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1. Characterizing the Reconfiguration Latency of Image Sensor Resolution on Android Devices

   https://doi.org/10.1145/3177102.3177109  

   Hu, Jinhan; Yang, Jianan; Delhivala, Vraj; LiKamWa, Robert (January 2018, Proceedings of the 19th International Workshop on Mobile Computing Systems & Applications - HotMobile '18)

   Advances in vision processing have ignited a proliferation of mobile vision applications, including augmented reality. However, limited by the inability to rapidly reconfigure sensor operation for performance-efficiency tradeoffs, high power consumption causes vision applications to drain the device's battery. To explore the potential impact of enabling rapid reconfiguration, we use a case study around marker-based pose estimation to understand the relationship between image frame resolution, task accuracy, and energy efficiency. Our case study motivates that to balance energy efficiency and task accuracy, the application needs to dynamically and frequently reconfigure sensor resolution. To explore the latency bottlenecks to sensor resolution reconfiguration, we define and profile the end-to-end reconfiguration latency and frame-to-frame latency of changing capture resolution on a Google LG Nexus 5X device. We identify three major sources of sensor resolution reconfiguration latency in current Android systems: (i) sequential configuration patterns, (ii) expensive system calls, and (iii) imaging pipeline delay. Based on our intuitions, we propose a redesign of the Android camera system to mitigate the sources of latency. Enabling smooth transitions between sensor configurations will unlock new classes of adaptive-resolution vision applications. 

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2. Squint: A Framework for Dynamic Voltage Scaling of Image Sensors Towards Low Power IoT Vision

   Kodukula, Venkatesh; Manetta, Mason; LiKamWa, Robert (January 2023, The 29th Annual International Conference on Mobile Computing and Networking (ACM MobiCom ’23))

   Energy-efficient visual sensing is of paramount importance to enable battery-backed low power IoT and mobile applications. Unfortunately, modern image sensors still consume hundreds of milliwatts of power, mainly due to analog readout. This is because current systems always supply a fixed voltage to the sensor’s analog circuitry, leading to higher power profiles. In this work, we propose to aggressively scale the analog voltage supplied to the camera as a means to significantly reduce sensor power consumption. To that end, we characterize the power and fidelity implications of analog voltage scaling on three off-the-shelf image sensors. Our characterization reveals that analog voltage scaling reduces sensor power but also degrades image quality. Furthermore, the degradation in image quality situationally affects the task accuracy of vision applications. We develop a visual streaming pipeline that flexibly allows application developers to dynamically adapt sensor voltage on a frame-by-frame basis. We develop a voltage controller that programmatically generates desired sensor voltage based on application request. We integrate our voltage controller into the existing RPi-based video streaming IoT pipeline. On top of this, we develop runtime support for flexible voltage specification from vision applications. Evaluating the system over a wide range of voltage scaling policies on popular vision tasks reveals that Squint imaging can deliver up to 73% sensor power savings, while maintaining reasonable task fidelity. Our artifacts are available at: https://gitlab.com/squint1/squint-ae-public 

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3. Collaborative Cloud-Edge-Local Offloading for Multi-Component Applications

   Gholami, Anousheh; Baras, John S. (December 2021, Proceedings of Sixth ACM/IEEE Symposium on Edge Computing - The 2nd Workshop on Edge Computing and Communications(EdgeComm 2021))

   With the explosion of intelligent and latency-sensitive applications such as AR/VR, remote health and autonomous driving, mobile edge computing (MEC) has emerged as a promising solution to mitigate the high end-to-end latency of mobile cloud computing (MCC). However, the edge servers have significantly less computing capability compared to the resourceful central cloud. Therefore, a collaborative cloud-edge-local offloading scheme is necessary to accommodate both computationally intensive and latency-sensitive mobile applications. The coexistence of central cloud, edge servers and the mobile device (MD), forming a multi-tiered heterogeneous architecture, makes the optimal application deployment very challenging especially for multi-component applications with component dependencies. This paper addresses the problem of energy and latency efficient application offloading in a collaborative cloud-edgelocal environment. We formulate a multi-objective mixed integer linear program (MILP) with the goal of minimizing the systemwide energy consumption and application end-to-end latency. An approximation algorithm based on LP relaxation and rounding is proposed to address the time complexity. We demonstrate that our approach outperforms existing strategies in terms of application request acceptance ratio, latency and system energy consumption. 

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4. RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone

   https://doi.org/10.1109/WACV61041.2025.00805  

   Munir, Mustafa; Rahman, Md Mostafijur; Marculescu, Radu (February 2025, IEEE)

   Vision transformers (ViTs) have dominated computer vision in recent years. However, ViTs are computationally expensive and not well suited for mobile devices; this led to the prevalence of convolutional neural network (CNN) and ViT-based hybrid models for mobile vision applications. Recently, Vision GNN (ViG) and CNN hybrid models have also been proposed for mobile vision tasks. However, all of these methods remain slower compared to pure CNN-based models. In this work, we propose Multi-Level Dilated Convolutions to devise a purely CNN-based mobile backbone. Using Multi-Level Dilated Convolutions allows for a larger theoretical receptive field than standard convolutions. Different levels of dilation also allow for interactions between the short-range and long-range features in an image. Experiments show that our proposed model outperforms state-of-the-art (SOTA) mobile CNN, ViT, ViG, and hybrid architectures in terms of accuracy and/or speed on image classification, object detection, instance segmentation, and semantic segmentation. Our fastest model, RapidNet-Ti, achieves 76.3% top-1 accuracy on ImageNet-1K with 0.9 ms inference latency on an iPhone 13 mini NPU, which is faster and more accurate than MobileNetV2x1.4 (74.7% top-1 with 1.0 ms latency). Our work shows that pure CNN architectures can beat SOTA hybrid and ViT models in terms of accuracy and speed when designed properly 

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5. Mobile Underwater Backscatter Networking

   https://doi.org/10.1145/3718958.3750507  

   Wang, Purui; Afzal, Sayed Saad; Adib, Fadel (August 2025, ACM)

   Underwater backscatter is a promising technology for ultra-lowpower underwater networking, but existing systems break down in mobile scenarios. This paper presents EchoRider, the first system to enable reliable underwater backscatter networking under mobility. EchoRider introduces three key components. First, it incorporates a robust and energy-efficient downlink architecture that uses chirp-modulated transmissions at the reader and a sub-Nyquist chirp decoder on backscatter nodes—bringing the resilience of LoRa-style signaling to underwater backscatter while remaining ultra-lowpower. Second, it introduces a NACK-based full-duplex retransmission protocol, enabling efficient, reliable packet delivery. Third, it implements a Doppler-resilient uplink decoding pipeline that includes adaptive equalization, polar coding, and dynamic retraining to combat channel variation. We built a full EchoRider prototype and evaluated it across over 1,200 real-world mobile experiments. EchoRider improves bit error rate by over 125× compared to a state-of-the-art baseline and maintains underwater goodput of 0.8 kbps at speeds up to 2.91 knots. In contrast, the baseline fails at speeds as low as 0.17 knots. Finally, we demonstrate EchoRider in end-to-end deployments involving mobile drones and sensor nodes, showing its effectiveness in practical underwater networked applications. 

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