Mission
Research
Publication
People
Funding
RESEARCH
 

Current projects

Past work

Deep Learning on Mobile Devices: The rapid progress of deep-learning techniques has enabled many emerging artificial intelligence applications, and there is a tremendous demand for running these applications on mobile devices. However, deep-learning models are by nature computationally intensive, making them challenging to deploy on battery-powered mobile devices. This research investigates the fundamental and challenging issues for running deep-learning applications on mobile devices by leveraging Neural Processing Units (NPUs). An NPU is a microprocessor that specializes in the acceleration of deep-learning algorithms; however, it incurs accuracy loss, and it is a challenge to address this problem. The goal of this research is to support deep learning on mobile devices with NPU by addressing the following issues: i) investigating model-partitioning techniques to decompose the deep-learning model into different layers running on heterogeneous processors to minimize processing time or maximize accuracy based on the application requirements; ii) designing energy- and thermal-aware techniques to address the performance limitations of the current mobile system; iii) exploring the collaborative intelligence between edge server and NPU-based mobile device to optimize performance by investigating how and where to run the computation.

SELECTED PUBLICATIONS (COMPLETE LIST)

  • Tianxiang Tan and Guohong Cao, "FastVA: Deep Learning Video Analytics Through Edge Processing and NPU in Mobile," IEEE INFOCOM, 2020. [[PDF]]

  • Tianxiang and Guohong Cao, "Deep Learning Video Analytics on Edge Computing Devices," IEEE Conference on Sensor, Mesh, and Ad Hoc Communications and Networks (SECON), 2021. [[PDF]]

  • Tianxiang and Guohong Cao, "Efficient Execution of Deep Neural Networks on Mobile Devices with NPU," ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), 2021. [[PDF]]

  • Tianxiang Tan and Guohong Cao, "Deep Learning Video Analytics Through Edge Computing and Neural Processing Units on Mobile Devices," IEEE Transactions on Mobile Computing, to appear. [[PDF]]

  • Tianxiang Tan and Guohong Cao, "Deep Learning on Mobile Devices Through Neural Processing Units and Edge Computing," IEEE INFOCOM, 2022. [[PDF]]

  • Tianxiang Tan and Guohong Cao, "Deep Learning on Mobile Devices with Neural Processing Units," IEEE Computer, 2023. [[PDF]]

  • Tianxiang Tan and Guohong Cao, "Thermal-Aware Scheduling for Deep Learning on Mobile Devices with NPU," IEEE Transactions on Mobile Computing, 2024. [[PDF]]

Edge-Assisted Cooperative Perception in Vehicular Networks Autonomous driving and advanced driver assistance systems have rapidly evolved in recent years. These systems rely on various sensors to perceive their surroundings and leverage AI techniques to understand traffic conditions and prevent accidents. However, the perception data gathered by sensors on a single vehicle may be insufficient due to their limited line of sight. To address this problem, multiple vehicles can share their perception data through the edge server; however, this approach raises many technical challenges due to the massive amount of data, significant computational demands, and limited wireless bandwidth, especially in busy intersections with numerous vehicles. This research addresses the challenges of collecting, processing, and disseminating perception data in real-time to improve driving safety under various resource constraints (i.e., computation and bandwidth).

SELECTED PUBLICATIONS (COMPLETE LIST)

  • Ruiqi Wang and Guohong Cao, "Edge-Assisted Camera Selection in Vehicular Networks," IEEE INFOCOM, 2024. [[PDF]]

  • Ruiqi Wang and Guohong Cao, "Cooperative Traffic Map Construction in Vehicular Networks," IEEE INFOCOM, 2025. [[PDF]]

  • Ruiqi Wang and Guohong Cao, "Edge-Assisted Relevance-Aware Perception Dissemination in Vehicular Networks," IEEE International Conference on Distributed Computing Systems (ICDCS), 2024. [[PDF]]

  • Ruiqi Wang and Guohong Cao, "Edge-Assisted Cooperative Perception in Vehicular Networks: System Design and Research Challenges," IEEE Vehicular Technology Magazine, 2026. [[PDF]]

  • Ruiqi Wang and Guohong Cao, "Occlusion-Aware Camera Selection in Vehicular Networks," IEEE Transactions on Vehicular Technology, to appear. [[PDF]]

Machine Learning Assisted High-Interaction Honeypots for IoT Devices: As IoT devices become widely deployed, they face an increasing number of security threats due to their inherent vulnerabilities. One effective approach to enhance IoT security is the deployment of honeypot systems, which can attract, engage, and deceive potential attackers, thereby exposing their attack methodologies and strategies. However, building high-interaction IoT honeypots is challenging due to the heterogeneity of IoT devices. Vulnerabilities in IoT devices can vary significantly based on factors such as the device brand, model, or firmware version, and simulating all these variations requires substantial resources. This research aims to develop machine learning-assisted high-interaction honeypots by addressing the following research issues: (i) leveraging reinforcement learning to train honeypot agents from real attack traces, (ii) applying generative AI, such as GPT, to emulate interactive shell sessions on IoT devices, and (iii) simulating key physical functions (e.g., video output) to mislead attackers that rely on cross-domain cues from both cyber and physical environments.

SELECTED PUBLICATIONS (COMPLETE LIST)

  • Chongqi Guan and Guohong Cao, "Cyber-Physical Deception Through Coordinated IoT Honeypots," USENIX Security, 2025 [[PDF]]

  • C. Guan, J. Zhang, G. Cao, T. La Porta, and J. Acosta, "Learning-Based Internet of Things Honeypots for Cyber Deception," IEEE Security & Privacy, Dec. 2025. [[PDF]]

  • Chongqi Guan, Guohong Cao, and Sencun Zhu, "HoneyLLM: Enabling Shell Honeypots with Large Language Models," IEEE Conference on Communications and Network Security, 2024 [[PDF]]

  • C. Guan, H. Liu, G. Cao, S. Zhu, and T. La Porta, "HoneyIoT: Adaptive High-Interaction Honeypot for IoT Devices Through Reinforcement Learning," ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec), 2023. [[PDF]]

