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1. Demo abstract: Towards Real-Time Rich Scene Analysis Using Vision-Guided Wireless Vibrometry

   https://doi.org/10.1145/3560905.3568060  

   Wang, Ziqi ; Sarker, Ankur ; Wu, Jason ; Hua, Derek ; Dong, Gaofeng ; Singh, Akash Deep ; Srivastava, Mani ( November 2022 , Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems)

   Intelligent systems commonly employ vision sensors like cameras to analyze a scene. Recent work has proposed a wireless sensing technique, wireless vibrometry, to enrich the scene analysis generated by vision sensors. Wireless vibrometry employs wireless signals to sense subtle vibrations from the objects and infer their internal states. However, it is difficult for pure Radio-Frequency (RF) sensing systems to obtain objects' visual appearances (e.g., object types and locations), especially when an object is inactive. Thus, most existing wireless vibrometry systems assume that the number and the types of objects in the scene are known. The key to getting rid of these presumptions is to build a connection between wireless sensor time series and vision sensor images. We present Capricorn, a vision-guided wireless vibrometry system. In Capricorn, the object type information from vision sensors guides the wireless vibrometry system to select the most appropriate signal processing pipeline. The object tracking capability in computer vision also helps wireless systems efficiently detect and separate vibrations from multiple objects in real time. 

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2. Challenges in Metaverse Research: An Internet of Things Perspective

   Abdelzaher, Tarek ( June 2023 , IEEE International Conference on Metaverse Computing, Networking and Applications (IEEE MetaCom 2023))

   The paper describes research challenges arising from the increasing interest in supporting more immersive and more intelligent environments that enable the next generation of seamless human and physical interactions. These environments span the gamut from augmented-physical to virtual, and are referred to hereafter as the Metaverse. We focus on challenges that constitute a natural extension of Internet of Things (IoT) research. Among the key applications of IoT has always been the integration of physical and cyber environments to endow "things" with a better contextual understanding of their surroundings, and endow human users with more seamless means of perception and control, ranging from smart home automation to industrial applications. This IoT vision was based on the premise that the number of physical "things" on the Internet will soon significantly outpace humans. Intelligent IoT further envisions a proliferation of edge intelligence with which humans will interact. The paper elaborates the research challenges that extrapolate the above trajectory. 

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3. 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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4. A Smart Walker for People with Both Visual and Mobility Impairment

   https://doi.org/10.3390/s21103488  

   Mostofa, Nafisa ; Feltner, Christopher ; Fullin, Kelly ; Guilbe, Jonathan ; Zehtabian, Sharare ; Bacanlı, Salih Safa ; Bölöni, Ladislau ; Turgut, Damla ( May 2021 , Sensors)

   In recent years, significant work has been done in technological enhancements for mobility aids (smart walkers). However, most of this work does not cover the millions of people who have both mobility and visual impairments. In this paper, we design and study four different configurations of smart walkers that are specifically targeted to the needs of this population. We investigated different sensing technologies (ultrasound-based, infrared depth cameras and RGB cameras with advanced computer vision processing), software configurations, and user interface modalities (haptic and audio signal based). Our experiments show that there are several engineering choices that can be used in the design of such assistive devices. Furthermore, we found that a holistic evaluation of the end-to-end performance of the systems is necessary, as the quality of the user interface often has a larger impact on the overall performance than increases in the sensing accuracy beyond a certain point. 

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5. MotionCompass: pinpointing wireless camera via motion-activated traffic

   https://doi.org/10.1145/3458864.3467683  

   He, Yan ; He, Qiuye ; Fang, Song ; Liu, Yao ( June 2021 , Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services (ACM MobiSys))

   null (Ed.)

   Wireless security cameras are integral components of security systems used by military installations, corporations, and, due to their increased affordability, many private homes. These cameras commonly employ motion sensors to identify that something is occurring in their fields of vision before starting to record and notifying the property owner of the activity. In this paper, we discover that the motion sensing action can disclose the location of the camera through a novel wireless camera localization technique we call MotionCompass. In short, a user who aims to avoid surveillance can find a hidden camera by creating motion stimuli and sniffing wireless traffic for a response to that stimuli. With the motion trajectories within the motion detection zone, the exact location of the camera can be then computed. We develop an Android app to implement MotionCompass. Our extensive experiments using the developed app and 18 popular wireless security cameras demonstrate that for cameras with one motion sensor, MotionCompass can attain a mean localization error of around 5 cm with less than 140 seconds. This localization technique builds upon existing work that detects the existence of hidden cameras, to pinpoint their exact location and area of surveillance. 

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