Abstract This article outlines an integrated strategy that combines fuzzy multi‐objective programming and a multi‐criteria decision‐making framework to achieve a number of transportation system management‐related objectives. To rank fleet cars using various criteria enhancement, the Fuzzy technique for order of preference by resemblance to optimum solution are initially integrated. We then offer a novel Multi‐Objective Possibilistic Linear Programming (MOPLP) model, based on the rankings of the vehicles, to determine the number of vehicles chosen for the work while taking into consideration the constraints placed on them. The search for optimal solutions to MOPs has benefited from the decades‐long development of classical optimisation techniques. As a result of its potential for use in the real world, multi‐objective optimisation (MOO) under uncertainty has gained traction in recent years. Recently, fuzzy set theory has been used to solve challenges in multi‐objective linear programming. In this paper, we present a method for solving MOPs that makes use of both linear and non‐linear membership functions to maximize user happiness. A hypothetical case study of transportation issue is taken here. This innovative approach improves management for the betterment of transportation networks in smart cities. The method is a more robust and versatile approach to the complex difficulties of contemporary urban transportation because it incorporates the TOPSIS method for vehicle ranking and then using Distance Operator and variable Membership Functions in fuzzy goal programming operation on the selected vehicles. The results provide valuable insights into the strengths and limitations of each technique, facilitating informed decision‐making in real‐world optimization scenarios.

Abstract The most important and difficult challenge the digital society has recently faced is ensuring data privacy and security in cloud‐based Internet of Things (IoT) technologies. As a result, many researchers believe that the blockchain's Distributed Ledger Technology (DLT) is a good choice for various clever applications. Nevertheless, it encountered constraints and difficulties with elevated computing expenses, temporal demands, operational intricacy, and diminished security. Therefore, the proposed work aims to develop a Decentralized Identifiable Distributed Ledger Technology‐Blockchain (DIDLT‐BC) framework that is intelligent and effective, requiring the least amount of computing complexity to ensure cloud IoT system safety. In this case, the Rabin algorithm produces the digital signature needed to start the transaction. The public and private keys are then created to verify the transactions. The block is then built using the DIDLT model, which includes the block header information, hash code, timestamp, nonce message, and transaction list. The primary purpose of the Blockchain Consent Algorithm (BCA) is to find solutions for numerous unreliable nodes with varying hash values. The novel contribution of this work is to incorporate the operations of Rabin digital data signature generation, DIDLT‐based blockchain construction, and BCA algorithms for ensuring overall data security in IoT networks. With proper digital signature generation, key generation, blockchain construction and validation operations, secured data storage and retrieval are enabled in the cloud‐IoT systems. By using this integrated DIDLT‐BCA model, the security performance of the proposed system is greatly improved with 98% security, less execution time of up to 150 ms, and reduced mining time of up to 0.98 s.

Abstract Plant leaf diseases are challenging to categorize due to the complexity of the pattern variations and the high degrees of inter‐class similarity. Plant ailments harm food quality and production. To ensure the quality and quantity of harvests, it is essential to protect plants from disease. Detection of diseases at an early stage is the main and the most complex task for farmers due to common morphological properties like colour, shape, texture, and edges. In this study, a Hybrid Deep Learning model named Hybrid‐Convolutional Support Machine (H‐CSM) based on ‘Support Vector Machine (SVM)’, ‘Convolutional Neural Network (CNN)’ and ‘Convolutional Block Attention Module (CBAM)’ is proposed for the early diagnosis and classification of leaf diseases in plants leaf. The suggested model can initially identify different plant leaf illnesses, although it is not constrained to these. A database of pictures of plant leaves is used to test the suggested method based on different evaluation parameters. The results were highly promising, with an accuracy of up to 98.72% which has been increased by applying better learning methods. Farmers can quickly identify 36 common diseases with a little instruction for 14 plant categories, enabling them to take prompt preventive measures using the proposed method.

Abstract In this article, we present two resilient algorithms, the improved alternative hard c‐means (IAHCM) and the improved alternative fuzzy c‐means (IAFCM). We implement the Gaussian distance‐dependent function proposed by Zhang and Chen (D.‐Q. Zhang and Chen, 2004). In some cases, Zhang and Chen's metric distance does not account for the clustering centroid effect predicted by the large value. R* is employed in IAHCM and IAFCM to discover robust results while minimizing its sensitivity. Experiments are conducted using two‐and three‐dimensional data, including Diamond and Iris real‐world data. The results are based on demonstrating the robust simplicity and applicability of the offered algorithms. Similarly, computational complexity is assessed.

