Real-time Constraints Research Articles

Secure Route-Obfuscation Mechanism with Information-Theoretic Security for Internet of Things.

As accessibility of networked devices becomes more and more ubiquitous, groundbreaking applications of the Internet of Things (IoT) find their place in many aspects of our society. The exploitation of these devices is the main reason for the cyberattacks in IoT networks. Security design is still an open problem and a crucial step in making IoT applications successful. In dicey environments, such as e-health, smart grid, and smart cities, real-time commands must reach the end devices in the scale of milliseconds. Traditional public-key cryptosystem, albeit necessary in the context of general Internet security, falls short in establishing new session keys in the scale of milliseconds for critical messages. In this paper, a systematic perspective for securing IoT communication, specifically satisfying the real-time constraint against certain adversaries in realistic settings. First, at the network layer, we propose a secret random route computation scheme using the software-defined network (SDN) based on a capability scheme using the network actions. The computed routes are random in the eyes of the eavesdropper. Second, at the application layer, the source breaks command messages into secret shares and sends them through the network to the destination. Only the legitimate destination device can reconstruct the command. The secret sharing scheme is efficient compared to PKI and comes with information-theoretic security against adversaries. Our proof formalizes the notion of security of the proposed scheme, and our simulations validate our design.

Automatic Guidance Method for Laser Tracker Based on Rotary-Laser Scanning Angle Measurement.

Laser-tracking measurement systems (laser tracker) have been playing a critical role in large-scale 3D high-precision coordinate measurement. However, the existing visual guidance of laser trackers is still limited by the disadvantages of operator-dependence, small-angle view field, time-consuming laser-guided process. This paper presents an automatic guidance method for laser trackers based on the rotary-laser scanning angle measurement technology. In this method, a special target consisting of six photoelectric receivers and a retroreflector is integrated into the rotary-laser scanning transmitter’ coordinate systems. Real-time constraints calculated by the proposed method would provide the coordinates of the target in a laser tracker coordinates system for guidance. Finally, the experimental results verified the automatic re-establish of sightline can be realized in horizontal 360° angle field within tens of arc-seconds, and this method is robust against the fast movement of the target.

An event-based approach for formally verifying runtime adaptive real-time systems

Real-time and embedded systems are required to adapt their behavior and structure to runtime unpredicted changes in order to maintain their feasibility and usefulness. These systems are generally more difficult to specify and verify owning to their execution complexity. Hence, ensuring the high-level design and the early verification of system adaptation at runtime is very crucial. However, existing runtime model-based approaches for adaptive real-time and embedded systems suffer from shortcoming linked to efficiently and correctly managing the adaptive system behavior, especially that a formal verification is not allowed by modeling languages such as UML and MARTE profile. Moreover, reasoning about the correctness and the precision of high-level models is a complex task without the appropriate tool support. In this work, we propose an MDE-based framework for the specification and the verification of runtime adaptive real-time and embedded systems. Our approach stands for Event-B method to formally verify resources behavior and real-time constraints. In fact, thanks to MDE M2T transformations, our proposal translates runtime models into Event-B specifications to ensure the correctness of runtime adaptive system properties, temporal constrains and nonfunctional properties using Rodin platform. A flood prediction system case study is adopted for the validation of our proposal.

Dependable Scheduling for Real-Time Workflows on Cyber–Physical Cloud Systems

Cyber-physical cloud systems (CPCS) are integrations of cyber-physical systems (CPS) and cloud computing infrastructures. Integrating CPS into cloud computing infrastructures could improve the performance in many aspects. However, new reliability and security challenges are also introduced. This fact highlights the need to develop novel methodologies to tackle these challenges in CPCS. To this end, this article is oriented toward enhancing the soft-error reliability of real-time workflows on CPCS while satisfying the lifetime reliability, security, and real-time constraints. In this article, we propose a dependable algorithm for scheduling workflow applications on CPCS. The proposed algorithm uses slack to recover failed tasks and allows all tasks to share the available slack in the system. To improve soft-error reliability, the algorithm first determines the priority of tasks, then assigns the maximum frequency to each task, and finally assigns the recoveries to tasks dynamically. Slack also can be used to utilize security services for satisfying system security requirements. The lifetime reliability constraint is met by dynamically scaling down the operating frequency of low-priority tasks. Extensive experiments on real-world workflow benchmarks demonstrate that the proposed scheme reduces the probability of failure by up to $52.1\%$ and improves the scheduling feasibility by up to $83.5\%$ compared to a number of representative approaches.

