate its performance via extensive experiments. Our results show that nShield has low power consumption (23 uW), can harvest significant amount of power (55 mW), and adaptively attenuates the signal strength of NFC in a variety of realistic settings, while only introducing insignificant delay (up to 2.2 s).

Time synchronization is a fundamental service for wireless sensor networks (WSNs). Although a number of message passing protocols can achieve satisfactory synchronization accuracy, they suffer poor scalability and high transmission overhead. An alternative approach is to utilize the global time references such as those induced by GPS and timekeeping radios. However, they require the hardware receiver to decode the out of band clock signal, which introduces extra cost and design complexity. This paper proposes a novel WSN time synchronization approach by exploiting the existing Wi-Fi infrastructure. Our approach leverages the fact that 802.15.4 sensors and Wi-Fi nodes often occupy the same or overlapping radio frequency bands in the 2.4 GHz unlicensed spectrum. As a result, a 802.15.4 node can detect and synchronize to the periodic beacons broadcasted by Wi-Fi access points (APs). A key advantage of our approach is that, due to the long communication range of Wi-Fi, a large number of 802.15.4 sensors can synchronize clock rates to the same beacons without any message exchange. This paper makes several key contributions. First, we experimentally characterize the spatial and temporal characteristics of Wi-Fi beacons in an enterprise Wi-Fi network consisting of over 50 APs deployed in a 300,000 square foot office building. Motivated by our measurement results, we design a novel synchronization protocol called WizSync. WizSync employs digital signal processing (DSP) techniques to detect periodic Wi-Fi beacons and use them to calibrate the frequency of native clocks. WizSync can intelligently predict the clock skew and adaptively schedules nodes to sleep to conserve energy. We implement WizSync in TinyOS 2.1.1 and conduct extensive evaluation on a testbed consisting of 19 TelosB motes. Our results show that WizSync can achieve an average synchronization error of 0.12 milliseconds over a period of 10 days with radio power consumption of 50.9 microwatts/node.

Wi-Fi networks have enjoyed an unprecedent penetration rate in recent years. However, due to the limited coverage, existing Wi-Fi infrastructure only provides intermittent connectivity for mobile users. Once leaving the current network coverage, Wi-Fi clients must actively discover new Wi-Fi access points (APs), which wastes the precious energy of mobile devices. Although several solutions have been proposed to address this issue, they either require significant modifications to existing network infrastructures or rely on context information that is not available in unknown environments. In this work, we develop a system called ZiFithat utilizes ZigBee radios to identify the existence of Wi-Fi networks through unique interference signatures generated by Wi-Fi beacons. We develop a new digital signal processing algorithm called common multiple folding (CMF) that accurately amplifies periodic beacons in Wi-Fi interference signals. ZiFi also adopts a constant false alarm rate (CFAR) detector that can minimize the false negative (FN) rate of Wi-Fi beacon detection while satisfying the user-specified upper bound on false positive (FP) rate. We have implemented ZiFi on two platforms, a Linux netbook integrating a TelosB mote through the USB interface, and a Nokia N73 smartphone integrating a ZigBee card through the miniSD interface. Our experiments show that, under typical settings, ZiFi can detect Wi-Fi APs with high accuracy (<;5 percent total FP and FN rate), short delay (~780 ms), and little computation overhead.

Monitoring important aquatic processes like harmful algal blooms is of increasing interest to public health, ecosystem sustainability, marine biology, and aquaculture industry. This article presents a novel approach to spatiotemporal aquatic field reconstruction using inexpensive, low-power mobile sensing platforms called robotic fish . Robotic fish networks are a typical example of cyber-physical systems where the design of cyber components (sensing, communication, and information processing) must account for inherent physical dynamics of the robots and the aquatic environment. Our approach features a rendezvous-based mobility control scheme where robotic fish collaborate in the form of a swarm to sense the aquatic environment in a series of carefully chosen rendezvous regions. We design a novel feedback control algorithm that maintains the desirable level of wireless connectivity for a sensor swarm in the presence of significant environment and system dynamics. Information-theoretic analysis is used to guide the selection of rendezvous regions so that the spatiotemporal field reconstruction accuracy is maximized subject to the limited sensor mobility. The effectiveness of our approach is validated via implementation on sensor hardware and extensive simulations based on real data traces of water surface temperature field and on-water ZigBee wireless communication.