Abstract Cephalometric analysis has an important and essential role to treat the patients with craniofacial and dentofacial deformities. Cephalometric analysis is a relationship of human geometry which can be quantified and derived from the linear and angular measurements. To treat any patient, such analysis is required to be performed on the Head X‐ray image of the patient. The objective of the proposed work is to detect cephalometric landmarks automatically on CT (computational tomography) images. Twenty cephalometric landmarks were automatically localized on 100 CT scans using hybrid coarse‐to‐fine computational method. The mean error for landmark detection was computed as 2.88 mm and standard deviation of 1.85 mm. The highest detection rate for cephalometric landmarks was received as 100% for Nasion landmark under 4‐mm error and the highest detection rate was received as 99% for Nasion landmark under 3‐mm error. The less number of datasets were used for the training and higher number of datasets were used for the testing. Compared to the literature methods, our method used higher number of datasets to demonstrate the accuracy of the proposed method.

The evolution of Internet-of-Vehicles (IoV) from IoT has revolutionized Smart cities, enabling vehicle communication for safety and traffic information dissemination. However, fulfilling time-sensitive applications like safety alerts via Cellular Vehicle-to-Everything (C-V2X) faces resource constraints. This study presents a Non-Orthogonal Multiple Access (NOMA) based resource allocation for C-V2X in Ultra-dense networks (UDN). This paper has also discussed the role of TinyML in unmanned aerial vehicle (UAV) and it is demonstrated with use case scenario. Additionally, a generalized expression for scheduling time fraction is derived for the proposed scheme. The proposed framework optimizes power allocation, accommodating high-speed users and UAV scenarios to improve performance in obstructed regions. Numerical analysis demonstrates an approximate 85% throughput increase over conventional schemes, affirming the efficiency of the NOMA-based approach for enhanced C-V2X performance.

The rapid growth of the Internet of Things (IoT) in the early 21st century has introduced complexities in delivering various services, including Quality-of-Service (QoS) management for smart agriculture sensors. Selecting optimal IoT nodes considering QoS parameters, such as energy consumption, latency, and network coverage area has become challenging. In response, this research proposes an extended form of differential evolution (DE) that incorporates a rapid adaptation approach using optimization-based design. By leveraging dynamic information from IoT devices, the proposed approach enhances exploration and exploitation capabilities, allowing for adaptive adjustment of algorithm parameters and strategies. Additionally, a novel fitness function for energy harvesting in IoT-based applications is introduced. The effectiveness of the proposed algorithm is evaluated in IoT-based applications and an IoT-service framework, with comparative analysis against state-of-the-art algorithms. The results demonstrate that the proposed approach achieves superior performance in energy harvesting QoS, delay, service cost, and maximum coverage area in the IoT-service network. This research contributes to the IoT field by offering an advanced DE algorithm that addresses limitations, providing valuable insights for QoS management in IoT-based services, particularly in the context of smart agriculture sensors.

Edge infrastructure and Industry 4.0 required services are offered by edge-servers (ESs) with different computation capabilities to run social application's workload based on a leased-price method. The usage of Social Internet of Things (SIoT) applications increases day-to-day, which makes social platforms very popular and simultaneously requires an effective computation system to achieve high service reliability. In this regard, offloading high required computational social service requests (SRs) in a time slot based on directed acyclic graph (DAG) is an NP-complete problem. Most state-of-art methods concentrate on the energy preservation of networks but neglect the resource sharing cost and dynamic subservice execution time (SET) during the computation and resource sharing. This article proposes a two-step deep reinforcement learning (DRL)-based service offloading (DSO) approach to diminish edge server costs through a DRL influenced resource and SET analysis (RSA) model. In the first level, the service and edge server cost is considered during service offloading. In the second level, the R-retaliation method evaluates resource factors to optimize resource sharing and SET fluctuations. The simulation results show that the proposed DSO approach achieves low execution costs by streamlining dynamic service completion and transmission time, server cost, and deadline violation rate attributes. Compared to the state-of-art approaches, our proposed method has achieved high resource usage with low energy consumption.

