No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Self-adaptive Internet of Things Systems: A Systematic Literature Review
To meet increasingly restrictive requirements and improve quality of service (QoS), Internet of Things (IoT) systems have embraced multi-layered architectures leveraging edge and fog computing. However, the dynamic and changing IoT environment can impact QoS due to unexpected events. Therefore, proactive evolution and adaptation of the IoT system becomes a necessity and concern. In this paper, we present a model-based approach for the specification and execution of self-adaptive multi-layered IoT systems. Our proposal comprises the design of a domain-specific language (DSL) for the specification of such architectures, and a runtime framework to support the system behaviuor and its self-adaptation at runtime. The code for the deployment of the IoT system and the execution of the runtime framework is automatically produced by our prototype code generator. Moreover, we also show and validate the extensibility of such DSL by applying it to the domain of underground mining. The complete infrastructure (modeling tool, generator and runtime components) is available in a online open source repository.
Cyber-physical systems (CPS) are characterized with their concurrency, heterogeneity and time sensitivity. In this context, it is crucial to have self-adaptive CPS systems in order to manage changing in their internal and external environment and supporting new requirements. Also, CPS systems are known with their restricted connectivity, and consequently, they must perform a decentralized adaptation. This is added to the general complexity of modeling the adaptation process. In fact, the MAPE-K (Monitoring, Analysis, Planning, Execution and Knowledge) control loop model has been identified as a crucial element for realizing self-adaptation in software systems. To respond to the design challenge of self-adaptive CPS, we propose a set of decentralized MAPE-K design patterns to help designer in building such complex systems thanks to software design patterns which provide a general reusable solution to a commonly occurring problem. In this paper, we provide a set of decentralized patterns to model CPS using the refinement technique. For this purpose, we proposed a standard notation based on the UML modeling language to describe the different MAPE-K patterns for decentralized control in self-adaptive CPS. We illustrate our approach by modeling the smart parking application using the coordinated control pattern.
In recent years, with the development of Unmanned Aerial Vehicle (UAV) and Cloud Internet-of-Things (Cloud IoT) technology, data collection using UAVs has become a new technology hotspot for many Cloud IoT applications. Due to constraints such as the limited power life, weak computing power of UAV and no-fly zones restrictions in the environment, it is necessary to use cloud server with powerful computing power in the Internet of Things to plan the path for UAV. This paper proposes a coverage path planning algorithm called Parallel Self-Adaptive Ant Colony Optimization Algorithm (PSAACO). In the proposed algorithm, we apply grid technique to map the area, adopt inversion and insertion operators to modify paths, use self-adaptive parameter setting to tune the pattern, and employ parallel computing to improve performance. This work also addresses an additional challenge of using the dynamic Floyd algorithm to avoid no-fly zones. The proposal is extensively evaluated. Some experiments show that the performance of the PSAACO algorithm is significantly improved by using parallel computing and self-adaptive parameter configuration. Especially, the algorithm has greater advantages when the areas are large or the no-fly zones are complex. Other experiments, in comparison with other algorithms and existing works, show that the path planned by PSAACO has the least energy consumption and the shortest completion time.
Socio-cyber-physical systems (SCPSs) are cyber-physical systems with social concerns. Many emerging SCPSs, often qualified as “smart”, need such concerns to be addressed not only at design time but also at runtime, often by adapting dynamically to surrounding contexts, to keep providing optimal value to users. A comprehensive requirements and design modeling approach is needed to incorporate social concerns (e.g., using goal modeling) into SCPS development activities. This paper introduces an optimization method that provides design-time and runtime solutions for self-adaptive SCPSs while supporting the validation of their design models. The method helps satisfying the goals of the SCPS and its stakeholders by monitoring the system’s environment and qualities, while enforcing correctness constraints specified in a feature model. We integrate arithmetic functions generated automatically from goal and feature models to build a combined goal-feature model and synchronize the values of the features shared between i) the objective function represented by goal functions, and ii) the constraints represented by feature functions. The goal-feature model is solved by an optimization tool (IBM CPLEX) in order to calculate optimal adaptation solutions for common situations at design time. Runtime optimization is also used by the system for adapting to situations unanticipated during design. We use a Smart Home Management System case study to assess how well the method can be used to manage selection among alternatives according to monitored environmental conditions while solving emergent conflicts. Further experiments on the use of the method for runtime adaptation show good performance for realistic models and good scalability overall. Some remaining challenges and limitations exist, including the availability of quantitative models as inputs.
