3rd International Workshop on Edge Computing and Artificial Intelligence based Sensor-Cloud System

Session ECAISS


8:00 AM — 10:40 AM GMT
Dec 13 Mon, 3:00 AM — 5:40 AM EST

Continuous Finger Tracking System based on Inertial Sensor

Yangyang Fang, Qun Fang, Xin He (Anhui Normal University, China)

Finger tracking has become an appropriate approach to interact with smart wearable devices or virtual reality (VR). However, designing circuit system almost required in many system. In this article, we present a continuous finger tracking system, which does not need special equipment or design a special environment. We regard the acceleration sequence of 2.4 seconds before as the features of displacement between current 0.04 seconds. In order to avoiding accumulative errors, caused by double integration,we using long short-term memory(LSTM) models to calculate the displacement directly at the corresponding time. In particular, there is no necessary to know the initial speed in this way. Our system has a resolution of 0.38mm and an accuracy of 2.32mm per frame under 25HZ sampling rate. The system can draw the target track accurately.

Sequence-based Indoor Relocalization for Mobile Augmented Reality

Kun Wang (Liaoning Police College, China), Jiaxing Che (Beihang University, China), Zhejun Shen (UnionSys Technology Co. Ltd, China)

In mobile Augmented Reality (AR) applications, relocalization plays a very important role in supporting persistence and multiple user interaction. Traditional methods usually utilize single-frame and integrated into the loop closure procedure in simultaneous localization and mapping (SLAM). However, indoor scenes include many challenges: similar patterns such as stairs and kitchen corners, textureless places such as white walls. To overcome these challenges, We propose a novel sequence-based relocalization method for mobile AR devices. Our method utilizes the commodity depth sensors such as ToF cameras. We firstly generate a sequence of depth maps with corresponding poses tracked via Visual Inertial Odometry (VIO). Based on keyframe-based visual relocalization, then choose a good subset of posed depth maps to verify and refine the pose. Results show that our method improves relocalization accuracy compared with simple sequence-based relocalization.

Toward Dispersed Computing: Cases and State-of-The-Art

Sen Yuan, Geming Xia, Jian Chen, Chaodong Yu (National University of Defense Technology, China)

With the growth of IoT and latency-sensitive applications (e.g., virtual reality, autonomous driving), massive amounts of data are generated at the network and the edge. Such proliferation drives the development of dispersed computing as a promising complementary paradigm to cloud computing and edge computing. Dispersed computing can leverage in-network computing resources to provide lower latency guarantees and more reliable computing power support than the cloud computing. On the other hand, dispersed computing shows excellent performance in highly dynamic and heterogeneous environments. This paper demonstrates the potential of dispersed computing in terms of providing low-latency services and adapting to highly dynamic and heterogeneous environments through several cases. To better grasp the current state of research in dispersed computing, several major research directions and advances are also given in the hope of attracting the attention of the community and inspiring more researches to promote the implementation of dispersed computing.

Trust Evaluation of Computing Power Network Based on Improved Particle Swarm Neural Network

Chaodong Yu, Geming Xia, Zhaohang Wang (National University of Defense Technology, China)

In order to satisfy the trust evaluation of efficient cooperative scheduling of computing power in Computing Power Network, we propose an adaptive detection trust evaluation management system and an efficient lightweight trust evaluation algorithm. In our work, the multi-attribute trust evaluation data combined with the active detection and global trust database are used as samples to train the BP neural network. And the structure and weight coefficient of the neural network are optimized by the improved particle swarm optimization algorithm, so as to reduce the size of neural network and improve its performance. The trust evaluation model in this study effectively improves the detection rate of malicious state nodes and reduces the detection time.

Session Chair

Chi Lin (Dalian University of Technology, China) Pengfei Wang (Dalian University of Technology, China)

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