Most computation-intensive industry applications and servers encounter service-reliability challenges due to the limited resource capability of the edge. Achieving quality data fusion and accurate service reliability with optimized service-x execution cost is challenging. While existing systems have taken into account factors such as device service execution, residual resource ratio, and channel condition; the service execution time, cost, and utility ratios of requested services from devices and servers also have a significant impact on service execution cost. To enhance service quality and reliability, we design a 2-step adaptive service-X cost consolidation (ASXC2) approach. This approach is based on the node-centric Lyapunov method and distributed Markov mechanism, aiming to optimize the service execution error rate during offloading. The node-centric Lyapunov method incorporates cost and utility functions and node-centric features to estimate the service cost before offloading. Additionally, the Markov mechanism-inspired service latency prediction model design assists in mitigating the ratio of offload-service execution errors by establishing a mobility-correlation matrix between devices and servers. In addition, the non-linear programming multi-tenancy heuristic method design help to predict the service preferences for improving the resource utilisation ratio. The simulations show the effectiveness of our approach. The model performance is enhanced with 0.13% service offloading efficiency, 0.82% rate of service completion when transmitting data size is 400 kb, and 0.058% average service offloading efficiency with 40 CPU Megacycles when the vehicle moves 60 Km/h speed around the server communication range. Our model simulations indicate that our approach is highly effective and suitable for lightweight, complex environments.

Computation and memory resource management strategies are the backbone of continuous object tracking in intelligent vehicle orchestration. Multi-object tracking generates enormous measurements of targets and extended object positions using light detection and ranging (Lidar) sensors. Designing an adequate object-tracking system is a global challenge because of dynamic object detection and data association uncertainties during scene understanding. In this regard, we develop an intelligent multi-objective tracking (IMOT) system with a novel measurement model, called the box data association inflate (BDAI) model, to assess each target’s object state and trajectory without noise by using the Bayesian approach. The box object filter method filters ambiguous detection responses during data association. The theoretical proof of the box object filter is derived based on binomial expansion. Prognosticating a lower-dimension object than the original point object reduces the computational complexity of vehicle orchestration. Two datasets (NuScenes dataset and our lab dataset) are considered during the simulations, and our approach measures the kinematic states adequately with reduced computation complexity compared to state-of-the-art methods. The simulation outcomes show that our proposed method is effective and works well to detect and track objects. The NuScenes dataset contains 28130 samples for training, 6019 examples for validation and 6008 samples for testing. IMOT achieves 58.09% tracking accuracy and 71% mAP with 5 ms pre-processing time. The Jetson Xavier NX consumes 49.63% GPU and 9.37% average power and exhibits 25.32 ms latency compared to other approaches. Our system trains a single pair frame in 169.71 ms with affinity estimation time of 12.19 ms, track association time of 0.19 ms and mATE of 0.245 compared to state-of-the-art approaches

Edge-enabled Deep Learning (DL) solutions for Predictive Quality Inspection (PQI) of products in Industry 4.0 are mostly designed for static manufacturing environments. In general, modern manufacturing processes are dynamic in nature. In this context, continual learning-based model retraining accommodates the dynamism for PQI of multiple processes (tasks) using a single DL model. However, the impact of the task ordering in sequentially arriving tasks and solution to reduce this impact on the overall PQI is yet to be solved. To this end, a novel mechanism using a light-weight similarity analysis module is introduced in the quality prediction system at the resource-limited edge. Sequential training of tasks above a similarity threshold (γ) is preferred, and dissimilar tasks are overlooked to train a separate model. This enables a PQI system to hover over training efficiency and model sustainability. The experimental results validate the impact of task order and the effectiveness of the proposed similarity-based analysis to reduce this impact by 70% on the model's overall performance in the real-world use case of plastic bricks.

Blockchain technology (BCT) has brought a paradigm shift to Business Process Management (BPM). BCT provides a trusted decentralized infrastructure to secure data and process executions using distributed ledgers and smart contract to manage complex business processes. Numerous efforts have been made to exploit BCT in supporting dynamic and trusted collaborations of business processes. This paper aims to understand recent BCT development for its BPM applications and identify the limitations and challenges for further development via a systematic literature review (SLR). It is found that numerous works have reported using BCT as technical solutions to fulfill some traditional BPM functions. This paper is distinguished from existing works, especially several relevant surveys in the sense that (1) the impact of using BCT in BPM is thoroughly explored to identify new constraints and challenges explicitly brought by blockchains; (2) the requirements for Business Process Compliance (BPC) are firstly analyzed in detail. Note that BPC is to assure the adherence of business processes to pre-defined policies, standards, specifications, regulations, and laws when business processes are executed. To fill the gaps of BCT applications in these two aspects, Blockchain-as-a-Service (BCaaS) is adopted in business process architecture, and the trends of BCT developments are identified accordingly.

