A hybrid framework for industrial data storage and exploitation

At the current pace of technological advancement, it is evident that IoT (Internet of Things) devices are becoming an inevitable part of daily life. Since the growth rate is exceptional, this situation has led to an alarming rise in concern for security threats and issues in the IoT devices. The purpose of this study is to introduce a novel and well-structured threat-modeling approach which is specifically tailored for IoT devices. Traditional threat models like STRIDE, LINDDUN, CORAS, etc., are effective in software applications. However, these models do not cover every aspect of threats specific to IoT devices. The proposed approach consists of a seven-step threat-modeling process. This methodology also addresses aspects of IoT devices which directly affect the user: Privacy threats; Safety threats; and Malfunction threats (PSM). The mentioned threats cannot be addressed effectively with existing traditional threat-modeling approaches. The proposed threat modeling has been applied to three IoT applications: IoT-based smart home environments; IoT-based garment units; and IoT-based water quality management systems, in order to demonstrate the work’s effectiveness. It was seen that when coupling the proposed methodology with the PSM technique and existing threat modeling methods, threats directly affecting users were also identified. This method increases the performance and effectiveness of threat modeling, which leads to increased mitigation of the identified threats. This also leads to an increase in the security of the devices and environment. The final part of the study presents practical mitigation techniques to eliminate threats in IoT devices.

With the evolution of the Internet of Things (IoT), the use of smart devices has completely changed the day-to-day life of the human being. IoT devices are of flexible use which is implemented to sense the environment and data efficiently. However, these devices have some constrained capabilities concerning fault tolerance, computation cost, and storage. This requires an improved framework and algorithms for performing effective operations. In this paper, a hybrid framework is proposed, which incorporates the various IoT devices in fog environments to enhance fault tolerance. The proposed framework implements a novel QoS-aware technique based on the combination of checkpoints and replication (CR) for diagnosing faults and the bee-mutation (BM) algorithm for optimal placement of service. A fog service monitor is established to observe the performance of fog nodes. Both the CR module and BM module access the service monitor to ensure that the proposed hybrid framework is fault-tolerant. Furthermore, the proposed CR-BM-based hybrid framework has been evaluated for its performance by using various performance metrics. In the comparative analysis, it is observed that the proposed hybrid framework outperforms the existing genetic algorithm-based framework.

To provide persistent data storage for Prolog programs we propose a simple interface between Prolog and relational data bases with SQL. The intention has been to make relations stored in data base tables behave as much as possible like relations defined by Prolog clauses, except for ordering of solutions. We introduce a syntactical device for simplifying translation of data base relations to efficient SQL queries and we describe the translation. A non-trivial problem has been to implement backtracking over data base relations, simulating the behaviour of Prolog. We suggest three different implementations and provide some benchmark figures for two of them. The data base system and our Prolog system are two components of a distributed and modular tool for writing applications such as expert systems.

The design and implementation of two expert systems for semiconductor manufacturing are described and compared. The first expert system is used for parametric test analysis in a CMOS wafer facility. It is designed as a user-interactive system, with all the expert's knowledge included in one knowledge base. This expert system tightly controls the flow of information between the knowledge base and the wafer data stored in a relational database. This expert system was implemented using the knowledge engineering environment shell with interface programs to INGRES. The second expert system is used for parametric test analysis in an analog wafer facility. This system is partitioned into several independent C programs running online and continuously communicating their results through the relational database. The interactive system is more flexible in representing expert knowledge and in explaining results, while the online system is faster, more open to new module integration, and better able to classify and diagnose large volumes of wafer data. >

