Mobile Cyber-physical Systems Research Articles

РОЗРОБКА МОБІЛЬНОЇ КІБЕРФІЗИЧНОЇ СИСТЕМИ ІНТЕЛЕКТУАЛЬНОГО МОНІТОРИНГУ КЛІМАТИЧНИХ ПАРАМЕТРІВ

The development of Mobile Cyber-Physical Systems (MCPS) is a promising research and development direction for many industries, such as manufacturing, healthcare, home automation, and many others. That is why a prototype MCPS based on a smartphone has been developed to collect, process, and transmit data from various devices and sensors in real-time, anywhere. The architecture of a mobile cyber-physical system for monitoring climatic parameters with telegram notifications has been designed. An algorithm for intelligent analysis and optimization of data transmission processes has been proposed for the developed MCPS prototype. The advantage of the developed system is the ability to determine the priority of monitoring parameters, which allows for a quick response to critical temperature changes at the object where the measurements are taken. Additionally, a unique method for measuring end-to-end data transmission delay using a timestamp in the packet header metadata has been implemented. This method enables the determination of the processing time of each component of the MCPS and, in case of exceeding the norms, automatically notifies about the necessary control decisions. Supporting such a method in MCPS is a particularly effective solution for monitoring the quality of real-time service delivery in critical infrastructure objects. Based on the conducted research, it has been established that the proposed algorithm for intelligent data analysis and optimization has reduced the number of messages by 3 times and the amount of transmitted information by 2.3 times. In the future, the developed system, in combination with artificial intelligence, will ensure reliable and high-quality data transmission even in unpredictable situations, making it a promising solution for improving the quality of life and the efficiency of smart infrastructures in various fields.

Joint Resource Scheduling for AMR Navigation Over Wireless Edge Networks

The future of autonomous systems will rely on the usage of wireless time-sensitive networks to connect mobile cyberphysical systems, such as Autonomous Mobile Robots (AMRs), to Edge compute platforms to offload computationally intensive workloads necessary to complete tasks. In the case of AMRs, due to their mobility, the offloading of expensive processes such as localization and tracking methods to the Edge computing infrastructure must also be done over dynamic wireless networks. In larger scale systems, the network and compute resource requirements can quickly become prohibitively large due to network traffic and heavy workloads and tight deadline requirements for proper execution of time-critical tasks. In this paper, we formulate the problem of jointly allocating network and compute resources for time sensitive systems as the state of the wireless channel changes over time. By characterizing a compute model for AMR workloads, we further demonstrate how the network and compute scheduling decisions can be serialized, thus making the optimal scheduling problem significantly more tractable, via the incorporation of a compute-utility aware network cost function. Simulation results of AMR systems in a Wi-Fi network demonstrate substantial gains over baseline scheduling methods in total resource efficiency.

A Model of a Decentralized Cyber-Physical System Resiliently Functioning in a Changing Environment

The article discusses the issues of decentralization and dynamic redistribution of roles in cyber-physical systems (CPS) designed for working in changing environments and especially open spaces, where there are increased risks of module failures and communication loss. In particular, decentralized methods for controlling the CPS behavior and ensuring the redundancy of their components and connections are investigated. A number of requirements for such systems are identified and it is noted what limitations in existing approaches impede the implementation of systems that satisfy these requirements at the physical, network and application layers. For different layers, behavior models are proposed, which provide autonomous role distribution between components. This made it possible to synthesize a structural-parametric model of an autonomous mobile CPS, focused on functioning in open areas and solving applied problems performed through the coordinated interaction of groups of mobile agents. The model takes into account aspects of system stability and its response to destructive influences. The advantages of the proposed models include task decentralization, absence of central, critical nodes and bottlenecks, no line of sight requirements or small distance between devices, ability to work in unexplored environments. The solutions can be applied primarily in the field of business and are suitable for use in industrial plants equipped with mobile robotic devices with cameras, for example, for agricultural tasks, or territorial exploration. The proposed approach makes possible to study the terrain more accurately, including unexplored areas with limited accessibility to humans. Also, the results obtained can be applied during rescue operations.