Undeniably, the increasing number of vehicles on the roads has automatically led to the unbelievable number of accidents occurring every day and the need for a safe communication system among the vehicles has become quite urgent. Using such systems, vehicles can communicate via wireless communication technologies and protocols like ZigBee, WiMax, Bluetooth, GSM, and RF. This paper introduces a communication system that works on preventing accidents by making all the surrounding vehicles communicate using RF, IoT, and LCD technologies to show all the warnings and the indications sent from other vehicles and a VLC that is connected to every vehicle to detect and send all needed information of occurring accidents on the road to all connected vehicles and to identify the hit or damaged ones. Moreover, the framework proposed in this paper attempts to perform mutual authentication procedures between vehicles and road-side units based on radio frequency identification devices and cloud computing. According to this framework, a low-cost device has been implemented using an LED lamp and an electrical circuit to control the LED luminous flux, and the modulated carrier signal has also been modified to effectively suppress the low-frequency noise. It should be noted that the experiments have shown that vehicular networks must be resilient and self-adapted to failures and potential threats to support coordinated transportation systems. Finally, the security analysis has also shown that the proposed framework is efficient, easily deployed and capable of finding the best routing paths between communicating vehicles.
A wide variety of Cyber Threat Information (CTI) is used by Security Operation Centres (SOCs) to perform validation of security incidents and alerts. Security experts manually define different types of rules and scripts based on CTI to perform validation tasks. These rules and scripts need to be updated continuously due to evolving threats, changing SOCs’ requirements and dynamic nature of CTI. The manual process of updating rules and scripts delays the response to attacks. To reduce the burden of human experts and accelerate response, we propose a novel Artificial Intelligence (AI) based framework, SmartValidator. SmartValidator leverages Machine Learning (ML) techniques to enable automated validation of alerts. It consists of three layers to perform the tasks of data collection, model building and alert validation. It projects the validation task as a classification problem. Instead of building and saving models for all possible requirements, we propose to automatically construct the validation models based on SOC’s requirements and CTI. We built a Proof of Concept (PoC) system with eight ML algorithms, two feature engineering techniques and 18 requirements to investigate the effectiveness and efficiency of SmartValidator. The evaluation results showed that when prediction models were built automatically for classifying cyber threat data, the F1-score of 75% of the models were above 0.8, which indicates adequate performance of the PoC for use in a real-world organization. The results further showed that dynamic construction of prediction models required 99% less models to be built than pre-building models for all possible requirements. Thus, SmartValidator is much more efficient to use when SOCs’ requirements and threat behaviour are constantly evolving. The framework can be followed by various industries to accelerate and automate the validation of alerts and incidents based on their CTI and SOC’s preferences.
Socio-cyber-physical systems (SCPSs) are cyber-physical systems with social concerns. Many emerging SCPSs, often qualified as “smart”, need such concerns to be addressed not only at design time but also at runtime, often by adapting dynamically to surrounding contexts, to keep providing optimal value to users. A comprehensive requirements and design modeling approach is needed to incorporate social concerns (e.g., using goal modeling) into SCPS development activities. This paper introduces an optimization method that provides design-time and runtime solutions for self-adaptive SCPSs while supporting the validation of their design models. The method helps satisfying the goals of the SCPS and its stakeholders by monitoring the system’s environment and qualities, while enforcing correctness constraints specified in a feature model. We integrate arithmetic functions generated automatically from goal and feature models to build a combined goal-feature model and synchronize the values of the features shared between i) the objective function represented by goal functions, and ii) the constraints represented by feature functions. The goal-feature model is solved by an optimization tool (IBM CPLEX) in order to calculate optimal adaptation solutions for common situations at design time. Runtime optimization is also used by the system for adapting to situations unanticipated during design. We use a Smart Home Management System case study to assess how well the method can be used to manage selection among alternatives according to monitored environmental conditions while solving emergent conflicts. Further experiments on the use of the method for runtime adaptation show good performance for realistic models and good scalability overall. Some remaining challenges and limitations exist, including the availability of quantitative models as inputs.
