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1. An Edge Based Smart Parking Solution Using Camera Networks and Deep Learning

   https://doi.org/10.1109/ICCC.2018.00010  

   Bura, Harshitha ; Lin, Nathan ; Kumar, Naveen ; Malekar, Sangram ; Nagaraj, Sushma ; Liu, Kaikai ( July 2018 , 2018 IEEE International Conference on Cognitive Computing (ICCC))

   The smart parking industry continues to evolve as an increasing number of cities struggle with traffic congestion and inadequate parking availability. For urban dwellers, few things are more irritating than anxiously searching for a parking space. Research results show that as much as 30% of traffic is caused by drivers driving around looking for parking spaces in congested city areas. There has been considerable activity among researchers to develop smart technologies that can help drivers find a parking spot with greater ease, not only reducing traffic congestion but also the subsequent air pollution. Many existing solutions deploy sensors in every parking spot to address the automatic parking spot detection problems. However, the device and deployment costs are very high, especially for some large and old parking structures. A wide variety of other technological innovations are beginning to enable more adaptable systems-including license plate number detection, smart parking meter, and vision-based parking spot detection. In this paper, we propose to design a more adaptable and affordable smart parking system via distributed cameras, edge computing, data analytics, and advanced deep learning algorithms. Specifically, we deploy cameras with zoom-lens and motorized head to capture license plate numbers by tracking the vehicles when they enter or leave the parking lot; cameras with wide angle fish-eye lens will monitor the large parking lot via our custom designed deep neural network. We further optimize the algorithm and enable the real-time deep learning inference in an edge device. Through the intelligent algorithm, we can significantly reduce the cost of existing systems, while achieving a more adaptable solution. For example, our system can automatically detect when a car enters the parking space, the location of the parking spot, and precisely charge the parking fee and associate this with the license plate number. 

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2. A Smartphone-Based Parking Guidance System with Predictive Parking Availability Information.

   Xiao, J. ; Lou, Y. ( January 2018 , 2018 Transportation Research Board Annual Meeting)

   Searching for parking has been a problem faced by many drivers, especially in urban areas. With an increasing public demand for parking information and services, as well as the proliferation of advanced smartphones, a range of smartphone-based parking management services began to emerge. Funded by the National Science Foundation, our research aims to explore the potential of smartphone-based parking management services as a solution to parking problems, to deepen our understandings of travelers’ parking behaviors, and to further advance the analytical foundations and methodologies for modeling and assessing parking solutions. This paper summarizes progress and results from our research projects on smartphone-based parking management, including parking availability information prediction, parking searching strategy, the development of a mobile parking application, and our next steps to learn and discover new knowledge from its deployment. To predict future parking occupancy, we proposed a practical framework that integrates machine-learning techniques with a model-based core approach that explicitly models the stochastic parking process. The framework is able to predict future parking occupancy from historical occupancy data alone, and can handle complex arrival and departure patterns in real-world case studies, including special event. With the predicted probabilistic availability information, a cost-minimizing parking searching strategy is developed. The parking searching problem for an individual user is a stochastic Markov decision process and is formalized as a dynamic programming problem. The cost-minimizing parking searching strategy is solved by value iteration. Our simulated experiments showed that cost-minimizing strategy has the lowest expected cost but tends to direct a user to visit more parking facilities compared with two greedy strategies. Currently, we are working on implementing the predictive framework and the searching algorithm in a mobile phone application. We are working closely with Arizona State University (ASU) Parking and Transit Services to implement a three-stage pilot deployment of the prototype application around the ASU main campus. In the first stage, our application will provide real-time information and we will incorporate availability prediction and searching guidance in the second and third stages. Once the mobile application is deployed, it will provide unique opportunities to collect data on parking search behaviors, discover emerging scenarios of smartphone-based parking management services, and assess the impacts of such systems. 

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3. A Smartphone-Based Parking Guidance System with Predictive Parking Availability Information

   Xiao, J. ; Lou, Y. ( January 2018 , 2018 Transportation Research Annual Meeting)