CPU-GPU-Memory DVFS for Power-Efficient MPSoC in Mobile Cyber Physical Systems

Most modern mobile cyber-physical systems such as smartphones come equipped with multi-processor systems-on-chip (MPSoCs) with variant computing capacity both to cater to performance requirements and reduce power consumption when executing an application. In this paper, we propose a novel approach to dynamic voltage and frequency scaling (DVFS) on CPU, GPU and RAM in a mobile MPSoC, which caters to the performance requirements of the executing application while consuming low power. We evaluate our methodology on a real hardware platform, Odroid XU4, and the experimental results prove the approach to be 26% more power-efficient and 21% more thermal-efficient compared to the state-of-the-art system.

The Handie system: Hand signs interaction with autonomous, mobile cyber-physical systems

Autonomous, mobile cyber-physical systems are becoming popular in the transportation, manufacturing and industry sectors, thanks to their ability to provide smart and twenty-four-seven services to society, production industries and companies. A smart service is, for example, autonomous transportation of people in traffic or goods in manufacturing industries without human control.The use of autonomous, mobile cyber-physical systems has led to changes and challenges. Especially, dynamic obstacles are affecting the cyber-physical systems and these must be handled to provide robust environments. Although the mobile cyber-physical systems are interacting and acting with the other moveable cyber-physical systems, they also need to handle interactions with human beings. These human beings' interactions can be verbal conversations with the system. However, this communication may struggle with language barriers where a cyber-physical system, for example, only works with one or two natural languages. These interaction limitations may not be obvious nor even known to human beings. In addition, the human being may not be able to handle verbal communication with the cyber-physical systems, therefore hand signs are required to communicate with the systems to provide a trustworthy and secure environment together with several cyber-physical systems.This paper presents Handie, a system handling hand sign interactions with autonomous mobile cyber-physical systems, aka mobile robots. The hand signs are simple, i.e., signs for "ok", "thumbs-up" and "stop" that constitute commands to the robot. Moreover, this non-verbal interaction includes human moods, with facial expressions like happiness, surprise, and fear. Both hand signs and facial expressions are handled by a deep learning recognition component in a humanoid, programmable robot Softbank Robotics' NAO robot v6. Results from testing the system show that the cyber-physical system can recognize hand signs and facial expressions to an acceptable extent to be useful, although the interaction is time-consuming and slow. Also, currently, the communication back to the end users is made verbally and not visually. The next step of Handle's will be to expand the number of hand signs and improve the interaction with the NAO robot.

Decomposed Task Scheduling for Security-Critical Mobile Cyber–Physical Systems

With the recent advances in mobile sensing, computing, and communication technologies, mobile cyber–physical systems (MCPSs) become a promising networking paradigm that provides mobile users with various applications and services from cyber space to the physical world. One of the main challenges in the MCPSs is to defend security threats launched by adversaries. Exploiting security services to defend threats is energy consuming, whereas the energy of mobile devices is generally limited since most of mobile devices are battery powered. This necessitates the need to design new methodologies to tackle the tradeoff between security and energy of MCPSs. To this end, this article aims to maximize MCPSs’ security under the constraints of energy and deadline. In this article, we first formulate the MCPS security maximization problem as a mixed-integer nonlinear programming (MINLP) problem and then transform it into a mixed-integer linear programming (MILP) problem without performance degradation. To solve the transformed MILP problem efficiently, we propose a decomposed algorithm to derive the optimum task scheduling solution instead of using MILP solvers that may be very time consuming for MCPSs of large granularity. The derived task scheduling solution decides the assignment, operating frequency, execution order, as well as security service selection for all tasks. We implement a series of simulation-based experiments to validate the proposed decomposed task scheduling scheme. Simulation results demonstrate that the proposed scheme increases system security level by 20.38% and 65.11% on average as compared to a state-of-art approach and a baseline method.