Models are used in both Software Engineering (SE) and Artificial Intelligence (AI). SE models may specify the architecture at different levels of abstraction and for addressing different concerns at various stages of the software development life-cycle, from early conceptualization and design, to verification, implementation, testing and evolution. However, AI models may provide smart capabilities, such as prediction and decision-making support. For instance, in Machine Learning (ML), which is currently the most popular sub-discipline of AI, mathematical models may learn useful patterns in the observed data and can become capable of making predictions. The goal of this work is to create synergy by bringing models in the said communities together and proposing a holistic approach to model-driven software development for intelligent systems that require ML. We illustrate how software models can become capable of creating and dealing with ML models in a seamless manner. The main focus is on the domain of the Internet of Things (IoT), where both ML and model-driven SE play a key role. In the context of the need to take a Cyber-Physical System-of-Systems perspective of the targeted architecture, an integrated design environment for both SE and ML sub-systems would best support the optimization and overall efficiency of the implementation of the resulting system. In particular, we implement the proposed approach, called ML-Quadrat, based on ThingML, and validate it using a case study from the IoT domain, as well as through an empirical user evaluation. It transpires that the proposed approach is not only feasible, but may also contribute to the performance leap of software development for smart Cyber-Physical Systems (CPS) which are connected to the IoT, as well as an enhanced user experience of the practitioners who use the proposed modeling solution.
The design of robots capable of operating autonomously in changing and unstructured environments, requires using complex software architectures in which, typically, robot engineers manually hard-code adaptation mechanisms allowing the robot to deal with certain situations. As adaptation is closely related with context monitoring, deliberation and actuation, its implementation typically spreads across several architecture components. Therefore, fine-tuning or extending the adaptation logic (e.g., to cope with new contingencies not foreseen at design-time) results in a very expensive and cumbersome process. This paper proposes a novel approach to deal with self-adaptation based on modeling behavior variability at design-time so that the robot can configure it at runtime, according to the contextual information only then available. This approach is supported by a model-based framework allowing robotic engineers to specify (1) the robot behavior variation points (open decision space); (2) the internal and external contextual information available; and (3) the non-functional properties (e.g. safety, performance, or energy consumption) in terms of which the robot Quality-of-Service (QoS) will be measured. Then, from these models, the framework will automatically generate the runtime infrastructure allowing the robot to self-adapt its behavior to achieve the best QoS possible according to its current context. The framework has been validated in two scenarios using two different well-known robotic software architectures.
Over the past few years, the relevance of the Internet of Things (IoT) has grown significantly and is now a key component of many industrial processes and even a transparent participant in various activities performed in our daily life. IoT systems are subjected to changes in the dynamic environments they operate in. These changes (e.g. variations in bandwidth consumption or new devices joining/leaving) may impact the Quality of Service (QoS) of the IoT system. A number of self-adaptation strategies for IoT architectures to better deal with these changes have been proposed in the literature. Nevertheless, they focus on isolated types of changes. We lack a comprehensive view of the trade-offs of each proposal and how they could be combined to cope with simultaneous events of different types.In this paper, we identify, analyze, and interpret relevant studies related to IoT adaptation and develop a comprehensive and holistic view of the interplay of different dynamic events, their consequences on QoS, and the alternatives for the adaptation. To do so, we have conducted a systematic literature review of existing scientific proposals and defined a research agenda for the near future based on the findings and weaknesses identified in the literature.
