Zicheng Chi

B2W2: N-Way Concurrent Communication for IoT Devices

The exponentially increasing number of internet of things (IoT) devices and the data generated by these devices introduces the spectrum crisis at the already crowded ISM 2.4 GHz band. To address this issue and enable more flexible and concurrent communications among IoT devices, we propose B2W2, a novel communication framework that enables N-way concurrent communication among WiFi and Bluetooth Low Energy (BLE) devices. Specifically, we demonstrate that it is possible to enable the BLE to WiFi cross-technology communication while supporting the concurrent BLE to BLE and WiFi to WiFi communications. We conducted extensive experiments under different real-world settings and results show that its throughput is more than 85X times higher than the most recently reported cross-technology communication system [22], which only supports one-way communication (i.e., broadcasting) at any specific time.

EMF: Embedding multiple flows of information in existing traffic for concurrent communication among heterogeneous IoT devices

The exponentially increasing number of IoT devices makes the unlicensed industrial, scientific, and medical (ISM) radio bands (e.g., 2.4 GHz) extremely crowded. Currently, there is no efficient solution to coordinate the large amount heterogeneous IoT devices that have different communication technologies (e.g., WiFi and ZigBee). To fill this gap, in this paper, we introduce embedded multiple flows (EMF) communication method, which (i) embeds different pieces of information in existing traffic and (ii)concurrently sends out these information from one IoT sender to multiple IoT receivers that have a different communication technology from the sender. By doing this, our EMF method (i) enables cross-technology communication among heterogeneous IoT devices, (ii) does not introduce any extra control traffic, and (iii) is transparent to the higher layer applications. Our approach is implemented on USRPs and commercial off-the-shelf (COTS) ZigBee devices. We also conducted extensive experiments to evaluate our approach in real-world settings. The evaluation results show that EMFs throughput is more than 14 times higher than the latest cross-technology communication technique (i.e. FreeBee[1]).

Harmony: Exploiting coarse-grained received signal strength from IoT devices for human activity recognition

The emerging smart health and smart home applications require pervasive and non-intrusive human activity recognition and monitoring. Traditional technologies (e.g., using cameras or accelerometers and gyroscopes) may introduce privacy issues or require people to wear sensors. To address these issues, recent approaches exploit fine-grained wireless signals for activity recognition. However, these approaches require devices that are costly or need to provide unique wireless features (e.g., Doppler shifts or phase information). With the increasingly available Internet of Things (IoT) devices, in this paper, we propose Harmony, a human activity recognition and monitoring middleware which can utilize the coarse-grained (but pervasively available) received signal strength (RSS) measurements from the radios of IoT devices. We implement a complete evaluation platform (from data collection to data analysis) of the middleware on top of low cost ZigBee compliant MICAz nodes and a laptop. We also conducted extensive experiments. Our results show that our design can achieve similar accuracy as fine-grained WiFi channel state information (CSI) measurement-based approaches. Specifically, our overall human activities recognition accuracy is up to 74% and 90% for RSS readings from a single pair and 3 pairs of IoT devices, respectively.

PMC: Parallel multi-protocol communication to heterogeneous IoT radios within a single WiFi channel

The exponentially increasing number of Internet of things (IoT) devices introduces spectrum crisis to the widely used industrial, scientific, and medical (ISM) frequency band. Since IoT devices use heterogeneous radios with different bandwidths (e.g., 20 MHz for WiFi and 2 MHz for ZigBee), traditional interference avoidance methods, such as time-division multiple access (TDMA) and carrier-sense multiple access (CSMA), have very low spectrum utilization. This is because TDMA and CSMA allocate the packets at time domain, without considering the bandwidth difference of different IoT radios. To address this issue, we propose PMC, a novel communication system that enables parallel multi-protocol communication to heterogeneous IoT radios (i.e., WiFi and ZigBee) within a single WiFi channel. Our extensive evaluations show that PMC achieves the throughput of up to 121.02 kbit/s and 319.76 Mbit/s for parallel communication to ZigBee and WiFi, respectively. Compared with TDMA and CSMA, the spectrum utilization of PMC is increased by 2.3 and 1.8 times, respectively.

