Self-adaptive Systems Research Articles

Disinfectant sodium dichloroisocyanurate synergistically strengthened sludge acidogenic process and pathogens inactivation: Targeted upregulation of functional microorganisms and metabolic traits via self-adaptation

Harmless and resourceful treatment of waste activated sludge (WAS) have been the crucial goal for building environmental-friendly and sustainable society, while the synergistic realization approach is currently limited. This work skillfully utilized the disinfectant sodium dichloroisocyanurate (NaDCC) to simultaneously achieve the pathogenic potential inactivation (decreased by 60.1 %) and efficient volatile fatty acids (VFAs) recovery (increased by 221.9 %) during WAS anaerobic fermentation in rather cost-effective way (Chemicals costs:0.4 USD/kg VFAs versus products benefits: 2.68 USD/kg chemical). Mechanistic analysis revealed that the C=O and NCl bonds in NaDCC could spontaneously absorb sludge (binding energy -4.9 kJ/mol), and then caused the sludge disintegration and organic substrates release for microbial utilization due to the oxidizability of NaDCC. The disruption of sludge structure along with the increase of bioavailable fermentation substrates contributed to the selectively regulation of microbial community via enriching VFAs-forming microorganisms (e.g., Pseudomonas and Streptomyces) and reducing VFAs-consuming microorganisms, especially aceticlastic methanogens (e.g., Methanothrix and Methanospirillum). Correspondingly, the metabolic functions of membrane transport, substrate metabolism, pyruvate metabolism, and fatty acid biosynthesis locating in the central pathway of VFAs production were all upregulated while the methanogenic step was inhibited (especially acetate-type methanogenic pathway). Further exploration unveiled that for those enriched functional anaerobes were capable to activate the self-adaptive systems of DNA replication, SOS response, oxidative stress defense, efflux pump, and energy metabolism to counteract the unfavorable NaDCC stress and maintain high microbial activities for efficient VFAs yields. This study would provide a novel strategy for synergistic realization of harmless and resourceful treatment of WAS, and identify the interrelations between microbial metabolic regulations and adaptive responses.

Adaptive Test Suits Generation for Self-Adaptive Systems Using SPEA2 Algorithm

Self-adaptive systems are capable of reconfiguring themselves while in use to reduce the risks forced by environments for which they may not have been specifically designed. Runtime validation techniques are required because complex self-adaptive systems must consistently offer acceptable behavior for important services. The runtime testing can offer further confidence that a self-adaptive system will continue to act as intended even when operating in unknowable circumstances. This article introduces an evolutionary framework that supports adaptive testing for self-adaptive systems. The objective is to ensure that the adaptive systems continue to operate following its requirements and that both test plans and test cases continuously stay relevant to shifting operational conditions. The proposed approach using the Strength Pareto Evolutionary Algorithm 2 (SPEA2) algorithm facilitates both the execution and adaptation of runtime testing operations.

Towards a Research Agenda for Understanding and Managing Uncertainty in Self-Adaptive Systems

Despite considerable research efforts on handling uncertainty in self-adaptive systems, a comprehensive understanding of the precise nature of uncertainty is still lacking. This paper summarises the findings of the 2023 Bertinoro Seminar on Uncertainty in Self- Adaptive Systems, which aimed at thoroughly investigating the notion of uncertainty, and outlining open challenges associated with its handling in self-adaptive systems. The seminar discussions were centered around five core topics: (1) agile end-toend handling of uncertainties in goal-oriented self-adaptive systems, (2) managing uncertainty risks for self-adaptive systems, (3) uncertainty propagation and interaction, (4) uncertainty in self-adaptive machine learning systems, and (5) human empowerment under uncertainty. Building on the insights from these discussions, we propose a research agenda listing key open challenges, and a possible way forward for addressing them in the coming years.