Wireless mesh network (WMN) is one of the most promising technologies for Internet of Things (IoT) applications because of its self-adaptive and self-organization nature. To meet different performance requirements on communications in WMNs, traditional approaches always have to program flow control strategies in an explicit way. In this case, the performance of WMNs will be significantly affected by the dynamic properties of underlying networks in real applications. With providing a more flexible solution in mind, in this article, for the first time, we present how we can apply emerging Deep Reinforcement Learning (DRL) on communication flow control in WMNs. Moreover, different from a general DRL based networking solution, in which the network properties are pre-defined, we leverage the adaptive nature of WMNs and propose a self-adaptive DRL approach. Specifically, our method can reconstruct a WMN during the training of a DRL model. In this way, the trained DRL model can capture more properties of WMNs and achieve better performance. As a proof of concept, we have implemented our method with a self-adaptive Deep Q-learning Network (DQN) model. The evaluation results show that the presented solution can significantly improve the communication performance of data flows in WMNs, compared to a static benchmark solution.
The Internet of Things (IoT) requires the integration of all available, highly specialized, and heterogeneous devices, ranging from embedded sensor nodes to servers in the cloud. The self-adaptive research domain provides adaptive capabilities that can support the integration in IoT systems. However, developing such systems is a challenging, error-prone, and time-consuming task. In this context, design patterns propose already used and optimized solutions to specific problems in various contexts. Applying design patterns might help to reuse existing knowledge about similar development issues. However, so far, there is a lack of taxonomies on design patterns for self-adaptive systems. To tackle this issue, in this paper, we provide a taxonomy on design patterns for self-adaptive systems that can be transferred to support adaptivity in IoT systems. Besides describing the taxonomy and the design patterns, we discuss their applicability in an Industrial IoT case study.
Emergent paradigms of Industry 4.0 and Industrial Internet of Things expect cyber-physical systems to reliably provide services overcoming disruptions in operative conditions and adapting to changes in architectural and functional requirements. In this paper, we describe a hardware/software framework supporting operation and maintenance of software-controlled systems enhancing resilience by promoting a Model-Driven Engineering (MDE) process to automatically derive structural configurations and failure models from reliability artifacts. Specifically, a reflective architecture developed around digital twins enables representation and control of system Configuration Items properly derived from SysML Block Definition Diagrams, providing support for variation. Besides, a plurality of distributed analytic agents for qualitative evaluation over executable failure models empowers the system with runtime self-assessment and dynamic adaptation capabilities. We describe the framework architecture outlining roles and responsibilities in a System of Systems perspective, providing salient design traits about digital twins and data analytic agents for failure propagation modeling and analysis. We discuss a prototype implementation following the MDE approach, highlighting self-recovery and self-adaptation properties on a real cyber-physical system for vehicle access control to Limited Traffic Zones.
Autonomic computing was the term coined by IBM in 2001. The term autonomic computing was used to define the self-adaptable nature of the human body. According to IBM, the same self-adaptable feature was the need to be incorporated in the software systems. Autonomic computing is the combination of few self-capabilities such as self-configuration, self-healing, self-optimization, self-protection, self-awareness, etc. So, autonomic computing approach was then used to develop autonomic software systems. This approach makes the computing systems self-adaptable and self-decision-making support systems for various activities. It also helps to reduce the human intervention in the software management process. Though, the implementation of autonomic self-capabilities may increase the software complexity, which further requires human intervention for the software maintenance-related specific tasks. Still, IT industries are approaching to develop autonomic features in their existing architecture or developing new self-adaptable software systems. Autonomic computing has its importance for providing a bridge for handling and managing the run-time computation-based issues/exceptions of the software. So, the discussion of this solution has become a necessity for making the vision of autonomic decision making more clear and understandable for researchers and developers for the improvement in an autonomic area. The paper provides an insight vision of the autonomic decision-making concept and its importance for the various purposes such as intrusion detection, cloud-based data security, wireless sensor network, Internet of Things, Big Data and many other areas where management cannot be handled by a human in real time. To assess the degree of autonomic feature, there is another term used which is known as autonomicity. The paper also discusses some solutions suggested and implemented by different researchers during their studies for estimating the system’s autonomicity level. These solutions will help in comparing different autonomic applications based on the autonomic features implemented in each application. This paper is an attempt to provide better understandability in the autonomic computational field.
