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.

/pdf/beyond-co-existence-exploiting-wifi-white-space-for-zigbee-4bjm2j242s.pdf

Beyond co-existence: Exploiting WiFi white space for Zigbee performance assurance

Managing energy efficiently is paramount in modern smartphones. The diverse range of wireless interfaces and sensors, and the increasing popularity of power-hungry applications that take advantage of these resources can reduce the battery life of mobile handhelds to few hours of operation. The research community, and operating system and hardware vendors found interesting optimisations and techniques to extend the battery life of mobile phones. However, the state of the art of lithium-ion batteries clearly indicates that energy efficiency must be achieved both at the hardware and software level. In this survey, we will cover the software solutions that can be found in the research literature between 1999 and May 2011 at six different levels: energy-aware operating systems, efficient resource management, the impact of users' interaction patterns with mobile devices and applications, wireless interfaces and sensors management, and finally the benefits of integrating mobile devices with cloud computing services.

Energy Management Techniques in Modern Mobile Handsets

This paper presents FreeBee, which enables direct unicast as well as cross-technology/channel broadcast among three popular wireless technologies: WiFi, ZigBee, and Bluetooth. Our design aims to shed the light on the opportunities that cross-technology communication has to offer including, but not limited to, cross-technology cooperation and coordination. The key concept of FreeBee is to modulate symbol messages by shifting the timing of periodic beacon frames already mandatory for wireless standards without incurring extra traffic. Such a generic cross-technology design consumes zero additional bandwidth, allowing continuous broadcast to safely reach mobile and/or duty-cycled devices. A new \emph{interval multiplexing} technique is proposed to enable concurrent broadcasts from multiple senders or boost the transmission rate of a single sender. Theoretical and experimental exploration reveals that FreeBee offers a reliable symbol delivery under a second and supports mobility of 30mph and low duty-cycle operations of under 5%.

https://www.sigmobile.org/mobicom/2015/papers/p317-kimA.pdf

FreeBee: Cross-technology Communication via Free Side-channel

Recent advances in Cross-Technology Communication (CTC) have improved efficient coexistence and cooperation among heterogeneous wireless devices (e.g., WiFi, ZigBee, and Bluetooth) operating in the same ISM band. However, until now the effectiveness of existing CTCs, which rely on packet-level modulation, is limited due to their low throughput (e.g., tens of bps). Our work, named WEBee, opens a promising direction for high-throughput CTC via physical-level emulation. WEBee uses a high-speed wireless radio (e.g., WiFi OFDM) to emulate the desired signals of a low-speed radio (e.g., ZigBee). Our unique emulation technique manipulates only the payload of WiFi packets, requiring neither hardware nor firmware changes in commodity technologies -- a feature allowing zero-cost fast deployment on existing WiFi infrastructure. We designed and implemented WEBee with commodity devices (Atheros AR2425 WiFi card and MicaZ CC2420) and the USRP-N210 platform (for PHY layer evaluation). Our comprehensive evaluation reveals that WEBee can achieve a more than 99% reliable parallel CTC between WiFi and ZigBee with 126 Kbps in noisy environments, a throughput about 16,000x faster than current state-of-the-art CTCs.

https://dl.acm.org/doi/pdf/10.1145/3117811.3117816

Demo: WEBee: Physical-Layer Cross-Technology Communication via Emulation

The vast array of small wireless sensors is a boon to body sensor network applications, especially in the context awareness and activity recognition arena. However, most activity recognition deployments and applications are challenged to provide personal control and practical functionality for everyday use. We argue that activity recognition for mobile devices must meet several goals in order to provide a practical solution: user friendly hardware and software, accurate and efficient classification, and reduced reliance on ground truth. To meet these challenges, we present PBN: Practical Body Networking. Through the unification of TinyOS motes and Android smartphones, we combine the sensing power of on-body wireless sensors with the additional sensing power, computational resources, and user-friendly interface of an Android smartphone. We provide an accurate and efficient classification approach through the use of ensemble learning. We explore the properties of different sensors and sensor data to further improve classification efficiency and reduce reliance on user annotated ground truth. We evaluate our PBN system with multiple subjects over a two week period and demonstrate that the system is easy to use, accurate, and appropriate for mobile devices.

/pdf/pbn-towards-practical-activity-recognition-using-smartphone-5bhhl5gmn9.pdf

PBN: towards practical activity recognition using smartphone-based body sensor networks

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 (ms), and little computation overhead

ZiFi: wireless LAN discovery via ZigBee interference signatures

Opportunistic Networks: Opportunistic Networks

The invention discloses a DTN network Anycast routing method based on opportunity communication, which belongs to the field of wireless communication. The method of the invention is: 1) the sending utility value u of each target group node is maintained by each node; 2) the maximum sending utility value u and the density perception measure rho of a node j meeting with a node i is obtained by the node i; 3) if the maximum sending utility value u of i is bigger than the setting threshold u, the size of u and u is compared; if u is smaller than u, the information is transmitted to j, otherwise, no transmission is carried out; 4) if u is not bigger than u, compare the density perception measure rho of i and compare rho and rho; if rho is less than rho, the information is transmitted by the node i to the node j, otherwise, no forwarding. Compared with current technology, the invention has the advantages of improving the success delivery rate of the network data, reducing the transmission delay of the data, and possibility of producing circle during the forwarding, and improving the approaching speed of information to the target node.

DTN network Anycast routing method based on opportunistic communication

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.

ZiFi: Exploiting Cross-Technology Interference Signatures for Wireless LAN Discovery

A mobile opportunistic network consists of sparsely scattered mobile nodes communicating via short range radios. It is characterized by frequent and unpredictable network partitions and intermittent connectivity. Anycast in opportunistic networks is anticipated in many application scenarios, and deserves great attention. In this paper, we propose an anycast routing algorithm in which each node is associated with a forwarding metric indicating its delivery probability to the destination anycast group and the node with lower value hands over the message to the encountered node with higher metric. The forwarding metric is determined according to historical node encounter information. We use three different forwarding metrics (variables) to guide the transmission of messages. The Group Forwarding Metric (GFM) treats the entire group as a whole, and it is defined as the probability of meeting any member in the anycast group to deliver a message. Similar to GFM, Probability Forwarding Metric (PFM) is defined as the probability of encountering at least one anycast group member, but it relies on the probability of meeting individual group members. The Distance Forwarding Metric (DFM) takes a function of the delivery probability to an anycast group member as the distance to the member. The DFM is the combination of these distances to forward messages towards the higher member density. Different metrics can be adopted for different mobile opportunistic networks based on the connectivity characteristics of the networks. We analyze the control overhead of the anycast algorithm and the message delivery delay of the routing protocol. Extensive simulations are carried out to evaluate the performance of the proposed solution under synthetic and realistic traces. The results show that our algorithm will significantly improve the anycast delivery performance when compared with simple routing algorithms in term of average message delivery delay and transmission overhead.

Yongping Xiong

Papers

ZiFi: wireless LAN discovery via ZigBee interference signatures

Opportunistic Networks: Opportunistic Networks

DTN network Anycast routing method based on opportunistic communication

ZiFi: Exploiting Cross-Technology Interference Signatures for Wireless LAN Discovery

Anycast routing in mobile opportunistic networks