A neural architecture for user-centric vehicular caching is introduced, termed as CacheFormer. It leverages autoformer for cache content prediction that positions vehicles as autonomous micro-servers that operate independently within personal area networks. The architecture employs multi-horizon prediction with five-minute slot granularity. It enables lookahead through decomposition of application patterns into trend and seasonal components. Integration of sparse auto-correlation mechanisms yields in reduced complexity while maintaining prediction accuracy. Further, an age-aware eviction mechanism combines forecasted demand with cache staleness metrics, which eliminates separate prediction and eviction modules. Experimental evaluation on 3,463 application sessions demonstrates convergence within 50 epochs in 27.5 minutes on consumer hardware, at high hit rates post-convergence across all popularity quartiles. Moreover, bimodal correlation distribution reveals successful temporal pattern extraction for 75% of applications. Results validate that adopting autoformer improves cache efficiency while preserving data sovereignty and eliminating dependencies on external cloud infrastructure during user mobility.

This paper addresses the critical challenge of resource saturation in fog computing paradigms by proposing novel solicitation-based migration methods that alleviate near-saturated nodes. Unlike traditional reactive approaches that trigger migration post-saturation, the proposed methods employ continuous monitoring wherein adjacent nodes transmit beacon messages to advertise their instantaneous load levels. First, a dual least load and delay (LD) solicitation approach that balances resource utilization with link delay. Second, an affinity-based solicitation that migrates virtual network functions (VNFs) to nodes already hosting related elements from the same service function chain (SFC), thus preserving locality and reducing fragmentation. Simulations demonstrate that both methods outperform baseline proactive load balancing and reactive greedy search approaches at high traffic volumes in terms of saturation levels, migration iterations, service downtime, and SFC fragmentation. The affinity method particularly excels in minimizing SFC fragmentation and service disruption, while the LD approach provides saturation resistance across varying traffic volumes.

This paper addresses the challenge of spectrum scarcity in centralized unmanned aerial vehicle (UAV) networks when multiple UAVs compete for exclusive access to a single licensed frequency channel. A novel priority-based access method is proposed that features an energy-aware UAV selection combined with weighted priority scoring that balance battery levels, service deadlines, signal strength, and altitude-dependent path loss conditions. A feasibility assessment mechanism ensures quality-of-service (QoS) guarantees while maintaining real-time computational efficiency through polynomial-time queue management. Simulations results demonstrate that the method outperforms conventional first-in-first-out (FIFO) scheduling, i.e., 73.3% success rates compared to 50% for baseline approach while reducing cumulative wait times by 80%. The method excels in managing heterogeneous mission requirements and energy constraints, providing robust spectrum utilization for mission-critical UAV operations in resource-constrained environments.

Fog computing and network function virtualization (NFV) technologies reduce latency and provide scalable services at the network edge. However, the limited resources of edge nodes pose a challenge in handling high traffic volumes from requests that demand high computation, extended lifetime, and low latency. To address this challenge, dynamic load migration schemes for NFV-based fog paradigms are proposed here. First, a content-specific scheme that diffuses excess loads to nearby locations hosting relevant virtual network functions (VNFs) of the requests. Second, a buffer scheme reserves a dedicated node to absorb loads from saturated nodes in the proximity of terminals. Third, a hybrid scheme integrates both strategies by initially migrating loads to the pre-allocated buffer to provide immediate relief without searching for candidate nodes. Upon the buffer saturation, it switches to the content-specific phase to distribute loads to the proximate nodes. The network introduces a novel request model comprised of dependent and independent VNFs of varying resource demands. Dependent VNFs are collectively mapped on a primary node while distributing independent VNFs across neighboring secondary nodes, forming a structured ring topology mapping method. These schemes enhance migration success rates, and reduce migration iterations, service downtime, and cost, as compared to prominent solutions.

Fog computing brings storage and computational capabilities closer to the data source, which reduces latency and enhances efficiency in processing data. However, these capabilities are resource-constrained at the fog nodes as compared to the cloud core. This limitation can result in high computational loads that yield in saturation and congestion at the fog nodes when processing large traffic volumes. Hence, this article introduces a novel load migration scheme for delay-sensitive and computation-intensive requests in fog computing without relaying to the cloud core, thus avoiding prolonged link delays. First, a grouped service function chain (SFC) provisioning framework is embedded on a heterogeneous architecture composed of super fog (SF) and ordinary fog (OF) nodes, where all the virtual network functions (VNFs) in the SFC are mapped on a single SF node to reduce resource use. Here, the SF nodes are always in active mode to receive incoming traffic, whereas the OF nodes are maintained in idle mode to save power and computing resources. When the SF node approaches a saturation threshold alarm, it diffuses its load (hosted VNFs) gradually to neighboring OF nodes in the vicinity, thus avoiding saturation at the SF node and providing service continuity. The framework is implemented on a resource-constrained network to achieve realistic operating conditions. Overall, the proposed work achieves high admission and resource utilization rates, reduced delays, and power consumption, as compared to key network architectures and provisioning schemes that separately map delay-sensitive and computation-intensive on hierarchical fog layers, which incur increased delays, network traffic, and power usage.

