Search

Intelligence Everywhere is predicated on the seamless integration of IoT networks transporting a vast amount of data streams through many computing resources across an edge-to-cloud continuum, relying on the orchestration of distributed machine learning models. The result is an interconnected and collective intelligent ecosystem where devices, systems, services, and users work together to support IoT applications. This paper discusses the state-of-the-art research and the principles of the Intelligence Everywhere framework for enhancing IoT applications in vertical sectors such as Digital Health, Infrastructure, and Transportation/Mobility in the context of intelligent society (Society 5.0). It also introduces a novel perspective for the development of horizontal IoT applications, capable of running across various IoT networks while fostering collective intelligence across diverse sectors. Finally, this paper provides comprehensive insights into the challenges and opportunities for harnessing collective knowledge from real-time insights, leading to optimised processes and better overall collaboration across different IoT sectors., Comment: Keywords: IoT collective intelligence, machine learning, edge intelligence, cloud computing, learning models, vertical IoT, IoT network, horizontal IoT applications, Society 5.0

This paper introduces an approach for multi-human 3D pose estimation and tracking based on calibrated multi-view. The main challenge lies in finding the cross-view and temporal correspondences correctly even when several human pose estimations are noisy. Compare to previous solutions that construct 3D poses from multiple views, our approach takes advantage of temporal consistency to match the 2D poses estimated with previously constructed 3D skeletons in every view. Therefore cross-view and temporal associations are accomplished simultaneously. Since the performance suffers from mistaken association and noisy predictions, we design two strategies for aiming better correspondences and 3D reconstruction. Specifically, we propose a part-aware measurement for 2D-3D association and a filter that can cope with 2D outliers during reconstruction. Our approach is efficient and effective comparing to state-of-the-art methods; it achieves competitive results on two benchmarks: 96.8% on Campus and 97.4% on Shelf. Moreover, we extends the length of Campus evaluation frames to be more challenging and our proposal also reach well-performed result., Comment: 12 pages with supplementary material; accepted to CVPR 2021 B-AMFG Workshop

With astonishing speed, bandwidth, and scale, Mobile Edge Computing (MEC) has played an increasingly important role in the next generation of connectivity and service delivery. Yet, along with the massive deployment of MEC servers, the ensuing energy issue is now on an increasingly urgent agenda. In the current context, the large scale deployment of renewable-energy-supplied MEC servers is perhaps the most promising solution for the incoming energy issue. Nonetheless, as a result of the intermittent nature of their power sources, these special design MEC server must be more cautious about their energy usage, in a bid to maintain their service sustainability as well as service standard. Targeting optimization on a single-server MEC scenario, we in this paper propose NAFA, an adaptive processor frequency adjustment solution, to enable an effective plan of the server's energy usage. By learning from the historical data revealing request arrival and energy harvest pattern, the deep reinforcement learning-based solution is capable of making intelligent schedules on the server's processor frequency, so as to strike a good balance between service sustainability and service quality. The superior performance of NAFA is substantiated by real-data-based experiments, wherein NAFA demonstrates up to 20% increase in average request acceptance ratio and up to 50% reduction in average request processing time.

Smartphones have been shipped with multiple wireless network interfaces in order to meet their diverse communication and networking demands. However, as smartphones increasingly rely on wireless network connections to realize more functions, the demand of energy has been significantly increased, which has become the limit for people to explore smartphones' real power. In this paper, we first review typical smartphone computing systems, energy consumption of smartphone, and state-of-the-art techniques of energy saving for smartphones. Then we propose a location-assisted Wi-Fi discovery scheme, which discovers the nearest Wi-Fi network access points (APs) by using the user's location information. This allows the user to switch to the Wi-Fi interface in an intelligent manner when he/she arrives at the nearest Wi-Fi network AP. Thus we can meet the user's bandwidth needs and provide the best connectivity. Additionally, it avoids the long periods in idle state and greatly reduces the number of unnecessary Wi-Fi scans on the mobile device. Our experiments and simulations demonstrate that our scheme effectively saves energy for smartphones integrated with Wi-Fi and cellular interfaces., Comment: accepted; Multimedia Systems, 2013. arXiv admin note: text overlap with arXiv:1201.0219