IEEE MSN 2021

Program at a Glance

The 17th International Conference on Mobility, Sensing and Networking (MSN 2021)

Session S1: Federated Learning I Session S2: Multi-Access Edge Computing I Session S3: Deep Reinforcement Learning Session S4: Federated Learning II Session S5: Multi-Access Edge Computing II Session S6: Network Security I

Session Opening: Opening Ceremony

Session Keynote-1: Keynote Speech 1: IoT Security Session Keynote-2: Keynote Speech 2: Deep Reinforcement Learning for Control and Management of Communications Networks

Session Panel: Digital Twins and their Applications in CPS

Session Poster: Poster Session

Session Poster

Poster Session

Conference

5:30 PM — 6:20 PM GMT

Local

Dec 14 Tue, 9:30 AM — 10:20 AM PST

Indoor Navigation for Users with Mobility Aids Using Smartphones and Neighborhood Networks

Bo Hui, Chen Jiang, Pavani Ankireddy (Auburn University, USA), Wenlu Wang (Texas A&M University-Corpus Christi (TAMUCC), USA), Wei-Shinn Ku (Auburn University, USA)

In this paper, we propose an indoor navigation strategy with users' positions updated based on both inertial data and Received Signal Strength (RSS). We focus on two open challenges for indoor navigation: (1) An indoor navigation system should support users with assistive devices (e.g., wheelchairs, scooters). One of the most used algorithms: the Pedestrian Dead Reckoning (PDR) algorithm does not support indoor navigation for wheelchair users since it is based on step detection; (2) The existing indoor positioning approach assumes that absolute position fixes from WLAN are available over long-term indoor positioning, which does not always hold in real-world scenarios. For Challenge (1), we categorize the moving pattern of a user first and estimate the displacement of the movements according to the moving pattern. Then, the estimated position is calibrated by the neighborhood Networks and signals from other users, thus addressing Challenge (2). We validate our design with real-world data. The experiments ascertain the efficiency of our proposed methods for various moving patterns and the scenario without WLAN coverage.

Demonstrator Game Showcasing Indoor Positioning via BLE Signal Strength

Felix Beierle (University of Würzburg, Germany), Hai Dinh-Tuan, Yong Wu (Technische Universität Berlin, Germany)

For a non-technical audience, new concepts from computer science and engineering are often hard to grasp. In order to introduce a general audience to topics related to Industry 4.0, we designed and developed a demonstrator game. The Who-wants-to-be-a-millionaire?-style quiz game lets the player experience indoor positioning based on Bluetooth signal strength firsthand. We found that such an interactive game demonstrator can function as conversation-opener and is useful in helping introduce concepts relevant for many future jobs.

Optimizing Microservices with Hyperparameter Optimization

Hai Dinh-Tuan, Katerina Katsarou, Patrick Herbke (Technische Universität Berlin, China)

In the last few years, the cloudification of applications requires new concepts to make the most out of the cloud computing paradigm. The microservices architectural style has gained attention from both industry and academia, inspired by service-oriented architectures. However, the complexity of distributed systems has also created many novel challenges in various aspects. In this work, we present our work-in-progress solution based on grid search and random search techniques to enable self-optimizing microservice systems. The initial results show our approach can help to optimize the latency performance of microservices to up to 10.56%

L-KPCA: an Efficient Feature Extraction Method for Network Intrusion Detection

Jinfu Chen, Shang Yin, Saihua Cai, Lingling Zhao, Shengran Wang (Jiangsu University, China)

Network intrusion detection identifies malicious activity in the network by analyzing the behavior of network traffic. As an important part of network intrusion detection, feature extraction plays a crucial role in improving the performance of intrusion detection. This research proposes a novel secondary feature extraction method called L-KPCA based on the Liner Discriminant Analysis (LDA) and Kernel Principal Component Analysis (KPCA), to provide efficient features for network intrusion detection. In the L-KPCA, the KPCA is used firstly to project the original linearly inseparable data samples into a highdimensional linearly separable space, to delete the redundant and irrelevant features; And then, the LDA is used in the new feature space to perform secondary feature extraction. While maintaining the effectiveness of processing nonlinear data in network traffic, the use of LDA effectively compensates for the problem that KPCA only focuses on the analysis of features in terms of variance and ignores the performance of features in terms of mean. Extensive experimental results verify that the use of the proposed, L-KPCA can make the intrusion detection classification model perform better in terms of recognition accuracy and recall.

Session Chair

Wei Wang (San Diego State University, USA)

Program at a Glance