Secure and Efficient Data-Privacy-Preserving Scheme for Mobile Cyber–Physical Systems

Research on mobile cyber–physical systems (MCPSs) that have the superiorities of cyber–physical systems (CPSs) and expand their application range has become a trend in recent years. The applications of MCPS in fields, such as intelligent transportation systems, smart home appliances, and mobile education, have also become increasingly mature. However, MCPS also has some shortcomings that need to be solved urgently. Sensors carried on mobile devices collect data and upload huge amounts of data to the cloud for statistics and analysis. Mobile devices need to directly interact with the cloud, causing both parties to bear huge computing and communication costs. Since the interaction process includes data sharing and storage, it is particularly important to protect the data itself and the security of sharing. Searchable encryption ensures the safety of communication among the MPEs and the cloud, while protecting the privacy of data on the cloud. However, the existing searchable encryption technology cannot provide efficient and reliable data storage and sharing services for MPEs in MCPS under the premise of ensuring security. In this article, we present a secure and efficient data sharing and privacy protection scheme in MCPS on the basis of the edge computing model (NESPS). Meanwhile, the introduction of edge computing (EC) significantly reduces the communication consumption between the device and the cloud. Furthermore, attribute-based encryption (ABE) enables the NESPS to achieve fine-grained management of device authorities. Moreover, we have carried out security analysis and simulation on the NESPS, the results demonstrate that the NESPS meets the proposed requirements.

Multi-agent deep reinforcement learning concept for mobile cyber-physical systems control

High complexity of mobile cyber physical systems (MCPS) dynamics makes it difficult to apply classical methods to optimize the MCPS agent management policy. In this regard, the use of intelligent control methods, in particular, with the help of artificial neural networks (ANN) and multi-agent deep reinforcement learning (MDRL), is gaining relevance. In practice, the application of MDRL in MCPS faces the following problems: 1) existing MDRL methods have low scalability; 2) the inference of the used ANNs has high computational complexity; 3) MCPS trained using existing methods have low functional safety. To solve these problems, we propose the concept of a new MDRL method based on the existing MADDPG method. Within the framework of the concept, it is proposed: 1) to increase the scalability of MDRL by using information not about all other MCPS agents, but only about n nearest neighbors; 2) reduce the computational complexity of ANN inference by using a sparse ANN structure; 3) to increase the functional safety of trained MCPS by using a training set with uneven distribution of states. The proposed concept is expected to help address the challenges of applying MDRL to MCPS. To confirm this, it is planned to conduct experimental studies.

Ontological modeling of micro and macro architectures Co-Design of cyber-physical systems

For effective design and modeling of modern mobile cyber-physical systems, it is proposed to use the theory of ontology, which has a number of important advantages: conceptual modeling of the target area in order to find hidden relationships, conflicts and description of incomplete (indefinite) information through abstraction at the physical or digital levels of the system, support for different styles modeling; modern interface of a large number of applications with a hierarchy of menus or work windows based on its developed conceptual model; the ability to combine two database applications or knowledge bases with completely different schemes of incompatible information systems into a single information space. Shown the importance of modern very high and is rapidly increasing mobile cyber-physical systems is determined by their wide range of application: almost all socially significant sectors of industry, economy, social systems: smart healthcare, smart home, smart city, smart e- and m-government. It is noted that the requirement for effective collaborative design and modeling leads to unusual complexity and ultra-high demands, application parallelism and heterogeneity, distribution of intelligence, computing resources для services and workloads in the Internet of Thinks hierarchy too. Suggested for modern complex applications require ultra-high performance and/or ultra-low power consumption иcпoльзoвать the authors propose using heterogeneous computing platforms, designing processors with parallel management multiprocessor macroarchitecture and parallel microarchitecture processors using various types of parallelism and application-specific parallel equipment based on super large-scale integrated circuit 1879VM6Y with high-performance processor core NMC4 of new generation architecture VLIW / SIMD.