THE MODEL OF THE FUNCTIONAL EVOLVING FOR MULTIFUNCTIONAL AUTOMATION SYSTEMS

The article considers the problem of modeling the functional evolving of multifunctional automated systems with dedicated subsystems. It is shown that the cyber-physical approach to the organization of automated systems allows us to move on to the creation of self-adaptive or self-developing multifunctional systems. At the same time, the management of adaptation processes requires embedding a model of a controlled object to predict the trajectories of changes in the suitability of the system. The model proposed in the paper is based on a self-developing three-component functional component (service, technical subsystem and control loop) and additionally takes into account the processes of degradation and recovery. Based on the values of the parameters of the evolving processes, the change in the functional suitability of the system as a whole is calculated. The influence of changes in the expectation value and the magnitude of the intensity of changes in the functional purpose of functional components, as well as the recovery time, is investigated. With a span of less than 0,1, the uneven loading of the modernization process control system increases. The critical values of the recovery time parameters are determined at which the full use of the control system capabilities is achieved, which leads to a sharp decrease in the coefficient of functional suitability of the system with an increase in these indicators. It is shown that the presence of interference when diagnosing the need for modernization leads to a significant decrease in the potential effect.

Self-aware Optimization of Adaptation Planning Strategies

In today’s world, circumstances, processes, and requirements for software systems are becoming increasingly complex. To operate properly in such dynamic environments, software systems must adapt to these changes, which has led to the research area of Self-Adaptive Systems (SAS). Platooning is one example of adaptive systems in Intelligent Transportation Systems, which is the ability of vehicles to travel with close inter-vehicle distances. This technology leads to an increase in road throughput and safety, which directly addresses the increased infrastructure needs due to increased traffic on the roads. However, the No-Free-Lunch theorem states that the performance of one adaptation planning strategy is not necessarily transferable to other problems. Moreover, especially in the field of SAS, the selection of the most appropriate strategy depends on the current situation of the system. In this article, we address the problem of self-aware optimization of adaptation planning strategies by designing a framework that includes situation detection, strategy selection, and parameter optimization of the selected strategies. We apply our approach on the case study platooning coordination and evaluate the performance of the proposed framework.

Design and Power Generation Characteristics Analysis of a Self-adaptive Thermoelectric Power Generation System Based on LTspice

Design and Power Generation Characteristics Analysis of a Self-adaptive Thermoelectric Power Generation System Based on LTspice

Integrating non-intrusive load monitoring based on graph-to-point learning into a self-adaptive home energy management system

Non-intrusive load monitoring (NILM) can extract the detailed consumption of target appliances from mains reading of a building, which is of great significance for consumers to optimize their electricity consumption and participate in demand response programs. Due to the fact that appliances with multiple states and complex working patterns often bring the most challenging obstacles in energy disaggregation, conventional deep learning methods tend to lead to unsatisfactory disaggregation results. Hence, a novel graph-to-point learning is proposed, in which residual graph convolutional network (ResGCN) and dual attention bidirectional long short-term memory (DABiLSTM) are combined to inherently capture the characteristics of time dependency. Then, accurate NILM results are integrated into a user-centric and self-adaptive home energy management system (HEMS), where a discrete Bayesian network for each appliance is adopted to automatically describe the end-users’ consumption behaviors. Lastly, the proposed algorithm is compared with state-of-the-art methods based on REFIT and REDD datasets for performance validation. Experiments results show that compared with traditional HEMS, NILM-based HEMS can markedly reduce operation cost by 53% and improve the overall consumer comfort by 31%.

Artificial Neural Networks in Membrane Bioreactors: A Comprehensive Review—Overcoming Challenges and Future Perspectives

Among different biological methods used for advanced wastewater treatment, membrane bioreactors have demonstrated superior efficiency due to their hybrid nature, combining biological and physical processes. However, their efficient operation and control remain challenging due to their complexity. This comprehensive review summarizes the potential of artificial neural networks (ANNs) to monitor, simulate, optimize, and control these systems. ANNs show a unique ability to reveal and simulate complex relationships of dynamic systems such as MBRs, allowing for process optimization and fault detection. This early warning system leads to increased reliability and performance. Integrating ANNs with advanced algorithms and implementing Internet of Things (IoT) devices and new-generation sensors has the potential to transform the advanced wastewater treatment landscape towards the development of smart, self-adaptive systems. Nevertheless, several challenges must be addressed, including the need for high-quality and large-quantity data, human resource training, and integration into existing control system facilities. Since the demand for advanced water treatment and water reuse will continue to expand, proper implementation of ANNs, combined with other AI tools, is an exciting strategy toward the development of integrated and efficient advanced water treatment schemes.

