Search

Purpose Considering the importance and the broad applications of the Fourth Industrial Revolution in various organizations and industries and enjoying the many benefits of this digital transformation framework, organizations need to measure their Industry 4.0 readiness as a starting point and take steps to achieve the strategic goals of Industry 4.0. This study aims to design a comprehensive and practical model that can determine Industry 4.0 readiness level, allowing organizations to implement and exploit technological constituents of this phenomenon. Design/methodology/approach A systematic literature review (SLR) methodology was used to evaluate and summarize a clear and comprehensive literature overview of Industry 4.0 readiness models and to certify the validity and transparency of the review process. After reviewing 71 articles and survey and then the consensus of Industry 4.0 experts, the 10 dimensions of the 4.0 Industry readiness model were finalized with their indicators having the most frequency in the published articles and models. Findings The application of the SLR to the development of the new Industry 4.0 readiness model which includes 10 dimensions and 37 indicators and can assess the Industry 4.0 readiness of firms and industries accurately and effectively. Research limitations/implications An extensive review of the previous literature yielded the current Industry 4.0 readiness model. The comprehensiveness of this model leads to its wide application in different companies. Future research suggestions are presented at the end of the manuscript. Practical implications The concept of the Fourth Industrial Revolution and the application of its technologies are vague and complicated for many organizations and managers, while the need to implement the components and technologies of Industry 4.0 is essential to achieve organizational goals. The presented readiness model helps companies to measure their readiness to enter the Fourth Industrial Revolution

Power system state forecasting has gained more attention in real-time operations recently. Unique challenges to energy systems are emerging with the massive deployment of renewable energy resources. As a result, power system state forecasting are becoming more crucial for monitoring, operating and securing modern power systems. This paper proposes an end-to-end deep learning framework to accurately predict multi-step power system state estimations in real-time. In our model, we employ a sequence-to-sequence framework to allow for multi-step forecasting. Bidirectional gated recurrent units (BiGRUs) are incorporated into the model to achieve high prediction accuracy. The dominant performance of our model is validated using real dataset. Experimental results show the superiority of our model in predictive power compared to existing alternatives.

In the event of large disturbances, the practice of controlled islanding is used as a last resort to prevent cascading outages. The application of the strategy at the right time is crucial to maintaining system security. A controlled islanding strategy may be deployed efficiently at the right time by predicting the time of uncontrolled system splitting. The purpose of this study is to predict the appropriate islanding time to prevent catastrophic blackout and uncontrolled islanding based on existing relationships between coherent generator groups. A new instability index is derived from the proximity of inter-area oscillations to power splitting indices. Power splitting indices are derived using synchronization coefficients, which recognize the conditions in the system that warrant controlled islanding. The critical values of indices are calculated in offline mode using simulation data from IEEE 39-Buses, and their online performance is evaluated following a controlled islanding strategy. Through the introduction of these indices, system degradation can be effectively evaluated, and blackouts can be predicted early and prevented by controlled islanding at the right time., Comment: NA

The paper presents a Gaussian/kernel process regression method for real-time state estimation and forecasting of phase angle and angular speed in systems with a high penetration of solar generation units, operating under a sparse measurements regime on both sunny and cloudy days. The method treats unknown terms in the swing equations, such as solar power, as random processes, thereby transforming these equations into stochastic differential equations. The proposed method accurately forecasts and estimates both observed and unobserved operating states, delivering forecasts comparable to those of the standard data-driven Gaussian/kernel process for observed system states. Additionally, the method demonstrates improved accuracy with increased observation frequency and reduced measurement errors in the IEEE 14-bus test system.