  • C. Guan, X. Chen, G. Cao, S. Zhu, and T. La Porta, "HoneyCam: Scalable High-Interaction Honeypot for IoT Cameras Based on 360-Degree Video," IEEE Conference on Communications and Network Security, 2022. [[PDF]]

Energy-Aware and QoE-Aware Video Streaming on Mobile Devices Video streaming on mobile devices has seen tremendous growth recently, and is a major marketing point of many wireless carriers. Since videos have very large data sizes, the mobile devices must spend a great deal of their battery power on downloading and processing the video over the wireless network. Existing research on video streaming over the Internet does not consider the new research challenges related to energy efficiency and the special characteristics of mobile devices and wireless networks. This project addresses these challenges by developing novel techniques to save energy while maintaining users' Quality of Experience (QoE) during video streaming. To support energy-aware and QoE-aware video streaming on mobile devices, this research addresses the challenges of how, when and how much data to download under different contexts to save energy and improve users' QoE through the following thrusts: (i) understanding how the processor frequency affects network throughput and energy consumption, and how to adjust the frequency to minimize energy or maximize QoE considering user context; (ii) development of protocols for buffer management considering that users skip scenes and abort watching under some circumstances; and (iii) reducing the energy consumption of video streaming through network quality aware downloading and context-aware downloading.

SELECTED PUBLICATIONS (COMPLETE LIST)

  • Wenjie Hu and Guohong Cao "Energy-Aware Video Streaming on Smartphones," IEEE infocom, 2015. [[PDF]]

  • W. Hu and G. Cao, "Energy-Aware CPU Frequency Scaling for Mobile Video Streaming," IEEE International Conference on Distributed Computing Systems (ICDCS), 2017. [[PDF]]

  • Yi Yang and Guohong Cao, "Prefetch-Based Energy Optimization on Smartphones," IEEE Transactions on Wireless Communications, Vol. 17, No. 1, January 2018. [[PDF]]

  • X. Chen, T. Tan, and G. Cao, "Energy-Aware and Context-Aware Video Streaming on Smartphones," IEEE International Conference on Distributed Computing Systems (ICDCS), 2019. [[PDF]]

  • Y. Yang, W. Hu, X. Chen, and G. Cao, "Energy-Aware CPU Frequency Scaling for Mobile Video Streaming," IEEE Transactions on Mobile Computing, November 2019. [[PDF]]

  • X. Chen, T. Tan, G. Cao, and T. La Porta, "Context-Aware and Energy-Aware Video Streaming on Smartphones," IEEE Transactions on Mobile Computing, to appear. [[PDF]]

  • Xianda Chen, Tianxiang and Guohong Cao, "Popularity-Aware 360-Degree Video Streaming," IEEE INFOCOM, 2021. [[PDF]]

  • X. Chen and G. Cao, "Energy-Efficient and QoE-Aware 360-Degree Video Streaming on Mobile Devices," IEEE International Conference on Distributed Computing Systems (ICDCS), 2022. [[PDF]]

  • Xianda Chen and Guohong Cao, "Energy-Efficient 360-Degree Video Streaming on Multicore-Based Mobile Devices," IEEE INFOCOM, 2023. [[PDF]]

  • Xianda Chen and Guohong Cao, "Macrotile: Toward QoE-Aware and Energy-Efficient 360-Degree Video Streaming," IEEE Transactions on Mobile Computing, February 2024. [[PDF]]

  • S. Hong, R. Wang, and G. Cao, "Adaptive 360-Degree Video Streaming with Super-Resolution and Interpolation", IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR), 2025. [[PDF]]

Network-Centric Mobile Cloud Computing (Edge Computing): Mobile cloud computing or edge computing has been used to address the resource limitation of mobile devices by offloading computational intensive applications from mobile devices to the cloud or edge nodes. However, such offloading requires wireless transmission which consumes energy. Hence, the key problem is how to minimize the energy consumption while preserving the mobile application performance. Different from traditional solutions which focus on reducing the cost of wireless transmission solely from the application perspective, this project focuses on designing solutions from a network-centric perspective, by mitigating the impacts of two special characteristics of wireless networks: the long-tail problem at the wireless interface and the quality variations of the wireless link. More specifically, this project consists of three closely intertwined research thrusts: (i) reducing the amount of tail energy when transmitting the program states, while ensuring that the performance requirements of mobile applications can be met; (ii) mitigating the impact of wireless link quality on both energy and performance, and minimizing the degradation of application performance when the wireless link quality is low; and (iii) exploiting the difference of wireless link quality among mobile users to further improve the energy efficiency via user cooperation.

SELECTED PUBLICATIONS (COMPLETE LIST)

  • Y. Geng, W. Hu, Y. Yang, W. Gao, and G. Cao, "Energy-Efficient Computation Offloading in Cellular Networks," IEEE International Conference on Network Protocols (ICNP), 2015. [[PDF]]

  • Yeli Geng, Yi Yang and Guohong Cao "Energy-Efficient Computation Offloading for Multicore-Based Mobile Devices," IEEE INFOCOM, 2018. [[PDF]]

  • Wenjie Hu and Guohong Cao, "Quality-Aware Traffic Offloading in Wireless Networks," IEEE Transactions on Mobile Computing, 16(11): 3182-3195, 2017. [[PDF]]

  • Yeli Geng and Guohong Cao "Peer-Assisted Computation Offloading in Wireless Networks," IEEE Transactions on Wireless Communications, Vol. 17, No. 7, July 2018. [[PDF]]

  • W. jiang, G. Feng, S. Qin, T. Yum, and G. Cao, "Multi-Agent Reinforcement Learning for Efficient Content Caching in Mobile D2D Networks," IEEE Transactions on Wireless Communication, 2019 [[PDF]]

  • Tianxiang Tan and Guohong Cao, "FastVA: Deep Learning Video Analytics Through Edge Processing and NPU in Mobile," IEEE INFOCOM, 2020. [[PDF]]

  • Heting Liu and Guohong Cao, "Deep Reinforcement Learning-Based Server Selection for Mobile Edge Computing," IEEE Transactions on Vehicular Technology, December 2021. [[PDF]]