Indoor localization becomes increasingly important as context-aware applications gain popularity in mobile users. A promising approach for indoor localization is to leverage the pervasive WiFi infrastructure via fingerprinting-based inference. However, a WiFi device must frequently scan for WiFi signals during localization, leading to high power consumption. Moreover, switching to the scanning mode introduces inevitable disruptions to data communication of WiFi interface. This paper presents a new indoor localization system called ZiFind that exploits the cross-technology interference in the unlicensed 2.4 GHz frequency spectrum. ZiFind utilizes low-power ZigBee interface to collect WiFi interference signals and adopts digital signal processing techniques to extract unique signatures as fingerprints for localization. To deal with the noise in the fingerprints, we design a new learning algorithm called R-KNN that can improve the accuracy of localization by assigning different weights to fingerprint features according to their importance. We implement ZiFind on TelosB motes and evaluate its performance through extensive experiments in a 16,000 ft2office building floor consisting of 28 rooms. Our results show that ZiFind leads to significant power saving compared with existing approaches based on WiFi interface, and yields satisfactory localization accuracy in a range of realistic settings.

Recent years have witnessed pilot deployments of inexpensive wireless sensor networks (WSNs) for active volcano monitoring. This paper studies the problem of picking arrival times of primary waves (i.e., P-phases) received by seismic sensors, one of the most critical tasks in volcano monitoring. Two fundamental challenges must be addressed. First, it is virtually impossible to download the real-time high-frequency seismic data to a central station for P-phase picking due to limited wireless network bandwidth. Second, accurate P-phase picking is inherently computation-intensive, and is thus prohibitive for many low-power sensor platforms. To address these challenges, we propose a new P-phase picking approach for hierarchical volcano monitoring WSNs where a large number of inexpensive sensors are used to collect fine-grained, real-time seismic signals while a small number of powerful coordinator nodes process collected data and pick accurate P-phases. We develop a suite of new in-network signal processing algorithms for accurate P-phase picking, including lightweight signal pre-processing at sensors, sensor selection at coordinators as well as signal compression and reconstruction algorithms. Testbed experiments and extensive simulations based on real data collected from a volcano show that our approach achieves accurate P-phase picking while only 16% of the sensor data are transmitted.

In this paper, we present the design and implementation of Nemo -- a practical in situ power metering system for wireless sensor networks. Nemo features a new circuit design called shunt resistor switch that can dynamically adjust the resistance of shunt resistors based on the current load. This allows Nemo to achieve a wide dynamic current range and high measurement accuracy. Nemo transmits real-time power measurements to the host node solely through the power line, by modulating the current load and the supply voltage. This feature leads to a noninvasive, plug & play design that allows Nemo to be easily installed on existing mote platforms without physical wiring or soldering. We have implemented a prototype of Nemo and conducted extensive experimental evaluation. Our results show that Nemo can transmit high-throughput measurement data to the host through voltage/current load modulation. Moreover, it has satisfactory measurement fidelity over a wide range of operating conditions. In particular, Nemo yields a dynamic measurement range of 250,000:1, which is 2.5X and 7X that of two state-of-the-art sensor network power meter systems, while only incurring an average measurement error of 1.34%. We also use a case study to demonstrate that Nemo is able to track the highly dynamic sleep current consumption of TelosB motes, which has important implications for the design of low duty-cycle sensor networks that operate in dynamic environments.

This demo abstract presents the design and the demonstration plan of Nemo -- a practical in situ power metering system for wireless sensor networks. Nemo features a new circuit design called shunt resistor switch that can dynamically adjust the resistance of shunt resistors based on the current load. This allows Nemo to achieve a wide dynamic current range and high measurement accuracy. Nemo transmits real-time power measurement to the host node solely through the power line, by modulating the current load and the supply voltage. This feature leads to a noninvasive, plug & play design that allows Nemo to be easily installed on existing mote platforms without physical wiring or soldering. We describe the set-up of demonstration and outline our demonstration plan.