Internet of Things (IoT) applications have the potential to derive sensitive information about individuals. Therefore, developers must exercise due diligence to make sure that data are managed according to the privacy regulations and data protection laws. However, doing so can be a difficult and challenging task. Recent research has revealed that developers typically face difficulties when complying with regulations. One key reason is that, at times, regulations are vague, and could be challenging to extract and enact such legal requirements. In our research paper, we have conducted a systematic analysis of the data protection laws that are used across different continents, namely: (i) General Data Protection Regulations (GDPR), (ii) the Personal Information Protection and Electronic Documents Act (PIPEDA), (iii) the California Consumer Privacy Act (CCPA), (iv) Australian Privacy Principles (APPs), and (v) New Zealand's Privacy Act 1993. In this technical report, we presented the detailed results of the conducted framework analysis method to attain a comprehensive view of different data protection laws and highlighted the disparities, in order to assist developers in adhering to the regulations across different regions, along with creating a Combined Privacy Law Framework (CPLF). After that, we gave an overview of various Privacy by Design (PbD) schemes developed previously by different researchers. Then, the key principles and individuals' rights of the CPLF were mapped with the privacy principles, strategies, guidelines, and patterns of the Privacy by Design (PbD) schemes in order to investigate the gaps in existing schemes., Comment: Technical Report

Digital vaccine passports are one of the main solutions which would allow the restart of travel in a post COVID-19 world. Trust, scalability and security are all key challenges one must overcome in implementing a vaccine passport. Initial approaches attempt to solve this problem by using centralised systems with trusted authorities. However, sharing vaccine passport data between different organisations, regions and countries has become a major challenge. This paper designs a new platform architecture for creating, storing and verifying digital COVID-19 vaccine certifications. The platform makes use of the InterPlanetary File System (IPFS) to guarantee there is no single point of failure and allow data to be securely distributed globally. Blockchain and smart contracts are also integrated into the platform to define policies and log access rights to vaccine passport data while ensuring all actions are audited and verifiably immutable. Our proposed platform realises General Data Protection Regulation (GDPR) requirements in terms of user consent, data encryption, data erasure and accountability obligations. We assess the scalability and performance of the platform using IPFS and Blockchain test networks.

Controlled islanding, which splits the whole power system into islands, is an effective strategy against rolling blackout during severe disturbances. Finding the islanding solutions in a real-time manner is complicated because of the combinatorial explosion of the solution space occurs for a large power network. In this work, a computationally efficient controlled islanding algorithm is proposed that uses constrained spectral clustering while addressing the generator coherency problem. The objective function used in this controlled islanding algorithm is the minimal power-flow disruption. The sole constraint applied to this solution is related to generator coherency. An undirected edge-weighted graph is created based on absolute values of apparent power flow and constraints related to transmission line availability and coherent generator groups are included by altering the edge weights of the graph and using a subspace projection. Spectral clustering is then applied to the constrained solution subspace to find the islanding solution. The methodology is tested on an IEEE-39 test system with a fully dynamic model. Simulation results demonstrate the efficacy of our approach., Comment: Accepted to be presented at the 2020 IEEE TPEC

The reported work points at developing a practical approach for power transmission planners to secure power networks from potential deliberate attacks. We study the interaction between a system planner (defender) and a rational attacker who threatens the operation of the power grid. In addition to the commonly used hardening strategy for protecting the network, a new sort of resource is introduced under the deception concept. Feint and deception are acknowledged as effective tools for misleading the attacker in strategic planning. To this end, the defender deception is mathematically formulated by releasing misinformation about his plan in the shared cognition-based model. To reduce the risk of damage in case of deception failure, preemptive-goal programming is utilized to prioritize the hardening strategy for the vital components. Furthermore, the value of posturing is introduced which is the benefits that the deception brings to the system. The problems are formulated as tri-level mixed-integer linear programming and solved by the constraint-and-column generation method. Comprehensive simulation studies performed on WSCC 9-bus and IEEE 118-bus systems indicate how the defender will save significant cost from protecting his network with posturing rather than hardening and the proposed approach is a promising development to ensure the secure operation of power networks., Comment: Accepted to be published in Journal of Electric Power Systems Research, 2018