Workflow is used to support and connect business processes (BP) in organizations. Historically, it is used to define the control of how tasks are coordinated and executed. Its importance has been continuously increasing with the incorporation of rapidly developed Service-oriented Architecture (SoA), Blockchain, and Internet-of-Thing (IoT); new information technologies have expanded the coverage of workflow across various applications. Workflows often interact with services, where SoA is a key driver to smoot service discovery and provide standards for interoperation. In decentralized collaborative environments, a workflow often deals with disparate services dynamically and interacts with services on demand. This not only unlocks the potentials of workflow applications in business process managements (BPM), but also precipitates significant challenges and has brought considerable attentions to the research community. This paper investigates the state-of-the-art of service workflow modelling and enabling technologies. It begins with the identification and examination of workflow and IoT characteristics; it proposes a workflow architecture to classify existing works on service workflows, and it summarizes the methods for workflow modeling, service interoperation to identify the limitations of existing works and clarify future research directions in using service workflows.

Wireless Sensor Networks (WSNs) are capable of achieving data dissemination between them such that exploration of their potential could be performed based on their frequency range. It is considered to be highly difficult for recharging sensor devices under adverse situations. The main drawbacks of WSNs concern to the issue of network lifetime, coverage area, scheduling and data aggregation. In particular, prolonging network lifetime confirms the success together with the energy conservation of sensor nodes, data transmission reliability and scalability of their operation in data aggregation. Clustering schemes are considered to be highly suitable for effectively utilising the resources with lower overhead, such that energy consumption is enhanced for upgrading the network lifespan. In this paper, a Hybrid Modified Artificial Bee Colony and Firefly Algorithm (HMABCFA) -Based Cluster Head Selection is proposed for ensuring energy stabilisation, delay minimisation and inter-node distance reduction for improving the network lifetime. This proposed HMABCFA integrates the benefit of the Firefly optimisation algorithm for generating a new position that which has the capability of replacing the position, which is not updated in the scout bee phase of ABC. This incorporation of Firefly optimisation algorithm into the ABC algorithm prevents the limitations of premature convergence, slow convergence and the possibility of being trapped into the local point of optimality in the clustering process. The modified ABC-based clustering process is phenomenal in improving the feasible dimensions for enhancing the process of exploitation and exploration. The results of the HMABCFA, on an average are confirmed to enhance the network lifetime by 23.21%, energy stability by 19.84% and reduce network latency by 22.88%, compared to the benchmarked approaches.

Abstract Handling the uncertainties in the utility grid and uncertainties in renewable energy sources is a main challenge to accomplish the modern grid code requirements. This article proposes a new control strategy based on interval type‐2 fuzzy sets for controlling the grid‐connected dispersed generation (DG) system tied to the distorted electric grid. The proposed control technique can easily model and handle the uncertainties in the system parameters and renewable energy sources. The existence of three‐dimensional membership functions of type‐2 fuzzy sets offers an additional degree of freedom to counter the uncertainties in the system. The proposed strategy is effective in improving the dynamic response during system uncertainties such as distortions in grid voltage, variations in grid frequency, variations in renewable energy sources and due to presence of non‐linear and unbalanced loads. Also, it improves the quality of the current being injected to the grid during uncertainties. The proposed control strategy is applied to control the dc side capacitor voltage as well as to control the current loop of the grid connected inverter. The proposed system is simulated in MATLAB Simulink environment and the performance is compared with traditional controllers to show the effectiveness of the proposed control strategy during system abnormalities.