Performance Monitoring of Hybrid All-Optical Fiber/FSO Communication Systems

The demand for network capacity has increased due to the introduction of new digital applications and services, which rely heavily on optical communication networks. While fiber networks serve as the optical networks’ backbone, deploying fiber in certain scenarios is not feasible, making it necessary to use other technologies conjointly. A hybrid all-optical fiber/free space optic (FSO) link is proposed to avoid such a challenge. The all-optical system avoids using electronics that have limited bandwidth. Hence, it supports high-capacity communication. However, the all-optical system comes with challenges arising from fiber and FSO channel impairments. To monitor the amount and type of distortion in the optical channel, machine learning (ML) techniques are exploited. In this work, Gaussian process regression (GPR) is utilized as an ML technique to predict three main channel impairments that arise in the hybrid all-optical fiber/FSO channels, which are turbulence, optical signal-to-noise ratio (OSNR), and chromatic dispersion (CD). The model’s performance is evaluated using boxplot graphs, root mean square error (RMSE) metric, and R-squared metric. The results indicate that the model can predict the various impairments with high accuracy, except under strong amplified spontaneous emission (ASE) noise, where the model demonstrated lower accuracy in predicting light turbulence parameters. The proposed approach provides a self-aware and self-adaptive communication system and can optimize network resources in the future.

A reinforcement learning-based approach for online optimal control of self-adaptive real-time systems

A reinforcement learning-based approach for online optimal control of self-adaptive real-time systems

Designing of Real-time Communication Method to Monitor Water Quality using WSN Based on IOT

Data accuracy has always been the essential and foremost requirement of any communication. A Real-time Water quality-monitoring system (WQMS) needs high-level data accuracy to process the water perfectly for the desired usage in any specific purpose. Exponential growth in man-made processes, human activities, Industrialization, and economic growth along with depleting safe water resources has made the water pollution issue a foremost threat to human survival and human civilisation as a whole. The Conventional WQMS lacks in providing data accuracy while testing and analysing the water samples at sites due to improper data transmission, human intervention, instrument node working and calibration issues. Therefore, monitoring the quality of the water is essential with a prime focus on data accuracy through proper testing, data analysis and data transmission methods to provide real-time data accuracy. In this research work, an IOT-based wireless sensor network (WSN) is proposed that uses mesh networking to connect the sensor nodes and message queuing telemetry transfer (MQTT) protocol to send the acquired data to a cloud server ADDA Fruit IO. ESP32 standalone microcontroller with in-built Wi-Fi is used as a transceiver on sensor nodes and master nodes. As far as sensor and master node power is concerned, a self-adapting power generation system is incorporated using solar power and water energy harvesting techniques. The sensor nodes are calibrated as per WHO standards using deionized water and buffer capsules. Five random samples are collected from river water, pond water, Borewell water, R.O water and Municipal committee water to analyse the proposed system’s accuracy. The accuracy test and analysis is done using statistical tools on water sample measurements by the proposed sensor node, and the same is compared with the actual certified instrument, hardware manual-based measurement, Laboratory value check and transmitted value on Cloud Server. The proposed WQMS is designed to measure various WQM parameters i.e. Total Dissolve Solid (TDS), pH, Temperature and turbidity and to ensure data accuracy.

A framework for AI-based self-adaptive cyber-physical process systems

AbstractDigital transformation is both an opportunity and a challenge. To take advantage of this opportunity for humans and the environment, the transformation process must be understood as a design process that affects almost all areas of life. In this paper, we investigate AI-Based Self-Adaptive Cyber-Physical Process Systems (AI-CPPS) as an extension of the traditional CPS view. As contribution, we present a framework that addresses challenges that arise from recent literature. The aim of the AI-CPPS framework is to enable an adaptive integration of IoT environments with higher-level process-oriented systems. In addition, the framework integrates humans as actors into the system, which is often neglected by recent related approaches. The framework consists of three layers, i.e., processes, semantic modeling, and systems and actors, and we describe for each layer challenges and solution outlines for application. We also address the requirement to enable the integration of new networked devices under the premise of a targeted process that is optimally designed for humans, while profitably integrating AI and IoT. It is expected that AI-CPPS can contribute significantly to increasing sustainability and quality of life and offer solutions to pressing problems such as environmental protection, mobility, or demographic change. Thus, it is all the more important that the systems themselves do not become a driver of resource consumption.