We propose a new methodology based on modularity clustering of synchronization coefficient, to identify coherent groups of generators in the power grid in real-time. The method uses real-time integrity indices, i.e., the Generators Connectivity Index (GCI) that represents how generators are coherently strong within the groups, the Generator Splitting Index (GSI) that reveals to what extent the generators in different groups tend to swing against the other groups, and the System Separation Index (SI) which discloses the overall system separation status. We demonstrate how these integrity indices can be used to study the dynamic behavior of the power system. Furthermore, a comparison analysis is conducted between the synchronization coefficient (KS) and the generator rotor angle correlation coefficient (CC). The proposed indices demonstrate the dynamic behavior of power system following occurrence the faults and thus represent a promising approach in power system islanding studies. Our methodology is simple, fast, and computationally attractive. Simulation case performed on IEEE 118-bus systems demonstrates the efficacy of our approach.

This thesis proposes solutions to four new problems stemming from a general framework of horizontal behavior composition, in which transition systems are used to model behaviors. The framework allows the realization of a new behavior from a set of available behaviors, through the synthesis of a controller, which delegates each action of the new behavior to an available behavior for execution. In this thesis, the behaviors are associated with software components—such as web services—, hardware components—such as connected objects—, or even agents. Besides, a composition consists of a controller and the behaviors interacting with the controller for realizing a target behavior, for example the one of a new agent. The first problem considers that the behaviors are subject to real-time constraints. The controller synthesis is done using the same algorithms as those of the general framework. Two additional steps are, however, required: one for modeling the interactions between the controller and behaviors in a closed-loop control system and another one for checking whether the closed-loop control system is deadlock free in all of its execution according to the set of real-time constraints. The second problem concerns the assembly of compositions. In contrast to the general framework that uses transition systems as modeling formalism in a purely monolithic control context, the proposed approach, on one hand, uses a process calculus as a formalism to represent all the elements of the closed-loop control system, and, on the other hand, performs a modular control to combine controllers using process calculus operators in order to obtain a global control. The third problem is an extension of the controller synthesis problem when the operations of the behaviors have qualitative or quantitative attributes and the operations of the target behavior are expressed in the form of preferences. The horizontal preference-based behavior composition makes it possible to realize a, Résumé : Cette thèse propose des solutions à quatre nouveaux problèmes issus d’un cadre général de composition horizontale de comportements modélisés à l’aide de systèmes à transition. Ce dernier permet la réalisation d’un nouveau comportement à partir d’un ensemble de comportements prédéfinis, à travers la synthèse d’un contrôleur qui délègue chacune de ses actions à un comportement prédéfini pour son exécution. Dans cette thèse, les comportements sont associés à des composants logiciels, comme des services Web, à des composants matériels, comme des objets connectés, ou à des agents. De plus, une composition est constituée d’un contrôleur et des comportements avec lesquels il interagit pour réaliser un comportement désiré, par exemple celui d’un nouvel agent. Le premier problème considère que les comportements sont soumis à des contraintes temps réel. La synthèse de contrôleur s’effectue en utilisant les mêmes algorithmes que ceux du cadre général. Toutefois, deux étapes additionnelles sont nécessaires : l’une pour modéliser les interactions entre les comportements et le contrôleur dans une boucle de rétroaction ; l’autre pour vérifier si la boucle de rétroaction est sans interblocage dans toutes ses exécutions considérant l’ensemble des contraintes temps réel. Le deuxième problème concerne l’assemblage de compositions. Contrairement au cadre général qui utilise des systèmes à transition comme formalisme de modélisation dans un contexte de contrôle purement monolithique, l’approche retenue suggère, d’une part, d’utiliser un calcul de processus comme formalisme pour représenter tous les éléments de la boucle de rétroaction et, d’autre part, d’effectuer un contrôle modulaire c’est-à-dire de combiner des contrôleurs à l’aide d’opérateurs du calcul de processus pour obtenir un contrôle global. Le troisième problème est une extension du problème de la synthèse de contrôleur lorsque les actions des comportements possèdent des attributs qualitatifs ou quantitatifs et que

