Security Threats to 5G Networks for Social Robots in Public Spaces: A Survey

This paper surveys security threats to 5G-enabled wireless access networks for social robots in public spaces (SRPS). The use of social robots (SR) in public areas requires specific Quality of Service (QoS) planning to meet its unique requirements. Its 5G threat landscape entails more than cybersecurity threats that most previous studies focus on. This study examines the 5G wireless RAN for SRPS from three perspectives: SR and wireless access points, the ad hoc network link between SR and user devices, and threats to SR and users’ communication equipment. The paper analyses the security threats to confidentiality, integrity, availability, authentication, authorisation, and privacy from the SRPS security objectives perspective. We begin with an overview of SRPS use cases and access network requirements, followed by 5G security standards, requirements, and the need for a more representative threat landscape for SRPS. The findings confirm that the RAN of SRPS is most vulnerable to physical, side-channel, intrusion, injection, manipulation, and natural and malicious threats. The paper presents existing mitigation to the identified attacks and recommends including physical level security (PLS) and post-quantum cryptography in the early design of SRPS. The insights from this survey will provide valuable risk assessment and management input to researchers, industrial practitioners, policymakers, and other stakeholders of SRPS.publishedVersio

DEVELOPMENT OF A LIGHTWEIGHT MODEL FOR COVID-19 FACE MASK WEARING-POSITION DETECTION

The Corona Virus Disease (Covid-19) spread has led to many infection cases with several resulting deaths. The increasing number of new Covid-19 variants has reinforced the need to develop a proactive critical preparedness, readiness response action plan. This study aims to develop a lightweight model for detecting face mask-wearing positions using a locally generated dataset of black people. A six-fold methodology of dataset generation, data preprocessing, model selection, model training and validation, and model deployment was adopted for this study. A dataset of black people from three universities in Nigeria was generated for males and females, indicating four distinct face mask-wearing positions and eight classes, using a digital camera, smartphones and medical face mask. The images were subject to preprocessing such as cropping, resizing, labelling and data augmentation. The generated dataset was used to train a modified YOLOv5s model and deployed using Roboflow’s webcam platform and local PC with Pycharm IDE. The developed model achieved 94.2%mAP, 94% recall, and 79% precision on Roboflow’s platform after training for 250 epochs. Training on Google Colab platform for 100 epochs resulted in 91.5% mAP, 91.4% recall and a precision of 86.8%. A dataset called "Black Face Mask Dataset" was generated from this study, with 13 different annotation formats. This study’s outcome will be beneficial to researchers in computer vision and the government of developing countries. The generated dataset can be merged with the existing face mask detection dataset to achieve a better model with good black people representation. the eight classes can be merged into smaller classes based on the application requirement to produce higher object numbers per class. The developed model can be cloned from GitHub for easy integration without the need for retraining

Security Aspects of Social Robots in Public Spaces: A Systematic Mapping Study

Background: As social robots increasingly integrate into public spaces, comprehending their security implications becomes paramount. This study is conducted amidst the growing use of social robots in public spaces (SRPS), emphasising the necessity for tailored security standards for these unique robotic systems. Methods: In this systematic mapping study (SMS), we meticulously review and analyse existing literature from the Web of Science database, following guidelines by Petersen et al. We employ a structured approach to categorise and synthesise literature on SRPS security aspects, including physical safety, data privacy, cybersecurity, and legal/ethical considerations. Results: Our analysis reveals a significant gap in existing safety standards, originally designed for industrial robots, that need to be revised for SRPS. We propose a thematic framework consolidating essential security guidelines for SRPS, substantiated by evidence from a considerable percentage of the primary studies analysed. Conclusions: The study underscores the urgent need for comprehensive, bespoke security standards and frameworks for SRPS. These standards ensure that SRPS operate securely and ethically, respecting individual rights and public safety, while fostering seamless integration into diverse human-centric environments. This work is poised to enhance public trust and acceptance of these robots, offering significant value to developers, policymakers, and the general public.publishedVersio

Control Design of an Inverted Pendulum on a Moving Base Using State Feedback

A fascinating and essential control problem for researchers is the inverted pendulum. It remains a generally accepted standard used in control and robotics for validation of emerging control technologies. A mathematical model of the inverted pendulum on a moving base system was derived, an expression of the linearised state space representation of the system was also presented, a controller to achieve specific criterion of the steady state error, rise and settling time, and was obtained, an implementation of this control on MATLAB was carried out together with the simulated animation of the system using Simulink and Simscap