Genetic state-grouping algorithm for deep reinforcement learning

Although Reinforcement learning has already been considered one of the most important and well-known techniques of machine learning, its applicability remains limited in the real-world problems due to its long initial learning time and unstable learning. Especially, the problem of an overwhelming number of the branching factors under real-time constraint still stays unconquered, demanding a new method for the next generation of reinforcement learning. In this paper, we propose Genetic State-Grouping Algorithm based on deep reinforcement learning. The core idea is to divide the entire set of states into a few state groups. Each group consists of states that are mutually similar, thus representing their common features. The state groups are then processed with the Genetic Optimizer, which finds outstanding actions. These steps help the Deep Q Network avoid excessive exploration, thereby contributing to the significant reduction of initial learning time. The experiment on the real-time fighting video game (FightingICE) shows the effectiveness of our proposed approach.

Local Market for TSO and DSO Reactive Power Provision Using DSO Grid Resources

This paper proposes a near to real-time local market to provide reactive power to the transmission system operator (TSO), using the resources connected to a distribution grid managed by a distribution system operator (DSO). The TSO publishes a requested reactive power profile at the TSO-DSO interface for each time-interval of the next delivery period, so that market agents (managing resources of the distribution grid) can prepare and send their bids accordingly. DSO resources are the first to be mobilized, and the remaining residual reactive power is supplied by the reactive power flexibility offered in the local reactive market. Complex bids (with non-curtailability conditions) are supported to provide flexible ways of bidding fewer flexible assets (such as capacitor banks). An alternating current (AC) optimal power flow (OPF) is used to clear the bids by maximizing the social welfare to supply the TSO required reactive power profile, subject to the DSO grid constraints. A rolling window mechanism allows a continuous dispatching of reactive power, and the possibility of adapting assigned schedules to real time constraints. A simplified TSO-DSO cost assignment of the flexible reactive power used is proposed to share for settlement purposes.

Dynamic speed scaling minimizing expected energy consumption for real-time tasks

This paper proposes a discrete time Markov decision process approach to compute the optimal on-line speed scaling policy to minimize the energy consumption of a single processor executing a finite or infinite set of jobs with real-time constraints. We provide several qualitative properties of the optimal policy: monotonicity with respect to the jobs parameters, comparison with on-line deterministic algorithms. Numerical experiments in several scenarios show that our proposition performs well when compared with off-line optimal solutions and out-performs on-line solutions oblivious to statistical information on the jobs.

Dynamic prioritization of surveillance video data in real-time automated detection systems

Automated object detection systems are a key component of modern surveillance applications. These systems rely on computationally expensive computer vision algorithms that perform object detection on visual data recorded by surveillance cameras. Due to the security and safety implications of these systems, this visual data st be processed accurately and in real-time. However, many of the frames that are created by the surveillance cameras may be of low importance, providing little or no useful information to the object detection system. Sub-sampling surveillance data by prioritizing important camera frames can greatly reduce unnecessary computation. Consequently, several works have explored dynamic visual data sub-sampling using various modalities of information (ie. spatial or temporal information) for prioritization. Few works, however, have combined and evaluated different modalities of information together for real-time prioritization of visual surveillance data. This work evaluates several individual and combined prioritization metrics derived from different modalities of information for use with a modern deep learning-based object detection algorithm. Both processing time and object detection rate are measured and used to rank the prioritization metrics. A novel approach that uses the historical detection confidences created by the object detection algorithm was demonstrated to be the best standalone prioritization metric. Additionally, a novel ensemble method that uses a KNN regressor to combine the best of the previously evaluated metrics to create a dynamic prioritization method is presented. This ensemble approach is shown to increase the object detection rate by up to 60% as compared to a static sub-sampling baseline as demonstrated using three publicly available datasets. The increased object detection rate was achieved while meeting the real-time constraints of the automated object detection system.

