IoT and Mobility in Smart Cities

Zafeiriou, Ioanna

doi:10.1109/wsce51339.2020.9275584

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…Along with blockchain, IoT is the evolutionary technology of this era. IoT technology has already been implemented in various fields including healthcare (Haghi et al, 2020;Asokan et al, 2021;Bhuiyan et al, 2021;Mehmood et al, 2021;Sharma et al, 2022); smart cities (Zafeiriou, 2020;Hajam and Sofi, 2021;Kaushik and Bagga, 2021;Nadeem et al, 2021); security (Siddiqui et al, 2019(Siddiqui et al, , 2020; business (Aagaard et al, 2019;Ali et al, 2019Ali et al, , 2020aFattouch et al, 2020;Torres et al, 2020); and agriculture (Azfar et al, 2018;Kassim, 2020;Syed et al, 2020) etc.…”

Section: Iot As Energy Trading Enabling Technologymentioning

confidence: 99%

A survey on peer-to-peer energy trading for local communities: Challenges, applications, and enabling technologies

Nadeem

2022

Front. Comput. Sci.

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Electricity is still a highly demanding resource in developing countries, especifically in rural areas. Local communities in developing countries are now focusing on generating their own electricity using solar and trading the energy which is in access to the supplier. Energy trading at the peer-to-peer level is a novel paradigm. It allows customers to contribute energy to the grid and power generation and supply companies. In this paper, we investigate the energy trading enabling technologies, their applications, challenges, and benefits to the local communities from various aspects. We analyse how blockchain and IoT technologies can help energy trading at the customer level from recent research proposals. We also present our analysis of recent research on the implementation of energy trading technologies and future directions.

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Section: Iot As Energy Trading Enabling Technologymentioning

confidence: 99%

A survey on peer-to-peer energy trading for local communities: Challenges, applications, and enabling technologies

Nadeem

2022

Front. Comput. Sci.

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“…Power resources and the location is also a concern for implementing security techniques. Mobility is a significant concern for connected cars, i.e., in smart automobiles [11]. In other applications where mobility is low, achieving trust is comparatively more comfortable than in fast-moving connected vehicles.…”

Section: Introductionmentioning

confidence: 99%

Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review

2021

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Internet of Things (IoT) technology is prospering and entering every part of our lives, be it education, home, vehicles, or healthcare. With the increase in the number of connected devices, several challenges are also coming up with IoT technology: heterogeneity, scalability, quality of service, security requirements, and many more. Security management takes a back seat in IoT because of cost, size, and power. It poses a significant risk as lack of security makes users skeptical towards using IoT devices. This, in turn, makes IoT vulnerable to security attacks, ultimately causing enormous financial and reputational losses. It makes up for an urgent need to assess present security risks and discuss the upcoming challenges to be ready to face the same. The undertaken study is a multi-fold survey of different security issues present in IoT layers: perception layer, network layer, support layer, application layer, with further focus on Distributed Denial of Service (DDoS) attacks. DDoS attacks are significant threats for the cyber world because of their potential to bring down the victims. Different types of DDoS attacks, DDoS attacks in IoT devices, impacts of DDoS attacks, and solutions for mitigation are discussed in detail. The presented review work compares Intrusion Detection and Prevention models for mitigating DDoS attacks and focuses on Intrusion Detection models. Furthermore, the classification of Intrusion Detection Systems, different anomaly detection techniques, different Intrusion Detection System models based on datasets, various machine learning and deep learning techniques for data pre-processing and malware detection has been discussed. In the end, a broader perspective has been envisioned while discussing research challenges, its proposed solutions, and future visions.

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