Multi‐agent system for the reactive fleet maintenance support planning of a fleet of mobile cyber–physical systems

Improving the availability and reliability of a fleet of mobile cyber–physical systems as well as the ability to adapt themaintenance planning decisions when facing unexpected events at the fleet level is a major stake faced by the manufacturersand the operators. In the context of this study, the authors propose a reactive multi-agent system model for the fleetmaintenance planning of mobile cyber–physical systems. For that purpose, the ANEMONA multi-agent design methodology isused. In this methodology, the agents are modelled and then their organisational and interaction views are described. Numericalexperiments are carried out in static and dynamic contexts. In a static context, the proposed multi-agent system is comparedwith a mathematical programming model to validate the effectiveness of the former in satisfying the fleet's availability andreliability expectations. In a dynamic context, simulated perturbations are used to illustrate the reactivity of the proposed multi-agent system. Lastly, an application to rail transport for the maintenance of a fleet of trains at Bombardier Transportation Franceis proposed. For that purpose, the proposed multi-agent system is integrated in the model layer of a decision support systemnamed ‘MainFleet’ which is currently under development

A Taxonomy for Security Flaws in Event-Based Systems

Event-based system (EBS) is prevalent in various systems including mobile cyber physical systems (MCPSs), Internet of Things (IoT) applications, mobile applications, and web applications, because of its particular communication model that uses implicit invocation and concurrency between components. However, an EBS’s non-determinism in event processing can introduce inherent security vulnerabilities into the system. Multiple types of attacks can incapacitate and damage a target EBS by exploiting this event-based communication model. To minimize the risk of security threats in EBSs, security efforts are required by determining the types of security flaws in the system, the relationship between the flaws, and feasible techniques for dealing with each flaw. However, existing security flaw taxonomies do not appropriately reflect the security issues that originate from an EBS’s characteristics. In this paper, we introduce a new taxonomy that defines and classifies the particular types of inherent security flaws in an EBS, which can serve as a basis for resolving its specific security problems. We also correlate our taxonomy with security attacks that can exploit each flaw and identify existing solutions that can be applied to preventing such attacks. We demonstrate that our taxonomy handles particular aspects of EBSs not covered by existing taxonomies.

The micro-architectures co-design of neuroprocessing cyber-physical systems

The importance of modern mobile cyber-physical Systems is determined by their wide range of application. Cyber-physical Systems cover almost all socially significant sectors of industry, economy, social systems: smart healthcare, smart home, smart city, smart government. The economic and social importance of mobile cyber-physical systems is very high and is rapidly increasing. However, design, creation and implementation of cyber-physical systems is accompanied by various challenges: unusual complexity and ultra-high demands, application parallelism and heterogeneity, distribution of intelligence, computing resources, services and workloads in the IoT hierarchical. Thus, modern complex applications require ultra-high performance and / or ultra-low power consumption. Therefore, to obtain ultra-high throughput and ultra-low power consumption, the authors propose using heterogeneous computing platforms, designing processors with parallel microarchitecture processors using various types of parallelism and application-specific parallel equipment.

A Real-Time Data Delivery for Mobile Sinks Group on Mobile Cyber-Physical Systems

Mobile Cyber-Physical Systems (MCPS) have extended the application domains by exploiting the advantages of Cyber-Physical Systems (CPS) through the mobile devices. The cooperation of various mobile equipment and workers based on the MCPS further improved efficiency and productivity in the industry. To support this cooperation of groups of workers (hereafter referred to as the Mobile Sink Groups), data should be delivered to appropriate groups of workers in a timely manner. Traditionally, the data dissemination for MSG relies on flooding-based geocasting into the movable area of the group due to frequent movements of each group member. However, the flooding-based data dissemination could not be directly applied to real-time data delivery that demands the required time deadline and the end-to-end delivery distance, because the flooding could not define the end-to-end distance and progress to each member in a group. This paper proposes a real-time data delivery mechanism for supporting MSG in time-critical applications. In our mechanism, a ring-based modeling and data transfer scheme on a virtual grid in the ring for group mobility provides the end-to-end distance and the progress to forward real-time data to each member. Simulation results show our mechanism is superior to the existing ones in terms of real-time communication for MSG.

