In the past decade, the inevitable increase in temperature has caused Malaysia to experience more extreme heat events, and yet very little research has been dedicated in exploring the heat-related vulnerability of exposed population. In this study, the extreme heat vulnerability index (EHVI) has been evaluated to identify the most vulnerable districts to extreme heat events. We evaluated exposure, population sensitivity and adaptive capacity from sociodemographic and remote sensing data. We have applied multivariate analysis on 13 indicators for every 87 districts to elucidate the extreme heat vulnerability in Peninsular Malaysia. The EHVI was generated by summing up the normalized extreme heat exposure scores and factor scores from the multivariate analysis. Our findings clarify that the most vulnerable populations are confined in the urban and northern region of Peninsular Malaysia. The source of vulnerability varied between both regions, with urbanization and population density increase the vulnerability in urban areas, high heat exposure and sensitive population are the dominant factors of vulnerability in the northern region. These findings are valuable in identifying districts vulnerable to extreme heat and help regulatory body; in designing effective adaptation and preparedness strategies to increase the population resilience towards extreme heat.
Advanced Metering Infrastructure (AMI) is a component of electrical networks that combines the energy and telecommunication infrastructure to collect, measure and analyze consumer energy consumptions. One of the main elements of AMI is a smart meter that used to manage electricity generation and distribution to end-user. The rapid implementation of AMI raises the need to deliver better maintenance performance and monitoring more efficiently while keeping consumers informed on their consumption habits. The convergence from analog to digital has made AMI tend to inherit the current vulnerabilities of digital devices that prone to cyber-attack, where attackers can manipulate the consumer energy consumption for their benefit. A huge amount of data generated in AMI allows attackers to manipulate the consumer energy consumption to their benefit once they manage to hack into the AMI environment. Anomalies detection is a technique can be used to identify any rare event such as data manipulation that happens in AMI based on the data collected from the smart meter. The purpose of this study is to review existing studies on anomalies techniques used to detect data manipulation in AMI and smart grid systems. Furthermore, several measurement methods and approaches used by existing studies will be addressed.
The continuous development of information communication technology facilitates the conventional grid in transforming into an automated modern system. Internet-of-Things solutions are used along with the evolving services of end-users to the electricity service provider for smart grid applications. In terms of various devices and machine integration, adequate authentication is the key to an accurate source and destination in advanced metering infrastructure (AMI). Various protocols are deployed to lead the identification between two parties, which require high computation time and communicational bit operations for system development. Therefore, Kerberos-based authentication protocols were designed in this study with the assistance of elliptic curve cryptography to manage the mutual authentication between two parties and reduce the time and bit operations. The protocols were evaluated in a widely adopted tool, AVISPA, which builds an understanding of the proposed protocol and ensures mutual authentication without unauthorized knowledge. In addition, upon comparing security and performance assessments to the current schemes, it was found that the protocol in this study required less time and bits to transmit information. Consequently, it effectively provides multiple security features making it suitable for resource constraint smart meters in AMI.
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