Modeling variability in self-adapting robotic systems

Autonomous robots operating in everyday environments, such as hospitals, private houses, and public roads, are context-aware self-adaptive systems, i.e. they exploit knowledge about their resources and the environment to trigger runtime adaptation, so that they exhibit a behavior adequate to the current context. For these systems, context-aware self-adaptation requires to design the robot control application as a dynamically reconfigurable software architecture and to specify the adaptation logic for reconfiguring its variable aspects (e.g. the modules that implement various obstacle detection algorithms or control different distance sensors) according to specific criteria (e.g. enhancing robustness against variable illumination conditions). Despite self-adaptation is an intrinsic capability of autonomous robots, ad-hoc approaches are used in practice to design reconfigurable robot architectures. In order to enhance system maintainability, the control logic and the adaptation logic should be loosely coupled. For this purpose, the adaptation logic should be defined against an explicit representation of software variability in the robot control architecture. In this paper we propose a modeling approach, which consists in explicitly representing robot software variability with the MARTE::ARM-Variability metamodel, which has been designed as an extension of the UML MARTE profile. We evaluate the applicability of the proposed approach by exemplifying the software architecture design of a robot navigation framework and by analyzing the support provided by the ROS infrastructure for runtime reconfiguration of its variable aspects.

SMACS: A framework for formal verification of complex adaptive systems

Abstract Self-adaptive systems (SASs) have the capability to evaluate and change their behavior according to changes occurring in the environment. Research in this field is being held since mid-60, and over the last decade, the importance of self-adaptivity is being increased. In the proposed research, colored petri nets (CPN) formal language is being used to model self-adaptive multiagent system. CPN is increasingly used to model self-adaptive complex concurrent systems due to its flexible formal specification and formal verification behavior. CPN being visually more expressive than simple, Petri Nets enable diverse modeling approaches and provides a richer framework for such a complex formalism. The main goal of this research is to apply self-adaptive multi-agent concurrent system (SMACS) for complex architectures. In our previous research, the SMACS framework is proposed and verified through traffic monitoring system. All agents of SMACS are also known as intelligent agents due to their self-adaptation behavior. Due to decentralized approach in this framework, each agent will intelligently adapt its behavior in the environment and send updates to other agents. In this research, we are choosing smart computer lab (SCL) as a case study. For internal structure of each agent modal, μ \mu -calculus will be used, and then a model checker TAPAs: a tool for the analysis of process algebras will be applied to verify these properties. CPN-based state space analysis will also be done to verify the behavioral properties of the model. The general objective of the proposed system is to maximize the utility generated over some predetermined time horizon.

Provenance-Based Trust-Aware Requirements Engineering Framework for Self-Adaptive Systems

With the development of artificial intelligence technology, systems that can actively adapt to their surroundings and cooperate with other systems have become increasingly important. One of the most important factors to consider during the process of cooperation among systems is trust. Trust is a social concept that assumes that cooperation with an object will produce positive results in the direction we intend. Our objectives are to propose a method for defining trust during the requirements engineering phase in the process of developing self-adaptive systems and to define the trust evidence models required to evaluate the defined trust at runtime. To achieve this objective, we propose in this study a provenance-based trust-aware requirement engineering framework for self-adaptive systems. The framework helps system engineers derive the user’s requirements as a trust-aware goal model through analysis of the trust concept in the requirements engineering process. We also propose a provenance-based trust evidence model to evaluate trust and provide a method for defining this model for the target domain. Through the proposed framework, a system engineer can treat trust as a factor emerging from the requirements engineering phase for the self-adaptive system and understand the factors affecting trust using the standardized format.