Financial strategy is a framework organization which directed the financial decision making resulted from different conditions, dynamic competitors and collection of relative reactions. Financial strategy is one of key factors in organizations and increase shareholder wealth by identifying value drivers. Accordingly, this study aimed to analyze the impact of financial strategy on financial operation of banks and also presented a pattern to develop and prioritize such strategies. To do this purpose, data were collected relative to Iran banks from 2010-2011. Independent variable of this study was determined as financing strategy (combine deposits) and investment strategy (payment facilities) and variables like net interest margin and economic added value were accounted as financial operation criteria (dependent variables). Data were analyzed by Kruskal-Wallis test and using SPSS software. Results of present study indicated that financing strategy influenced significantly on economic added value, but did not influence on net interest margin. In contrast, impact of investment strategy on economic added value was rejected, but on net interest margin was confirmed. In second part, after using Delphi method, viewpoints of scholars in a private bank were collected and relative strategies in four fields of opportunities, threats, strengths and weaknesses were determined and after creating SWOT matrix, they were prioritized using AHP technique and presented a pattern to develop such strategies in an organization.

Financial strategy is a framework organization which directed the financial decision making resulted from different conditions, dynamic competitors and collection of relative reactions. Financial strategy is one of key factors in organizations and increase shareholder wealth by identifying value drivers. Accordingly, this study aimed to analyze the impact of financial strategy on financial operation of banks and also presented a pattern to develop and prioritize such strategies. To do this purpose, data were collected relative to Iran banks from 2010-2011. Independent variable of this study was determined as financing strategy (combine deposits) and investment strategy (payment facilities) and variables like net interest margin and economic added value were accounted as financial operation criteria (dependent variables). Data were analyzed by Kruskal-Wallis test and using SPSS software. Results of present study indicated that financing strategy influenced significantly on economic added value, but did not influence on net interest margin. In contrast, impact of investment strategy on economic added value was rejected, but on net interest margin was confirmed. In second part, after using Delphi method, viewpoints of scholars in a private bank were collected and relative strategies in four fields of opportunities, threats, strengths and weaknesses were determined and after creating SWOT matrix, they were prioritized using AHP technique and presented a pattern to develop such strategies in an organization.

Financial strategy is a framework organization which directed the financial decision making resulted from different conditions, dynamic competitors and collection of relative reactions. Financial strategy is one of key factors in organizations and increase shareholder wealth by identifying value drivers. Accordingly, this study aimed to analyze the impact of financial strategy on financial operation of banks and also presented a pattern to develop and prioritize such strategies. To do this purpose, data were collected relative to Iran banks from 2010-2011. Independent variable of this study was determined as financing strategy (combine deposits) and investment strategy (payment facilities) and variables like net interest margin and economic added value were accounted as financial operation criteria (dependent variables). Data were analyzed by Kruskal-Wallis test and using SPSS software. Results of present study indicated that financing strategy influenced significantly on economic added value, but did not influence on net interest margin. In contrast, impact of investment strategy on economic added value was rejected, but on net interest margin was confirmed. In second part, after using Delphi method, viewpoints of scholars in a private bank were collected and relative strategies in four fields of opportunities, threats, strengths and weaknesses were determined and after creating SWOT matrix, they were prioritized using AHP technique and presented a pattern to develop such strategies in an organization.

Financial strategy is a framework organization which directed the financial decision making resulted from different conditions, dynamic competitors and collection of relative reactions. Financial strategy is one of key factors in organizations and increase shareholder wealth by identifying value drivers. Accordingly, this study aimed to analyze the impact of financial strategy on financial operation of banks and also presented a pattern to develop and prioritize such strategies. To do this purpose, data were collected relative to Iran banks from 2010-2011. Independent variable of this study was determined as financing strategy (combine deposits) and investment strategy (payment facilities) and variables like net interest margin and economic added value were accounted as financial operation criteria (dependent variables). Data were analyzed by Kruskal-Wallis test and using SPSS software. Results of present study indicated that financing strategy influenced significantly on economic added value, but did not influence on net interest margin. In contrast, impact of investment strategy on economic added value was rejected, but on net interest margin was confirmed. In second part, after using Delphi method, viewpoints of scholars in a private bank were collected and relative strategies in four fields of opportunities, threats, strengths and weaknesses were determined and after creating SWOT matrix, they were prioritized using AHP technique and presented a pattern to develop such strategies in an organization.

