Handover (HO) management plays a key role in ensuring uninterrupted connectivity across evolving wireless networks. While previous generations such as 4G and 5G have introduced several HO strategies, these techniques are insufficient to meet the rigorous demands of sixth-generation (6G) networks in ultra-dense, heterogeneous smart city environments. Existing studies often fail to provide integrated HO solutions that consider key concerns such as energy efficiency, security vulnerabilities, and interoperability across diverse network domains, including terrestrial, aerial, and satellite systems. Moreover, the dynamic and high-mobility nature of smart city ecosystems further complicate real-time HO decision-making. This survey aims to highlight these critical gaps by systematically categorizing state-of-the-art HO approaches into AI-based, fuzzy logic-based, and hybrid frameworks, while evaluating their performance against emerging 6G requirements. Future research directions are also outlined, emphasizing the development of lightweight AI–fuzzy hybrid models for real-time decision-making, the implementation of decentralized security mechanisms using blockchain, and the need for global standardization to enable seamless handovers across multi-domain networks. The key outcome of this review is a structured and in-depth synthesis of current advancements, which serves as a foundational reference for researchers and engineers aiming to design intelligent, scalable, and secure HO mechanisms that can support the operational complexity of next-generation smart cities.

The evaluation of research and development (R&D) project proposals plays a critical role in shaping national scientific and technological priorities. However, existing expert review systems are often characterized by fragmented digital workflows, limited traceability of decisions, and a strong reliance on manual coordination, which reduces transparency and auditability. This paper proposes a conceptual and methodological framework for a national research project evaluation system that integrates artificial intelligence and blockchain technologies as complementary decision-support and data integrity mechanisms. The framework formalizes the complete evaluation lifecycle, including applicant authorization, formal compliance verification, originality and plagiarism analysis, expert selection and assessment, analytical consolidation of reviews, and fixation of final decisions. Artificial intelligence modules are introduced to support thematic classification, compliance checking, expert matching, and analytical processing of expert evaluations, while blockchain technology is incorporated as an immutable integrity layer for recording critical evaluation events and ensuring data provenance. The proposed approach focuses on architectural design, governance principles, and process modeling rather than system implementation or empirical validation. The framework is intended to serve as a reference model for the design and future development of transparent, accountable, and scalable research project evaluation platforms at national and institutional levels.

Handover management is a fundamental process in modern mobile networks, ensuring service continuity under user mobility. However, the relationship between network conditions and handover behavior remains insufficiently understood under real-world measurement conditions. This study presents a data-driven analysis of handover behavior based on drive-test measurements collected in an urban environment. A formal definition of handover events is proposed and implemented for automatic detection using changes in the serving cell identifier. The dataset is further analyzed to assess the influence of radio signal indicators, QoS metrics, and mobility-related variables on handover occurrence. Logistic Regression is used as an interpretable baseline, while Random Forest is applied to capture nonlinear feature interactions. The results show that individual QoS indicators demonstrate limited direct explanatory capability when considered independently. Random Forest achieved higher predictive performance than Logistic Regression, with AUC = 0.902 compared to 0.787, indicating the importance of nonlinear relationships in handover behavior. Degradation events are additionally identified using a threshold-based proxy, showing that latency is a more sensitive indicator of degraded conditions than throughput. Overall, the findings suggest that handover behavior depends on multiple interacting network conditions rather than a single dominant predictor, highlighting the importance of QoS-aware and data-driven mobility analysis in 5G networks and beyond.

Quantitative assessment of information security in 5G telecommunications infrastructure is difficult because individual security metrics are heterogeneous and rarely interpreted together. Conventional multi-criteria decision-making (MCDM) applications rank alternatives by compensatory aggregation, so a violated metric can be offset by others. This study proposes a standard-traceable method in which MCDM only weights the metrics, while measured values are evaluated against tolerable values by a non-compensatory exceedance indicator and a three-level status rule. Twenty-four metrics were derived from ISO/IEC 27011:2024 controls through the ISO/IEC 27004 measurement chain and grouped into three domains. The analytic hierarchy process (AHP) weighted three quality criteria of a metric (significance 62.6%, correctness 28.9%, measurability 8.5%), and the weighted aggregated sum product assessment (WASPAS) method used them to derive the metric weights, cross-checked by the fuzzy technique for order of preference by similarity to ideal solution (Fuzzy TOPSIS). Validation combined synthetic deterioration scenarios, Monte Carlo analysis and comparison with alternative MCDM methods. In three 12-month scenarios, the exceedance indicator flagged each violation in the month it occurred, whereas compensatory aggregation lagged by three to six months or did not react. The domain status remained stable in 85.3–100% of Monte Carlo draws under perturbed tolerable values and measurements. Compared with conventional MCDM-based aggregation, the advantage lies in traceable metric selection, normalization against tolerable values and the non-compensatory status rule.