802.11-based wireless LANs (WLANs) have become an important communication infrastructure for today's pervasive computing applications. Nevertheless, WLAN users often experience various performance issues such as highly variable signal quality. To diagnose such transient service degradations and plan for future network upgrades, it is essential to closely monitor the performance of a WLAN and collect user statistics. This paper proposes a new WLAN performance monitoring approach motivated by the fact that many low-power wireless technologies such as ZigBee and Bluetooth co-exist with WLAN in the same open radio spectrum and are capable of sensing Received Signal Strength (RSS) of 802.11 transmissions. We have developed a ZigBee-based WLAN monitoring system called WizNet. Powered by batteries, ZigBee sensors of WizNet can be deployed in large quantities to monitor the spatial performance of a WLAN in long periods of time. By adopting digital signal processing techniques, WizNet automatically identifies 802.11 signals from ZigBee RSS measurements and associates them with wireless access points. To ensure the monitoring fidelity, WizNet accounts for the significant differences in ZigBee and WLAN radios, such as bandwidth and susceptibility to multipath and frequency-selective fading. A simple yet accurate linear estimator derived from a signal propagation model is used to infer the access points' signal to noise ratio (SNR). Moreover, WizNet can measure the congestion level of the channel and detect rogue APs. WizNet can also collect WLAN client statistics and classify device models based on RSS signatures of 802.11 access point scans. We have implemented WizNet in TinyOS 2.x and extensively evaluated its performance on a wireless testbed. Our results over a period of 140 hours show that WizNet can accurately capture the spatial and temporal performance variability of a large-scale production WLAN.

This paper presents COBRA - a visible light communication (VLC) system for off-the-shelf smartphones. COBRA encodes information into specially designed 2D color barcodes and streams them between screen and camera of smartphones. Due to the directionality and short range of visible light, COBRA can preserve user privacy and security in many near field communication scenarios such as opportunistic data exchange between smartphones. We develop a new 2D color barcode for COBRA that is optimized for streaming between small-size screen and low-speed camera of smartphones. COBRA adapts the size and layout of code blocks in streamed barcodes to deal with the significant image blur in mobile environments, and adopts new image processing techniques to achieve real-time barcode stream decoding. Our approach is evaluated through extensive experiments on Android smartphones.

No abstract available.

Monitoring important aquatic processes like harmful algal blooms is of increasing interest to public health, ecosystem sustainability, marine biology, and aquaculture industry. This paper presents a novel approach to spatiotemporal aquatic field reconstruction using inexpensive, low-power, mobile sensing platforms called robotic fish. Robotic fish networks are a typical example of Cyber-Physical Systems where the design of cyber components (sensing, communication, and information processing) must account for inherent physical dynamics of the robots and the aquatic environment. Our approach features a rendezvous-based mobility control scheme where robotic fish collaborate in the form of a swarm to sense the aquatic environment in a series of carefully chosen rendezvous regions. We design a novel feedback control algorithm that maintains the desirable level of wireless connectivity for a sensor swarm in the presence of significant environment and system dynamics. Information-theoretic analysis is used to guide the selection of rendezvous regions so that the spatiotemporal field reconstruction accuracy is maximized subject to the limited sensor mobility. The effectiveness of our approach is validated via implementation on sensor hardware and extensive simulations based on real data traces of water surface temperature field and on-water ZigBee wireless communication.

Clock synchronization is critical for Wireless Sensor Networks (WSNs) due to the need of inter-node coordination and collaborative information processing. Although many message passing protocols can achieve satisfactory clock synchronization accuracy, they incur prohibitively high overhead when the network scales to more than tens of nodes. An alternative approach is to take advantage of the global time reference induced by existing infrastructures including GPS, timekeeping radio stations, or power grid. However, high power consumption and geographic constraints present them from being widely adopted in WSNs. In this paper, we propose ROCS, a new clock synchronization approach exploiting the Radio Data System (RDS) of FM radios. First, we design a new hardware FM receiver that can extract a periodic pulse from FM broadcasts, referred to as RDS clock. We then conduct a large-scale measurement study of RDS clock in our lab for a period of six days and on a vehicle driving through a metropolitan area of over 40 $km^2$. Our results show that RDS clock is highly stable and hence is a viable means to calibrate the clocks of large-scale city-wide sensor networks. To reduce the high power consumption of FM receiver, ROCS intelligently predicts the time error due to drift, and adaptively calibrates the native clock via the RDS clock. We implement ROCS in TinyOS on our hardware FM receiver and a TelosB-compatible WSN platform. Our extensive experiments using a 12-node testbed and our driving measurement traces show that ROCS achieves accurate and precise clock synchronization with low power consumption.