Due to energy efficiency and near real-time finality settlement, permissioned blockchain is mainly used in blockchain-based Internet of Things (BIoT) services. This blockchain requires validators to reach the consensuses. Currently, the validators are chosen based on generic attributes of trusted nodes capriciously. However, service users have no mechanism to provide their requirements in assessing validators. Existing works are biased from users’ perspectives, which risks collusion attacks due to improperly selected validators. This article aims to solve this by considering users’ expectations on services. A user-oriented framework is proposed to evaluate the trust of BIoT services with the consideration of validator attributes. It provides an effective mechanism to express requirements. Specification language is adopted in our algorithm to perform compliance checking. The feasibility has been verified in a simulation. We found that the permissioned blockchain has also benefited from the proposed user-oriented selection of validators in terms of computing efficiency due to additional constraints.

With the fast development of information technologies, traditional value-added chain business models are shifting to service-based applications (SBAs) to cope with ever-changing user behaviors and the modern social system. An SBA allows an organization to utilize external and distributed resources to achieve its business goals, especially when the Internet of Thing (IoT) will soon become mainstream. However, the development of SBAs is at its early stage with a number of unsolved issues, such as the availability of effective methods for services selection and composition, semantic representation of specifications, and security assurances. This article aims to address two main issues in SBAs: 1) the standardization of formulation and 2) the security assurance. A systematic method is proposed to formulate the specifications of services, the prevalent blockchain technology (BCT) is adopted to enable SBAs, and the proposed specification patterns and BCT are integrated as a BCT-based quality-of-service (QoS) framework to support service selections and workflow compositions.

Internet of Things (IoT) refers to networks with billions of physical devices for collecting, sharing, and utilizing data in the virtual world. Most of IoT applications centralize security assurance in creating, authenticating, transferring, or delating system components. However, the centralization exposes its limitations to meet security needs of a rapidly growing number of things world-widely. How to scale up the applications with assured security becomes a critical challenge. Blockchain technology (BCT) is a promising solution to provide security and protect privacy in a large scale; especially, smart contracts offer opportunities to improve the reliability of IoT applications. Smart contracts establish trusts for both of data and executed processes. Recently, many literature surveys and positioning articles have been published on the integration of BCT with IoT, but they are limited to superficial discussions of technical potentials, and very few of them have a thorough exploration of the challenges in developing BCT for IoT at technical levels. This paper uses the system design approach to scrutinize the state of the art of study on BCT-based applications and clarify critical research areas of enabling BCT for security assurance: 1) the relations of BCT and IoT are modeled and discussed; 2) the needs of eliminating threats in IoT-based applications are defined as functional requirements (FRs), existing works on enabling technologies of BCT are defined as the physical solutions (PSs); and 3) the mappings between FRs and PSs are established to identify the limitations and the critical areas for the applications of BCT in large-scale distributed environment.

Internet of Things (IoT) is able to integrate the computation and physical processes as services in the social world. The number of services at the edge of IoT is rising rapidly due to the prevalent uses of smart devices and cyber-physical systems (CPSs). To explore the promising applications of IoT services, one of the challenges is to enable the interoperability of the services in a decentralized environment. The blockchain technology (BCT) has been proven as a promising solution to establish the trust of data and call for executions; theoretically, it can be used to support the interoperability of services. BCT verifies data or a process and stores it as a transaction in a distributed ledger. Similar to the topology to IoT, applying BCT at the edges of the network exhibits the distributed characteristic. However, currently, BCT is still facing the challenges for interoperability due to a number of factors such as consensus protocols, block sizes, and interval of blocks. Prominent protocols such as proof-of-work (PoW) may cause excessive delays in finality settlement. One promising protocol Practical Byzantine Fault Tolerant offers a fast finality settlement and uses hyperledger to support the scalability; however, the trust might also be a concern if the validators are chosen improperly. This paper discusses the interoperability of IoT services and the challenges and proposes an architecture solution by integrating BCT, service-oriented architecture (SoA), and enablers of key performance indicators (KPIs) and service selections. The proposed architecture aims to solve both interoperability and trust issues for IoT services. The feasibility of the proposed method is validated by the examples of smart contract implementations.