A systematic review on security and safety of self-adaptive systems

Context:Cyber–physical systems (CPS) are increasingly self-adaptive, i.e. they have the ability to introspect and change their behavior. This self-adaptation process must be considered when modeling the safety and security aspects of the system. Objective:This study collects and compares security attacks and safety hazards on self-adaptive systems (SAS) described in the literature. In addition, mitigation and treatment strategies, as well as the modeling and analysis approaches, are investigated. Method:We conducted a systematic literature review on 21 selected papers. The selection process included a database search on four scientific databases using a common search string (1430 papers), forward and backward snowballing (1402 papers), and filtering the results based on predefined inclusion and exclusion criteria. The coding scheme to analyze the content of the papers was obtained through research questions, existing domain-specific taxonomies, and open coding. Results:Safety and security are not jointly modeled in the context of self-adaptive systems. The adaptation process is often not considered in the attack and hazard analysis due to naïve assumptions and modeling. The proposed approaches are mostly verified and validated through simulation often using simple use cases and scenarios. Conclusion:A thorough and joint modeling approach for safety and security in self-adaptive systems is still an open challenge that needs to be addressed. Further work is needed to address the gap between safety and security modeling in self-adaptive systems.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Autonomous Self-Adaptive and Self-Aware Optical Wireless Communication Systems.

The future age of optical networks demands autonomous functions to optimize available resources. With autonomy, the communication network should be able to learn and adapt to the dynamic environment. Among the different autonomous tasks, this work considers building self-adaptive and self-awareness-free space optic (FSO) networks by exploiting advances in artificial intelligence. In this regard, we study the use of machine learning (ML) techniques to build self-adaptive and self-awareness FSO systems capable of classifying the modulation format/baud rate and predicting the number of channel impairments. The study considers four modulation formats and four baud rates applicable in current commercial FSO systems. Moreover, two main channel impairments are considered. The results show that the proposed ML algorithm is capable of achieving 100% classification accuracy for the considered modulation formats/baud rates even under harsh channel conditions. Moreover, the prediction accuracy of the channel impairments ranges between 71% and 100% depending on the predicted parameter type and channel conditions.

Design and Simulation-Based Optimization of an Intelligent Autonomous Cruise Control System

Significant progress has recently been made in transportation automation to alleviate human faults in traffic flow. Recent breakthroughs in artificial intelligence have provided justification for replacing human drivers with digital control systems. This paper proposes the design of a self-adaptive real-time cruise control system to enable path-following control of autonomous ground vehicles so that a self-driving car can drive along a road while following a lead vehicle. To achieve the cooperative objectives, we use a multi-agent deep reinforcement learning (MADRL) technique, including one agent to control the acceleration and another agent to operate the steering control. Since the steering of an autonomous automobile could be adjusted by a stepper motor, a well-known DQN agent is considered to provide the discrete angle values for the closed-loop lateral control. We performed a simulation-based analysis to evaluate the efficacy of the proposed MADRL path following control for autonomous vehicles (AVs). Moreover, we carried out a thorough comparison with two state-of-the-art controllers to examine the accuracy and effectiveness of our proposed control system.

Second order self-adaptive dynamical system for sparse signal reconstruction and applications to image recovery

In this article, we consider sparse signal reconstruction problems by an alternative second order self-adaptive dynamical system. By split feasibility problem of sparse signal reconstruction, we introduce a new second order self-adaptive dynamical system. Then, we prove that the proposed system has a unique solution under reasonable conditions. Furthermore, it is shown that the corresponding orbit of the system always converges. Finally, all kinds of numerical results on synthetic data and data from practical problems verify the efficiency of the proposed approach.

Real-Time Self-Adaptive Traffic Management System for Optimal Vehicular Navigation in Modern Cities

The increase in private car usage in cities has led to limited knowledge and uncertainty about traffic flow. This results in difficulties in addressing traffic congestion. This study proposes a novel technique for dynamically calculating the shortest route based on the costs of the most optimal roads and nodes using instances of road graphs at different timeslots to help minimize congestion for actual drivers in urban areas. The first phase of the study involved reducing traffic congestion in one city. The data were collected using a mobile application installed on more than 10 taxi drivers’ phones, capturing data at different timeslots. Based on the results, the shortest path was proposed for each timeslot. The proposed technique was effective in reducing traffic congestion in the city. To test the effectiveness of the proposed technique in other cities, the second phase of the study involved extending the proposed technique to another city using a self-adaptive system based on a similarity approach regarding the structures and sub-regions of the two cities. The results showed that the proposed technique can be successfully applied to different cities with similar urban structures and traffic regulations. The proposed technique offers an innovative approach to reducing traffic congestion in urban areas. It leverages dynamic calculation of the shortest route and utilizes instances of road graphs to optimize traffic flow. By successfully implementing this approach, we can improve journey times and reduce fuel consumption, pollution, and other operating costs, which will contribute to a better quality of urban life.