  • Heting Liu and Guohong Cao, "Deep Learning Video Analytics Through Online Learning Based Edge Computing," IEEE Transactions on Wireless Communication, to appear. [[PDF]]

  • Heting Liu, Fang He and Guohong Cao, "Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization," IEEE INFOCOM, 2023. [[PDF]]

  • Ruiqi Wang and Guohong Cao, "Edge-Assisted Camera Selection in Vehicular Networks," IEEE INFOCOM, 2024. [[PDF]]

Smarthealth with Smartwatch: Nowadays, people are paying more attention to their health; however, the high cost and the inefficiency of the existing healthcare systems have prevented many people from receiving affordable and accessible services. Thus, there is an urgent need for the transformation of healthcare from expensive, reactive and hospital-centered to low-cost, proactive and person-centered. The goal of this research is to achieve user-friendly smartwatch-based health monitoring by exploiting smartwatch's embedded sensors while addressing the research challenges posed by its computation, energy, and communication limitations. Smartwatch's accelerometer sensor is leveraged to estimate the respiratory rate during sleep, which is critical for assessing sleep quality and predicting sleep related diseases such as sleep apnea. By leveraging embedded sensors, a smartwatch-based system is designed to detect common daily activities, which can provide helpful information for healthcare and clinical medicine.

SELECTED PUBLICATIONS (COMPLETE LIST)

  • X. Chen, Y. Xiao, Y. Tang, J. Fernandez-Mendoza, and G. Cao, "ApneaDetector: Detecting Sleep Apnea with Smartwatches," ACM International Conference on Pervasive and Ubiquitous Computing (UbiComp), 2021. [[PDF]]

  • Z. Liu, X. Chen, F. Ma, J. Fernandez-Mendoza, and G. Cao, "DeepApnea: Deep Learning Based Sleep Apnea Detection Using Smartwatches," IEEE International Conference on Pervasive Computing and Comunications (Percom), 2024. [[PDF]]

  • X. Sun, L. Qiu, Y. Wu, Y. Tang, and G. Cao, "SleepMonitor: Monitoring Respiratory Rate and Body Position During Sleep Using Smartwatch," ACM International Conference on Pervasive and Ubiquitous Computing (UbiComp), 2017 [[PDF]]

  • Yi Yang and Guohong Cao, "Characterizing and Optimizing Background Data Transfers on Smartwatches," IEEE ICNP, 2017. [[PDF]]

  • X. Sun, L. Qiu, Y. Wu, and G. Cao "ActDetector: Detecting Daily Activities Using Smartwatches," Int'l Conference on Mobile Ad-hoc and Sensor Systems (IEEE MASS), 2017. [[PDF]]

  • X. Sun, Z. Lu, X. Zhang, M. Salathe, and G. Cao, "Infectious Disease Containment Based on a Wireless Sensor System," IEEE Access, April 2016. [[PDF]]

  • X. Sun, Z. Lu, W. Hu, and G. Cao, "SymDetector: Detecting Sound-Related Respiratory Symptoms Using Smartphones," ACM International Conference on Pervasive and Ubiquitous Computing (UbiComp), 2015. [[PDF]]

Resource-Aware Crowdsourcing in Wireless Networks Transmitting large numbers of photos in a wireless environment with bandwidth constraints is challenging. In this project, we develop a framework to quantify the quality of crowdsourced photos based on the accessible geographical and geometrical information (called metadata) including the smartphone's orientation, position and all related parameters of the built-in camera. From the metadata, information such as where and how the photo is taken can be inferred, and then only the most useful photos may be transmitted. Specifically, the project addresses three closely intertwined issues in resource-aware crowdsourcing. The first part investigates how to select photos based on the collected metadata by considering two scenarios: Point of Interest where the selected photos should be about a specific location or object, and Area of Interest where selected photos are related to an area. For both cases, various algorithms are designed to quantify the coverage of the photos based on the metadata, and then to select the minimum number of photos based on the coverage requirement, or to select a predefined number of photos to maximize the photo coverage. The second part focuses on metadata transmission and redundancy removal when crowdsourcing is based on peer-to-peer (P2P) communications. The third part investigates techniques to automatically and accurately generate metadata based on sensors available on most off-the-shelf smartphones.

SELECTED PUBLICATIONS (COMPLETE LIST)

  • Yi Wang, Wenjie Hu, Yibo Wu, and Guohong Cao, "SmartPhoto: A Resource-Aware Crowdsourcing Approach for Image Sensing with Smartphones," ACM Mobihoc, 2014. [[PDF]]

  • Yi Wang and Guohong Cao, "On Full-View Coverage in Camera Sensor Networks," IEEE INFOCOM, 2011. [[PDF]]

  • Yi Wang and Guohong Cao, "Achieving full-view coverage in camera sensor networks," ACM Transactions on Sensor Networks (ToSN), Vol. 10, No. 1, November, 2013. [[PDF]]

  • Y. Wu, Y. Wang, W. Hu, and G. Cao, "SmartPhoto: A Resource-Aware Crowdsourcing Approach for Image Sensing with Smartphones," IEEE Transactions on Mobile Computing, May 2016. [[PDF]]

  • Y. Wu, Y. Wang, W. Hu, X. Zhang, and G. Cao, "Resource-Aware Photo Crowdsourcing Through Disruption Tolerant Networks," IEEE International Conference on Distributed Computing Systems (ICDCS), 2016. [[PDF]]

  • Y. Wu, Y. Wang, and G. Cao, "Photo Crowdsourcing for Area Coverage in Resource Constrained Environments," IEEE INFOCOM, 2017. [[PDF]]

  • Yibo Wu and Guohong Cao, "VideoMec: A Metadata-Enhanced Crowdsourcing System for Mobile Videos," ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), 2017. [[PDF]]

  • X. Zhang, Y. Wu, L. Huang, H. Ji, G. Cao, "Expertise-Aware Truth Analysis and Task Allocation in Mobile Crowdsourcing," IEEE International Conference on Distributed Computing Systems (ICDCS), 2017. [[PDF]]

