Effective local governance increasingly relies on dependable digital infrastructure, accurate asset monitoring, timely risk identification, and accountable public-service delivery. Radio frequency interference, ground reflection and multipath propagation, however, can affect the positioning accuracy of such real-time locating systems (RTLS) based on wireless sensor networks (WSN) used in municipal buildings and facilities, leading to inaccurate estimations of the real-time location. These constraints can cause incomplete asset visibility, poor coordination in emergencies, poor planning of operations, and administration based on distorted facts. To overcome these challenges, this paper presents an adaptive WSN framework, which combines artificial neural network (ANN) -based localisation, the mitigation of ground-reflection error and a risk-informed decision support to local governance. The asset locations are estimated using a multilayer perceptron (MLP) that is able to learn the relationship between the characteristic of the transmitted wireless signals and reference positions, while an adaptive correction mechanism aims to enhance the asset's localisation stability under various propagation scenarios. Next, a multi-criteria risk prioritisation mechanism applies to the quantitative assessment of municipal operational risks, based on certain criteria that are set beforehand, such as the urgency, the disruption of services, the risk to safety and the requirement for the resources.This proposed research takes the traditional research on wireless positioning beyond the field of technical performance assessments and draws methodological linkages between wireless positioning reliability and public-sector decision making. The system endeavors to provide evidence based support at the local level by bridging the gap between the sensor intelligence level and operation risk assessment to aid municipal managers in asset management, emergency preparedness and service continuity. The results will feed into building reliable, transparent and accountable technology-enabled local governance systems.
Reliability and security for digital public services depend more now than ever upon robust network infrastructure as well as strong cyber-security management systems. Interconnected digital platforms are used by local government organizations for different purposes; however, there are issues like network failures and cyber attacks that can affect their operations as well as people’s confidence in them. The paper presents a new model called SG-ACGF which is an adaptive cybersecurity governance framework for improving resiliency and responsibility within internet-based government services' infrastructure systems. Graph Attention Networks are combined with Deep Reinforcement Learning for evaluating network connectivity and adapting to changing threats. This is an intrusion detection system based on sensors that can identify anomalies as well as predictive models of future events so we can take preventive measures beforehand. The Explainability Risk Prioritization Module helps to assess severity levels of incidents as well as their importance to services so that they can be addressed by local governments more effectively. Governance is based on roles of responsibility and transparency in making decisions. This model would be evaluated by means of simulation studies as well as comparison to current ones based upon parameters such as packet delivery rate and response time for governance issues. In conclusion, our study aims to integrate an adaptive network security system with local government administration so that there is better resilience in providing services of citizens through technology.
Reliability and security for digital public services depend more now than ever upon robust network infrastructure as well as strong cyber-security management systems. Interconnected digital platforms are used by local government organizations for different purposes; however, there are issues like network failures and cyber attacks that can affect their operations as well as people’s confidence in them. The paper presents a new model called SG-ACGF which is an adaptive cybersecurity governance framework for improving resiliency and responsibility within internet-based government services' infrastructure systems. Graph Attention Networks are combined with Deep Reinforcement Learning for evaluating network connectivity and adapting to changing threats. This is an intrusion detection system based on sensors that can identify anomalies as well as predictive models of future events so we can take preventive measures beforehand. The Explainability Risk Prioritization Module helps to assess severity levels of incidents as well as their importance to services so that they can be addressed by local governments more effectively. Governance is based on roles of responsibility and transparency in making decisions. This model would be evaluated by means of simulation studies as well as comparison to current ones based upon parameters such as packet delivery rate and response time for governance issues. In conclusion, our study aims to integrate an adaptive network security system with local government administration so that there is better resilience in providing services of citizens through technology.
Condition monitoring plays a role in wind turbine maintenance. But supervised machine learning is constrained in the presence of unlabeled data in datasets related to Supervisory Control and Data Acquisition (SCADA). This paper suggests method to detect anomalies in wind turbines based on 8,760 observations per hour from the Texas Wind Turbine SCADA dataset. Power estimation is performed using a 300-tree model of Random Forest regression that uses wind speed, wind direction, air pressure, and air temperature. The power difference between the measured and expected powers is used to label the anomaly using a threshold of 4.1767 kW. The anomalous observations are identified as 700 and normal observations are 8,060. The optimized Random Forest and XGBoost models have a 93.06% and 93.24% accuracy, respectively, and their F1 scores are 41.54% and 43.51%, respectively. LOF finds 438 density anomalies, and McNemar's test shows that there is no significant difference between classifiers.
Digital technologies are increasingly used in the delivery of local public services, which makes it important to have strong authentication, privacy, and accountable access control on digital governance platforms. In this study, an authentication solution named Governance-Aware PhishGuard++ from the Web3 space is proposed that combines Zero Knowledge Proofs (ZKPs), Decentralized Identifiers (DIDs), and Graph Neural Network (GNN) based threat intelligence to augment the identity verification process, safeguard sensitive information, and enhance the capability of phishing threat detection. The principal contribution is called a Governance-Aware Risk-Adaptive Access Control (GRAAC) mechanism, which combines identity verification, dynamic phishing-risk estimation, institutional authorisation policies, and accountable access enforcement. The proposed extension differs from the original framework in that it also considers factors such as the probability of a threat, institutional permission, resource sensitivity, and governance requirements in its evaluation of access requests rather than just the mutual authentication and real-time threat detection in the original framework. Only a policy-constrained decision mechanism will decide whether to allow or disallow requests as well as whether to escalate to authorised review. Enforcement and privacy-conscious audit records via smart-contracts or the application level support traceability without necessarily revealing sensitive identity information. It will be tested using the available phishing data sources and synthetic municipal-services phishing scenarios. The experiments will compare accuracy of phishing detection, unauthorised access acceptance, denial of legitimate access requests; policy compliance, latency in decision making and computation overhead; and completeness of auditing. The proposed mechanism will be evaluated by means of baseline comparison, ablation studies, and sensitivity analysis. The research aims to combine explicit institutional governance policies with privacy-preserving authentication and graph-based threat intelligence to lay a foundation for measurable and accountable cybersecurity for local digital public services.