The expert system is one tangible manifestation of the development of artificial intelligence which can be useful in assisting problem solving to obtain effective results in the process of determining a diagnostic decision. The expert system itself adopts expert knowledge in a field then provides diagnostic output in accordance with the expert's knowledge in solving diagnostic problems. The problems raised are the topic of this research to be resolved using an expert system, namely solving the problem of uncertainty in the diagnosis of Hepatomegaly which is experienced by the general public so that it must require the assistance of an alhi who is called an expert in diagnosing Hepatomegaly. Hepatomegaly is an increase in the size of the liver. This condition generally occurs due to disease in the liver, one of which is hepatitis. But it could also be due to other diseases. The expert system method applied in this study is to obtain accurate diagnosis results in accordance with the expert knowledge of diagnosis of Hepatomegaly, namely the Fuzy Logic Sugeno method. Fuzy Logic Sugeno which is used in expert systems in this study works to find an accurate diagnostic solution to the problem of uncertainty in the initial diagnosis of Hepatomegaly. The expert system for diagnosing Hepatomegaly in this study was built using the Visual Basic Net 2008 programming language and using the MySQL database as a data storage space for this expert system. The results of this study are expected to be an alternative application that can solve the problems of diagnosing Hepatomegaly in the future. Keywords : Expert System, Diagnosis, Hepatomegaly, Fuzzy Logic Sugeno

Semantic and pragmatic processing in FIDO: A flexible interface for data-base operations

IoT devices are pillars for the Industry 4.0 software applications. However, clustering these edge nodes are interesting open challenges in several dimensions, because of mandatory integration of diverse hardware and software packages. Different type of industrial cameras and a supercomputer node to support 3D reconstructions is not a trivial approach, especially considering aspects of the IoT software engineering. In this paper, we present a research, which could be classified as an implementation science effort. The target is a heterogenous edge computing platform, utilized to support a real case study of an electrical engineering field application. This application is characterized by a 3D virtual reconstruction paradigm for the hydropower project. Our results indicate interesting aspects related to implementation science and challenges found in the composition and operationalization of this heterogenous edge platform.

This study primarily focuses on the implementation of extended Hamming code within Internet of Things (IoT) devices and examines its impact on device performance, particularly in relation to communication protocols. The research begins by introducing and explaining the essential principles surrounding the extended Hamming code and its system. This introduction is followed by a detailed analysis of its practical application in IoT device communication and the subsequent influence on performance. Additionally, the study explores the potential role of extended Hamming code in strengthening the security measures of IoT devices. Experimental findings indicate that incorporating extended Hamming code can effectively enhance the communication efficiency of IoT devices, ensuring accurate data transmission. It also improves the overall operational efficiency of the devices and fortifies their security framework. Yet, despite these promising outcomes, the real-world application of extended Hamming code presents significant challenges. These hurdles highlight the need for continued research and exploration to maximize the potential of the extended Hamming code in the IoT domain. The study concludes with an optimistic outlook, encouraging ongoing investigation and innovation to further optimize the benefits of this code and drive advancements in IoT technology.