Architecting IoT systems able to guarantee Quality of Service (QoS) levels can be a challenging task due to the inherent uncertainties (induced by changes in e.g., energy availability, network traffic) that they are subject to. Existing work has shown that machine learning (ML) techniques can be effectively used at run time for selecting self-adaptation patterns that can help maintain adequate QoS levels. However, this class of approach suffers from learning bias, which induces accuracy problems that might lead to sub-optimal (or even unfeasible) adaptations in some situations. To overcome this limitation, we propose an approach for proactive self-adaptation which combines ML and formal quantitative verification (probabilistic model checking). In our approach, ML is tasked with selecting the best adaptation pattern for a given scenario, and quantitative verification checks the feasibility of the adaptation decision, preventing the execution of unfeasible adaptations and providing feedback to the ML engine which helps to achieve faster convergence towards optimal decisions. The results of our evaluation show that our approach is able to produce better decisions than ML and quantitative verification used in isolation.
Cyber-Physical Systems (CPSs) are large-scale complex systems that monitor and control physical processes by using computer algorithms tightly integrated with networking and their users. Monitoring and controlling the physical environment is a hot topic for today’s researchers and engineers in academia and industry. Within this realm, an important feature of current and future Information and Communications Technology (ICT) systems is self-adaptation—yet there is a shortage of information focusing on this characteristic in the literature, particularly as it relates to CPSs. Here, we investigate current state-of-the-art research on CPSs from this perspective, and evaluate the main self-adaptive approaches proposed in the literature, along with their results, strengths, and weaknesses. We also discuss appropriate techniques for enabling self-adaptation capabilities within CPSs at different architectural layers. Overcoming the challenges associated with designing and implementing self-adaptive mechanisms in CPSs will provide a path for bolstering a new generation of CPSs with greater robustness and reliability.
Many Cyber-Physical Systems (CPSs) today are self-adaptive, in order to handle frequent changes in environmental conditions and requirements. In CPSs, goal-based reasoning is often used to include stakeholder and social concerns in decision making during design and runtime adaptation activities. To better support some of these activities, arithmetic semantics for goal models were proposed to enable the generation of mathematical functions usable by systems. However, goal models often allow invalid combinations of alternatives, which can be prevented by companion feature models. In this paper, to enable the generation of valid and optimal configurations for adaptive CPSs, we propose new arithmetic semantics for feature models that enable their transformations to mathematical functions (in several programming languages) further restricting the ones generated from goal models. The composition of feature and goal functions results in a smaller design space, leading to fewer but valid solutions that can be generated (e.g., through optimization) and used in simulations and running adaptive CPSs with social concerns. Finally, a simulation model in SysML is proposed in this paper to demonstrate the feasibility and usefulness of this composition.
City services are frequently supported by software services that are managed by service-oriented architectures. However, a large number of software services is likely to cause performance issues when discovering software services. The distributed organisation of services information improves discovery performance. Existing research proposes to organise services information according to service location, domains, or city context, keeping that organisation constant under an assumption that cities do not change. However, cities are dynamic environments where entities interact, causing events that in turn, effect changes in the city. The organisation of services information must evolve or it will become outdated, negatively impacting discovery performance. We propose a self-adaptive service model for smart cities to support service discovery. This model adapts the organisation of services information according to city events. We introduce a self-adaptive architecture that keeps track of the discovery metrics and moves information about services between registries to maintain the discovery efficiency. We evaluate the proposed model in simulated environments and a real IoT testbed. Results show that our model outperforms competitors when reactive adaptation is triggered by a specific event. However, proactive adaptation needs further research. Results from the real IoT testbed present the costs of the proposed model.