Performance Optimization of Reconfigurable Real-Time Wireless Sensor Networks

Wireless sensor networks (WSNs) can be seriously impacted by several changes in behavior. As a measure of optimality, a network should react in real-time. However, even if some reactions can make the system flexible, they can cause significant damages when they are not well-structured. Moreover, the scenarios of reconfigurations can affect several parameters within a wireless sensor network. Among these parameters it is possible to name the energy efficiency, the memory limitation within a node, the real-time constraints at the level of the nodes and the network in general. Either through the application or the transmission of the reconfiguration scenarios, some parameters are severely altered. A metamorphosis of the internal architecture of the nodes is proposed in this research work as well as a policy of communication between several nodes aiming to adapt the network to any change. The resulting proposition offers general efficiency in energy and real-time constraints. The efficiency is realized by the application of a pipelined approach, dealing with the incoming reconfiguration scenarios, and a communication protocol, based on a priority energy and deadline aware scheduling algorithm (a multicriteria scheduling algorithm). This protocol is capable of optimizing the transmission of both reconfiguration scenarios and sensed data simultaneously. The solution is tested on a network connecting several cars during an automobile journey and the results validate the efficiency of this proposition.

Design and Analysis of Secure and Dependable Automotive CPS: A Steer-by-Wire Case Study

The next generation of automobiles (also known as cybercars) will increasingly incorporate electronic control units (ECUs) in novel automotive control applications. Recent work has demonstrated the vulnerability of modern car control systems to security attacks that directly impacts the cybercar's physical safety and dependability. In this paper, we provide an integrated approach for the design of secure and dependable automotive cyber-physical systems (CPS) using a case study: a steer-by-wire (SBW) application over controller area network (CAN). The challenge is to embed both security and dependability over CAN while ensuring that the real-time constraints of the automotive CPS are not violated. Our approach enables early design feasibility analysis of automotive CPS by embedding essential security primitives (i.e., confidentiality, integrity, and authentication) over CAN subject to the real-time constraints imposed by the desired quality of service and behavioral reliability. Our method leverages multicore ECUs for providing fault tolerance by redundant multi-threading (RMT) and also further enhances RMT for quick error detection and correction. We quantify the error resilience of our approach and evaluate the interplay of performance, fault tolerance, security, and scalability for our SBW case study.

Security-Critical Energy-Aware Task Scheduling for Heterogeneous Real-Time MPSoCs in IoT

Internet of Things (IoT) devices, such as intelligent road side units and video-based detectors, are being deployed in emerging applications like sustainable and intelligent transportation systems. The primary obstacles against the development of these IoT devices are various security threats and huge energy consumption. In this article, we study the problem of scheduling tasks onto a heterogeneous multiprocessor system on a chip (MPSoC) deployed in IoT for optimizing quality of security under energy, real-time, and task precedence constraints. We first provide a mixed-integer linear programming (MILP) formulation for allocating and scheduling dependent tasks with energy and real-time constraints on a heterogeneous MPSoC system to maximize system quality of security. In order to efficiently solve the formulated MILP, we then propose an analysis-based two-stage scheme that determines the allocation, operating frequency, and security service of tasks to maximize system quality of security while satisfying the design constraints. We finally carry out extensive simulation experiments to validate our proposed two-stage scheme and MILP approach. Simulation results demonstrate that the proposed two-stage scheme outperforms a number of representative existing approaches in saving energy and improving system quality of security. The results also show that the proposed MILP approach can achieve the best performance and the proposed two-stage scheme has a close performance to the MILP approach.

Tool System for Testing Real-Time Constraints for Modular Computational System Configurations

The problem of testing real-time constraints for modular computational systems (MCSes) is exemplified by systems of integrated modular avionics. The requirements to the software for testing these constraints are formulated. An approach to modeling MCSes based on the mathematical apparatus of timed automata with suspended timers is described, and a tool system that implements this approach is presented. An experimental study is performed of the parameterized MCS model entering the developed tool system. The model is tested with the CAD IMA Scheduler used in Russian industry.

