Zhichuan Huang

Sharing renewable energy in smart microgrids

Renewable energy harvested from the environment is an attractive option for providing green energy to homes. Unfortunately, the intermittent nature of renewable energy results in a mismatch between when these sources generate energy and when homes demand it. This mismatch reduces the efficiency of using harvested energy by either i) requiring batteries to store surplus energy, which typically incurs ~20% energy conversion losses; or ii) using net metering to transmit surplus energy via the electric grids AC lines, which severely limits the maximum percentage of possible renewable penetration. In this paper, we propose an alternative structure wherein nearby homes explicitly share energy with each other to balance local energy harvesting and demand in microgrids. We develop a novel energy sharing approach to determine which homes should share energy, and when, to minimize system-wide efficiency losses. We evaluate our approach in simulation using real traces of solar energy harvesting and home consumption data from a deployment in Amherst, MA. We show that our system i) reduces the energy loss on the AC line by 60% without requiring large batteries, ii) scales up performance with larger battery capacities, and iii) is robust to changes in microgrid topology.

iDES: Incentive-driven distributed energy sharing in sustainable microgrids

Buildings consume a significant amount of electricity, which is normally generated from dirty sources causing an increase in carbon footprints. To reduce carbon footprint, distributed renewable energy generation has been proposed. However, the amount of renewable energy harvested normally does not match the amount of energy consumed in individual homes. To address this mismatch, we propose a distributed solution to share renewable energy among homes, which form a microgrid. Specifically we (i) design an incentive-driven distributed energy sharing system (iDES) in a microgrid to enable effective energy sharing and reduce the communication overhead, and (ii) develop energy sharing pricing model to incentivize energy sharing. The energy sharing price generally reflects the installation costs of on-site renewable and energy storage units, the dynamic changes of energy supply-demand relationship, and the remaining energy level of batteries. We validated the effectiveness of our system with extensive evaluations that use empirical traces. The results show that our energy sharing pricing model can effectively motivate and encourage homes to share energy.

Minimizing electricity costs by sharing energy in sustainable microgrids

Buildings account for over 75% of the electricity consumption in the United States. To reduce electricity usage and peak demand, many utilities are introducing market-based time-of-use (TOU) pricing models. In parallel, government programs that increase the fraction of renewable energy are incentivizing residential consumers to adopt on-site renewables and energy storage. Connecting on-site renewables and energy storage between homes forms a sustainable microgrid capable of generating, storing, and sharing electricity to balance local generation and consumption in residential areas. In this paper, we investigate how to minimize the costs of electricity from a utility for a microgrid under market-based TOU pricing models. In particular, we (i) present a system architecture for an energy-sharing microgrid; and (ii) develop optimal energy-sharing algorithms for homes within the microgrid. We conduct an extensive evaluation under two typical TOU pricing models that use data from more than 40 homes. Our results indicate that our system reduces the costs of Alternating Current (AC) electricity by 20%, even for homes with similar energy usage patterns.

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.

EEP2P: An energy-efficient and economy-efficient P2P network protocol

Peer-To-Peer (P2P) file-sharing protocols have been widely used for distributing massive data over the Internet. To satisfy the requirement of P2P platforms, like BitTorrent, edge devices have to be powered on continuously to either download files or assist other peers to download files, which could lead to the wasted energy, especially for those time-insensitive files such as online games or high-definition movies. Energy problem is not only related to energy consumption, but also to economic problems in the smart grid area for P2P. In this work, we present a framework combining the Time-Of-Use (TOU) pricing model and P2P protocols in smart grid area. Peers adjust their energy consumption per unit time based on the TOU pricing model automatically, by switching between a dormant state and an active state in accordance with a time schedule sequence. Our simulation results show that our protocol is both economically efficient and energy efficient for transferring files of various sizes on different systems such as Personal Computers (PCs) and mobile devices.

Accurate power quality monitoring in microgrids

Traditional power grid is not resistant to severe weather conditions, especially in remote areas. For some areas with few people, such as islands, it is difficult and expensive to maintain their connectivity to the traditional power grid. Therefore, a self-sustainable microgrid is desired. However, given the limited local energy storage and energy generation, it is extremely challenging for a microgrid to balance the power demand and generation in real-time. To realize the real-time power quality monitoring, the power quality information of microgrid, such as voltage, frequency and phase angle in each home, needs to be collected in real- time. Furthermore, the unreliable sensing results and data collection in a microgrid make the real-time data collection more difficult. To address these challenges, we designed an accurate real-time power quality data sensing hardware to sense the voltage, frequency and phase angle in each home. A novel data management technique is also proposed to reconstruct the missing data caused by unreliable sensing. We implemented our system over off-the-shelf smartphones with a few peripheral hardware components, and realized an accuracy of 1.7 mHz and 0.01 rad for frequency and phase angle monitoring, respectively. We also show our data management technique can reconstruct the missing data with more than 99% accuracy.

A Survey on Spectrum Utilization in Wireless Sensor Networks

In recent years, the industrial, scientific, and medical (ISM) bands have been intensively shared with unlicensed wireless communications applications such as wireless sensor networks (WSNs). With flourishing popularity of sensor devices and increasing installation of wireless sensor nodes, the cross technology interference (CTI) has become a considerable real-world problem. Because of CTI, wireless devices suffer significant communication dilemma. Moreover, ISM band, as the main communication medium of WSN, should be reasonably utilized in an efficient and effective manner. Extensive approaches have been proposed to explore spectrum utilization in WSN. However, there is no such one, which systematically organizes these works. In this paper, we present a comprehensive survey on spectrum utilization in WSNs. To achieve this goal, We first illustrate the background of WSN and spectrum utilization. Our concern on CTI is then noted. Later we demonstrate the importance of efficient spectrum utilization. Eventually, through classification and summary of recent related works, we provide an essential structure of research in titled field and detailed intellectual merits of published works. Our survey covers more than 80 studies in the scope of spectrum utilization in WSN.

Distributed and dynamic spectrum management in airborne networks

Airborne network plays an important role in operations of Air Force. Due to the rapidly-changing topologies, limited wireless communication spectrum, latency, and priorities of tasks, it is vitally important to have dynamic spectrum management between aircrafts and base station in airborne networks. Particularly, we encounter two major challenges: (1) uncertainty about channel quality, in terms of the long-term channel statistics and real-time channel states; (2) the dynamics and the possibly correlated nature of channel quality of different aircrafts. To address these two challenges, we propose a distributed framework for spectrum management in airborne networks, which includes: 1) channel quality prediction based on correlation of channel quality among aircrafts; 2) task scheduling in the aircraft. With real-world trace data, we conduct extensive experiments. Our simulation results show that our design can effectively optimize the channel utilization in airborne network.

Wearable sensor based human posture recognition

Human posture recognition has a wide range of applications including elderly care and video surveillance. This paper discusses how to recognize human postures using wearable devices. From real-world data, we analyze the challenges in terms of result performance, recognition efficiency and sensor selection. To deal with the challenges, we present our design with five techniques: i) oversampling and undersampling methods, ii) ensemble learning, iii) sensor selection, iv) stream data classification and v) post-processing techniques. We verify our design and show our findings through extensive experiments on real-world data, which shows our approach can achieve up to 91.5% overall weighted average accuracy for all three postures. We also discuss possible extensions of our work.