Owing to the exponential growth of Internet of Things (IoTs), ensure that the Quality of Service (QoS) over IoT becomes challenges at the network edge or on cloud. The traditional mechanisms for QoS measurements rely on the centralized trusted third parties who use specialized agents to collect data and measure the performances of services. However, these mechanisms are ineffective to deal with highly dynamic and distributed nature of IoT-based services. Moreover, the dynamism of QoS needs to collect, update, and access reliable quality relevant data frequently, while lacking trust becomes a major hurdle for data utilization. It is our argument that the QoS measurement of IoT-based services would be decentralized and the trusts be built from collectively trusted subnetworks. In this paper, we propose to integrate the blockchain technologies (BCT) with a multi-agent approach to warrantee the trustiness of real-time data for the measurement of QoS in the IoT environment. The proposed approach is verified by some demonstrative examples in addressing QoS specification patterns commonly found in service-based applications (SBAs), where qualitative analyses are conducted for the evaluation of the patterns.

In addition to functionalities, business process management (BPM) involves several key indicators such as openness, security, flexibility, and scalability. Optimizing system performance is becoming a great challenge for an ever-increasing large-scale distributed application system in the digital economy on the Internet of Things (IoT) era. In a centralized BPM, many indicators, such as security and openness, or cost and flexibility, are conflicting with each other. For example, inviting new partners across enterprises, domains, and regions to form a service workflow exposes new risks and needs additional security mechanisms for scrutiny; enhancing the flexibility of business workflow compositions increases the cost of security assurance. Blockchain technology (BCT) has thrown the light on the development of vital solutions to various BPM problems. BCT has to be integrated with other BPM system components that often involve IoT devices to implement specified functionalities related to the application. Currently, the potentials of using BCT have been explored although still at an early stage. In this paper, the states of the art are presented to identify emerging research topics, challenges, and promising applications in integrating BCT into the development of BPM.

Innovating business processes involves cutting-edge technologies where the Internet of Things (IoT) and Blockchain are technological breakthroughs. IoT is envisioned as a global network infrastructure consisting of numerous connected devices over the Internet. Many attempts have been made to improve and adapt business workflows for best utilizing IoT services. One possible solution is to digitize and automate internal processes using IoT services, in which Blockchain smart contract is a viable solution to establish the trust of process executions without intermediaries. Modern business processes are composed of disparate services; many of them tend to be delivered based on IoT. Interoperating with such services poses major challenges: 1) time for finality settlement of transactions is unpredictable and usually experiencing delay; 2) several implementations of permissioned Blockchain pose a major concern of trust regarding nodes that perform consensus; and 3) trust of process executions and IoT information is the major factor to the success of modern business processes, which require the composition of distributed IoT services. Traditional business processes are mostly managed by a single entity, which induces the problem of trust of process executions. In this paper, a smart contract for establishing the trust of process executions that fits into the IoT environment is presented. A consensus approach with selected validators extended from Practical Byzantine Fault Tolerance (PBFT) is introduced to address time and prejudice challenges.

Service-Oriented Computing (SOC) is changing the way modern information systems that are designed, operated, and evolved. SOC makes possible to aggregate distributed resources at the phases of decision-making support and system operations. When myriads of resources with similar functionalities are available, effective methodologies are demanded to select services and compose them as service workflows for the specified goals. The computation for workflow composition is very complex since it depends on the numbers of services and their dynamic characteristics. Therefore, composing optimized workflows in a timely manner poses a great challenge. We are highly motivated to reduce the complexity of service selection and composition. The formalized semantics in SWSpec is extended so that unqualified or inferior services can be eliminated directly from the scope of the design solution space. In this paper, a brief review of the proposed SWSpec language is given and the focus is on the sematic formalization. A new compositional proof-system is developed with a set of inference rules and the proven system properties. The proposed semantic formalization has its great significance in reducing the complexity of composing workflows and developing efficient algorithms for compliance checking.

Service-oriented computing (SOC) lays the foundation for modern enterprise systems where services are incorporated as building blocks in application workflows. However, SOC is still at development stage with many unsolved issues; particularly, composing services for a workflow pose a significant challenge when some factors including distribution and dynamic characteristics of services are in consideration. The number of vendors providing similar services is increasing, and this requires effective methodologies for service workflow compositions. Service workflow specification (SWSpec) language was proposed to specify requirements of service attributes and verify them in a workflow during service selection processes automatically. One requirement for SWSpec is that all services must be represented for service selections adequately, while existing SWSpec exhibits its limitation in addressing significant specification patterns found in service-based applications (SBAs). This paper proposes a strengthened SWSpec, called real-time monitoring SWSpec that incorporates new modalities to represent missing specification patterns of events occurring in SBAs.