  • Y. Khazbak, J. Qiu, T. Tan, and G. Cao, "TargetFinder: Privacy Preserving Target Search through IoT Cameras," ACM/IEEE International Conference on Internet of Things Design and Implementation (IoTDI), 2019. [[PDF]]

Privacy-Aware Mobile Sensing: Smartphones with various sensors have been widely used to providing location-based service, gathering data about people and their environments as well as enabling participatory sensing. However, many people have not realized that sensor data can leak their private information, and current user's data privacy is being sacrificed to trade for the good of the community in mobile sensing. The goal of this research is to design privacy-aware mobile sensing systems by addressing the following issues. i) We propose privacy-preserving schemes for aggregation of stream data, which allow the data collector to obtain desired aggregate statistics without knowing the content of individual users' data; ii) to provide privacy for more general data, we consider mobile sensing as a service and propose a quality-of-sensing based sensor management framework, which provides users with fine-grained access control over each onboard sensor; iii) to provide trustworthy sensing data collection, we provide a distributed privacy-preserving location proof system which allows multiple mobile users to mutually verify their locations (i.e., sensing context) in an anonymous way, and propose an incentive-based corroboration mechanism to boost the trustworthiness of the reported sensing data.

SELECTED PUBLICATIONS (COMPLETE LIST)

  • Q. Li and G. Cao, "Privacy-Preserving Participatory Sensing," IEEE Communication Magazine, August 2015. [[PDF]]

  • Qinghua Li and Guohong Cao, "Providing Efficient Privacy-Aware Incentives for Mobile Sensing," IEEE International Conference on Distributed Computing Systems (ICDCS), 2014. [[PDF]]

  • Qinghua Li and Guohong Cao, "Providing Privacy-Aware Incentives for Mobile Sensing" IEEE International Conference on Pervasive Computing and Comunications (Percom), 2013. [[PDF]]

  • Qinghua Li, and Guohong Cao, "Efficient Privacy-Preserving Stream Aggregation in Mobile Sensing with Low Aggregation Error," The 13th Privacy Enhancing Technologies Symposium (PETS), 2013. [[PDF]]

  • Q. Li and G. Cao, "Efficient and Privacy-Preserving Data Aggregation in Mobile Sensing," IEEE International Conference on Network Protocols (ICNP), 2012. [[PDF]]

  • Zhichao Zhu and Guohong Cao, "APPLAUS: A Privacy-Preserving Location Proof Updating System for Location-based Services," IEEE INFOCOM, 2011. [[PDF]]

  • B. Niu, Q. Li, X. Zhu, G. Cao, H. Li, "Enhancing Privacy through Caching in Location-Based Services," IEEE INFOCOM, 2015. [[PDF]]

  • B. Niu, Q. Li, X. Zhu, G. Cao, and H. Li, "Achieving k-anonymity in Privacy-Aware Location-Based Services," IEEE infocom, 2014. [[PDF]]

  • Y. Khazbak and G. Cao, "Deanonymizing Mobility Traces With Co-Location Information," IEEE Conference on Communications and Network Security (CNS), 2017. [[PDF]]

  • Y. Khazbak, J. Fan, S. Zhu, and G. Cao, "Preserving Location Privacy in Ride-Hailing Service," IEEE Conference on Communications and Network Security (CNS), 2018. [[PDF]]

Coopertive caching in wireless P2P networks: Wireless P2P networks such as ad hoc networks, mesh networks and sensor networks have received considerable attention due to their potential applications in many civilian and military environments such as disaster recovery, wireless office, battlefield and outdoor assemblies. Design of such networks considering performance and power optimization has become a recent research focus. As nodes in wireless P2P networks may perform similar tasks using common data sets, cooperative data access, which allows sharing and coordination of cached or replicated data among multiple nodes, can be used to reduce the bandwidth and power consumption. The specific goal of this project is to provide a collaborative data access framework for wireless P2P networks. The proposed research addresses four intertwined issues. First, we design and evaluate two cooperative caching schemes, called CachePath and CacheData. We identify the tradeoffs between these two schemes, and rely on HybridCache to further improve the system performance. Second, various cache replacement and cache admission control algorithms are proposed and evaluated to balance the tradeoffs between access latency and data accessibility. Third, to further reduce the access latency and increase the data accessibility, data replication techniques are designed and evaluated. Finally, we identify possible security violations to maintain data consistency and propose solutions to defend against such attacks.

SELECTED PUBLICATIONS (COMPLETE LIST)

  • G. Cao, L. Yin, and C. Das, " Cooperative Cache-Based Data Access in Ad Hoc Networks," IEEE Computer, pp. 32-39, Feb. 2004.

  • L. Yin and G. Cao, "Supporting Cooperative Caching in Ad Hoc Networks,'' IEEE Transactions on Mobile Computing , Vol. 5, No. 1, pp. 77- 89, January, 2006. ( A preliminary version appeared in IEEE INFOCOM'04).

  • J. Cao, Y. Zhang, G. Cao, and L. Xie ``Data Consistency for Cooperative Caching in Mobile Environments," IEEE Computer, pp. 60-66, April 2007. [[PDF]]

  • Jing Zhao, Ping Zhang, Guohong Cao, and Chita R. Das, "Cooperative Caching in Wireless P2P Networks: Design, Implementation, and Evaluation," IEEE Transactions on Parallel and Distributed Systems, Vol. 21, No. 2, pp. 229-241, February 2010. [[PDF]]

  • W. Gao, G. Cao, A. Iyengar, and M. Srivatsa, "Supporting Cooperative Caching in Disruption Tolerant Networks," IEEE International Conference on Distributed Computing Systems (ICDCS), 2011. [[PDF]]

  • X. Zhuo, Q. Li, G. Cao, Y. Dai, B. Szymanski, T. La Porta, "Social-Based Cooperative Caching in DTNs: A Contact Duration Aware Approach," International Conference on Mobile Ad-hoc and Sensor Systems (IEEE MASS), 2011. [[PDF]]

  • Y. Zhang, L. Yin, J. Zhao, and G. Cao, "Balancing the Tradeoffs between Query Delay and Data Availability in MANETs," IEEE Transactions on Parallel and Distributed Systems, to appear. [[PDF]]

