Nowadays, the usage of the Industrial Internet of Things (IIoT) in practical applications has increased. The primary utilization is a fog cloud network, which offers different services, such as network and remote edges, at different places. Existing studies implemented the Service-Oriented Architecture (SOA) based on the fog-cloud network to run IIoT applications, such as e-healthcare, e-agriculture, renewable energy, etc. However, due to the applications' monolithic property, issues like failures, security, and cost factors occur, e.g. the failure of one service in SOA affects monolithic applications' performance in the system. With this motivation, this study suggests a microservice-based system to deal with the cost, security, and failure risks of IIoT applications in the fog-cloud system. The study improves the existing SOA systems for e-healthcare, e-agriculture, and renewable energy and minimizes the applications' overall cost. The performance evaluation shows that the devised systems outperform the existing SOA system in terms of failure, cost, and the deadline for all applications.

Named Entity Recognition (NER) System aims to extract the existing information into the following categories such as: Person's Name, Organization, Location, Date and Time, Term, Designation and Short forms. Now, it is considered to be important aspect for many natural languages processing (NLP) tasks such as: information retrieval system, machine translation system, information extraction system and question answering. Even at a surface level, the understanding of the named entities involved in a document gives richer analytical framework and cross referencing. It has been used for different Arabic Script-Based languages like, Arabic, Persian and Urdu but, Sindhi could not come into being yet. This paper explains the problem of NER in the framework of Sindhi Language and provides relevant solution. The system is developed to tag ten different Named Entities. We have used Ruled based approach for NER system of Sindhi Language. For the training and testing, 936 words were used and calculated performance accuracy of 98.71%.

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Present-day intelligent healthcare applications offer digital healthcare services to users in a distributed manner. The Internet of Healthcare Things (IoHT) is the mechanism of the Internet of Things (IoT) found in different healthcare applications, with devices that are attached to external fog cloud networks. Using different mobile applications connecting to cloud computing, the applications of the IoHT are remote healthcare monitoring systems, high blood pressure monitoring, online medical counseling, and others. These applications are designed based on a client–server architecture based on various standards such as the common object request broker (CORBA), a service-oriented architecture (SOA), remote method invocation (RMI), and others. However, these applications do not directly support the many healthcare nodes and blockchain technology in the current standard. Thus, this study devises a potent blockchain-enabled socket RPC IoHT framework for medical enterprises (e.g., healthcare applications). The goal is to minimize service costs, blockchain security costs, and data storage costs in distributed mobile cloud networks. Simulation results show that the proposed blockchain-enabled socket RPC minimized the service cost by 40%, the blockchain cost by 49%, and the storage cost by 23% for healthcare applications.

Crowdsourcing is a complex task-solving model that utilizes humans for solving organizational specific problems. For assigning a crowdsourced task to an online crowd, crowd selection is carried out to select appropriate crowd for achieving the task. The efficiency and effectiveness of crowdsourcing may fail if irrelevant crowd is selected for performing a task. Early decisions regarding selection of a crowd can ultimately lead to successful completion of tasks. To select most appropriate crowd from crowdsourcing, this paper presents a decision support system (DSS) for appropriate selection of crowd. The system has been implemented in the Superdecision tool by plotting hierarchy of goals, criteria, and alternatives. Various calculations have been done for performing the proposed research. Results of the study reveal that the proposed system is effective and efficient for selection of crowd in crowdsourcing by performing various pairwise computation of the study.