Internet of Things (IoT) Platform for Multi-Topic Messaging

IoT-based applications operate in a client–server architecture, which requires a specific communication protocol. This protocol is used to establish the client–server communication model, allowing all clients of the system to perform specific tasks through internet communications. Many data communication protocols for the Internet of Things are used by IoT platforms, including message queuing telemetry transport (MQTT), advanced message queuing protocol (AMQP), MQTT for sensor networks (MQTT-SN), data distribution service (DDS), constrained application protocol (CoAP), and simple object access protocol (SOAP). These protocols only support single-topic messaging. Thus, in this work, an IoT message protocol that supports multi-topic messaging is proposed. This protocol will add a simple “brain” for IoT platforms in order to realize an intelligent IoT architecture. Moreover, it will enhance the traffic throughput by reducing the overheads of messages and the delay of multi-topic messaging. Most current IoT applications depend on real-time systems. Therefore, an RTOS (real-time operating system) as a famous OS (operating system) is used for the embedded systems to provide the constraints of real-time features, as required by these real-time systems. Using RTOS for IoT applications adds important features to the system, including reliability. Many of the undertaken research works into IoT platforms have only focused on specific applications; they did not deal with the real-time constraints under a real-time system umbrella. In this work, the design of the multi-topic IoT protocol and platform is implemented for real-time systems and also for general-purpose applications; this platform depends on the proposed multi-topic communication protocol, which is implemented here to show its functionality and effectiveness over similar protocols.

MUACLP Modeling Unified Architecture of Cross Layer Parameters Strategy of Handoff for Next Generation Wireless Network

The Quality of Service and Quality of experience is always a goal of future generation wireless systems. A suitably designed handoff plays an important role in achieving these goals. The traditional methods of handoff for wireless systems pose challenges in handling the smooth transition in the heterogeneous architecture of future generation architecture. This paper induces a model for synchronization of seamless handoff for a mobile user from a unified access point of a large cell to the small cell. The study is carried out considering a novel analytical model for facilitating a better form of vertical handoff mechanism in order to facilitate communication over 5G networks. The modeling is carried out considering the efficiency of channel capacity as well as considering near real-time constraint of latency and throughput. The typical design assumption for the unified architecture of the model for the handoff is for future generation wireless network that includes various physical layer parameters. For an effective analysis, the study considers that the receiving node will be identified on the basis of the non-erroneous node as the idea is to increase the proper utilization of the network resources. The core agenda of the study will be to ensure that irrespective of any form of traffic intensity as well as variable resource availability, the proposed scheme should be able to offer an extensive communication with the seamless performance during handoff operation. The study outcome exhibits that the proposed system offers better vertical handoff performance in contrast to the existing system on various test cases.

Combined compression of multiple correlated data streams for online-diagnosis systems

Online fault-diagnosis is applied to various systems to enable an automatic monitoring and, if applicable, the recovery from faults to prevent the system from failing. For a sound decision on occurred faults, typically a large amount of sensor measurements and state variables has to be gathered, analyzed and evaluated in real-time. Due to the complexity and the nature of distributed systems all this data needs to be communicated among the network, which is an expensive affair in terms of communication resources and time. In this paper we present compression strategies that utilize the fact that many of these data streams are highly correlated and can be compressed simultaneously. Experimental results show that this can lead to better compression ratios compared to an individual compression of the data streams. Moreover, the algorithms support real-time constraints for time-triggered architectures and enable the data to be transmitted by means of shorter messages, leading to a reduced communication time and improved scheduling results. With an example data set we show that, depending on the parameters of the compression algorithm, more than one third of the bits (34.3%) in the data communication can be saved while only on about 0.2% of all data values a slight loss of accuracy occurs. This means 99.8% of the data values can be correctly delivered without any loss but with a significant reduction of bandwidth demands.

OpenCL implementation of a SLAM system on an SoC-FPGA

• We propose a full embedded implementation of a SLAM on a low cost SoC-FPGA. • Hardware/Software partitioning is done after the profiling results. • OpenCL is used for High Level Synthesis on the FPGA. • Implementations constraints are discussed and solutions are proposed. • Results shows an acceleration near 2 times compared to the software implementation. Simultaneous localization and mapping (SLAM) becomes a solved problem in terms of the algorithms. In order to use them in autonomous mobile robots, researches are focusing on reducing computation complexity and developing embedded systems with appropriate software and hardware architecture. In this work, we aim to perform a fully embedded implementation of a SLAM on an FPGA based SoC combining an embedded Hard Processor System (HPS) with an FPGA in the same chip. The followed implementation methodology is based on software profiling. The SLAM is profiled to perform an appropriate hardware/software partitioning and decide which part of the SLAM will be accelerated on the FPGA. The software is running on Linux on the HPS of the SoC-FPGA and the accelerated function is executed on the FPGA. The FPGA is programmed using OpenCL which is a high level language that can reduce significantly the developpement time. The results show that the SLAM algorithm implemented on SoC-FPGA is accelerated to nearly the twice comparing to the pure software implementation, while ensuring the real-time constraint