Summary We present a new approach that uses artificial intelligence to identify thewell-test-interpretation model that best describes the behavior of a reservoir. This knowledgebased (expert) system is composed of carefully extracted rulesand facts for buildup and drawdown test analysis. The rules simulate thereasoning process used by a human expert to identify the appropriateinterpretation model for a well test. The elaborate control needed to simulatea human's reasoning process was simplified with a blackboard architecture. Toexplain the application of the expert system, we use it to analyze a fieldexample. Introduction The accuracy of reservoir properties estimated from well tests depends onprior identification of a well-test-interpretation model that accuratelydescribes the underground system. Identifying the appropriatewell-test-interpretation model is the most fundamental and difficult problemthat an engineer faces when analyzing a pressure-transient test. Recent use ofinvolved mathematical models and techniques has not completely solved thisproblem because choosing an applicable model is not a completely quantitativeprocedure. The choice is based on experience and on a complex reasoning processthat is often deeply buried in the process that is often deeply buried in thehuman expert's mind. If successful, the process of interpreting a well test should provide areservoir model that predicts the behavior of the reservoir being tested. Thisprocess, known as the inverse problem, is characterized by identifying anunknown system (model) by studying the input (rate) and output (pressure)signals. Because a number of models can produce the same output responses froma given input signal, there is no single solution to this problem, suggestingthat a successful interpretation depends on the skills and experience of theinterpreters and their conception of the true model. We developed a knowledge-based system that seeks the appropriateinterpretation model by imitating the reasoning process of human well-testingexperts. The knowledge base uses interactive well-test-analysis and automatichistory-matching modules, and should minimize-and sometimes eliminate-thelack-of-uniqueness problem. Literature Review Although much literature exists on well-test interpretation, work related tothe identification of well-test-interpretation models is very limited. Gringarten discussed the difficulty and complexity of identifying thewell-test model and-validating the analysis. He concluded that, because theprocess involved subjective reasoning, this was an artificial-intelligenceproblem that required an expert system. Using a logical and systematic approach, McVay et al. developed interactivesoftware for well-test analysis. In their work, they indicated that the humanexpert's knowledge needed to be incorporated into an expert system to preventthe user from imposing preconceived modeling ideas on the data. In a recent study, Watson et al. discussed the importance of identifying theinterpretation model as a precondition to successful analysis. They used astatistical approach to choose the applicable model from a group of candidatemodels. Automatic history-matching has been used extensively in recent years toenhance the efficiency and speed of well-test analysis. The interpretationmodel in this case is preselected and a match is sought by changing the modelparameters. This solution, however, is apt to result in misleadinginterpretations because, as indicated earlier, different models can respondsimilarly to a given set of input signals. Erdle et al. described a general interactive software package for well-testinterpretation and introduced expert advice into the well-test-interpretationprocess. In this paper, we introduce a new approach that simplifies the processof determining the applicable well-test-interpretation model. Our approach isbased on artificial-intelligence technology. Problem Solving Model Problem Solving Model Blackboard Architecture. Theknowledge-based architecture consists of three principal components: aknowledge base (facts and rules), a data base (supplied data and solutionstate), and a control (problem-solving) strategy. Although knowledge is the most important part, the control strategy is acritical design part, the control strategy is a critical design component forknowledge-based systems because simulating the human expert's reasoning processrequires that the reasoning steps process requires that the reasoning steps anddomain knowledge be organized such that they can be used efficiently to solvethe problem. A number of problem-solving models have been described in theartificialintelligence literature. JPT P. 654

The research aimed to develop expert system for helping lecturers to decide which courses to be taken by students in the next semester. The expert system contained specific pieces of knowledge to solve specific problems involved in the forms of system development and maintenance. This expert system was in the form of a website using the PHP programming language and MySQL database. The researcher used the Ripple Down Rules (RDR) method to identify the courses by putting forward the questions so that the system could decide which courses the students should take in next semester. The result shows that this web-based expert system can identify which courses that the students have to enroll after the students have answered questions generated by the system. The available data on the courses in the system adapt to the rules, so it is in line with the enrolled courses.

At school, especially in Automotive Vocational High Schools, a learning process is carried out by helping students to recognize and overcome problems that occur in motorbike/car damage. One of the SMKs that has an Automotive expertise program is SMK Negeri 2 Bengkulu City. So far, the automotive learning system has used books and pictures to support the learning process at school. However, this sometimes has problems, where students do not fully understand the material given, and on the other hand students have difficulty learning independently either in the school environment or at home. Expert systems in helping students diagnose car engine damage using the Expert System Development Life Cycle (ESDLC) method can be accessed online with internet access via the web link http://riska.vad.my.id. This expert system application is made using the PHP programming language and MySQL database. This expert system application has been embedded with the ESDLC method with the Forward Chaining inference engine which is used to trace damage to car engines based on the symptoms experienced by the car engine. This expert system application can help facilitate the understanding of students at SMK Negeri 2 Bengkulu City in diagnosing car engine damage, by providing consultation directions starting from entering symptoms to getting consultation results. Based on the system testing that has been done, it can be concluded that the functionality of the application has been running well and this expert system can provide consultation results based on the symptoms selected by the user through the stages of the Expert System Development Life Cycle (ESDLC) method.

