Soft Real Time Research Articles

IIoT Protocols for Edge/Fog and Cloud Computing in Industrial AI

Various industrial applications deal with high-frequency data. Traditionally, these systems have analyzed high-frequency data directly on the data source or at the commanding PLC. However, currently, Industry 4.0 technologies support new monitoring scenarios for high-frequency data monitoring where raw data is transmitted in soft-real time to an Edge/Fog or Cloud node for processing, enabling centralized computing. This demands efficient communication protocols capable of handling high-frequency, high-throughput data. This paper focuses on analyzing the performance of key IIoT (Industrial Internet of Things) messaging protocols—AMQP, MQTT, KAFKA, ZeroMQ, and OPCUA—to evaluate their suitability, in terms of latency and jitter, for transmitting high-frequency data within these new scenarios. The analysis reveals MQTT, AMQP, and ZeroMQ as top performers in Edge/Fog computing, while ZeroMQ exhibits the lowest latency and jitter in Cloud computing. Finally, a guideline for protocol selection is proposed, aiding industrial enterprises in protocol selection for specific AI use cases.

Development of Advanced Monitoring System in Real Time Environment

In this paper, the problem of providing a good computerized 
 monitoring system is discussed. As an application, Advanced 
 Monitoring System (AMS) is designed and built for this purpose. 
 AMS is an image processing, a soft real time system, which is 
 concerned with enhancement of the monitoring services with lower 
 cost and a high performance. It seems to be very broad area of 
 research as it relates to many important subjects of computer science. 
 The produced system has accomplished its intended functions and 
 services in trusted fashion.

Tardiness Bounds for Sporadic Gang Tasks Under Preemptive Global EDF Scheduling

Following the trend of increasing autonomy in cyber-physical systems, parallel embedded architectures have enabled devices to better handle the large streams of data and intensive computation required by such autonomous systems. However, while the explosion of highly-parallel platforms has seen a proportional growth in the number of applications/devices that utilize these platforms, the embedded systems community's understanding of how to build time-predictable, safety-critical systems with parallel platforms has not kept pace. As a well-motivated but challenging parallel scheduling model, gang scheduling requires all parallel threads of each parallel task to simultaneously execute in unison, which is in contrast to traditional, multi-threaded parallel scheduling, where a parallel task may spawn multiple threads, and each thread will be scheduled independently of other threads of the same task. While increasing research efforts on hard real-time (HRT) gang scheduling have recently been seen, the problem of gang scheduling in the context of soft real-time (SRT) systems, where provably bounded deadline tardiness can be tolerated, has hardly been studied yet. In this article, we derive and prove the first tardiness bounds for sporadic gang task systems under preemptive GEDF scheduling. A total utilization bound for SRT-schedulability is required for ensuring such tardiness bounds but it is shown to be tight with respect to the platform capacity and maximum parallelism-induced idleness. Furthermore, we also empirically evaluate the effects of different degrees of task parallelism upon the SRT-schedulability.

Statically optimal dynamic soft real-time semi-partitioned scheduling

Semi-partitioned scheduling is an approach to multiprocessor real-time scheduling where most tasks are fixed to processors, while a small subset of tasks is allowed to migrate. This approach offers reduced overhead compared to global scheduling, and can reduce processor capacity loss compared to partitioned scheduling. Prior work has resulted in a number of semi-partitioned scheduling algorithms, but their correctness typically hinges on a complex intertwining of offline task assignment and online execution. This brittleness has resulted in few proposed semi-partitioned scheduling algorithms that support dynamic task systems, where tasks may join or leave the system at runtime, and few that are optimal in any sense. This paper introduces EDF-sc, the first semi-partitioned scheduling algorithm that is optimal for scheduling (static) soft real-time (SRT) sporadic task systems and allows tasks to dynamically join and leave. The SRT notion of optimality provided by EDF-sc requires deadline tardiness to be bounded for any task system that does not cause over-utilization. In the event that all tasks can be assigned as fixed, EDF-sc behaves exactly as partitioned EDF. Heuristics are provided that give EDF-sc the novel ability to stabilize the workload to approach the partitioned case as tasks join and leave the system.

