Cyber-physical Solution Research Articles

AutoDRIVE: A Comprehensive, Flexible and Integrated Digital Twin Ecosystem for Autonomous Driving Research & Education

Prototyping and validating hardware–software components, sub-systems and systems within the intelligent transportation system-of-systems framework requires a modular yet flexible and open-access ecosystem. This work presents our attempt to develop such a comprehensive research and education ecosystem, called AutoDRIVE, for synergistically prototyping, simulating and deploying cyber-physical solutions pertaining to autonomous driving as well as smart city management. AutoDRIVE features both software as well as hardware-in-the-loop testing interfaces with openly accessible scaled vehicle and infrastructure components. The ecosystem is compatible with a variety of development frameworks, and supports both single- and multi-agent paradigms through local as well as distributed computing. Most critically, AutoDRIVE is intended to be modularly expandable to explore emergent technologies, and this work highlights various complementary features and capabilities of the proposed ecosystem by demonstrating four such deployment use-cases: (i) autonomous parking using probabilistic robotics approach for mapping, localization, path-planning and control; (ii) behavioral cloning using computer vision and deep imitation learning; (iii) intersection traversal using vehicle-to-vehicle communication and deep reinforcement learning; and (iv) smart city management using vehicle-to-infrastructure communication and internet-of-things.

Towards Sim2Real Transfer of Autonomy Algorithms using AutoDRIVE Ecosystem

The engineering community currently encounters significant challenges in the development of intelligent transportation algorithms that can be transferred from simulation to reality with minimal effort. This can be achieved by robustifying the algorithms using domain adaptation methods and/or by adopting cutting-edge tools that help support this objective seamlessly. This work presents AutoDRIVE, an openly accessible digital twin ecosystem designed to facilitate synergistic development, simulation and deployment of cyber-physical solutions pertaining to autonomous driving technology; and focuses on bridging the autonomy-oriented simulation-to-reality (sim2real) gap using the proposed ecosystem. In this paper, we extensively explore the modeling and simulation aspects of the ecosystem and substantiate its efficacy by demonstrating the successful transition of two candidate autonomy algorithms from simulation to reality to help support our claims: (i) autonomous parking using probabilistic robotics approach; (ii) behavioral cloning using deep imitation learning. The outcomes of these case studies further strengthen the credibility of AutoDRIVE as an invaluable tool for advancing the state-of-the-art in autonomous driving technology.

DepTSol: An Improved Deep-Learning- and Time-of-Flight-Based Real-Time Social Distance Monitoring Approach under Various Low-Light Conditions

Social distancing is an utmost reliable practice to minimise the spread of coronavirus disease (COVID-19). As the new variant of COVID-19 is emerging, healthcare organisations are concerned with controlling the death and infection rates. Different COVID-19 vaccines have been developed and administered worldwide. However, presently developed vaccine quantity is not sufficient to fulfil the needs of the world’s population. The precautionary measures still rely on personal preventive strategies. The sharp rise in infections has forced governments to reimpose restrictions. Governments are forcing people to maintain at least 6 feet (ft) of safe physical distance to stay safe. With summers, low-light conditions can become challenging. Especially in the cities of underdeveloped countries, where poor ventilated and congested homes cause people to gather in open spaces such as parks, streets, and markets. Besides this, in summer, large friends and family gatherings mostly take place at night. It is necessary to take precautionary measures to avoid more drastic results in such situations. To support the law and order bodies in maintaining social distancing using Social Internet of Things (SIoT), the world is considering automated systems. To address the identification of violations of a social distancing Standard Operating procedure (SOP) in low-light environments via smart, automated cyber-physical solutions, we propose an effective social distance monitoring approach named DepTSol. We propose a low-cost and easy-to-maintain motionless monocular time-of-flight (ToF) camera and deep-learning-based object detection algorithms for real-time social distance monitoring. The proposed approach detects people in low-light environments and calculates their distance in terms of pixels. We convert the predicted pixel distance into real-world units and compare it with the specified safety threshold value. The system highlights people violating the safe distance. The proposed technique is evaluated by COCO evaluation metrics and has achieved a good speed–accuracy trade-off with 51.2 frames per second (fps) and a 99.7% mean average precision (mAP) score. Besides the provision of an effective social distance monitoring approach, we perform a comparative analysis between one-stage object detectors and evaluate their performance in low-light environments. This evaluation will pave the way for researchers to study the field further and will enlighten the efficiency of deep-learning algorithms in timely responsive real-world applications.

