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1. Energy-Efficient and Robust Middleware Prototyping for Smart Mobile Computing

   Tiku, S.; Pasricha, S. (January 2017, IEEE International Symposium on Rapid System Prototyping (RSP))

   A large amount of data is produced by mobile devices today. The rising computational abilities and sophisticated operating systems (OS) on these devices have allowed us to create applications that are able to leverage this data to deliver better services. But today’s mobile technology is heavily limited by low battery capacity and limited cooling capabilities, which has motivated a search for new ways to optimize for energy-efficiency. A challenge in conducting such optimizations for today’s mobile devices is to be able to make changes in complex OS and application software architectures. Middleware has been becoming an increasingly popular solution for inserting energy-efficient solutions and optimizations in a robust manner, without altering the OS or application code. This is because of the flexibility and standardization that can be achieved through middleware. In this paper, we discuss some powerful and promising developments in prototyping middleware for energy efficient and robust execution of a variety of applications on commodity mobile computing devices. 

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2. Intent to share: enhancing Android inter-component communication for distributed devices

   https://doi.org/10.1145/3197231.3197245  

   Cruz, Breno Dantas; Tilevich, Eli (January 2018, Proceedings of the 5th International Conference on Mobile Software Engineering and Systems)

   Data-intensive applications in diverse domains, including video streaming, gaming, and health monitoring, increasingly require that mobile devices directly share data with each other. However, developing distributed data sharing functionality introduces low-level, brittle, and hard-to-maintain code into the mobile codebase. To reconcile the goals of programming convenience and performance efficiency, we present a novel middleware framework that enhances the Android platform's component model to support seamless and efficient inter-device data sharing. Our framework provides a familiar programming interface that extends the ubiquitous Android Inter-Component Communication (ICC), thus lowering the learning curve. Unlike middleware platforms based on the RPC paradigm, our programming abstractions require that mobile application developers think through and express explicitly data transmission patterns, thus treating latency as a first-class design concern. Our performance evaluation shows that using our framework incurs little performance overhead, comparable to that of custom-built implementations. By providing reusable programming abstractions that preserve component encapsulation, our framework enables Android devices to efficiently share data at the component level, providing powerful building blocks for the development of emerging distributed mobile applications. 

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3. Self-trainable and adaptive sensor intelligence for selective data generation

   https://doi.org/10.3389/frai.2024.1403187  

   Rezvani, Arghavan; Huang, Wenjun; Chen, Hanning; Ni, Yang; Imani, Mohsen (January 2025, Frontiers in Artificial Intelligence)

   With the increasing integration of machine learning into IoT devices, managing energy consumption and data transmission has become a critical challenge. Many IoT applications depend on complex computations performed on server-side infrastructure, necessitating efficient methods to reduce unnecessary data transmission. One promising solution involves deploying compact machine learning models near sensors, enabling intelligent identification and transmission of only relevant data frames. However, existing near-sensor models lack adaptability, as they require extensive pre-training and are often rigidly configured prior to deployment. This paper proposes a novel framework that fuses online learning, active learning, and knowledge distillation to enable adaptive, resource-efficient near-sensor intelligence. Our approach allows near-sensor models to dynamically fine-tune their parameters post-deployment using online learning, eliminating the need for extensive pre-labeling and training. Through a sequential training and execution process, the framework achieves continuous adaptability without prior knowledge of the deployment environment. To enhance performance while preserving model efficiency, we integrate knowledge distillation, enabling the transfer of critical insights from a larger teacher model to a compact student model. Additionally, active learning reduces the required training data while maintaining competitive performance. We validated our framework on both benchmark data from the MS COCO dataset and in a simulated IoT environment. The results demonstrate significant improvements in energy efficiency and data transmission optimization, highlighting the practical applicability of our method in real-world IoT scenarios. 

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4. SecDeep: Secure and Performant On-device Deep Learning Inference Framework for Mobile and IoT Devices

   https://doi.org/10.1145/3450268.3453524  

   Liu, Renju; Garcia, Luis; Liu, Zaoxing; Ou, Botong; Srivastava, Mani (May 2021, IoTDI '21: Proceedings of the International Conference on Internet-of-Things Design and Implementation)

   null (Ed.)

   There is an increasing emphasis on securing deep learning (DL) inference pipelines for mobile and IoT applications with privacy-sensitive data. Prior works have shown that privacy-sensitive data can be secured throughout deep learning inferences on cloud-offloaded models through trusted execution environments such as Intel SGX. However, prior solutions do not address the fundamental challenges of securing the resource-intensive inference tasks on low-power, low-memory devices (e.g., mobile and IoT devices), while achieving high performance. To tackle these challenges, we propose SecDeep, a low-power DL inference framework demonstrating that both security and performance of deep learning inference on edge devices are well within our reach. Leveraging TEEs with limited resources, SecDeep guarantees full confidentiality for input and intermediate data, as well as the integrity of the deep learning model and framework. By enabling and securing neural accelerators, SecDeep is the first of its kind to provide trusted and performant DL model inferencing on IoT and mobile devices. We implement and validate SecDeep by interfacing the ARM NN DL framework with ARM TrustZone. Our evaluation shows that we can securely run inference tasks with 16× to 172× faster performance than no acceleration approaches by leveraging edge-available accelerators. 

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5. ERSAM: Neural Architecture Search for Energy-Efficient and Real-Time Social Ambiance Measurement

   https://doi.org/10.1109/ICASSP49357.2023.10095360  

   Li, Chaojian; Chen, Wenwan; Yuan, Jiayi; Lin, Yingyan Celine; Sabharwal, Ashutosh (June 2023, 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   Social ambiance describes the context in which social interactions happen, and can be measured using speech audio by counting the number of concurrent speakers. This measurement has enabled various mental health tracking and human-centric IoT applications. While on-device Socal Ambiance Measure (SAM) is highly desirable to ensure user privacy and thus facilitate wide adoption of the aforementioned applications, the required computational complexity of state-of-the-art deep neural networks (DNNs) powered SAM solutions stands at odds with the often constrained resources on mobile devices. Furthermore, only limited labeled data is available or practical when it comes to SAM under clinical settings due to various privacy constraints and the required human effort, further challenging the achievable accuracy of on-device SAM solutions. To this end, we propose a dedicated neural architecture search framework for Energy-efficient and Real-time SAM (ERSAM). Specifically, our ERSAM framework can automatically search for DNNs that push forward the achievable accuracy vs. hardware efficiency frontier of mobile SAM solutions. For example, ERSAM-delivered DNNs only consume 40 mW • 12 h energy and 0.05 seconds processing latency for a 5 seconds audio segment on a Pixel 3 phone, while only achieving an error rate of 14.3% on a social ambiance dataset generated by LibriSpeech. We can expect that our ERSAM framework can pave the way for ubiquitous on-device SAM solutions which are in growing demand. 

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