The method of real-time acoustic measurement of dynamical changes in the speaker’s emotional state

In this paper we consider the issues in implementations of interactive voice response systems with remote access. Their efficiency can be improved by automatically analyzing changes in the user's emotional state during the dialogue. In order to measure the indicator of the dynamics of the emotional statein real time, it is proposed to use the effect of sound (phonetic) variability of the user's speech at short intervals (fractions of a minute). The novel method of acoustic measurements in conditions of small samples has been developed based on information-theoretic approach by using a scale-invariant gain-optimized dissimilarity measure of the speech signals in the frequency domain. An example of its practical implementation in soft real time is considered. It is shown that the delay in obtaining the measurement results does not exceed in this case 10–20 sec. The experimental results confirmed the high speed of the proposed method and its sensitivity to changes in the emotional state under the influence of external noise. The proposed method can be used for automated quality control of voice samples of users in unified biometric systems, as well as to improve safety by non-contact identification of potentially dangerous persons with short-term psycho-emotional disorders.

Maximizing Utilization and Minimizing Migration in Thermal-Aware Energy-Efficient Real-Time Multiprocessor Scheduling

This work proposes CAlECs, a clustered scheduling system for MPSoCs subject to thermal and energy constraints. It calculates off-line a cyclic executive honoring temporal and thermal constraints, for a hard real-time (HRT) task set at minimum frequency to reduce consumed energy, minimizing context switches and migrations. It also provides an on-line controller able to manage system and task parametric variations and soft real-time (SRT) tasks, always meeting the HRT task set constraints and the system thermal bound. CAlECS maximizes CPU utilization to help avoid overprovisioning contributing to a low SWaP factor. Its modular design allows the utilization of different modeling and scheduling approaches, and makes the off-line and on-line components independent from each other to better suit the requirements of a specific system. We experimentally show that the cyclic executive provided by CAlECS for HRT task sets outperforms RUN, a reference off-line algorithm in terms of optimal number of context switches.

Traffic-type Assignment for TSN-based Mixed-criticality Cyber-physical Systems

This article focuses on mixed-criticality applications with functions that have different timing requirements, i.e., hard real-time (HRT), soft real-time (SRT), and functions that are not time-critical (NC). The applications are implemented on distributed cyber-physical systems that use IEEE Time-sensitive Networking (TSN). TSN is the product of an IEEE effort to bring deterministic real-time capabilities to IEEE 802.3 Ethernet. TSN supports the convergence of multiple traffic types, i.e., critical, real-time, and regular “best-effort” traffic within a single network: Time-triggered (TT), where the messages are transmitted based on static schedule tables, Audio-video Bridging (AVB), for dynamically scheduled messages with a guaranteed bandwidth and bounded delays, and Best Effort (BE), for which no timing guarantees are provided. The HRT messages have deadlines, whereas we capture the quality-of-service for the SRT messages using “utility functions.” Given the network topology, the set of application messages, including their routing, and the set of available AVB classes, we are interested in determining the traffic type of each message, such that all the HRT messages are schedulable and the total utility for the SRT messages is maximized. We propose a Tabu Search-based metaheuristic to solve this optimization problem. The proposed proof-of-concept tool has been evaluated using several benchmarks, including two realistic test cases.

An Adaptive Emergency First Intelligent Scheduling Algorithm for Efficient Task Management and Scheduling in Hybrid of Hard Real-Time and Soft Real-Time Embedded IoT Systems

Industrial revolution is advancing, and the augmented role of autonomous technology and embedded Internet of Things (IoT) systems is at its vanguard. In autonomous technology, real-time systems and real-time computing are of core importance. It is crucial for embedded IoT devices to respond in real-time; along with fulfilling all the constraints. Many combinations for existing approaches have been proposed with different trade-offs between the resources constraints and tasks dropping rate. Hence, it highlights the significance of a task scheduler which not only takes care of complex nature task input; but also maximizes the CPU throughput. A complex nature task input is when combinations of hard real-time tasks and soft real-time tasks, with different priorities and urgency measures, arrive at the scheduler. In this work, we propose a custom tailored adaptive and intelligent scheduling algorithm for the efficient execution and management of hard and soft real time tasks in embedded IoT systems. The proposed scheduling algorithm aims to distribute the CPU resources fairly to the possibly starving, in overloaded cases, soft real-time tasks while focusing on the execution of high priority hard real-time tasks as its primary objective. The proposal is achieved with the help of two intelligent measures; Urgency Measure (UM) and Failure Measure (FM). The proposed mechanism reduces the rate of tasks missed and the rate of tasks starved, by utilizing the free CPU units for maximum CPU utilization and quick response times. We have performed comparisons of our proposed scheme based on performance metrics as percentage of task instances missed, number of tasks with missed instances, and tasks starvation rate to evaluate the CPU utilization. We first compare our proposed approach with multiple traditional and combined scheduling approaches, and then we evaluate the effect of intelligent modules by comparing the intelligent FEF with non-intelligent FEF. We also evaluate the proposed algorithm in contrast to the most commonly-used hybrid scheduling scheme in embedded systems. The results show that the proposed algorithm out performs the other algorithms, by significantly reducing the task starvation rate and increasing the CPU utilization.