Chiron: Concurrent High Throughput Communication for IoT Devices

The exponentially increasing number of heterogeneous Internet of Things (IoT) devices motivate us to explore more efficient and higher throughput communication, especially at the bottleneck (i.e., edge) of the IoT networks. Our work, named Chiron, opens a promising direction for Physical (PHY) layer concurrent high throughput communication to heterogeneous IoT devices (e.g., wider-band WiFi and narrower-band ZigBee). Specifically, at the PHY layer, Chiron enables concurrently transmitting (or receiving) 1 stream of WiFi data and up to 4 streams of ZigBee data to (or from) commodity WiFi and ZigBee devices as if there is no interference between these simultaneous connections. We extensively evaluate our system under different real-world settings. Results show that Chirons concurrent WiFi and ZigBee communication can achieve similar throughput as the sole WiFi or ZigBee communication. Chirons spectrum utilization is more than 16 times better than the traditional gateway.

Enhanced Wearable Medical Systems for Effective Blood Glucose Control

Wearable medical systems are prevalent in recent years. Researchers have done enormous experiments and thousands of people have got benefits from them. Wearable medical systems involve many fields, and we mainly talk about blood glucose-insulin control in this paper. The general living standards continue to improve, even though the prevalence of diabetes is dramatically rising. Current methods for treating diabetes is mainly confined to manually injecting insulin for the patient, which is inconvenient and highly expensive. Meanwhile, they are not fine-grained for doctors to accurately control insulin levels, so we try to improve the whole system for blood glucose-insulin control. Wearable medical system for blood glucose-insulin control mainly consists of three parts, Continuous Glucose Monitoring System (CGMS), insulin pump and loop control algorithm. CGSM and insulin pump have made great advances in recent years, but we are still trying to find a better method to decrease errors introduced by mechanical measurement. Loop control algorithm is vitally important and complex to study in this system. We introduce a novel algorithm which can better control blood glucose and insulin levels. To optimize this algorithm and solve storage problem, we also add big data analysis to this system. Our simulations are based on real data from 8 patients during 3 days stay in hospital. We have finally concluded that our system performs well. The research of blood control is one part of wearable medical systems which is of great significance.

Passive-ZigBee: Enabling ZigBee Communication in IoT Networks with 1000X+ Less Power Consumption

Within heterogenous IoT sensor networks, users of ZigBee devices expect long-lasting battery usage due to its ultra-low power and duty cycle. In IoT networks, to demonstrate even further ultra-low power consumption, we introduce Passive-ZigBee that demonstrates we can transform an existing productive WiFi signal into a ZigBee packet for a CoTS low-power consumption receiver while consuming 1,440 times lower power compared to traditional ZigBee. Moreover, this low power backscatter radio can bridge between the ZigBee and WiFi devices by relaying data allowing heterogenous radios to communicate with each other. We built a hardware prototype and implement these devices on a commodity ZigBee, WiFi, and an FPGA platform. Our experimental evaluation demonstrates the backscattered WiFi packets can be decoded by CoTS ZigBee receivers over a distance of 55 meters in none-line-of-sight and with human movements. Our Passive-ZigBee can consume only 25μW when transferring sensor data and relay ZigBee and WiFi data compared to traditional ZigBee (36mW). Our FPGA synthesis tool demonstrated the extremely low power consumption.

EAR: Exploiting Uncontrollable Ambient RF Signals in Heterogeneous Networks for Gesture Recognition

The exponentially increasing number of Internet-of-Thing (IoT) devices introduces a spectrum crisis in the shared ISM band. However, it also introduces opportunities for conducting radio frequency (RF) sensing using pervasively available signals generated by heterogeneous IoT devices. In this paper, we explore how to leverage the ambient wireless traffic that i) generated by uncontrollable IoT devices and ii sensed by ambient noise floor measurements (a widely available metric in IoT devices) for human gesture recognition. Specifically, we introduce our system EAR, which can conduct fine-grained human gesture recognition using coarse-grained measurements (i.e., noise floor) of ambient RF signals generated from uncontrollable signal sources. We conducted extensive evaluations in both residential and academic buildings. Experimental results show that although EAR uses coarse-grained noise floor measurements to sense the uncontrollable signal sources, the signal sources can be distinguished with an accuracy up to 99.76%. Moreover, EAR can recognize fine-grained human gestures with high accuracy even under extremely low traffic rate (i.e., 4%) from uncontrollable ambient signal sources.

Minimizing amortized cost of the on-demand irrigation system in smart farms

Irrigation is one of the most important food-water-energy problems in smart farms. It has a huge impact on the growth of food, while the water and energy cost for irrigation is the main cost of smart farms. In this paper, we propose an on-demand irrigation scheduling system which considers not only hourly numerical weather prediction to avoid over-irrigation but also TOU price model of electricity to minimize irrigation cost. To evaluate our design, we conduct extensive simulations with real-world trace data from Austin. The result shows that our proposed method can save 7.97% water and energy resources and reduce the amortized cost by 25.34% compared to a soil moisture based irrigation method.