Low Power Wide Area Networks (LPWANs) can provide long-distance low-power communication at a very low SNR. It is a very promising technique for different application scenarios, e.g., Industrial networks, smart city, smart agriculture, network for harsh environments. We aim to push LPWANs to real applications. More specifically, we work on collision decoding, high concurrency communication, wireless reprogramming for LPWANs. We also design and build real LPWAN hardware.

Backscatter has been shown as a promising techniques for connecting Internet of Things through battery-less communication. Typical backscatter system can achieve a very limited range (e.g., several meters). We are working on providing long distance (km level) backscatter platforms. As there are no batteries in devices for backscatter networks, they can be used for connection for very small things (e.g., cups, bottles, micro-robots) for a long life time. We believe those will be among the key communication techniques for Internet of Things.

We are working on using different types of wireless signal to achieve different levels of localization, tracking and sensing. For example, we are using WiFi, UWB, Acoustic signal, Bluetooth signal for wireless sensing. We can integrate sensing and communication on the same device. Based on our research on long-range battery-less backscatter networks, we can integrate sensing and communication in complex environments, and enable sensing and communication for ubiquitous devices.

We deploy large scale wireless sensor networks GreenOrbs in the forest of Tianmu Mountain, Zhejiang, a national nature reserved area and national 5A tourist attractions. We build system with low power wireless protocols and multi-hop network to collect data from the forest. The system can monitor different forest environment parameters such as temperature, humidity, carbon dioxide, etc.

Cooperating with Chilwee Corporation (超威集团), one of the largest battery industrial manufacturer, we start the life cycle management of battery system. The main goal of the system is to 1) facilitate the recycling as each battery has a very high recycling value, 2) improve the management of lead-acid battery, and 3) use the life cycle management data to improve manufacturing, sale, recycling, and trading of recycling battery. The entire system incorporates Internet of things, mobile computing, clouding computing technologies, etc. We design IoT systems, Apps and web management systems for this life cycle management system.

We deploy large scale online water monitoring system to measure the water PH, DO (dissolved oxygen), residual chlorine, turbidity, etc., which are essential to the water quality. We designed an App of online water monitoring (水质在线) for using the data online. The App can be downloaded in App Store.

We deployed a large scale wireless sensor network CitySee in Wuxi City, Jiangsu. The network consists of over 1000 sensor nodes and is the largest outdoor wireless sensor network. The entire system covers an area of over 1 square kilometers. We work on network monitoring, network analysis, network diagnosis, low power multi-hop wireless protocols, etc., to build a reliable and efficient operational network.

We designed wireless sensor network testbed to facilitate network experiments. The testbed consists of 50 nodes. Users can online upload programs to specified sensor nodes on the testbed, monitor each node’s status and download data from sensor nodes. This significantly improves the performance for design, testing, and implementation wireless sensor network applications.

We take the first attempt to build a ubiquitous passive smoking detection system, which leverages the patterns smoking leaves on WiFi signals to identify the smoking activity even in the non-line-of-sight and through-wall environments. We study the behaviors of smokers and leverage the common features to recognize the series of motions during smoking, avoiding the target-dependent training set to achieve the high accuracy. We design a foreground detection based motion acquisition method to extract the meaningful information from multiple noisy subcarriers even influenced by posture changes. Without requirements of target’s compliance, we leverage the rhythmical patterns of smoking to reduce the detection false positives. We prototype Smokey with the commodity WiFi infrastructure and evaluate its performance in real environments. Experimental results show Smokey is accurate and robust in various scenarios.

We propose Vernier, an efficient and accurate acoustic tracking method on commodity mobile devices. In the heart of Vernier lies a novel method to efficiently and accurately derive phase change and thus moving distance. Vernier significantly reduces the tracking delay/overhead by removing the complicated frequency analysis and long window of signal accumulation, while keeping a high tracking accuracy. We implement Vernier on Android, and evaluate its performance on COTS mobile. Evaluation results show that Vernier outperforms previous approaches with a tracking error less than 4 mm. The tracking speed achieves 3 × improvement to existing phase based approaches and 10 × to Doppler Effect based approaches. Vernier is also validated in applications like controlling and drawing, and we believe it is generally applicable in many real applications.

We propose NiFi, the first attempt for a non-intrusive, automatical user identification approach using physical signals on WiFi APs. NiFi can be deployed on most COTS WiFi routers and requires no special software and hardware support. In NiFi, we transfer the user identification problem to a path matching problem in signal space. We further propose effective similarity measure methods for signal sequences and path matching algorithm for user identification. We demonstrated this system at ACM MobiHoc 2016.

We propose ViTrack, an efficient tracking framework with limited computation resource on the edge for commodity video surveillance systems. This system can be used in various applications such as searching for the trajectory of a particular car, person, hit-and-run thieves and missing children. ViTrack leverages a two-layer compressive target detection and a Markov Model to obtain the moving trajectory. We evaluate ViTrack on a real deployed video surveillance system with 110 cameras.