Cyber-physical systems (CPS) have been used in many applications, especially in smart cities and industrial systems. As a result of the exponential development of CPSs connected components, cyber-attacks against CPSs have exploded. Moreover, new critical CPS have high-security constraints that must be detected and predicted at an early stage of the communication process. Thus, it has become harder to detect these attacks. Machine learning is one of the most effective techniques for identifying and detecting CPS vulnerabilities. As a result of the heterogeneity of traffic and attacks only one machine learning algorithm is unreliable. To provide a self-adaptive and scalable prediction/detection mechanism we propose a framework called AAPF-CPS, which combines several machine learning algorithms with statistical tests. With multiple classification algorithms, AAPF-CPS analyzes CPS network logs simultaneously and in real time. Friedman’s test is also used to rank each classifier for each context in AAPF-CPS. Experimental results showed that AAPF-CPS could adapt ML algorithms based on traffic, allowing it to predict and detect potential attacks more efficiently.
The performance and reliability of Cyber-Physical Systems are increasingly aided through the use of digital twins, which mirror the static and dynamic behaviour of a Cyber-Physical System (CPS) in software. Digital twins enable the development of self-adaptive CPSs which reconfigure their behaviour in response to novel environments. It is crucial that these self-adaptations are formally verified at runtime, to avoid expensive re-certification of the reconfigured CPS. In this paper, we demonstrate formally verified self-adaptation in a digital twinning system, by constructing a non-deterministic model which captures the uncertainties in the system behaviour after a self-adaptation. We use Signal Temporal Logic to specify the safety requirements the system must satisfy after reconfiguration and employ formal methods based on verified monitoring over Flow* flowpipes to check these properties at runtime. This gives us a framework to predictively detect and mitigate unsafe self-adaptations before they can lead to unsafe states in the physical system.KeywordsDigital twinSelf-adaptationReachability analysisSignal temporal logicOptimizationCyber-physical system
Artificial Intelligence of Things (AIoT) has recently accepted significant interests. Remarkably, embedded artificial intelligence (e.g., deep learning) on-device transforms IoT devices into intelligent systems that robustly and privately process data. Quantization technique is widely used to compress deep models for narrowing the resource gap between computation demands and platform supply. However, existing quantization schemes induce unsatisfaction for IoT scenarios since they are oblivious to dynamic changes of application context (e.g., battery and hierarchical memory availability) during the long-term operation. Subsequently, they will mismatch the user-desired resource efficiency and application lifetime. Also, to adapt to the dynamic context, we can neither accept the latency for model retraining with existing hand-crafted quantization nor the overhead for quantization bit width researching with prior on-demand quantization. This article presents a context-aware and self-adaptive deep model quantization (CAQ) system for IoT application scenarios. CAQ integrates a novel switchable multigate quantization framework, optimizing the quantized model accuracy and energy efficiency in diverse contexts. Based on the learned model, CAQ can switch among different gating networks in a context-aware manner and then adopt it to automatically capture the representation importance of various layers for optimal quantization bit-width selection. The experimental results show that CAQ achieves up to 50% storage savings with even 2.61% higher accuracy than the state-of-the-art baselines.
Context:
Modern cyber–physical systems (CPSs) are embedded in the physical world and intrinsically operate in a continuously changing and uncertain environment or operational context. To meet their business goals and preserve or even improve specific adaptation goals, besides the variety of run-time uncertainties and changes to which the CPSs are exposed—the systems need to self-adapt.
Objective:
The current literature in this domain still lacks a precise definition of what self-adaptive systems are and how they differ from those considered non-adaptive. Therefore, in order to answer how to engineer self-adaptive CPSs or self-adaptive systems in general, we first need to answer what is adaptivity, correspondingly self-adaptive systems.
Method:
In this paper, we first formally define the notion of adaptivity. Second, within the frame of the formal definitions, we propose a logical architecture for engineering decentralised self-adaptive CPSs that operate in dynamic, uncertain, and partially observable operational contexts. This logical architecture provides a structure and serves as a foundation for the implementation of a class of self-adaptive CPSs.
Results:
First, our results show that in order to answer if a system is adaptive, the right framing is necessary: the system’s adaptation goals, its context, and the time period in which the system is adaptive. Second, we discuss the benefits of our architecture by comparing it with the MAPE-K conceptual model.