Collaborative Multi-Expert Active Learning for Mobile Health Monitoring: Architecture, Algorithms, and Evaluation.

Mobile health monitoring plays a central role in the future of cyber physical systems (CPS) for healthcare applications. Such monitoring systems need to process user data accurately. Unlike in other human-centered CPS, in healthcare CPS, the user functions in multiple roles all at the same time: as an operator, an actuator, the physical environment and, most importantly, the target that needs to be monitored in the process. Therefore, mobile health CPS devices face highly dynamic settings generally, and accuracy of the machine learning models the devices employ may drop dramatically every time a change in setting happens. Novel learning architecture that specifically address challenges associated with dynamic environments are therefore needed. Using active learning and transfer learning as organizing principles, we propose a collaborative multiple-expert architecture and accompanying algorithms for the design of machine learning models that autonomously adapt to a new configuration, context, or user need. Specifically, our architecture and its constituent algorithms are designed to manage heterogeneous knowledge sources or experts with varying levels of confidence and type while minimizing adaptation cost. Additionally, our framework incorporates a mechanism for collaboration among experts to enrich their knowledge, which in turn decreases both cost and uncertainty of data labeling in future steps. We evaluate the efficacy of the architecture using two publicly available human activity datasets. We attain activity recognition accuracy of over (for the first dataset) and (for the second dataset) by labeling only of unlabeled data.

Black Block Recorder: Immutable Black Box Logging for Robots via Blockchain

Event data recording is crucial in robotics research, providing prolonged insights into a robot's situational understanding, progression of behavioral state, and resulting outcomes. Such recordings are invaluable when debugging complex robotic applications or profiling experiments ex post facto. As robotic developments mature into production, both the roles and requirements of event logging will broaden, to include serving as evidence for auditors and regulators investigating accidents or fraud. Given the growing number of high profile public incidents involving self-driving automotives resulting in fatality and regulatory policy making, it is paramount that the integrity, authenticity and non-repudiation of such event logs are maintained to ensure accountability. Being mobile cyber-physical systems, robots present new threats, and vulnerabilities beyond traditional IT: unsupervised physical system access or postmortem collusion between robot and OEM could result in the truncation or alteration of prior records. In this letter, we address immutablization of log records via integrity proofs and distributed ledgers with special considerations for mobile and public service robot deployments.

Structural Reliability Estimation with Participatory Sensing and Mobile Cyber-Physical Structural Health Monitoring Systems

With the help of community participants, smartphones can become useful wireless sensor network (WSN) components, form a self-governing structural health monitoring (SHM) system, and merge structural mechanics with participatory sensing and server computing. This paper presents a methodology and framework of such a cyber-physical system (CPS) that generates a bridge finite element model (FEM) integrated with vibration measurements from smartphone WSNs and centralized/distributed computational facilities, then assesses structural reliability based on updated FEMs. Structural vibration data obtained from smartphones are processed on a server to identify modal frequencies of an existing bridge. Without design drawings and supportive documentation but field measurements and observations, FEM of the bridge is drafted with uncertainties in the structural mass, stiffness, and boundary conditions (BCs). Then, 2700 FEMs are autonomously generated, and the baseline FEM is updated by minimizing the error between the crowdsourcing-based modal identification results and the FEM analysis. Furthermore, using 151 strong ground motion records from databases, the bridge response time history simulations are conducted to obtain displacement demand distribution. Finally, based on reference performance criteria, structural reliability of the bridge is estimated. Integrating the cyber (FEM analysis) and the physical (the bridge structure and measured vibration characteristics) worlds, this crowdsourcing-based CPS can provide a powerful tool for supporting rapid, remote, autonomous, and objective infrastructure-related decision-making. This study presents a new example of the emerging fourth industrial revolution from structural engineering and SHM perspective.