  • W. Gao, G. Cao, M. Srivatsa and A. Iyengar, "Distributed Maintenance of Cache Freshness in Opportunistic Mobile Networks," IEEE International Conference on Distributed Computing Systems (ICDCS), 2012. [[PDF]]

  • Wei Gao, Guohong Cao, Arun Iyengar, and Mudhakar Srivatsa, "Cooperative Caching for Efficient Data Access in Disruption Tolerant Networks," IEEE Transactions on Mobile Computing, March 2014. [[PDF]]

  • Li Qiu and Guohong Cao, "Cache Increases the Capacity of Wireless Networks," IEEE INFOCOM, 2016. [[PDF]]

  • Li Qiu and Guohong Cao, "Popularity-Aware Caching Increases the Capacity of Wireless Networks," IEEE INFOCOM, 2017. [[PDF]]

Efficient Energy-Aware Web Access in Wireless Networks: Web access is one of the most common and important services provided by smartphones. However, it currently suffers from long delays and creates a huge drain on the battery lifetime of smartphones. These limitations are caused by complex interactions between the processing flow in mobile web browsers and specialized characteristics of the wireless radio interface, and by the processing limitations of the smartphones. This project addresses these limitations by focusing on three intertwined issues: (i) various techniques to reorganize the computation sequence of the web browser to let the wireless radio interface enter sleep earlier, are designed, implemented and evaluated; (ii) practical data mining based methods are introduced to predict the user viewing time of webpages and determine when the smartphone should switch to low power state, considering the resource limitations of the smartphone and various tradeoffs between delay and power; and (iii) a new architecture is proposed to shift the computing from smartphones to the virtual-machine based proxy to address the computation limitations in smartphones considering scalability issues and bandwidth constraints.

SELECTED PUBLICATIONS (COMPLETE LIST)

  • Wenjie Hu and Guohong Cao, "Quality-Aware Traffic Offloading in Wireless Networks," ACM Mobihoc, 2014. [[PDF]]

  • B. Zhao, Q. Zheng, G. Cao, and S. Addepalli, "Energy-Aware Web Browsing in 3G Based Smartphones," IEEE International Conference on Distributed Computing Systems (ICDCS), 2013. [[PDF]]

  • Wenjie Hu and Guohong Cao "Energy Optimization Through Traffic Aggregation in Wireless Networks," IEEE infocom, 2014. [[PDF]]

  • W. Hu, G. Cao, S. Krishanamurthy, P. Mohapatra, "Mobility-Assisted Energy-Aware User Contact Detection in Mobile Social Networks," IEEE International Conference on Distributed Computing Systems (ICDCS), 2013. [[PDF]]

  • Bo Zhao, Byung Chul Tak and Guohong Cao, "Reducing the Delay and Power Consumption of Web Browsing on Smartphones in 3G networks," IEEE International Conference on Distributed Computing Systems (ICDCS), 2011. [[PDF]]

  • H. Zhu and G. Cao, ``A Power-Aware and QoS-Aware Service Model on Wireless Networks,'' IEEE INFOCOM, March 2004. An extended version appeared in IEEE Transactions on Mobile Computing, Vol. 4, No. 4, July/August, pp. 391-403, 2005.

Resilient and Efficient Data Access in Cognitive Radio Networks In cognitive radio networks, to avoid interference with licensed users, unlicensed users must vacate the spectrum accessed by the primary users. Since it takes some time for the unlicensed users to detect and switch to other available spectrum, the ongoing data transmission may have to be interrupted, leading to poor data access performance. Although there is a lot of research on cognitive radio networks, not much work has been done on data access. This research focuses on three intertwined issues to support resilient and efficient data access: (i) Various topology control protocols which carefully assign communication channels considering network robustness and channel interference to achieve better data accessibility, are designed and evaluated; (ii) Delay-constrained caching techniques are introduced to deal with primary user appearance, where data is cached/replicated at appropriate nodes to statistically limit the data access delay; (iii) Spectrum-aware data replication schemes are designed to improve data access performance in intermittently connected cognitive radio networks, by considering both node mobility pattern and primary user appearance.

SELECTED PUBLICATIONS (COMPLETE LIST)

  • Jing Zhao, Wei Gao, Yi Wang, and Guohong Cao, "Delay-Constrained Caching in Cognitive Radio Networks," IEEE infocom, 2014. [[PDF]]

  • Jing Zhao and Guohong Cao, "Spectrum-Aware Data Replication in Intermittently Connected Cognitive Radio Networks," IEEE infocom, 2014. [[PDF]]

  • Jing Zhao and Guohong Cao, "Robust Topology Control in Multi-hop Cognitive Radio Networks," IEEE infocom, 2012. [[PDF]]

  • J. Zhao X. Zhuo, Q. Li, W. Gao, G. Cao, "Contact Duration Aware Data Replication in DTNs with Licensed and Unlicensed Spectrum," IEEE Transactions on Mobile Computing, April, 2016. [[PDF]]

  • Jing Zhao, Wei Gao, Yi Wang, and Guohong Cao, "Delay-Constrained Caching in Cognitive Radio Networks," IEEE Transactions on Mobile Computing, March 2016. [[PDF]]

Social-Aware Data Dissemination in Delay Tolerant Networks: In Delay Tolerant Networks (DTNs), due to node mobility and low node density, the network topology is highly dynamic and end-to-end connection is hard to maintain. To deal with these problems, researchers adopt the idea of carry and forward, where a node carries the packet when no route exists, and later forwards the packet to a new relay or the receiver that moves into its vicinity. Then, the key problem for data dissemination in DTNs becomes how to determine the appropriate relay selection strategy, and many researchers design different metrics for choosing the relays. The goal of this research is to design social-aware data dissemination schemes for DTNs. We focus on designing and evaluating user-centric data dissemination schemes based on social network phenomenon such as Homophily, and social network techniques such as centrality. Our solutions will allow users to specify their interests and only forward data to the interested nodes with maximum cost-effectiveness. We also investigate the social selfishness behavior. Since a node may be more willing to relay packets coming from a friend than that from a stranger, data dissemination in DTN needs to deal with this kind of selfish behavior when selecting the relays.