A multi-agent framework for cloud-based management of collaborative robots

This article presents a cloud-based multi-agent architecture for the intelligent management of aerial robots in a disaster response situation. In a disaster scenario, a team of highly maneuverable quadcopters is deployed to carry out surveillance and decision support in disaster-affected areas. In Pakistan, such events usually result from sudden unpredictable calamities such as earthquakes. The aim of this work is to develop a robust mechanism to autonomously manage and react to sensory inputs received in soft real time from an unstructured environment. The immediate goal is to locate the maximum number of trapped, injured people within a large area, and help first responders plan rescue activities accordingly. To evaluate the proposed framework, a number of simulations are carried out using GAMA platform to emulate a disaster environment. Subsequently, algorithms are developed to survey an affected geographical area through the use of small flight drones. The key challenges in this work are related to the combination of the domains of multi-agent technology, robotics, and cloud computing for effectively bridging the cyber world with the physical world. Therefore, the proposed work demonstrates the effective use of a limited number of drones to capture inputs from a disaster situation in the physical world, and such inputs are used for timely planning of rescue efforts. The results of fixed resource assignment are compared with the proposed reactive assignment strategy, and it clearly shows a significant improvement in terms of resource usage compared to traditional approach.

Energy efficiency of soft real time service in hyper-cellular network with users' behavior prediction

Many users require the same content, such as breaking news, newly released movies, and favorites videos. The same content is transmitted multiple times across wireless cellular network because current wireless network is designed to transmit data based on user requests, which exhausts wireless bandwidth and incurs high energy costs. This paper presents a soft real time service frame that is user-centric and content-aware. The frame broadcasts popular content to user equipment before users ask for it to achieve higher network energy efficiency and enhance quality of service. We propose a popularity prediction scheme, a target user group prediction scheme, a dynamic broadcast area calculation scheme, and a content pushing scheme. We build a system model to analyze key parameters in the service framework. As shown in simulations, the wireless network energy efficiency is improved 2 times with news contents in public cellular scenarios, and is improved 50 times with online study contents in campus cellular scenarios.

FPGA Based Pattern Generation and Synchonization for High Speed Structured Light 3D Camera

Recently, structured light 3D imaging devices have gained a keen attention due to their potential applications to robotics, industrial manufacturing and medical imaging. Most of these applications require high 3D precision yet high speed in image capturing for hard and/or soft real time environments. This paper presents a method of high speed image capturing for structured light 3D imaging sensors with FPGA based structured light pattern generation and projector-camera synchronization. Suggested setup reduces the time for pattern projection and camera triggering to 16msec from 100msec that should be required by conventional methods.

Facilitating agronomic mobile data management in a crop field

One of the enterprises that has heavily adopted mobile technology today is the agriculture sector. Farmers and agro specialists can use their smartphones and tablets to easily communicate, advertise goods and services, as well as access agronomic data in soft-real time. The palpable barrier however is the mode of communication of these mobile devices, which is the use of wireless channels orthodoxly. As a result, agronomic mobile applications can experience bandwidth fluctuations and inefficient propagation of data in mobile cloud computing architectures. To address this issue, some existing works proposed a P2P synchronisation technique that is based on three algorithms namely: bloom filters, whole state data transfer, and exchange of deltas only. In this paper, we have totally redesigned and replaced the P2P synchronisations with the data-centric publish-subscribe model that improves the efficiency of the agronomic data synchronisation. As part of this research, a real-world agronomic mobile app is developed and tested where empirical evaluations show that the data-centric publish-subscribe approach is better in terms of latency optimisation in agronomic data management. The well-researched app is called FlagThis and it is available in the App store for free download by farmers and agro-experts.