Agile polymorphic software-defined fog computing platform for mobile wireless controllers and sensors

Softwarisation approaches in networks, storage systems, and smart devices aim to optimise costs and processes and bring new infrastructure definitions and functional values. A recent integration of wireless and mobile cyber-physical systems, with dramatically growing smart sensors, enable new types of pervasive smart and mobile urban surveillance infrastructures that open up new opportunities for boosting the accuracy, efficiency, and productivity of uninterrupted target tracking and situational awareness. Wireless sensors provide the tool for communications and security applications. They offer low-power, multi-functioning and computational capabilities. In this paper, we present a design and prototype of an efficient and effective fog system using light-weight agile software-defined control for mobile wireless nodes. Fog computing or edge computing, a recently proposed extension and complement for cloud computing, enables computing at the network edge in a smart device without outsourcing jobs to a remote cloud. We investigate an effective softwarisation approach in the Fog environment for dynamic big data driven, real-time urban surveillance tasks of uninterrupted target tracking. We address key technical challenges of node mobility to improve the system awareness. We built a preliminary proof-of-concept Light-weight controller architecture on both Android-and Linux-based smart devices and tested various collaborative scenarios among the mobile nodes.

Dependable Visual Light-Based Indoor Localization with Automatic Anomaly Detection for Location-Based Service of Mobile Cyber-Physical Systems

Indoor localization has become popular in recent years due to the increasing need of location-based services in mobile cyber-physical systems (CPS). The massive deployment of light emitting diodes (LEDs) further promotes the indoor localization using visual light. As a key enabling technique for mobile CPS, accurate indoor localization based on visual light communication remains nontrivial due to various non-idealities such as attenuation induced by unexpected obstacles. The anomalies of localization can potentially reduce the dependability of location-based services. In this article, we develop a novel indoor localization framework based on relative received signal strength. Most importantly, an efficient method is derived from the triangle inequality to automatically detect the abnormal LED lamps that are blocked by obstacles. These LED lamps are then ignored by our localization algorithm so that they do not bias the localization results, which improves the dependability of our localization framework. As demonstrated by the simulation results, the proposed techniques can achieve superior accuracy over the conventional approaches, especially when there exist abnormal LED lamps.

Discovering communities of malapps on Android-based mobile cyber-physical systems

Android-based devices like smartphones have become ideal mobile cyber-physical systems (MCPS) due to their powerful processors and variety of sensors. In recent years, an explosively and continuously growing number of malicious applications (malapps) have posed a great threat to Android-based MCPS as well as users’ privacy. The effective detection of malapps is an emerging yet crucial task. How to establish relationships among malapps, discover their potential communities, and explore their evolution process has become a challenging issue in effective detection of malapps. To deal with this issue, in this work, we are motivated to propose an automated community detection method for Android malapps by building a relation graph based on their static features. First, we construct a large feature set to profile the behaviors of malapps. Second, we propose an E-N algorithm for graph construction by combining epsilon graph and k-nearest neighbor (k-NN) graph. It solves the problem of an incomplete graph led by epsilon method and the problem of noise generated by k-NN graph. Finally, a community detection method, Infomap, is employed to explore the underlying structures of the relation graph, and obtain the communities of malapps. We evaluate our community detection method with 3996 malapp samples. Extensive experimental results show that our method outperforms the traditional clustering methods and achieves the best performance with rand statistic of 94.93% and accuracy of 79.53%.

Lightweight attribute based encryption scheme for mobile cloud assisted cyber-physical systems

Protecting data in Cyber-Physical Systems (CPS) has been a longstanding challenge. The tradition Attribute Based Encryption (ABE) is known for its high computation load which creates a significant challenge for resource constrained mobile devices. In this paper, we propose a Lightweight Attribute Based Encryption Scheme (LABE) for mobile cloud-assisted CPS based on a proxy service architecture and a new ciphertext policy ABE. In particular, mobile devices will solely perform symmetric encryption by joining authentication and encryption proxy services encapsulated in RESTful. Encryption shall not need pairing and then ciphertext can be decrypted with one pairing. Security analysis shows that the proposed LABE is secure with fine grained access control and users revocation capability. The computation complexity shows that the proposed scheme imposes low overhead on mobile devices and performs very well in mobile cloud-assisted CPS.