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 Keynote-1

Keynote Speech 1: IoT Security

Conference
1:30 PM — 2:30 PM GMT
Local
Dec 14 Tue, 5:30 AM — 6:30 AM PST

IoT Security

Prof. Elisa Bertino (Purdue University, USA)

0
The Internet of Things (IoT) paradigm refers to the network of physical objects or "things" embedded with electronics, software, sensors, and connectivity to enable objects to exchange data with servers, centralized systems, and/or other connected devices based on a variety of communication infrastructures. IoT makes it possible to sense and control objects creating opportunities for more direct integration between the physical world and computer-based systems. Furthermore, the deployment of AI techniques enhances the autonomy of IoT devices and systems. IoT will thus usher automation in a large number of application domains, ranging from manufacturing and energy management (e.g. SmartGrid), to healthcare management and urban life (e.g. SmartCity). However, because of its fine-grained, continuous and pervasive data acquisition and control capabilities, IoT raises concerns about security, privacy, and safety. Deploying existing solutions to IoT is not straightforward because of device heterogeneity, highly dynamic and possibly unprotected environments, and large scale. In this talk, after outlining key challenges in IoT security and privacy, we outline a security lifecycle approach to securing IoT data, and then focus on our recent work on security analysis for cellular network protocols and edge-based anomaly detection based on machine learning techniques.
Session Chair

Xiaohua Jia (City University of Hong Kong, Hong Kong)

Session Keynote-2

Keynote Speech 2: Deep Reinforcement Learning for Control and Management of Communications Networks

Conference
2:30 PM — 3:30 PM GMT
Local
Dec 14 Tue, 6:30 AM — 7:30 AM PST

Deep Reinforcement Learning for Control and Management of Communications Networks

Kin K. Leung (EEE and Computing Departments Imperial College, London, UK)

0
Deep RL techniques have been applied to many application domains. In communications networks, deep RL has been used to solve routing, service-placement and power-allocation problems in the software defined networks (SDN) as well as the software defined coalitions (SDC) developed in the DAIS ITA Program. This speaker begins with a brief introduction to RL. For illustration purposes, he presents use of RL to train a smart policy for synchronization of domain controllers in order to maximize performance gains in SDN. Results show that the RL policy significantly outperforms other algorithms for inter-domain routing tasks. As shown in the above work, a challenging issue for deep RL is the huge state and action spaces, which increase model complexity and training time beyond practical feasibility. The speaker will present a method to decouple actions from the state space for the value-function learning process and a relatively simple transition model is learned to determine the action that causes the associated state transition. Experimental results show that the state-action separable RL can greatly reduce training time without noticeable performance degradation. The speaker will conclude by highlighting the open issues for use of RL for control of large-scaled communications networks.
Session Chair

Jia Hu (University of Exeter, U. K.)

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