The Internet of Medical Things (IoMT) is a new paradigm in the IoT-based healthcare and medical applications which has brought many advantages to this. Increasing in the number of smart medical devices has led to security challenges within IoMT environments. Cryptography and other cuttingedge approaches have been widely used to mitigate the challenges and prevent sensitive data leakage. One of such approaches is the attribute-based encryption (ABE), which enables a fine-grained and flexible access control. However, the ABE does not provide a dynamic encryption mechanism when patients’ conditions change over the period of remote monitoring. To address this, we present a novel security architecture for IoMT that relies on ciphertext policy attributebased encryption (CP-ABE). The architecture applies a dynamic access structure for the encryption in accordance with the status of the medical data collected by IoMT devices. We define some new components mainly hosted on the IoMT gateways in order to encrypt and decrypt the medical data in the architecture. An IoMT use case is presented to show how our proposed architecture supports dynamic encryption in ehealthcare systems.

Digital vaccination passports are being proposed by various governments internationally. Trust, scalability and security are all key challenges in implementing an online vaccine passport. Initial approaches attempt to solve this problem by using centralised systems with trusted authorities. However, sharing vaccine passport data between different organisations, regions and countries has become a major challenge. A platform for creating, storing and verifying digital COVID-19 vaccine certifications is presented, making use of InterPlanetary File System (IPFS) to guarantee that there is no single point of failure and to allow data to be securely distributed globally. Blockchain and smart contracts are also integrated into the platform to explicitly determine policies and log access rights to the passport data while ensuring all actions are audited and verifiably immutable. Our proposed platform realises General Data Protection Regulation (GDPR) requirements in terms of user consent, data encryption, data erasure and accountability obligations. We assess the scalability and performance of the platform using IPFS and Blockchain test networks.

The General Data Protection Regulation (GDPR) remains an important requirement for many electronic services which utilise user data. GDPR compliance verification for a cloud provider is aimed to confirm that personal data provided by a user is shared in-line with the requirements of this legislation, so that any subsequent audit carried out on the provider does not lead to a financial penalty. This verification involves two aspects: (i) ensuring that user consent has been obtained; (ii) sharing of data with external cloud providers is undertaken in a transparent way, so that the user is aware of which providers the information was shared with and for what purpose. Using a survey we describe why users are still ambivalent about the use of GDPR - and how its adoption can be improved using a Blockchain-based architecture that can provide greater transparency on how GDPR compliance is supported by cloud providers.

Internet of Things (IoT) applications have the potential to derive sensitive information about individuals. Therefore, developers must exercise due diligence to make sure that data are managed according to the privacy regulations and data protection laws. However, doing so can be a difficult and challenging task. Recent research has revealed that developers typically face difficulties when complying with regulations. One key reason is that, at times, regulations are vague, and could be challenging to extract and enact such legal requirements. In our research paper, we have conducted a systematic analysis of the data protection laws that are used across different continents, namely: (i) General Data Protection Regulations (GDPR), (ii) the Personal Information Protection and Electronic Documents Act (PIPEDA), (iii) the California Consumer Privacy Act (CCPA), (iv) Australian Privacy Principles (APPs), and (v) New Zealand’s Privacy Act 1993. In this technical report, we presented the detailed results of the conducted framework analysis method to attain a comprehensive view of different data protection laws and highlighted the disparities, in order to assist developers in adhering to the regulations across different regions, along with creating a Combined Privacy Law Framework (CPLF). After that, we gave an overview of various Privacy by Design (PbD) schemes developed previously by different researchers. Then, the key principles and individuals’ rights of the CPLF were mapped with the privacy principles, strategies, guidelines, and patterns of the Privacy by Design (PbD) schemes in order to investigate the gaps in existing schemes.