Conclusion:
Commonly accepted definitions of adaptivity and self-adaptive systems are necessary for work in this domain to be compared and discussed since the same terms are often used with different semantics. Furthermore, in modern self-adaptive CPSs, which operate in dynamic and uncertain contexts, it is insufficient if the adaptation logic is specified during the system’s design, but instead, the adaptation logic itself needs to adapt and “learn” during run-time.
The Internet of Things (IoT) connects a wide range of entities and can be applied to various types of environments. In addition, IoT environments can be dynamically changed at runtime; thus, IoT systems can be deployed in various environments. To support stable operation, IoT systems must adapt to dynamic environmental changes. The self-adaptive software aims to adjust various artifacts or attributes of software to adapt the detected context by itself, and various studies have applied self-adaptive methods in IoT-related research. In this study, we proposed a self-adaptive software framework with master–slave architecture-based finite-state machine modeling. In addition, model checking is applied, which is a formal method to verify IoT systems at runtime, and a cache-based mechanism is applied to reduce the computational time required for verification. To demonstrate the efficiency of the proposed framework, an empirical evaluation was performed with several model-checking tools (RINGA, NuSMV, nuXmv, and CadenceSMV), and the results showed the efficiency of the proposed framework with the cache-based mechanism. In addition, an example application was investigated with smart greenhouse scenarios, and the application was implemented on Android and Arduino. The application was operated in physical environments, and the results showed the practical usability of the proposed framework with verification at runtime.
As the number of IoT applications increases, IoT devices are becoming more and more ubiquitous. Therefore, they need to adapt their functionality in response to the uncertainties of their environment to achieve their goals. In Human-centered IoT, objects and devices have direct interactions with human beings. Self-adaptation of such applications is a crucial subject that needs to be addressed in a way that respects human goals and human values. This paper presents SMASH: a multi-agent approach for self-adaptation of IoT applications in human-centered environments. SMASH agents are provided with a 4-layer architecture based on the BDI agent model that integrates human values with goal-reasoning, planning, and acting. It also takes advantage of a semantic-enabled platform called Home’In to address interoperability issues among non-identical agents and devices with heterogeneous protocols and data formats. This approach is compared with the literature and is validated by developing a scenario as the proof of concept. The timely responses of SMASH agents show the feasibility of the proposed approach in human-centered environments.
Human-Cyber-Physical-Systems (HPCS), such as critical infrastructures in modern society, are subject to several systemic threats due to their complex interconnections and interdependencies. Management of systemic threats requires a paradigm shift from static risk assessment to holistic resilience modeling and evaluation using intelligent, data-driven and run-time approaches. In fact, the complexity and criticality of HCPS requires timely decisions considering many parameters and im-plications, which in turn require the adoption of advanced mon-itoring frameworks and evaluation tools. In order to tackle such challenge, we introduce those new paradigms in a framework named RESILTRON, envisioning Digital Twins (DT) to support decision making and improve resilience in HCPS under systemic stress. In order to represent possibly complex and heterogeneous HCPS, together with their environment and stressors, we leverage on multi-simulation approaches, combining multiple formalisms,data-driven approaches and Artificial Intelligence (AI) modelling paradigms, through a structured, modular and compositional framework. DT are used to provide an adaptive abstract representation of the system in terms of multi-layered spatially-embedded dynamic networks, and to apply self-adaptation totime-warped What-If analyses, in order to find the best sequence of decisions to ensure resilience under uncertainty and continuous HPCS evolution.
Engineering Internet of Things (IoT) systems is a challenging task partly due to the dynamicity and uncertainty of the environment including the involvement of the human in the loop. Users should be able to achieve their goals seamlessly in different environments, and IoT systems should be able to cope with dynamic changes. Several approaches have been proposed to enable the automated formation, enactment, and self-adaptation of goal-driven IoT systems. However, they do not address deployment issues. In this paper, we propose a goal-driven approach for deploying self-adaptive IoT systems in the Edge-Cloud continuum. Our approach supports the systems to cope with the dynamicity and uncertainty of the environment including changes in their deployment topologies, i.e., the deployment nodes and their interconnections. We describe the architecture and processes of the approach and the simulations that we conducted to validate its feasibility. The results of the simulations show that the approach scales well when generating and adapting the deployment topologies of goal-driven IoT systems in smart homes and smart buildings.