The emergence of the Internet-of-Things (IoT) refers to not only the ability to identify physical objects, but also to identify many types of virtual objects, including services. Such identification plays a crucial role in service workflow. The success of a service workflow requires the composition of services where requirements must be satisfied. However, the large-scale open environment of today's Internet poses significant challenges for efficient compliance checking algorithms of those requirements. This paper is based on the previous progressive work on Service Workflow Specification language (SWSpec), the uniformed representation of requirements, and the compliance checking algorithms based on Constrained Truth Table (CTT) and Exclusive Disjunctive Normal Form (EDNF). In this paper, a new algorithm is proposed, which significantly reduces the cost of time complexity. In some cases, this algorithm is able to run in polynomial time. Experiments are conducted to evaluate and compare the performance of these algorithms.

The Internet of Things (IoT) not only identifies physical and virtual objects, but also enables the connection of such objects in an internet-like structure. In this context, services are one of the most important technologies where IoT can be utilized to enhance dynamic interoperation in the form of service workflows. In opened environments, because services are dynamically gathered, IoT identification is an essential feature which leads to more complicated service composition than before. Since services may possess different requirements, it becomes very challenging to determine suitable services to be a part of a workflow. Compliance of such requirements, reflecting trust-based decision of a service to join a workflow, plays a significant role in the success of service interoperation. This paper presents the compliance checking algorithms for service workflow specification (SWSpec) and service workflow net (SWN) to support trust-based decision for service workflow participation. The application of SWSpec is illustrated using an example of a disaster warning system. Finally, the prototype is developed to evaluate the performance of the proposed algorithms.

The internet-of-things (IoT) technology allows auto-organized and intelligent entities such as services to be interoperable and able to act independently. This enables the advanced form of service composition by allowing individual services to dynamically form a service workflow. In this context, services possess different requirements where the compliance of such requirements reflects trust-based decision for participating in a workflow. Large-scale service interoperations pose significant challenges for compliance checking of those requirements. These include inconsistency of requirements that can be represented in different formats and dynamicity, where a workflow can be modified based on service creation, modification, or termination. These factors directly affect the decision of a service to be part of a workflow. To solve these problems, service workflow specification (SWSpec) has been proposed as a consistent and uniformed representation of requirements, and algorithms based on constrained truth table (CTT) have been developed for automatic compliance checking. In this paper, the architecture of these elements is created to facilitate 1) a workflow owner in specifying properties of services to be part of a workflow and 2) services to express their requirements where their compliance reflects trust-based participation decision.

Advanced technologies have changed the nature of business processes in the form of services. In coordinating services to achieve a particular objective, service workflow is used to control service composition, execution sequences as well as path selection. Since existing mechanisms are insufficient for addressing the diversity and dynamicity of the requirements in a large-scale distributed environment, developing formal requirements specification is necessary. In this paper, we propose a Service Workflow Specification language, called SWSpec, which allows arbitrary services in a workflow to formally and uniformly impose their requirements. As such, the solution will provide a formal way to regulate and control workflows as well as enrich the proliferation of service provisions and consumptions in opened environments.

This paper presents a requirement-oriented automated framework for formal verification of service workflows. It is based on our previous work describing the requirement-oriented service workflow specification language called SWSpec. This language has been developed to facilitate workflow composer as well as arbitrary services willing to participate in a workflow to formally and uniformly impose their own requirements. As such, SWSpec provides a formal way to regulate and control workflows. The key component of the to-be-proposed framework centers on verification algorithms that rely on propositional logic. We demonstrate that logic-based workflow verification can be applied to SWSpec which is capable of checking compliance and also detecting conflicts of the imposed requirements. By automating compliance checking process, this framework will support scalable services interoperation in the form of workflows in opened environments.

The emergence of the Internet has changed the nature of face-to-face towards online interactions. This leads to the concept of virtual interoperation such as Web Services, Grid, and Cloud Computing. Since existing security mechanisms are insufficient to cover the diversity of workflow application domains, trust is considered as an adaptive, high-level abstraction, and platform-independent solution that fits into this context. This paper proposes a formal trust specification which covers a wide range of intuitive trust characteristics such as trust transitivity and mutual relationship. We develop a new trust definition and three modes of trust with algebraic operators to form specification formulas. A method for determining the closeness of a matched trust value on a service using Euclidean Distance is presented and the basic analysis is conducted.