Cloud-hosted business processes require access to customer data to complete a transaction, to improve a customer’s on-line experience or provide useful product recommendations. However, privacy concerns associated with the use of this data have led to legal regulations that impose restrictions on how such data is requested or processed by an on-line service, with large penalties for violating these restrictions, e.g. the European General Data Protection Regulation (GDPR). We propose a framework for helping cloud-hosted services automate GDPR compliance checking. The framework comprises three steps: represent data flow in business processes with an appropriate abstraction (timed transition systems), formalise GDPR rules and obligations and incorporate them into the same abstraction, and implement the abstraction in a model checking tool (Uppaal) in order to automatically verify compliance of business process activities with GDPR. We demonstrate the approach using a cloud-based purchase order system.

The development of Internet of Things (IoT) industries has raised significant questions in terms of accountability of smart devices and user privacy. The advent of European General Data Protection Regulation (GDPR) in such industries enabled users to control their collected data and be informed about the collecting devices. This paper by using blockchain technology provides the audit trail of IoT devices under GDPR rules. It translates a set of such rules into smart contracts to protect personal data in a transparent and automatic way. By proposing an abstract model and designing some business processes, the paper shows how the integration of GDPR and blockchain can appear in the design patterns of IoT devices to achieve a greater transparency of privacy.

With recent adoption of Internet of Things (IoT) technologies and their use in industry, user data privacy concerns remain a major preoccupation of regulation bodies. The European General Data Protection Regulation (GDPR) enables users to control their data and be informed about any devices involved in collecting and processing this data. The overall objective is to enable individuals to have full rights and control over their data assets and to be able to transfer their data without any unmitigated risk. Blockchains provide the benefits of a distributed ledger that can securely manage digital transactions -- where the centralisation of data is eliminated. Blockchains have recently entered as an enabling technology into the IoT market, and used in a variety of different application areas. Blockchains enable the implementation of a more trusted system capable of processing operations between IoT services and sources of data. In smart buildings, for example, Blockchains support the formation of smart contracts as a means to give transactional capabilities to IoT devices, allowing users to keep data ownership and privacy using an immutable dataset. We describe how Blockchain technology can be used to develop an audit trail of data generated in IoT devices, enabling GDPR rules to be verified on such a trail. We describe how to translate a set of such rules into smart contracts to protect personal data in a transparent and automatic way.

Understanding how cloud providers support the European General Data Protection Regulation (GDPR) remains an imporant challenge for new providers emerging on the market. GDPR influences access to, storage, processing and tranmission of data, requiring these operations to be exposed to a user to seek explicit consent. A privacy-aware cloud architecture is proposed that improves transparency and enables the audit trail of providers who accessed the user data to be recorded. The architecture not only supports GDPR compliance by imposing several data protection requirements on cloud providers, but also benefits from a blockchain network that securely stores the providers' operations on the user data. A blockchainbased tracking approach based on a shared privacy agreement implemented as a smart contract is described - providers who violate GDPR rules are automatically reported through a voting mechanism.

A team formation problem consists in finding an effective group of experts in a social network to accomplish a job with a minimum expenditure of energy and time. This problem has been transposed into the domain of multiagent systems to form a team of autonomous agents whose mission is to achieve a given goal. There is a wide range of such problems. This paper generalizes one of them by assigning explicit behaviors to agents whose tasks are equipped with multiple attributes. Their values are compared with preferences attached to the desired tasks of the goal. A synthesized controller realizes the goal by invoking tasks of a subset of the available agents, called a composition in this paper. Furthermore, utility values are assigned to compositions and robustness is considered to be an important property of a team to prevent its deterioration when one or more of its agents fail. Finding a robust team that satisfies the goal’s preferences with better utility values for compositions constitutes a difficult optimization problem. The proposed method to solve this problem consists in three phases: controller synthesis with filtering on tasks with respect to some qualitative preferences, composition ranking based on their fitness, and multiobjective mathematical optimization.