Support vector machine (SVM) is a powerful machine learning technology and the distinctive generalization ability makes it one of the most popular approximation tools in the field of internet of things (IoT) based marine data processing. However, SVM has been criticized for trial and error of parameters, especially kernel function. How to determine a suitable kernel for SVM in a specific problem has been rather tricky. To give a systematic research of the field, we concentrate on the self-adaptive selection of kernel functions in the framework of SVM for internet of things based marine data prediction. Specifically, we adopt the optimal kernel for obtaining competitive SVM and devises a kernel selection criteria of such high-efficiency models. Experiments are conducted via IoT based real-world marine datasets of different characteristics. The results demonstrate that our proposed self-adaptive SVM model can autonomously provide a suitable kernel for given marine environmental factor prediction, and outperform the alternative with the linear combination of multiple kernels. Besides, the superior performance is verified from the perspective of statistic analysis.
The complex factory environment of the Industrial Internet of Things (IIoT) greatly increases the energy consumption of sensor nodes and reduces production profits. Especially in mines, the terrain will change continuously as the mining progresses. Additionally, heavy traffic load on a single sink node and unbalanced load on multiple sink nodes also reduce battery life. Therefore, how to build an energy-efficient topology based on the unique mine terrain characteristics is a critical issue. To address this problem, this paper proposes a three-dimensional Topology Evolution Scheme with Self-Adaption for mining areas (3D-TES) to reduce energy consumption. We build the multi-peak terrain model according to the characteristics of the mining environment. Blocked by the undulating peaks on the mine, the strength of the node signal is quantified by the slope and aspect. The 3D-TES is then applied to determine the optimal number of sink nodes and find the best data transmission path between sensor nodes and multiple sink nodes. The experiment results show that 3D-TES outperforms the Directed Angulation toward the Sink Node Model (DASM) in terms of reliability, average path length, and data load on sink nodes.
Ambient Assisted Living (AAL) aims to improve people’s quality of life through the use of information technologies. Due to the critical nature of AAL systems, quality is a priority. However, as AAL is a relatively new domain, its main limitation is the lack of consensus and standardization in quality assessment. This work presents a systematic review to determine the state of the art on the quality assessment of AAL systems from a multidimensional vision (software product, in use, data and context). Initially, 1308 primary studies were extracted, from them 21 relevant studies related to models, frameworks, taxonomies and other approaches of quality assessment were selected after applying the corresponding inclusion and exclusion criteria. The selected studies were subject to a comparative analysis that determined the most recurrent and critical quality attributes for AAL systems, being an important contribution to generate consensus in the construction of more complete quality models. Furthermore, this work allowed to recognize the strengths and limitations of the quality proposals studied and to identify research gaps and challenges.
High mobility in ITS, especially V2V communication networks, allows increasing coverage and quick assistance to users and neighboring networks, but also degrades the performance of the entire system due to fluctuation in the wireless channel. How to obtain better QoS during multimedia transmission in V2V over future generation networks (i.e., edge computing platforms) is very challenging due to the high mobility of vehicles and heterogeneity of future IoT-based edge computing networks. In this context, this article contributes in three distinct ways: to develop a QoS-aware, green, sustainable, reliable, and available (QGSRA) algorithm to support multimedia transmission in V2V over future IoT-driven edge computing networks; to implement a novel QoS optimization strategy in V2V during multimedia transmission over IoT-based edge computing platforms; to propose QoS metrics such as greenness (i.e., energy efficiency), sustainability (i.e., less battery charge consumption), reliability (i.e., less packet loss ratio), and availability (i.e., more coverage) to analyze the performance of V2V networks. Finally, the proposed QGSRA algorithm has been validated through extensive real-time datasets of vehicles to demonstrate how it outperforms conventional techniques, making it a potential candidate for multimedia transmission in V2V over self-adaptive edge computing platforms.