SELECTED PUBLICATIONS (COMPLETE LIST)

  • Wei Gao, Qinghua Li, Bo Zhao and Guohong Cao, "Multicasting in Delay Tolerant Networks: A Social Network Perspective," ACM Mobihoc, 2009. [[PDF]]

  • Q. Li, S. Zhu, and G. Cao, "Routing in Socially Selfish Delay Tolerant Networks," IEEE INFOCOM, 2010. [[PDF]]

  • Wei Gao and Guohong Cao, "On Exploiting Transient Contact Patterns for Data Forwarding in Delay Tolerant Networks," IEEE International Conference on Network Protocols (ICNP), 2010. [[PDF]]

  • Wei Gao and Guohong Cao, "User-Centric Data Dissemination in Disruption Tolerant Networks," IEEE INFOCOM, 2011. [[PDF]]

  • X. Zhuo, W. Gao, G. Cao, and Y. Dai, "Win-Coupon: an Incentive Framework for 3G Traffic Offloading," IEEE International Conference on Network Protocols (ICNP), 2011. [[PDF]]

  • X. Zhuo, Q. Li, W. Gao, G. Cao, and Y. Dai, "Contact Duration Aware Data Replication in Delay Tolerant Networks," IEEE International Conference on Network Protocols (ICNP), 2011. [[PDF]]

  • Wei Gao, Qinghua Li, Bo Zhao, and Guohong Cao, "Social-aware Multicast in Disruption Tolerant Networks," IEEE/ACM Transactions on Networking, October, 2012. [[PDF]]

  • Wei Gao, Guohong Cao, Tom La Porta and Jiawei Han, "On Exploiting Transient Social Contact Patterns for Data Forwarding in Delay Tolerant Networks," IEEE Transactions on Mobile Computing, January, 2013. [[PDF]]

  • Wei Gao, Qinghua Li, and Guohong Cao "Forwarding Redundancy in Opportunistic Mobile Networks: Investigation and Elimination," IEEE infocom, 2014. [[PDF]]

  • Xiaomei Zhang and Guohong Cao, "Transient Community Detection and Its Application to Data Forwarding in Delay Tolerant Networks," IEEE International Conference on Network Protocols (ICNP), 2013. [[PDF]]

  • Xiaomei Zhang and Guohong Cao, "Efficient Data Forwarding in Mobile Social Networks with Diverse Connectivity Characteristics," IEEE International Conference on Distributed Computing Systems (ICDCS), 2014. [[PDF]]

Data dissemination in vehicular networks: Vehicular ad hoc networks (VANET) have been envisioned to be useful in road safety and many commercial applications. Although data dissemination techniques have been widely studied in the database community and the network community, many unique characteristics of VANET bring out new research challenges. The specific goal of this project is to provide a unified data dissemination framework for VANET. The proposed research addresses four intertwined issues. First, for infrastructure-assisted data dissemination, we design and evaluate service scheduling protocols, and protocols to efficiently utilization the bandwidth. We also propose a data pouring scheme to push data to the users to reduce the query delay. Second, for infrastructureless data dissemination, we design and evaluate vehicle-assisted data delivery protocols for sparsely connected VANET. Our solution makes use of the predictable mobility in VANET, which is limited by the traffic pattern and the road layout. Third, besides using network support for data dissemination, we also look into user-centric data dissemination to maximize data accessibility to individual users. Finally, we investigate mobility characterization techniques which enable individual vehicles to characterize the fine-grained mobility patterns of their neighbors, which can be used for clustering or finding a stable multi-hop route.

SELECTED PUBLICATIONS (COMPLETE LIST)

  • J. Zhao and G. Cao, "VADD: Vehicle-Assisted Data Delivery in Vehicular Ad Hoc Networks," IEEE Transactions on Vehicular Technology, Vol. 57, No. 3, May 2008. [[PDF]] (Early version appeared in IEEE INFOCOM'06).

  • J. Zhao, Y. Zhang, and G. Cao, "Data Pouring and Buffering on The Road: A New Data Dissemination Paradigm for Vehicular Ad Hoc Networks," IEEE Transactions on Vehicular Technology , Volume 56, Issue 6, pp. 3266-3277, November, 2007. [[PDF]]

  • J. Zhao, T. Arnold, Y. Zhang and G. Cao, "Extending Drive-Thru Data Access by Vehicle-to-Vehicle Relay," ACM International Workshop on Vehicular Ad Hoc Networks (VANET) , 2008. [[PDF]]

  • Y. Zhang, J. Zhao and G. Cao, "Roadcast: A Popularity Aware Content Sharing Scheme in VANETs", IEEE International Conference on Distributed Computing Systems (ICDCS), 2009. [[PDF]]

  • Wei Gao and Guohong Cao, "Fine-Grained Mobility Characterization: Steady and Transient State Behaviors," ACM Mobihoc, 2010. [[PDF]]

  • Y. Zhang, J. Zhao, and G. Cao, "Service Scheduling of Vehicle-Roadside Data Access," ACM/Springer Journal on Mobile Networks and Applications Volume 15, Number 1, pp. 83-96, February, 2010. [[PDF]]

  • Yang Zhang and Guohong Cao, "V-PADA: Vehicle-Platoon-Aware Data Access in VANETs," IEEE Transactions on Vehicular Technology, Vol. 60, NO. 5, June 2011. [[PDF]]

Supporting multi-missions in wireless sensor networks: Wireless sensor networks have been adopted to many military and commercial applications. However, the sensor network envisioned so far is targeted for a single mission and often designed for some particular application. As sensors become widely deployed, multiple missions, each with different requirements, may share common sensors to achieve their goals. Each mission may have its own requirements for the type of data being reported, the sampling rate, accuracy, and location of the sampling. From resource management point of view, it will be cost effective for the wireless sensor network to support multiple missions instead of a single mission. The specific goal of this project is to support multi-missions in wireless sensor networks. The project addresses four intertwined issues: (i) various mission-driven scheduling protocols which can optimize the sensor coverage will be designed, implemented, and evaluated; (ii) novel techniques will be developed to disseminate the mission switch code/command to the affected sensor nodes quickly and efficiently; (iii) mission-driven sensor assignment schemes will be designed to maximize the network utility; (iv) mission-specific network configurations will be investigated to meet the real-time requirements of data transfer for dynamic and competing missions.