   Searching for parking has been a problem faced by many drivers, especially in urban areas. With an increasing public demand for parking information and services, as well as the proliferation of advanced smartphones, a range of smartphone-based parking management services began to emerge. Funded by the National Science Foundation, our research aims to explore the potential of smartphone-based parking management services as a solution to parking problems, to deepen our understandings of travelers’ parking behaviors, and to further advance the analytical foundations and methodologies for modeling and assessing parking solutions. This paper summarizes progress and results from our research projects on smartphone-based parking management, including parking availability information prediction, parking searching strategy, the development of a mobile parking application, and our next steps to learn and discover new knowledge from its deployment. To predict future parking occupancy, we proposed a practical framework that integrates machine-learning techniques with a model-based core approach that explicitly models the stochastic parking process. The framework is able to predict future parking occupancy from historical occupancy data alone, and can handle complex arrival and departure patterns in real-world case studies, including special event. With the predicted probabilistic availability information, a cost-minimizing parking searching strategy is developed. The parking searching problem for an individual user is a stochastic Markov decision process and is formalized as a dynamic programming problem. The cost-minimizing parking searching strategy is solved by value iteration. Our simulated experiments showed that cost-minimizing strategy has the lowest expected cost but tends to direct a user to visit more parking facilities compared with two greedy strategies. Currently, we are working on implementing the predictive framework and the searching algorithm in a mobile phone application. We are working closely with Arizona State University (ASU) Parking and Transit Services to implement a three-stage pilot deployment of the prototype application around the ASU main campus. In the first stage, our application will provide real-time information and we will incorporate availability prediction and searching guidance in the second and third stages. Once the mobile application is deployed, it will provide unique opportunities to collect data on parking search behaviors, discover emerging scenarios of smartphone-based parking management services, and assess the impacts of such systems. 

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4. SoundFence: Securing Ultrasonic Sensors in Vehicles Using Physical-Layer Defense

   https://doi.org/10.1109/SECON52354.2021.9491590  

   Lou, Jianzhi ; Yan, Qiben ; Hui, Qing ; Zeng, Huacheng ( July 2021 , 2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON))

   null (Ed.)

   Autonomous vehicles (AVs), equipped with numerous sensors such as camera, LiDAR, radar, and ultrasonic sensor, are revolutionizing the transportation industry. These sensors are expected to sense reliable information from a physical environment, facilitating the critical decision-making process of the AVs. Ultrasonic sensors, which detect obstacles in a short distance, play an important role in assisted parking and blind spot detection events. However, due to their weak security level, ultrasonic sensors are particularly vulnerable to signal injection attacks, when the attackers inject malicious acoustic signals to create fake obstacles and intentionally mislead the vehicles to make wrong decisions with disastrous aftermath. In this paper, we systematically analyze the attack model of signal injection attacks toward moving vehicles. By considering the potential threats, we propose SoundFence, a physical-layer defense system which leverages the sensors’ signal processing capability without requiring any additional equipment. SoundFence verifies the benign measurement results and detects signal injection attacks by analyzing sensor readings and the physical-layer signatures of ultrasonic signals. Our experiment with commercial sensors shows that SoundFence detects most (more than 95%) of the abnormal sensor readings with very few false alarms, and it can also accurately distinguish the real echo from injected signals to identify injection attacks. 

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5. Stealthy-Shutdown: Practical Remote Power Attacks in Multi - Tenant FPGAs

   https://doi.org/10.1109/ICCD50377.2020.00097  

   Luo, Yukui ; Gongye, Cheng ; Ren, Shaolei ; Fei, Yunsi ; Xu, Xiaolin ( October 2020 , IEEE International Conference on Computer Design: VLSI in Computers and Processors)

   null (Ed.)

   With the deployment of artificial intelligent (AI) algorithms in a large variety of applications, there creates an increasing need for high-performance computing capabilities. As a result, different hardware platforms have been utilized for acceleration purposes. Among these hardware-based accelerators, the field-programmable gate arrays (FPGAs) have gained a lot of attention due to their re-programmable characteristics, which provide customized control logic and computing operators. For example, FPGAs have recently been adopted for on-demand cloud services by the leading cloud providers like Amazon and Microsoft, providing acceleration for various compute-intensive tasks. While the co-residency of multiple tenants on a cloud FPGA chip increases the efficiency of resource utilization, it also creates unique attack surfaces that are under-explored. In this paper, we exploit the vulnerability associated with the shared power distribution network on cloud FPGAs. We present a stealthy power attack that can be remotely launched by a malicious tenant, shutting down the entire chip and resulting in denial-of-service for other co-located benign tenants. Specifically, we propose stealthy-shutdown: a well-timed power attack that can be implemented in two steps: (1) an attacker monitors the realtime FPGA power-consumption detected by ring-oscillator-based voltage sensors, and (2) when capturing high power-consuming moments, i.e., the power consumption by other tenants is above a certain threshold, she/he injects a well-timed power load to shut down the FPGA system. Note that in the proposed attack strategy, the power load injected by the attacker only accounts for a small portion of the overall power consumption; therefore, such attack strategy remains stealthy to the cloud FPGA operator. We successfully implement and validate the proposed attack on three FPGA evaluation kits with running real-world applications. The proposed attack results in a stealthy-shutdown, demonstrating severe security concerns of co-tenancy on cloud FPGAs. We also offer two countermeasures that can mitigate such power attacks. 

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