Dynamic Bargain Game Theory in the Internet of Things for Data Trustworthiness

Smart home and smart building systems based on the Internet of Things (IoT) in smart cities currently suffer from security issues. In particular, data trustworthiness and efficiency are two major concerns in Internet of Things (IoT)-based Wireless Sensor Networks (WSN). Various approaches, such as routing methods, intrusion detection, and path selection, have been applied to improve the security and efficiency of real-time networks. Path selection and malicious node discovery provide better solutions in terms of security and efficiency. This study proposed the Dynamic Bargaining Game (DBG) method for node selection and data transfer, to increase the data trustworthiness and efficiency. The data trustworthiness and efficiency are considered in the Pareto optimal solution to select the node, and the bargaining method assigns the disagreement measure to the nodes to eliminate the malicious nodes from the node selection. The DBG method performs the search process in a distributed manner that helps to find an effective solution for the dynamic networks. In this study, the data trustworthiness was measured based on the node used for data transmission and throughput was measured to analyze the efficiency. An SF attack was simulated in the network and the packet delivery ratio was measured to test the resilience of the DBG and existing methods. The results of the packet delivery ratio showed that the DBG method has higher resilience than the existing methods in a dynamic network. Moreover, for 100 nodes, the DBG method has higher data trustworthiness of 98% and throughput of 398 Mbps, whereas the existing fuzzy cross entropy method has data trustworthiness of 94% and a throughput of 334 Mbps.

Ensuring data trustworthiness within SMART Monitoring of environmental processes

<p>The term SMART Monitoring was also defined by the project Digital Earth (DE) , a central joint project of eight Helmholtz centers in Earth and Environment. SMART Monitoring in the sense of DE means that measured environmental parameters and values need to be specific/scalable, measurable/modular, accepted/adaptive, relevant/robust, and trackable/transferable (SMART) for sustainable use as data and improved real data acquisition. SMART Monitoring can be defined as a reliable monitoring approach with machine-learning, and artificial intelligence (A.I.) supported procedures for an “as automated as possible” data flow from individual sensors to databases. SMART Monitoring Tools must include various standardized data flows within the entire data lifecycle, e.g., specific sensor solutions, novel approaches for sampling designs, and defined standardized metadata descriptions. One of the SMART Monitoring workflows' essential components is enhancing metadata with comprehensive information on data quality. On the other hand, SMART Monitoring must be highly modular and adaptive to apply to different monitoring approaches and disciplines in the sciences.</p><p>In SMART monitoring, data quality is crucial, not only with respect to data FAIRness. It is essential to ensure data reliability and representativeness. Hence, comprehensively documented data quality is essential and required to enable meaningful data selection for specific data blending, integration, and joint interpretation. Data integration from different sources represents a prerequisite for parameterization and validation of predictive tools or models. This data integration demonstrates the importance of implementing the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) for sustainable data management (Wilkinson et al. 2016). So far, the principle of FAIRdata does not include a detailed description of data quality and does not cover content-related quality aspects. Even though data may be FAIR in terms of availability, it is not necessarily “good" in accuracy and precision. Unfortunately, there is still considerable confusion in science about the definition of good or trustworthy data.</p><p>An assessment of data quality and data origin is essential to preclude the possibility of inaccurate, incomplete, or even unsatisfactory data analysis applying, e.g., machine learning methods, and avoid poorly derived, misleading or incorrect conclusions. The terms trustworthiness and representativeness summarise all aspects related to these issues. The central pillars of trustworthiness/representativeness are validity, provenience/provenance, and reliability, which are fundamental features in assessing any data collection or processing step for transparent research. For all kinds of secondary data usage and analysis, a detailed description and assessment of reliability and validity involve an appraisal of applied data collection methods.</p><p>The presentation will give exemplary examples to show the importance of data trustworthiness and representativeness evaluation and description, allowing scientists to find appropriate tools and methods for FAIR data handling and more accurate data interpretation.</p>

Lecturer’s Motivational Strategies in Teaching English

This research was aimed to describe the lecturer’s motivational strategies in Teaching English at STKIP Yayasan Abdi Pendidikan Payakumbuh.This research was descriptive research in qualitative method. Participants in this research were three English lecturers who teach in Indonesian Department. Those were chosen by using total sampling. Researcher used observation, recording and field notes as the sources of the data. Checking data trustworthiness had been done by using peer briefing. After doing the research, the result showed that the most motivational strategies used by the lecturers was creating basic motivational conditions. Percentage was 24,65%. concluded that the English lecturer had used some kinds of motivational strategies in teaching English, strategy that used by the lecturers is creating the basic motivational conditions Keywords : motivational strategies, teaching english

Digital Risk Management for Data Attacks against State Evaluation

Understanding reasonable framework cyber attacks is essential for creating material assurance and recuperation measures. Propelled attacks follow exploited contact at diminished expenses and recognize capacity. This paper behaviors chance investigation of joined data trustworthiness and handiness attacks against the office framework state evaluation. We will in general contrast the consolidated attacks and unadulterated honesty attacks - false data infusion attacks. A safety record for defenselessness appraisal to those two sorts of attacks is arranged and created because a blended number connected science drawback. We will in general demonstrate that such joined attacks will prevail with less assets than false data infusion attacks. The consolidated attacks with confined data of the framework design also open gifts to keep camouflage against the undesirable data location. At last, the risk of joined attacks to dependable framework activity is assessed abuse the outcomes from defenselessness evaluation and attacks sway examination. The discoveries during this paper are substantial and upheld by a top to bottom contextual investigation