Privacy and cybersecurity systems in smart cities are critically reviewed and assessed in the present paper by synthesizing 21 peer-reviewed publications, published between 2018 and 2024, but the works on the topic published prior to 2018 are also acknowledged to provide background information. The paper not only classifies these mechanisms, but further compares them against the frameworks of privacy protection, scalability, governance and resilience, a separation between the technical and legal approaches. The systematic literature review was conducted based on the five-phase protocol presented by Kitchenham with the assistance of a PRISMAlike quality assessment of the studies included. These mechanisms were categorized into five themes that comprise blockchain-based architectures, intrusion detection and machine learning models, privacy-preserving learning systems, legal and governance frameworks and hybrid trust-based architectures. An evaluation lens Technology, Governance, and Resilience (TGR) triad was used to make comparative synthesis and prioritize both performance and limitation and applicability at the city level. As determined in the review, blockchain-enabled schemes dominate in the recent researches and offer immutability, auditability, but have a limitation of latency, interoperability and high power-consumption. Methods of machine learning are more accurate in detection, yet can not be interpreted or adversarial resistant. These regulations such as GDPR are enforced but not dynamic and thus, they lag behind the evolving technologies. Hybrid trust based models are promising to be integrated yet they are computationally expensive. The review has identified ten gaps, which include dynamic consent, adversarial robustness, explainable AI and cross-jurisdictional interoperability. The results indicate that scalable, modular, and legally entrenched structures are immediately required in order to balance privacy, cost, and social trust of urban infrastructures. Practice implications may be reduced costs of cybersecurity breaches, enhanced citizen trust in smart services, and advice to policymakers on how compliance may be incorporated into technical design instead of ex-post regulation. The current research is the only one that combines technological, governance and economical views into a comparative framework. It provides a resilient, privacy-conscious and economically viable roadmap towards co-designing smart city systems by placing post-2018 mechanisms in a broader historical context to guide researchers, practitioners, and policymakers to implement a coherent approach to smarter cities
M. AlThabahi, Sohail Abbas· 2026 6th International Confe...· 0 citations
Semi-grant-free non-orthogonal multiple access (SGF-NOMA) schemes group one grant-based (GB) user with multiple grant-free (GF) users into one time/frequency resource block (RB) to enhance spectral efficiency. Due to the sporadic traffic of GF users and the stringent quality of service (QoS) requirement of the GB user, the access collision problem becomes severe in SGF-NOMA. To solve this problem, this paper firstly designs an RB-based power pool (PP), which directs GF users to adjust their transmit power without disrupting the ongoing transmission of the internal GB user. After that, this work proposes an efficient multi-agent deep reinforcement learning (MA-DRL) framework to jointly optimize the PP and access strategy for maximizing the network throughput. In particular, this work exploits the fast-response feature of the traditional competitive MA-DRL and the increased-performance feature of the traditional cooperative MA-DRL to redesign a mixed reward system, which contributes to a hybrid MA-DRL mode for enhancing the learning efficiency of agents, i.e., GF users. We investigate the performance of the proposed algorithm at the network level and the NOMA-cluster level. We show that the proposed hybrid MA-DRL at the cluster level converges faster to an optimal solution than that at the network level but at an extra cost of user clustering. The numerical results show that the proposed scheme increases the successful decoded users by 42.38% when compared to the traditional schemes without learning capability. The proposed hybrid MA-DRL mode performs better than the pure competitive and cooperative MA-DRL modes, especially under a heavy-load network. It is able to achieve a 69% success rate of access in a time-varying environment with high packet arrival rates.
M. Fayaz, Sohail Abbas, Abdullah Alajmi et al.· IEEE Transactions on Cogniti...· 0 citations