Facilitating agronomic mobile data management in a crop field

One of the enterprises that has heavily adopted mobile technology today is the agriculture sector. Farmers and agro specialists can use their smartphones and tablets to easily communicate, advertise goods and services, as well as access agronomic data in soft-real time. The palpable barrier however is the mode of communication of these mobile devices, which is the use of wireless channels orthodoxly. As a result, agronomic mobile applications can experience bandwidth fluctuations and inefficient propagation of data in mobile cloud computing architectures. To address this issue, some existing works proposed a P2P synchronisation technique that is based on three algorithms namely: bloom filters, whole state data transfer, and exchange of deltas only. In this paper, we have totally redesigned and replaced the P2P synchronisations with the data-centric publish-subscribe model that improves the efficiency of the agronomic data synchronisation. As part of this research, a real-world agronomic mobile app is developed and tested where empirical evaluations show that the data-centric publish-subscribe approach is better in terms of latency optimisation in agronomic data management. The well-researched app is called FlagThis and it is available in the App store for free download by farmers and agro-experts.

Middleware for mobile medical data management with minimal latency

A promise of mHealth is its capacity to facilitate the consumption of Electronic Health Record (EHR) data using mobile devices, which is central to promoting remote healthcare delivery. Our ongoing project, called SOPHRA, in collaboration with the City Hospital in Saskatoon, Canada, focuses on supporting care providers (e.g., physicians) in the collection/recording of medical data from patients remotely. A major challenge that needs to be addressed is the potential loss of communication between the mobile-clients and the health information system (HIS) during the transfer of the electronic health record (EHR). Overcoming this challenge will foster soft-real time medical data exchanges. However, the issue of communication loss can be as a result of the over-reliance on wireless networks such as Wi-Fi, which can sometimes be unstable. Thus, the goal of this work is to propose a mobile health (mHealth) architectural environment that can exploit the limited available bandwidth during the mobile medical data transfer. A middleware is proposed with the capability of managing different states of the medical data as the physicians updates the EHR. The work details the employment of the Bernoulli model as a means of determining and controlling different updates of the mHealth data for effective propagation.

Traffic class assignment for mixed-criticality frames in TTEthernet

In this paper we are interested in mixed-criticality applications, which have functions with different timing requirements, i.e., hard real-time (HRT), soft real-time (SRT) and functions that are not time-critical (NC). The applications are implemented on distributed architectures that use the TTEthernet protocol for communication. TTEthernet supports three traffic classes: Time-Triggered (TT), where frames are transmitted based on static schedule tables; Rate Constrained (RC), for dynamic frames with a guaranteed bandwidth and bounded delays; and Best Effort (BE), for which no timing guarantees are provided. HRT messages have deadlines, whereas for SRT messages we capture the quality-of-service using "utility functions". Given the network topology, the set of application messages and their routing, we are interested to determine the traffic class of each message, such that all HRT messages are schedulable and the total utility for SRT messages is maximized. For the TT frames we decide their schedule tables, and for the RC frames we decide their bandwidth allocation. We propose aTabu Search-based metaheuristic to solve this optimization problem. The proposed approach has been evaluated using several benchmarks, including two realistic test cases.

Mobile Medical Data Synchronization on Cloud-Powered Middleware Platform

Our research focuses on supporting patients (persons with mild hemophilia) to self-manage injuries in cases of minor incidents. This involves bi-directional exchanges of the Electronic Health Record (EHR) between patients and the care facility. However, mobile devices rely on wireless communication channels (e.g., Wi-Fi, 3.5/4G, etc.) to transmit data and these channels can experience sporadic disconnections due to bandwidth fluctuations and user mobility. As a result, the collected medical data may not be transmitted back to the Health Information system (HIS) in soft-real time. Further, when new updates are submitted by the patients who are disconnected (remote and offline), synchronization can fail; leading to decision-making based on outdated or incomplete information. In this work, we focus on how to ensure efficient synchronization of the EHR in unreliable mobile environments. This work took advantage of the ubiquitous nature of mobile cloud computing and proposes a middleware, which facilitates efficient process of medical data synchronization, and with minimal latency. The work details state-of-the-art architecture of the cloud-based middleware that is built and tested for real-world use following four methodologies namely: reflective, tuple space, context-awareness, and event-based.