The behavior composition problem consists in the automatic synthesis of a controller being able to realize a desired target behavior by coordinating a set of available behaviors. Although many frameworks have been proposed for this problem, one of the open issues concerns how to realize the desired target behavior at lower cost when several behaviors are available to perform the same actions. This paper provides an answer to this question by proposing a new framework in which values or costs are assigned to transitions of the available behaviors. Furthermore, controllers are synthesized by using two strategies: one suitable for on-line computation and another for off-line computation. Some experiments show how the two strategies perform on typical data.

One of the open issues in cloud computing is service discovery. Another issue, which has received less attention by practitioners in the cloud computing industry, is service composition based on formal methods, particularly those based on automata and formal languages. In recent years, semantic techniques and cloud ontology have been used to improve the process of finding similar services in the cloud environment. Cloudle, which is the most recent agent-based and semantic-supported search engine in cloud systems, exploits such techniques. Though many models and architectures have been proposed to develop Cloudle, none of them used formal methods for composing services. In this paper, behavior composition, as a formal approach and a sound and complete controller synthesis technique, is adapted to provide a formal approach for resource matching and service composition.

The behavior composition problem consists in the synthesis of a controller that coordinates a set of available behaviors with the aim of realizing a desired target behavior. This problem suffers from a serious drawback because, for many instances, the target behavior cannot be achieved. In that case, the user is responsible for bringing changes until the problem becomes solvable. This inherent limitation is due to the fact that an exact match is required between the actions initiated by the user and those offered by the available behaviors. The main question is, how can a controller select a suitable behavior such that the functionality of one of its available actions is close to that currently requested by the desired target behavior? This paper provides an answer to this question by associating semantics to actions based on expectations and exploiting a semantic similarity function between actions. Similar actions that have different names, but comparable tasks, are considered equivalent. This renders the behavior composition framework more flexible so that it can fit with semantic environments.

With the evolution of software engineering since the advent of structured programming until now, software engineers are faced with tremendous challenges mostly due to the development of large software programs that behave as open systems. Multi-agent systems, which consist of multiple cooperating intelligent agents within an environment, form a particular class of such systems. This sort of software program, which carries out operations by repeatedly interacting with dynamic environments, has become more and more complex with the emergence of ubiquitous communication and computing technologies that constantly grow and evolve. Agent-oriented computing constitutes an appealing solution for coping with this level of complexity because systems can be built by combining agents. The automated composition of software artifacts to generate new ones may rest on recent progress in artificial intelligence and automatic control that has its roots in the tradition of program synthesis. The goal is to provide software engineers with effective methods in which the planning and control of software actions are integral parts of composition operators, together with synthesis procedures that automatically generate an execution strategy that governs the discrete dynamics of the composed artifact in order to satisfy given requirements. This approach ensures a higher degree of safety because it relies on formal methods. This paper shows how the behavior composition problem issued from the artificial intelligence community can be solved within the framework of the supervisory control theory with the aim to benefit from all of its rich facets.

Internet of Things (IoT) applications have the potential to derive sensitive information about individuals. Therefore, developers must exercise due diligence to make sure that data are managed according to the privacy regulations and data protection laws. However, doing so can be a difficult and challenging task. Recent research has revealed that developers typically face difficulties when complying with regulations. One key reason is that, at times, regulations are vague, and could be challenging to extract and enact such legal requirements. In our research paper, we have conducted a systematic analysis of the data protection laws that are used across different continents, namely: (i) General Data Protection Regulations (GDPR), (ii) the Personal Information Protection and Electronic Documents Act (PIPEDA), (iii) the California Consumer Privacy Act (CCPA), (iv) Australian Privacy Principles (APPs), and (v) New Zealand’s Privacy Act 1993. In this technical report, we presented the detailed results of the conducted framework analysis method to attain a comprehensive view of different data protection laws and highlighted the disparities, in order to assist developers in adhering to the regulations across different regions, along with creating a Combined Privacy Law Framework (CPLF). After that, we gave an overview of various Privacy by Design (PbD) schemes developed previously by different researchers. Then, the key principles and individuals’ rights of the CPLF were mapped with the privacy principles, strategies, guidelines, and patterns of the Privacy by Design (PbD) schemes in order to investigate the gaps in existing schemes.