We have implemented a gaze based gesture recognition system that enables user to interact with mobile devices with gaze movement. Our system can be used in user-device interaction, such as device unlocking, device control and game play. Our proposed method differs from previous work in that they implement interaction interface on desktop computers rather than mobile devices. Our system considers continuous posture changes and speed-power balance in mobile environment. We demonstrated this system at ACM Ubicomp 2017.

We propose a side-channel video identification method using eavesdropped network traffic while video streaming. Based on the bitrate variation caused by Variable Bit-Rate (VBR) encoding and video segmentation of Dynamic Adaptive Streaming over HTTP (DASH), our method can extract stable video traffic trends and video fingerprints despite bitrate adaptation. By comparing video traffic trends and fingerprints, we can finally find the video identity.

We designed a pervasive usage of the sensor network infrastructure as a cyber-physical system for navigating internal users in locations of potential danger. This system can be used in various scenarios such as underground coal mine monitoring and emergency navigation for buildings. Our proposed application differs from previous work in that they typically treat the sensor network as a media of data acquisition while in our navigation application, in-situ interactions between users and sensors become ubiquitous. This system considers both human safety and time factors in navigation. We demonstrated this system at ACM SenSys 2018.

It is of essential importance to measure the forwarding quality of multi-hop paths and such information shall be utilized in designing efficient routing strategies. The experience on GreenOrbs, a large-scale sensor network with 330 nodes, reveals that the quality of forwarding inside each sensor node is at least an equally important factor that contributes to the path quality in data delivery. We propose QoF, Quality of Forwarding, a new metric which explores the performance in the gray zone inside a node left unattended in previous studies. By combining the QoF measurements within a node and over a link, we are able to comprehensively measure the intact path quality in designing efficient multi-hop routing protocols. We implement QoF and build a modified Collection Tree Protocol (CTP).

We study the ubiquitous data collection for mobile users in wireless sensor networks. People with handheld devices can easily interact with the network and collect data. We propose a novel approach for mobile users to collect the network-wide data. The routing structure of data collection is additively updated with the movement of the mobile user. With this approach, we only perform a limited modification to update the routing structure while the routing performance is bounded and controlled compared to the optimal performance. The proposed protocol is easy to implement. Our analysis shows that the proposed approach is scalable in maintenance overheads, performs efficiently in the routing performance, and provides continuous data delivery during the user movement. We implement the proposed protocol in a prototype system and test its feasibility and applicability by a 49-node testbed. We further conduct extensive simulations to examine the efficiency and scalability of our protocol with varied network settings.

We propose ZiSense, an energy efficient rendezvous mechanism which is resilient to interference. Instead of checking the signal strength or decoding the probe packets, ZiSense detects the existence of ZigBee transmissions and wakes up nodes accordingly. On sensor nodes with limited information and resource, we carefully study and extract short-term features purely from the time-domain RSSI sequence, and design a rule-based approach to efficiently identify the existence of ZigBee. We theoretically analyze the benefit of ZiSense in different environments and implement a prototype in TinyOS with TelosB motes. We examine ZiSense performance under controlled interference and office environments. The evaluation results show that, compared with state-of-the-art rendezvous mechanisms, ZiSense significantly reduces the energy consumption.

We propose Hitchhike, a technique that utilizes the preamble field to carry control messages. Hitchhike completely decouples the control messages from the payload and therefore the superposition of (multiple) control messages has little adverse effect on the operation of the payload decoding. We implement and evaluate Hitchhike in the USRP2 platform with 5 nodes. Evaluation results demonstrate the feasibility and effectiveness of Hitchhike. Compared with the state-of-the-art, e.g., Side-channel in 802.15.4, Hitchhike improves the detection accuracy of control messages by 40% and reduces the data loss caused by control messages by 15%.

We propose STAIRS, a time and energy efficient collision resolution mechanism for wireless sensor networks. STAIRS incorporates the constructive interference technique in its design and explicitly forms superimposed colliding signals. Through extensive observations and theoretical analysis, we show that the RSSI of the superimposed signals exhibit stairslike phenomenon with different number of contenders. That principle offers an attractive feature to efficiently distinguish multiple contenders and in turn makes collision-free schedules for channel access. In the design and implementation of STAIRS, we address practical challenges such as contenders alignment, online detection of RSSI change points, and fast channel assignment. The experiments on real testbed show that STARIS realizes fast and effective collision resolution, which significantly improves the network performance in terms of both latency and throughput.

We propose Chase++, a Fountain code based concurrent broadcast control layer to enable fast flooding in asynchronous duty cycle networks. Chase++ uses Fountain code to alleviate the negative influence of the continuous loss of a certain part of flooding payload. Chase++ partitions long payload into several short payload blocks, which are further encoded into many encoded payload blocks by Fountain code. Receivers can recover original flooding payload after several independent encoded payload blocks are collected. We implement Chase++ in TinyOS with TelosB nodes. We further evaluate Chase++ on local testbed with 50 nodes and Indriya testbed with 95 nodes. The improvement of network flooding speed can reach 23.6% and 13.4%, respectively.