A Novel Decentralized Analytical Methodology for Cyber Physical Networks Attack Detection

In many functional implementations of considerable engineering significance, cyber physical solutions have recently been developed where protection and privacy are essential. This led to the recent increase in interest in the development of advanced and emerging technology for anomaly and intrusion detection. The paper suggests a new frame for the distributed blind intrusion detection by modelling sensor measurements as the graph signal and using the statistical features of the graph signal for the detection of intrusion. The graphic similarity matrices is generated using the measured data of the sensors as well as the proximity of the sensors to completely take account of the underlying network structure. The scope of the collected data is modelled on the random field Gaussian Markov and the required precision matrix can be determined by adjusting to a graph called Laplacian matrix. For research statistics, the suggested technique for intrusion detection is based on the modified Bayesian probability ratio test and the closed-form expressions are derived. In the end, the time analysis of the actions of the network is calculated by computing the Bhattacharyya distance at consecutive times among the measurement distributions. Experiments are carried out, evaluated and equate the efficiency of the proposed system to the modern method. The findings indicate a detection value better than that offered by other existing systems via the proposed intrusion detection frame.

Lessons From 10 Years of Demand Response Research: Smart Energy for Customers?

To accommodate more energy from renewable sources, the existing paradigm in the electricity grid of supply follows demand has its limitations. On the other hand, demand response aims to exploit the available flexibility in electricity consumption to follow the supply of renewables. This is possible by decoupling the demand for electricity from the demand of the associated services (such as heating, cooling, and hot water) and presents many challenges, especially if flexibility is harvested at a local scale. For 10 years, research groups worldwide have looked into demand response. This article describes the open challenges of demand response in a residential context and outlines different coordination mechanisms that optimize market profit, mitigate technical issues, and reach prosumer objectives. Several considerations are added on how these cyberphysical solutions must be complemented by other, nontechnical fields to get customers engaged.

MIH-SPFP: MIH-based secure cross-layer handover protocol for Fast Proxy Mobile IPv6-IoT networks

With the proliferation of mobile devices characterizing modern cyber-physical systems, service switching and handoff over large coverage areas become key aspects of the Internet of Things (IoT), mainly when remotely controlling and interacting with mission-critical autonomous vehicles that potentially may cover quite large distances such as driverless cars and Unmanned Aerial Vehicles (UAVs). These requirements can now be fully satisfied by the widespread Fast handover for Proxy Mobile IPv6 (F-PMIPv6) technology, that can be yet considered as a cornerstone in emerging 5G communications, but, unfortunately, such an approach only supports homogeneous handover, that may result in a nontrivial problem due to the heterogeneity in mobile communications technologies characterizing the available cyber-physical solutions and IoT network access devices. Recently, many researchers developed efficient solutions for the integration of F-PMIPv6 and Media Independent Handover (MIH) to allow fast handover in a highly heterogeneous mobile network. However, these models lack the security features which are necessary to protect IoT devices during handoffs. In this paper, a new security protocol, MIH-based secure cross-layer handover protocol for Fast Proxy Mobile IPv6 networks (MIH-SPFP), is proposed, incorporating the features of Secure Protocol for Fast-PMIPv6 (SPFP) into F-PMIPv6-MIH and reducing the security risks during the handover. The proposed solution also provides low latency by reducing the re-authentication path during the inter-Mobile Access Gateway (MAG) handovers. The security of the proposed protocol has been analyzed by using Burrows–Abadi–Needham (BAN) logic and Automated Validation of Internet Security Protocols and Applications (AVISPA) tool and its performance has been evaluated through numerical simulation by selecting “Marathon Broadcasting” as a case study. Results show that the proposed protocol not only effectively secures the handover process but is also more efficient compared with the standard MIH handover solution.

New Resource-Wise Planning Strategies for Smart Urban-Rural Development in Finland

This article discusses the opportunities and challenges for resource-wise development strategies in regional planning. Spatial planning integrates the key aspects, transportation, housing, and food production which are, on many occasions, stated as the most significant consumption factors causing environmental impacts. In light of the challenges that regions are currently facing in Finland, we are drawing attention to the role of strategic spatial planning as demand-responsive resource management, a theme which is still inadequately addressed within regional development and planning in Finland. In many other fields of society, innovative data-based products and demand- and user-driven services are considered important sources of success in the future. Such strategies combine different types of service providers, like deliveries for groceries or restaurant meals, mobile healthcare services, or public transport with on-demand services. We highlight the fact that a regionally large and sparsely populated country, such as Finland, cannot achieve success solely through centralisation. Instead, smart networking, co-creation, and innovative cyber-physical solutions are vital for the utilisation of the entire country’s resource potentiality. In conclusion, we underpin the need for a framework, which would offer a strategic support scheme for resource-wise development, resource optimization, and closure of yield gaps. In our view it is necessary to begin to envision, strategise, and develop user- and demand-responsive development strategies with a specific aim for sustainable, resource-wise ways of life in northern regions, also outside the growing urban centres, and innovate solutions that help individuals, communities, and the whole society to renew and manage resources wisely.