Performance Enhancement with Multipath Routing in Term of Bandwidth and Security on MANETs

In Mobile Ad hoc Networks (MANETs) providing performance enhancement in term of bandwidth, security for soft real time processing services is very difficult. All routing protocols are intended for operation in a trusted environment in which all nodes are honest, and they do not consider the disruptions that can be caused by a malicious attacker sending arbitrary (e.g. forged) routing packets. In this paper, we have proposed a performance enhancement with multipath routing in term of bandwidth and security on MANETs that reactively collects link-state information from source to destination in order to dynamically construct a flow network. The QoS enabled multipath routes between source and destination has been detected by destination node under the CDMA-over-TDMA channel model of MAC layer, which collectively satisfy required bandwidth and security services during route discovery process. For secure route discovery, we use hashing chain and onion encryption an asymmetric key cryptography for authenticating source to destination route, so that no modification is done during en-route process.

PCA Based Data Reconciliation in Soft Sensor Development - Application for Melt Flow Index Estimation

Melt flow index (MFI) is a very important property of thermoplastic polymers. Laboratory measurements follow standard methods (ASTM D1238 or ISO 1133) to determine MFI and give accurate values, but these measurements are available only in 2-4 hour sampling intervals. Using soft sensors real time estimation of MFI is available for process control and monitoring. When detailed knowledge about the process is not available, data-driven soft sensors can be applied. In this case historical process data are used to build statistical models to determine the relationship between inputs and outputs. Since these methods are based on measurements, the performance of soft sensor depends on the quality of data. Measurements are always affected by errors so pre-processing of data should be necessary. The measurement noise and process variable can be correlated with each other so one opportunity to improve measurement accuracy is using multivariate statistical methods (PCA, PLS). Statistical methods can be improved when phenomenological knowledge is taken into account (e.g. balance equations). The aim of the presented research is to propose a methodology to support the data-driven development of process monitoring systems. We developed a method which improves the effectiveness of data based MFI soft sensor. This method includes advantages of a priori knowledge based models and data-driven multivariate statistical process monitoring tools. As a case study we developed a soft-sensor to estimate MFI of the products of industrial polypropylene reactor at TVK Plc. The proposed method is able to detect undesirable operation states and it can be used for fault detection.

Improvised Optimum Multilevel Dynamic Round Robin Algorithm for Optimizing CPU Scheduling

scheduling has strong effect on resource utilization as well as overall performance of the system. In order to simulate the behavior of multiple jobs in a multiprogramming computer system needs to be specified. The most important aspect of job scheduling is the ability to create a multi-tasking environment. The intention should be allowed as many as possible running processes at all time in order to make best use of CPU. Round Robin algorithm performs optimally in timeshared systems, but it is not suitable for soft real time systems, because it gives more number of context switches, larger waiting time and larger response time. The main objective of this paper is to improve the previous OMDRRS with calculates intelligent time slice and warps after every round of execution and assumed that all the processes were come at randomly as well as all the processes have priority. In order to simulate the behavior of various CPU scheduling algorithms and to improve Round Robin scheduling algorithm using dynamic time slice concept, we purpose new improved CPU scheduling algorithm called Optimum Dynamic Round Robin Scheduling (OMDRR). Our experimental results show that our proposed algorithm performs better in terms of reducing the number of context switch, average waiting time and average turnaround time.

Guest editorial: large-scale Web virtualized environment

Virtualization-based paradigms like cloud computing provide a powerful model that allows to simply and securely connect users to information resources on-demand and anywhere. Modern Web applications are increasingly hosted in a cloud-computing infrastructure, able to communicate and interact with external mobile devices. Such applications should dynamically scale in response to changes in the workload to guarantee service level agreements. This trend brought about a number of new modeling, design and implementation issues for Web applications. Solving these key issues requires both first-class theoretical research and careful experimental evaluation to show that model predictions are faithful to observed application behavior. Results need to be validated in the context of hybrid provisioning on multiple, heterogeneous virtualized platforms and cloud stacks. In particular, Web-based information systems rely on models and tools for representing, integrating and linking data or resources from heterogeneous sources or platforms. This special issue received many submissions from researchers and practitioners working on Web environments analyzing the impact of virtualization on them. After two rounds of review, eventually eight high quality papers were chosen. The paper titled “MUBaaS: Mobile Ubiquitous Brokerage as a Service” presents a cloudbased application middleware, called Mobile Ubiquitous Brokerage as a Service (MUBaaS), which enables n-devices of a user to access multiple cloud services endpoints in soft-real time. This is achieved by proposing distributed brokers that coordinates the transactions of the user while taking load balancing into account. Pilot evaluations of the proposed architecture prove real-time application synchronization with reasonable scalability. The use of multiple World Wide Web (2016) 19:1–3 DOI 10.1007/s11280-015-0366-9