Wireless video surveillance system is one of the cyber-physical security systems kinds, which transmits the signal of IP cameras through a wireless medium using a radio band. WVSSs are widely deployed with large systems for use in strategic places such as city centers, public transportation, public roads, airports, and play a significant role in critical infrastructure protection. WVSSs are vulnerable to jamming attacks creating an unwanted denial of service. Hence, it is essential to secure this system from jamming attacks. In this paper, three models of IoT-fuzzy inference system-based jamming detection system are proposed for detecting and countermeasure the presence of jamming by computing two jamming detection metrics; PDR and PLR, and based on the result, the system countermeasures this attack by storing the video feed locally in the subsystem nodes. FIS models are based on Mamdani, Tsukamoto, and Sugeno fuzzy logic which optimizes the jamming detection metrics for detecting the jamming attack. The efficiency of these proposed models is compared in detecting jamming signals. The experimental results show that the proposed Tsukamoto model detects jamming attacks with high accuracy and efficiency. Finally, the proposed IoT-Tsukamoto-based model was compared with the existing systems and proved to be superior to them in terms of central processing complexity, accuracy, and countermeasure for this attack.

Fog computing complements cloud paradigms to provide fast service provisioning by processing user demands at the edge of the network. This offers local services without the need to offload or relay demands back to the cloud core, thus saving time and energy. However, the distribution of fog nodes comes at the detriment of limited resource capacity. Therefore, fog nodes are vulnerable to saturation under high traffic volumes. Hence, fog paradigms can act as a bottleneck to incoming requests and degrade network capacity and quality-of-experience (QoE). Along this, resource management is vital for fog nodes to maintain efficient node utilization and high admission rates. Hence, this paper proposes a load migration scheme that redistributes load assignment between fog nodes, i.e., from highly-to lightly-loaded nodes. The scheme leverages Luus-Jaakola meta-heuristic to allocate the optimal recipient node that accepts the excess load, while maintaining minimal migration delays at high success rates.

Network function virtualization (NFV) technologies are being rapidly adopted across many enterprise and service provider networks to help replace costly, proprietary hardware systems. This paradigm runs virtual “softwarized” networking functions on low-cost off-the-shelf servers and can be used to construct highly-customized service function chains (SFC) for clients. NFV also offers many advantages at the network edge, and hence newer studies have started to look at SFC deployment in specialized fog computing domains, i.e., to support clients with stringent delay and localized contextual service needs. As these trends accelerate, there is a growing need to further address service survivability in these settings, particularly for clients with stringent/rapid failure recovery requirements. Hence this paper presents novel “on-demand” heuristic solutions and batch sorting methodologies for SFC service mapping and protection in fog domains. These schemes take into account key end-user client and operator concerns, including service delay, bandwidth, and node/link resource parameters.

Routing protocols in wireless sensor network are vulnerable to various malicious security attacks that can degrade network performance and lifetime. This becomes more important in cluster routing protocols that is composed of multiple node and cluster head, such as low energy adaptive clustering hierarchy (LEACH) protocol. Namely, if an attack succeeds in failing the cluster head, then the entire set of nodes fail. Therefore, it is necessary to develop robust recovery schemes to overcome security attacks and recover packets at short times. Hence this paper proposes a detection and recovery scheme for selective forwarding attacks in wireless sensor networks using LEACH protocol. The proposed solution features near-instantaneous recovery times, without the requirement for feedback or retransmissions once an attack occurs.

Cellular networks at millimeter wave frequencies experience tremendous propagation impairments and channel fluctuations. One major challenge here is the aggregated path loss levels attributed to obstacles of various shapes and density in the propagation links, i.e., link blockage. Therefore, once the base and mobile stations establish the optimum beamforming and combining vectors during the initial beam access procedure in the control-plane, efficient link maintenance techniques are required to maintain communication sessions with sufficient signal levels at various blockage densities. Hence this paper presents a novel link recovery scheme based upon beam aggregation during link transitions from line-of-sight to non-line-of-sight (NLoS) environments. The proposed scheme utilizes circular antenna arrays and hybrid beamforming designs to achieve near-instantaneous recovery times without the need for repeated beam scanning. Simulation results show that the proposed recovery scheme exhibits significant improvements compared to alternative traditional methods in terms of signal-to-noise ratios and recovery times.

Millimeter wave channels suffer from large path losses, penetration losses and atmospheric attenuation. Although beamforming techniques can significantly enhance link margins here, they also introduce initial beam access problems at the base and mobile stations prior to data transmission. Hence this paper presents an effective access scheme inspired by the Hooke Jeeves direct pattern search for analog beamforming cascaded codebooks. Simulation results show that the proposed algorithm delivers substantial performance improvements versus existing solutions in terms of computational complexity, access times and power and energy consumption.

The use of beamforming techniques in millimeter wave systems is crucial for overcoming poor scattering channel impairments. This directional transmission and reception method compels the base station and mobile station to conduct exhaustive spatial beam search, i.e., to determine directions with optimum gains. Consequently, this technique results in high computational complexity, prolonged discovery times and energy inefficiency. Hence this paper proposes a novel initial beam discovery algorithm for sparse millimeter wave channels in non-line of sight outdoor environments. The discovery problem is formulated as an optimization problem, and a modified form of coordinated generalized pattern search is presented to mitigate the aforementioned limitations. Results show that the proposed algorithm delivers significant performance enhancement compared to existing solutions.