Linear Weakly Hard Real-Time Control Systems: Time- and Event-Triggered Stabilization

This article considers the stabilization of linear control systems under weakly hard real-time (WHRT) constraints. In the context of networked control systems, such WHRT constraints allow us to specify packet loss properties of a communication network over windows of a given length, e.g., at least n consecutive transmissions in every window of m consecutive transmission attempts are successful. Time- and event-triggered stabilization results are derived. The control design exploits a graph that captures the available information on possible packet losses. An algorithm for computing such a graph for given WHRT constraints is provided. Both stabilization results are illustrated and compared using a numerical example from literature.

REAL-TIME INTERACTIVE MECHANISM FOR LIVE STREAMING ON PEER-TO-PEER NETWORKS

Quality of live video streaming technology is based on quality of Experiences parameters (QoE). Approaching the peer-to-peer (P2P) or peer-assisted networks as a sympathetic solution is highly required, especially in light of its authentic scalability and its extremely low initial cost requirements. However, the design of robust, efficient, and performing P2P streaming systems remains a high challenge when real-time constraints are part of the quality of service (QoS), as in TV distribution or conferencing applications. One of the P2P main issues that affect the quality of streaming is the neighbor selection methodology. The proposed work presents an effective mesh-based neighbor selection approaches for video streaming – Uniform Peer Distribution Algorithm (UPDA) – based on QoS and QoE Parameters. UPDA shortens the latency to be ranging from 10 ms to 50 ms servicing up to 4000 online peers under failure / recovery tests. Simulation results demonstrate that the proposed UPDA achieves good performance in End-to End delay with a percentage of 10.4 % and packet delay variation about 2% compared to random neighbor selection method.

Bridging the gap between formal verification and schedulability analysis: The case of robotics

The challenges of deploying robots and autonomous vehicles call for further efforts to bridge the gap between the robotics, the real-time systems and the formal methods communities. Indeed, with robots being more and more involved in costly missions and contact with humans, a rigorous formal verification of their behavior in the presence of various real-time constraints is crucial. In order to increase our trust in its results, such verification should be carried out on models that are the closest possible to reality, and thus take into account hardware and OS specificities such as the number of cores provided by the robotic platform and the scheduling policy. In this paper, we propose a novel binary-search-inspired technique that allows to extend timed automata models of robotic specifications with dynamic-priority schedulers. Given a number of cores, the extended models can then be checked against various real-time and behav-ioral properties, including schedulability, within the same model checking framework. Our technique is implemented in an automatic translation from a robotic framework to UPPAAL, and evaluated on a real robotic case study, where it shows a significant gain in scalability as opposed to the counting technique used in the literature.

A guidance framework for synthesis of multi-core reconfigurable real-time systems

Nowadays, multi-core architectures are being more and more used in the design of real-time systems. Those systems react usually to their environment that requires to amend their behaviors by applying reconfiguration scenarios. This paper deals with multi-core reconfigurable real-time systems that should be adapted to their environment under real-time constraints. Meanwhile, their synthesis induces a heavy system code and time overhead due to a huge number of threads. The setting up of those systems involves many stages: i) definition of system functionalities, ii) generating of tasks, iii) placement and scheduling of tasks, and iv) generating system code. Correct transition among these steps has an impact on the final system implementation. Thus the need of a designer’s experience is definitely required. However, many problems related to design decisions can be caused due to the complexity of real-time analysis, scheduling, and placement. Those problems may conduct to infeasible implementations. The proposed approach presents a guidance framework to avoid these problems from specification to code generation. This framework is performed by mixed-integer linear programming. It aims to resolve a task partitioning/scheduling problem while optimizing some metrics. The viability of the proposed framework is illustrated by a case study and performance evaluation.