SELECTED PUBLICATIONS (COMPLETE LIST)

  • C. Liu and G. Cao, "Minimizing the Cost of Mine Selection Via Sensor Networks," IEEE INFOCOM, 2009. [[PDF]]

  • L. Su, C. Liu, H. Song, and G. Cao, "Routing in Intermittently Connected Sensor Networks," IEEE International Conference on Network Protocols (ICNP) , 2008 [[PDF]]

  • C. Liu and G. Cao, "Spatial-Temporal Coverage Optimization in Wireless Sensor Networks," IEEE Transactions on Mobile Computing, April 2011. [[PDF]]

  • Yi Wang and Guohong Cao, "Minimizing Service Delay in Directional Sensor Networks," IEEE INFOCOM, 2011. [[PDF]]

  • Yi Wang and Guohong Cao, "On Full-View Coverage in Camera Sensor Networks," IEEE INFOCOM, 2011. [[PDF]]

  • Yi Wang and Guohong Cao, "Barrier Coverage in Camera Sensor Networks," ACM Mobihoc, 2011. [[PDF]]

  • L. Su, Y. Gao, Y. Yang, and G. Cao, "Towards Optimal Rate Allocation for Data Aggregation in Wireless Sensor Networks," ACM Mobihoc, 2011. [[PDF]]

  • Changlei Liu and Guohong Cao, "Distributed Critical Location Coverage in Wireless Sensor Networks with Lifetime Constraint," IEEE INFOCOM, 2012. [[PDF]]

  • Yi Wang and Guohong Cao, "Achieving full-view coverage in camera sensor networks," ACM Transactions on Sensor Networks (ToSN), Vol. 10, No. 1, November, 2013. [[PDF]]

Security in cellular networks: With the introduction of the iPhone, Openmoko and Android smartphones amongst several others, we see an increasing trend in the mobile devices domain to keep the user always connected to the Internet. At the same time, surveys and research show that there is an increasing number of mobile phone malware. Besides viruses, other types of malware like worms, spyware, adware, trojan horses, backdoors etc. are emerging. Moreover, these mobile phones can be used to exploit the vulnerabilities of the cellular network to launch Denial of Service attacks and battery attacks. Despite the severity of these threats, research on security in cellular networks has not received much attention and many research challenges remain open. This research will investigate device-side and network-side solutions for defending against various security attacks in cellular networks.

SELECTED PUBLICATIONS (COMPLETE LIST)

  • Z. Zhu, G. Cao, S. Zhu, S. Ranjan, A. Nucci, "A Social Network Based Patching Scheme for Worm Containment in Cellular Networks," IEEE INFOCOM, 2009. [[PDF]]

  • B. Zhao, C. Chi, W. Gao, S. Zhu, G. Cao, "A Chain Reaction DoS Attack on 3G Networks: Analysis and Defenses," IEEE INFOCOM, 2009. [[PDF]]

  • Zhichao Zhu and Guohong Cao, "Towards Privacy Preserving and Collusion Resistance in Location Proof Updating System," IEEE Transactions on Mobile Computing, January, 2013. [[PDF]] (A preliminary version appeared in IEEE INFOCOM'2011)

Secure wireless sensor networks: It is a big challenge to secure wireless sensor networks because of the network scale, the highly constrained system resource, and the fact that sensor networks are often deployed in unattended and hostile environments. The objective of this project is to develop a framework for defending against node compromises in unattended sensor networks. The framework consists of a suite of security mechanisms spanning three phases: prevention, detection, and reaction. This research will provide fundamental security services covering key management, authentication, compromise detection, and revocation. These services are essential for the successful deployment of sensor networks. In addition, most of the proposed solutions are designed and implemented in a distributed manner, where no central authority is involved. This distributed property is critical for unattended sensor networks deployed in adversarial environments because the central authority is a single point of failure from security and performance perspectives.

SELECTED PUBLICATIONS (COMPLETE LIST)

  • W. Zhang and G. Cao, "Group Rekeying for Filtering False Data in Sensor Networks: A Predistribution and Local Collaboration-Based Approach," IEEE INFOCOM, March 2005. [PDF]

  • W. Zhang, H. Song, S. Zhu, and G. Cao, "Least Privilege and Privilege Deprivation: Towards Tolerating Mobile Sink Compromises in Wireless Sensor Networks," ACM MOBIHOC, May 2005. [PDF]

  • Y. Yang, X. Wang, S. Zhu, and G. Cao, "SDAP: A Secure Hop-by-Hop Data Aggregation Protocol for Sensor Networks," ACM MobiHoc, May 2006. [PDF]

  • P. Traynor, R. Kumar, H. Bin Saad, G. Cao, S. Zhu, and T. La Porta, "Efficient Hybrid Security Mechanisms for Heterogeneous Sensor Networks", IEEE Transactions on Mobile Computing, Vol. 6, No. 6, pp. 663-677, June 2007. [[PDF]]

  • M. Shao, S. Zhu, W. Zhang, and G. Cao, "pDCS: Security and Privacy Support for Data-Centric Sensor Networks," IEEE INFOCOM, 2007. [[PDF]]

  • M. Shao, Y. Yang, S. Zhu, and G. Cao, "Towards Statistically Strong Source Anonymity for Sensor Networks," IEEE INFOCOM, 2008. [[PDF]]

  • Y. Yang, M. Shao, S. Zhu, B. Urgaonkar, and G. Cao, "Towards Event Source Unobservability with Minimum Network Traffic in Sensor Networks," ACM Conference on Wireless Network Security (WiSec), 2008. [[PDF]]

  • Y. Yang, S. Zhu, and G. Cao. "Improving Sensor Network Immunity under Worm Attacks: A Software Diversity Approach." ACM Mobihoc 2008. [[PDF]]

Mobile sensor networks: Traditional sensor networks have limitations when applied to support multiple missions or when the network conditions change. Mobile sensors can be used to address these problems as mobility can significantly increase the capability of the sensor network by making it resilient to failures, reactive to events, and be able to support disparate missions with a common set of sensors. To support mobility in sensor networks, this project investigates various research issues in mobility assisted sensing, network monitoring, mobility assisted routing, and integrated mobility management for sensing and routing. The expected results from this project are: (i) Significant theoretical and technical advances in supporting mobility in sensor networks; (ii) Understanding various performance and power tradeoffs in designing and implementing sensor relocation protocols; (iii) Development of network monitoring protocols, coverage hole estimation and failure effect estimation protocols; (iv) Theoretical advances on mobility assisted routing; and (v) Understanding of how sensing and routing interact and how to satisfy different mission requirements and maximize the network capability.