(1) Institute of Software, Chinese Academy of Sciences, China; (2) Graduate University, Chinese Academy of Sciences, China; (3) Department of Computer Science and Engineering, Michigan State University, United States

Time synchronization is a fundamental service for Wireless Sensor Networks (WSNs). This paper proposes a novel WSN time synchronization approach by exploiting the existing Wi-Fi infrastructure. Our approach leverages the fact that ZigBee sensors and Wi-Fi nodes often occupy the same or overlapping radio frequency bands in the 2.4 GHz unlicensed spectrum. As a result, a ZigBee node can detect and synchronize to the periodic beacons broadcasted by Wi-Fi access points (APs). We experimentally characterize the spatial and temporal characteristics of Wi-Fi beacons in an enterprise Wi-Fi network consisting of over 50 APs deployed in a 300,000 square foot office building. Motivated by our measurement results, we design a novel synchronization protocol called WizSync. WizSync employs advanced Digital Signal Processing (DSP) techniques to detect periodic Wi-Fi beacons and use them to calibrate the frequency of native clocks. WizSync can intelligently predict the clock skew and adaptively schedules nodes to sleep to conserve energy. We implement WizSync in TinyOS 2.1x and conduct extensive evaluation on a testbed consisting of 19 TelosB motes. Our results show that WizSync can achieve an average synchronization error of 0.12 milliseconds over a period of 10 days with radio power consumption of 50.9 microwatts/node.

Recent years have witnessed the increasing adoption of ZigBee technology for performance-sensitive applications such as wireless patient monitoring in hospitals. However, operating in unlicensed ISM bands, ZigBee devices often yield unpredictable throughput and packet delivery ratio due to the interference from ever increasing WiFi hotspots in 2.4 GHz band. Our empirical results show that, although WiFi traffic contains abundant white space, the existing coexistence mechanisms such as CSMA are surprisingly inadequate for exploiting it. In this paper, we propose a novel approach that enables ZigBee links to achieve assured performance in the presence of heavy WiFi interference. First, based on statistical analysis of real-life network traces, we present a Pareto model to accurately characterize the white space in WiFi traffic. Second, we analytically model the performance of a ZigBee link in the presence of WiFi interference. Third, based on the white space model and our analysis, we develop a new ZigBee frame control protocol called WISE, which can achieve desired trade-offs between link throughput and delivery ratio. Our extensive experiments on a testbed of 802.11 netbooks and 802.15.4 TelosB motes show that, in the presence of heavy WiFi interference, WISE achieves 4× and 2× performance gains over B-MAC and a recent reliable transmission protocol, respectively, while only incurring 10.9% and 39.5% of their overhead.

WiFi networks have enjoyed an unprecedent penetration rate in recent years. However, due to the limited coverage, existing WiFi infrastructure only provides intermittent connectivity for mobile users. Once leaving the current network coverage, WiFi clients must actively discover new WiFi access points (APs), which wastes the precious energy of mobile devices. Although several solutions have been proposed to address this issue, they either require significant modifications to existing network infrastructures or rely on context information that is not available in unknown environments. In this work, we develop a system called ZiFi that utilizes ZigBee radios to identify the existence of WiFi networks through unique interference signatures generated by WiFi beacons. We develop a new digital signal processing algorithm called Common Multiple Folding (CMF) that accurately amplifies periodic beacons in WiFi interference signals. ZiFi also adopts a constant false alarm rate (CFAR) detector that can minimize the false negative (FN) rate of WiFi beacon detection while satisfying the user-specified upper bound on false positive (FP) rate. We have implemented ZiFi on two platforms, a Linux netbook integrating a TelosB mote through the USB interface, and a Nokia N73 smartphone integrating a ZigBee card through the miniSD interface. Our experiments show that, under typical settings, ZiFi can detect WiFi APs with high accuracy (<5% total FP and FN rate), short delay (~780 ms), and little computation overhead