Expert systems (ES) are computer programs which make recommendations and draw conclusions from heuristic rules and from relationships derived out of human experience. A decision support system (DSS) is a computer-based management information system (MIS) linking one or more data bases to computer software for exploration and analysis of the data. Combined in a single system an ES and a DSS comprise an expert support system. A simple expert support system for use in fishery stock assessment and management is described. It demonstrates a number of practical benefits of the use of expert systems technology to implement and integrate some simple tasks, i.e., bioeconomic modelling, cost/benefit sampling analyses, and technical interpretation, namely: (1) the effects of different population structures and patterns of fishing on modelling results can be examined under the programmed guidance of the expert system; (2) the effects of different levels of sampling precision on modelling results can be similarly examined; (3) cost/benefit analyses of different sampling budgets can be examined; and (4) management advice specific to each sampling, surplus production (four different models), and net present value (NPV) case is given. An examination of the future of expert support systems in fishery stock assessment and management considers four possible levels of support, as follows: (1) to provide technical explanations, (2) to manage and catalog repetitive processes, (3) to guide “low-level decisions”, and (4) to contribute directly to strategic and policy decisions. Currently, movement in expert systems usage is toward so-called “embedded systems”, for example, an expert support system which comprises, in addition to computational models and databases, one or more expert systems. Problems related to the design and development of ESS for fishery stock assessment and management are challenging conceptual problems; the construction and programming of ESS are reasonably tractable tasks. Determining what an ESS will do is often the principle conceptual question. Expert support systems provide another tool for fishery stock assessment scientists and managers to increase their productivity.

The technique of two-mode evaluation described allows function-free ground unit clauses to be stored as relations in a relation data base instead of as statements in a Prolog program and provides a real-time mechanism for retrieving such clauses as necessary when executing the program without programmed data base calls. The technique includes a look-ahead facility for reasonably effective (logical) data base operations. All interactions between the program and the data base are transparent to the programmer, i.e. two-mode evaluation provides a one-memory-level virtual machine from a programming point of view. The formal semantics of an integrated Prolog/RDBMS environment is presented and explained.

A relational data base is herein defined as a collection of normalized relations (relations in first normal form) and a collection of domains. A normalized relation may be viewed as a table, wherein each row of the table corresponds to a tuple of the relation, and the entries in a given column belong to the set of values constituting the domain underlying that column. The domains of a data base have an abstract existence apart from the data base relations.The data base also includes various types of semantic integrity rules, which specify additional properties of the data in the data base. One such type of semantic integrity rule is the domain definition. A domain definition includes the precise description of the set of values (objects) constituting the domain. In a normalized data base, all domains are sets of atomic data values. A domain definition also includes a specification of the ordering on the values in a domain, for comparability purposes. In addition, a domain definition contains a specification of the action that is to occur if an attempt is made to violate the restriction that every entry in each column of a relation must be from the underlying domain of that column.A nonprocedural language permitting the high level expression of domain definitions is defined. Language details and examples are presented, and the syntax and informal semantics of the domain definition language are given. This approach to domain definition is analyzed in terms of its impact on other aspects of data base semantic integrity. The relationship with the data base system in general is outlined. An analysis of intradomain and interdomain comparability is included. An introduction to relevant implementation issues is also presented. Emphasis is placed on a general approach to implementation and implementation techniques, rather than on a specific system.

A relational data base is herein defined as a collection of normalized relations (relations in first normal form) and a collection of domains. A normalized relation may be viewed as a table, wherein each row of the table corresponds to a tuple of the relation, and the entries in a given column belong to the set of values constituting the domain underlying that column. The domains of a data base have an abstract existence apart from the data base relations. The data base also includes various types of semantic integrity rules, which specify additional properties of the data in the data base. One such type of semantic integrity rule is the domain definition. A domain definition includes the precise description of the set of values (objects) constituting the domain. In a normalized data base, all domains are sets of atomic data values. A domain definition also includes a specification of the ordering on the values in a domain, for comparability purposes. In addition, a domain definition contains a specification of the action that is to occur if an attempt is made to violate the restriction that every entry in each column of a relation must be from the underlying domain of that column. A nonprocedural language permitting the high level expression of domain definitions is defined. Language details and examples are presented, and the syntax and informal semantics of the domain definition language are given. This approach to domain definition is analyzed in terms of its impact on other aspects of data base semantic integrity. The relationship with the data base system in general is outlined. An analysis of intradomain and interdomain comparability is included. An introduction to relevant implementation issues is also presented. Emphasis is placed on a general approach to implementation and implementation techniques, rather than on a specific system.