SELECTED PUBLICATIONS (COMPLETE LIST)

  • G. Wang, G. Cao, T. La Porta, and W. Zhang, "Sensor Relocation in Mobile Sensor Networks," IEEE INFOCOM, March 2005. [PDF]

  • G. Wang, G. Cao, and T. La Porta, ``Movement-Assisted Sensor Deployment,'' IEEE Transactions on Mobile Computing, Vol. 5, No. 6, pp. 640 - 652, June 2006. [PDF] (A preliminary version appeared in IEEE infocom'2004)

  • G. Wang, G. Cao, P. Berman, and T. La Porta, "Bidding Protocols for Deploying Mobile Sensors," IEEE Transactions on Mobile Computing, Vol. 6, No. 5, pp. 515-528, May 2007. [[PDF]]

Data centric sensor networks: Sensor nodes are limited in sensing capacity and are prone to failure, drift and loss of calibration, and hence we can not rely on a single sensor node to obtain reliable data. Instead, multiple nodes should be deployed in close proximity with the target of interest to obtain fine-grained and high-precision data. The involvement of many senor nodes in a sensing task and the constrained energy supply of the sensor nodes pose new challenges for designing scalable, self-organizing, and energy efficient data collection and dissemination schemes in sensor networks. The specific goal of this proposal is to provide a data-centric framework for mobile target tracking and data dissemination in sensor networks. The proposed research addresses three intertwined issues. The first part focuses on building a dynamic convoy tree-based framework for data collection. Tree reconfiguration protocols and collaborative failure detection and recovery schemes will be designed and evaluated considering the energy efficiency and scalability issues. The second part proposes an index-based data dissemination framework. Considering the scalability and reliability issues, an adaptive ring-based index solution will be designed and evaluated. The final part attempts to build a heterogeneous storage structure which allows the collected sensing data to be saved locally. Protocols will be designed to help sensor nodes find local storage, and the tradeoffs between data quality and cost will be investigated.

SELECTED PUBLICATIONS (COMPLETE LIST)

Cache invalidation and power-aware data access: Caching is an effective technique to reduce the query latency, bandwidth and power consumption in mobile environments. When cache is used, cache consistency issues must be addressed. One attractive approach is based on invalidation reports (IR), where the server periodically broadcasts an IR in which the changed data items are indicated. The IR-based solution is attractive because it can tolerate client disconnections and it has good scalability. However, it has some drawbacks such as long query latency and low bandwidth utilization. To reduce the query latency, we proposed to replicate the IR several times. Since the IR contains a large amount of update history information, we proposed an optimization technique which is called updated invalidation report (UIR), to remove the redundancy [Mobicom00]. With the UIR-based model, we designed and evaluated stateful and stateless server approaches to improve the bandwidth utilization by actively prefetching the right data into the local cache. In the stateful server approach [Monet02], a counter is used to identify the most frequently accessed data. Techniques are also proposed to deal with client and server failures. In the stateless server approach [TC02], we proposed novel solutions to organize the broadcast channel to help clients prefetch the right data. Since prefetch consumes power, we investigated the tradeoffs between performance and power [Twireless04], and extended the solution to achieve a balance between performance and power considering various factors such as access rate, update rate, and data size.

SELECTED PUBLICATIONS (COMPLETE LIST)

Resource management in wireless networks: Putting the wireless network interface (WNI) into sleep when the WNI is idle is an effective technique to save power. To support streaming applications, existing techniques cannot put the WNI into sleep due to strict delay requirements. we have proposed a novel power-aware and QoS-aware service model [infocom04b], where mobile nodes use proxies to buffer data so that the WNIs can sleep for a long time period. To achieve power-aware communication while satisfying the delay requirement of each flow, a scheduling scheme is designed to decide which flow should be served at which time. To deal with channel errors, a novel adaptive technique is developed to adjust the sleep time of the WNI according to the channel condition. We are also investigating techniques to improve the performance of Wireless LANs through scheduling. Since wireless LAN supports multiple data rates in response to different channel conditions, data packets may be delivered faster through a relay node than through a direct link if the direct link has low quality and low rate. To enable MAC layer relay, we have designed and evaluated protocols [infocom05] to help mobile nodes collect information about the channel conditions, and notify each other which data rate to use and whether to transmit the data through a relay station.

SELECTED PUBLICATIONS (COMPLETE LIST)

Distributed fault-tolerant computing: Coordinated checkpointing is an attractive approach for transparently adding fault tolerance to distributed applications. We have developed a theory of ``z-dependency'' [TPDS98] to catch the essence of coordinated checkpointing and proved that there does not exist a non-blocking algorithm, which forces only a minimum number of processes to take their checkpoints. Based on this impossibility result, we have proposed a min-process algorithm which relaxes the non-blocking condition while tries to minimize the blocking time, and a non-blocking algorithm [TPDS01] which relaxes the min-process condition while minimizing the number of checkpoints saved on the stable storage. The proposed non-blocking algorithm is based on a novel concept called ``mutable checkpoint'' which is neither a tentative checkpoint nor a permanent checkpoint. Mutable checkpoints can be saved anywhere; e.g., the main memory or local disk of the mobile nodes. In this way, taking a mutable checkpoint avoids the overhead of transferring large amount of data to the stable storage at the base station over the wireless network. We have also designed and evaluated techniques to minimize the number of mutable checkpoints.

SELECTED PUBLICATIONS (COMPLETE LIST)

 

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