Large language models (LLMs) have demonstrated remarkable capabilities in natural language processing. Their massive computational and memory requirements often necessitate cloud-based deployment, introducing challenges related to cost, latency, privacy, and network reliability. Deploying on-device LLMs alleviates these challenges, but is hindered by the severe resource constraints of edge hardware. This survey reviews efficient inference techniques for edge LLMs, with a focus on two key strategies of speculative decoding and model offloading. We categorize strategies into single-device and multi-device types, systematically analyzing the principles, recent advancements, implementations, and support within edge frameworks. Finally, we highlight the open challenges and future research directions that will advance the field of edge LLM inference.

The rapid growth of the Internet of Things (IoT) demands efficient system architectures and protocols to ensure consistent performance at scale. This paper explores the scalability of IoT systems across three key layers: sensing, network, and control. IoT scalability is the ability of a system to maintain consistent and reliable performance despite a continuous increase in connected devices. To evaluate scalability, we introduce the scalability indicator (SI), a metric designed to assess an IoT system’s scalability capability. Through extensive research and real-world deployments, we identify key challenges in data sensing, routing, and system control. Our study presents a model to understand these challenges and proposes strategies to optimize resource utilization, ensuring efficient data collection. The findings also emphasize the key influencing factors for the stable performance of large-scale IoT systems, providing valuable insights for how to design scalable systems that can meet the growing demand for interconnected devices.

Motion tracking via Inertial Measurement Units (IMUs) on mobile and wearable devices has attracted significant interest in recent years. High-accuracy IMU-tracking can be applied in various applications, such as indoor navigation, gesture recognition, text input, etc. Many efforts have been devoted to improving IMU-based motion tracking in the last two decades, from early calibration techniques on ships or airplanes, to recent arm motion models used on wearable smart devices. In this paper, we present a comprehensive survey on IMU-tracking techniques on mobile and wearable devices. We also reveal the key challenges in IMU-based motion tracking on mobile and wearable devices and possible directions to address these challenges.

Camera-equipped mobile devices are encouraging people to take more photos and the development and growth of social networks is making it increasingly popular to share photos online. When objects appear in overlapping Fields Of View (FOV), this means that they are drawing much attention and thus indicates their popularity. Successfully discovering and locating these objects can be very useful for many applications, such as criminal investigations, event summaries, and crowdsourcing-based Geographical Information Systems (GIS). Existing methods require either prior knowledge of the environment or intentional photographing. In this paper, we propose a seamless approach called “Spotlight”, which performs passive localization using crowdsourced photos. Using a graph-based model, we combine object images across multiple camera views. Within each set of combined object images, a photographing map is built on which object localization is performed using plane geometry. We evaluate the system’s localization accuracy using photos taken in various scenarios, with the results showing our approach to be effective for passive object localization and to achieve a high level of accuracy.