Feasibility Study of The IoT-Connectivity Deployment for AMI Service: A Case Study in Surabaya City

Purnama, Arrizky Ayu Faradila; Nashiruddin, Muhammad Imam; Murti, Muhammad Ary

doi:10.1109/comnetsat50391.2020.9328934

Cited by 5 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AMI composes a set of smart devices for monitoring and estimating utility usage between companies and customers. The Internet of Things (IoT) assists in building bidirectional communication in AMI applications [1]. Owing to the high scalability of the IoT network, providing security during bidirectional communication becomes a primary concern in AMI.…”

Section: Introductionmentioning

confidence: 99%

Optimized Federated Learning with Ensemble of Sequential Models for Detecting RPL Routing Attacks for AMI Networks

Sharma

Sakthivel²,

Alnajjar

2023

Preprint

View full text Add to dashboard Cite

With the rapid expansion of smart applications and services, the Internet of Things (IoT) plays a vital role in enabling changes in the usage of information and communication technology. Advanced Metering Infrastructure (AMI) is the key component of the smart grid which often meets privacy and security threats from the routing layer. The Routing Protocol for Low-Power and Lossy Networks (RPL) is the prominent routing protocol for AMI networks, and securing the RPL is the cornerstone of all security controls in the AMI for successful deployment. Recently, IoT security systems have increasingly adopted privacy-preserving Federated Learning (FL) that trains a local data model across multiple decentralized edge devices and takes global decisions without the need for global data sharing. Relying on a single learning model significantly degrades the FL performance due to the frequent transmission of weights and training on heterogeneous IoT networks. This paper proposes an Optimized FL model for Securing RPL (OFL-SRPL) over AMI devices. By reducing the size of the data transferred from the multiple local models to the server, the OFL-SRPL adopted the Particle Swam Optimization (PSO) algorithm to select the best local model from the Home Area Network (HAN) gateway and build the global model. The proposed OFL-SRPL applies an ensemble of sequential classifiers with its appropriate loss function to improve the decision-making quality and reduce the FL communication burden. Instead of transferring all the local model weights to the global model, the OFL-SRPL iteratively selects the best ensemble model with sequential classifies and only sends the weight of the best model to build the global model. The sequential classifier design improves the ownership weight and handles class imbalance issues. After taking an optimized global model decision, the devices execute ensemble classifiers successfully with appropriate learning parameters. Thus, the proposed OFL-SRPL achieves 10.6% higher detection accuracy than a centralized setup of the Long Short-Term Memory (LSTM) model without incurring multiple communication rounds like traditional FL.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimized Federated Learning with Ensemble of Sequential Models for Detecting RPL Routing Attacks for AMI Networks

Sharma

Sakthivel²,

Alnajjar

2023

Preprint

View full text Add to dashboard Cite

show abstract

IoT-enabled smart cities: a hybrid systematic analysis of key research areas, challenges, and recommendations for future direction

Omrany,

Al-Obaidi,

Hossain

et al. 2024

Discov Cities

View full text Add to dashboard Cite

Cities are expected to face daunting challenges due to the increasing population in the near future, putting immense strain on urban resources and infrastructures. In recent years, numerous studies have been developed to investigate different aspects of implementing IoT in the context of smart cities. This has led the current body of literature to become fairly fragmented. Correspondingly, this study adopts a hybrid literature review technique consisting of bibliometric analysis, text-mining analysis, and content analysis to systematically analyse the literature connected to IoT-enabled smart cities (IESCs). As a result, 843 publications were selected for detailed examination between 2010 to 2022. The findings identified four research areas in IESCs that received the highest attention and constituted the conceptual structure of the field. These include (i) data analysis, (ii) network and communication management and technologies, (iii) security and privacy management, and (iv) data collection. Further, the current body of knowledge related to these areas was critically analysed. The review singled out seven major challenges associated with the implementation of IESCs that should be addressed by future studies, including energy consumption and environmental issues, data analysis, issues of privacy and security, interoperability, ethical issues, scalability and adaptability as well as the incorporation of IoT systems into future development plans of cities. Finally, the study revealed some recommendations for those interconnected challenges in implementing IESCs and effective integrations within policies to support net-zero futures.

show abstract

Adaptation of Seven-Layered IoT Architecture for Energy Efficiency Management in Smart House

Kutseva

2022

2022 10th International Scientific Conference on Computer Science (COMSCI)

View full text Add to dashboard Cite

Feasibility Study of The IoT-Connectivity Deployment for AMI Service: A Case Study in Surabaya City

Cited by 5 publications

References 7 publications

Optimized Federated Learning with Ensemble of Sequential Models for Detecting RPL Routing Attacks for AMI Networks

Optimized Federated Learning with Ensemble of Sequential Models for Detecting RPL Routing Attacks for AMI Networks

IoT-enabled smart cities: a hybrid systematic analysis of key research areas, challenges, and recommendations for future direction

Adaptation of Seven-Layered IoT Architecture for Energy Efficiency Management in Smart House

Contact Info

Product

Resources

About