Security is one of the main concerns with regard to the Internet of Things (IoT) networks. Since most IoT devices are restricted in resource and power consumption, it is not easy to implement robust security mechanisms. There are different methods to secure network communications; however, they are not applicable to IoT devices. In addition, most authentication methods use certificates in which signing and verifying certificates need more computation and power. The main objective of this paper is to propose a lightweight authentication and encryption mechanism for IoT constrained devices. This mechanism uses ECDHE-PSK which is the Transport Layer Security (TLS) authentication algorithm over Message Queuing Telemetry Transport (MQTT) Protocol. This authentication algorithm provides a Perfect Forward Secrecy (PFS) feature that makes an improvement in security. It is the first time that this TLS authentication algorithm is implemented and evaluated over the MQTT protocol for IoT devices. To evaluate resource consumption of the proposed security mechanism, it was compared with the default security mechanism of the MQTT protocol and the ECDHE-ECDSA that is a certificate-based authentication algorithm. They were evaluated in terms of CPU utilization, execution time, bandwidth, and power consumption. The results show that the proposed security mechanism outperforms the ECDHE-ECDSA in all tests.
Fifth Generation (5G) is the final generation in mobile communications, with minimum latency, high data throughput, and extra coverage. The 5G network must guarantee very good security and privacy levels for all users for these features. Therefore, researchers have deliberated the privacy and security solution of 5G users. The 5G wireless network offers a futuristic concept that helps to solve challenges affecting previous communications generations. The key concern to many scholars in the field of mobile networking is user privacy, which is long‐term subscription identifier as International Mobiles Subscribers Identifiers (IMSIs) and short‐term subscription identifier as Temporary Mobiles Subscribers Identifiers and Cell‐Radio Networks Temporary Identifiers (TMSIs and C‐RNTIs), which are used for permanent identifying, paging, and location update. This article investigates the existing literature survey about user privacy for 5G networks, which continues the identity and location privacy. Also, it discusses most of the studies that handle user identifications in authentication, paging, and location update. This article discusses the various privacy issues in the 5G network that use IMSI in clear text or temporary identities such as TMSI & C‐RNTI with IMSI to disclose user identity privacy. This article also investigates the existing literature on user identity and location privacy and highlights the key parameters, issues, challenges, and future recommendations with potential solutions.
A contactless system became necessary for smart mobility during the COVID-19 pandemic. There are many touchpoints in private and public areas where contact is essential, such as intelligent transportation systems for vaccine carriers, patient ambulances, elevators, metros, buses, hospitals, and banks. A secured contactless device reduces the chances of COVID-19 infection spread. Several devices use smart cards, fingerprint identification, or code-based access. Most of these devices require some form of touch. The cost of such devices varies, depending on their capability and intended use. Sensors developed by using artificial intelligence (AI) to provide secured access are an emerging area. This paper presents an AI-powered contactless face recognition system. The solution has the Internet of Things (IoT) enabled access system. To identify a person, it uses AI assistance for face recognition with the help of Python Dlib’s facial recognition network. Dlib offers a wide range of functionality across several machine learning sectors and is open-source. The Arduino Uno (ATmega328P) and STK500 protocol has been used for communication to testify and validate the performance of the proposed technique. The objective is to detect and recognize faces by the proposed contactless approach. The obtained result shows 92% accuracy, 94% sensitivity, 96% precision and FRR 6% for face detection. There is a significant improvement in FRR in our work compared to the published 27.27%. The implemented solution in this paper provides accurate and secure contactless access to conventional, readily available techniques in public health safety.
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