We present an efficient and fully distributed concurrent broadcast layer for flooding in asynchronous duty cycle networks. The main idea of Chase is to meet the strict signal timing and strength requirements (e.g., Capture Effect) for concurrent transmission while reducing contentions and collisions. We propose a distributed random inter-preamble packet interval adjustment approach to constructively satisfy the requirements. We implement Chase in TinyOS and TelosB platform and extensively evaluate its performance. The implementation does not have any specific requirement on the hardware and can be easily extended to other platforms. The evaluation results also show that Chase can significantly improve flooding efficiency in asynchronous duty cycle networks.

Collision avoidance comes at the cost of miscellaneous overhead, which oppositely hurts channel utilization, not to mention the poor resiliency and performance of those protocols in face of dense networks or intensive traffic. Discovering the ability to tolerate collisions at the physical layer implementations of wireless networks, we propose Coco, a MAC protocol that advocates simultaneous accesses from multiple senders to a shared channel, i.e., optimistically allowing collisions instead of simply avoiding them. With a simple but effective design, Coco addresses the key challenges in achieving collision tolerance, such as precise sender alignment and fine control of the transmission concurrency. We implement Coco in 802.15.4 networks and evaluate its performance through extensive experiments with 21 TelosB nodes. The results demonstrate that Coco is light-weight and enhances channel utilization by at least 20% in general cases, compared with state-of-the-arts protocols.

We present GeneWave, a fast device authentication and key agreement protocol for commodity mobile devices. GeneWave first achieves bidirectional initial authentication based on the physical response interval between two devices. GeneWave requires neither special hardware nor pre-built fingerprint database, and thus it is easy-to-use on commercial mobile devices. We implement GeneWave on mobile devices (i.e., Nexus 5X and Nexus 6P) and evaluate its performance through extensive experiments. Experimental results show that GeneWave efficiently accomplish secure key agreement on commodity smartphones with a key generation rate 10x faster than the state-of-the-art approach.

We analyze the energy consumption for duty cycled sensor networks with different data rates. Our analysis shows that existing protocols cannot lead to an efficient energy consumption in various scenarios. Based on the analysis, we design a light-weight adaptive duty-cycling protocol (LAD), which reduces the energy consumption under different data rates and protocol dynamics. LAD can adaptively adjust the protocol parameters according to network conditions such as data rate and achieve an optimal energy efficiency. To make LAD practical in real network, we further precalculate optimal parameters offline and store them on sensor nodes, which significantly reduces the computation time. We theoretically validate the performance improvement of the protocol. We implement the protocol in TinyOS and extensively evaluate it on 40 TelosB nodes. The evaluation results show the energy consumption can be reduced by 28.2%∼40.1% compared with state-of-the-art protocols.

We present the first comprehensive link-level measurements in an operational large-scale urban sensor network. By carefully analyzing the performance metrics, we seek to answer several fundamental questions: what are the characteristics of links in a real large-scale network, and what causes link performance degradation? The key findings of this study are that (1) the performance of intermediate links is the most unpredictable and some links exhibit highly periodic patterns, (2) the width of the reception “transitional region” is much larger than those reported in previous experiments, indicating that an outdoor environment might have a larger impact on the link performance and current protocol parameters should be carefully designed, and (3) different from previously reported results, link performance degradation has a relatively weak correlation with the corresponding RSSI (Received Signal Strength Indicator) values fluctuating near the noise floor.

We present findings from a large-scale operating sensor network system, GreenOrbs, with up to 330 nodes deployed in the forest. We instrument such an operating network throughout the protocol stack and present observations across layers in the network. Based on our findings from the system measurement, we propose and make initial efforts to validate three conjectures that give potential guidelines for future designs of large-scale sensor networks. 1) A small portion of nodes bottlenecks the entire network, and most of the existing network indicators may not accurately capture them. 2) The network dynamics mainly come from the inherent concurrency of network operations instead of environment changes. 3) The environment, although the dynamics are not as significant as we assumed, has an unpredictable impact on the sensor network. We suggest that an event-based routing structure can be trained and thus better adapted to the wild environment when building a large-scale sensor network.

We present a comprehensive delay performance measurement and analysis in a large-scale wireless sensor network. We build a light-weight delay measurement system and present a robust method to calculate the per-packet delay. We show that the method can identify incorrect delays and recover them with a bounded error. Through analysis of delay and other system metrics, we seek to answer the following fundamental questions: What are the spatial and temporal characteristics of delay performance in a real network? What are the most important impacting factors and is there any practical model to capture those factors? What are the implications to protocol designs? In this paper, we identify important factors from the data trace, and show that the important factors are not necessarily the same with those in Internet. Further, we propose a delay model to capture those factors. We revisit several prevalent protocol designs such as Collection Tree Protocol, opportunistic routing and Dynamic Switching based Forwarding, and show the our model and analysis are useful to practical protocol designs.