Search Results (34)

The evolution toward sixth generation (6G) wireless networks promises higher performance, greater flexibility, and enhanced intelligence. However, it also introduces a substantially enlarged attack surface driven by open, disaggregated, and multi-vendor Open RAN (O-RAN) architectures that will be utilised in 6G networks. This paper addresses the urgent need for a practical Zero Trust (ZT) deployment model tailored to O-RAN specification. To do so, we introduce a novel hybrid ZT deployment model that establishes the trusted foundation for AI/ML-driven security in O-RAN, integrating macro-level enclave segmentation with micro-level application sandboxing for xApps/rApps. In our model, the Policy Decision Point (PDP) centrally manages dynamic policies, while distributed Policy Enforcement Points (PEPs) reside in logical enclaves, agents, and gateways to enable per-session, least-privilege access control across all O-RAN interfaces. We demonstrate feasibility via a Proof of Concept (PoC) implemented with Kubernetes and Istio and based on the NIST Policy Machine (PM). The PoC illustrates how pods can represent enclaves and sidecar proxies can embody combined agent/gateway functions. Performance discussion indicates that enclave-based deployment adds 1–10 ms of additional per-connection latency while CPU/memory overhead from running a sidecar proxy per enclave is approximately 5–10% extra utilisation, with each proxy consuming roughly 100–200 MB of RAM. Full article

Extra edge connectivity is an important parameter for measuring the reliability of interconnection networks. Given a graph G and a non-negative integer h, the h-extra edge connectivity of G, denoted by ${\lambda }_h\left(G\right)$, is the minimum cardinality of a set of edges in G (if it exists) whose deletion disconnects G such that each remaining component contains at least $h+1$ vertices. In this paper, we obtain the h-extra edge connectivity of Cayley graphs generated by transposition trees for $h\le 5$. As byproducts, we derive the h-extra edge connectivity of the star graph $S_n$ and the bubble-sort graph $B_n$ for $h\le 5$. Full article

An interconnection network is usually modeled by a graph, and fault tolerance of the interconnection network is often measured by connectivity of the graph. Given a connected subgraph L of a graph G and non-negative integer t, the t-extra connectivity ${\kappa }_t\left(G\right)$, the L-structure connectivity $\kappa (G;L)$ and the t-extra L-structure connectivity ${\kappa }_g(G;L)$ of G can provide new metrics to measure the fault tolerance of a network represented by G. Fully connected cubic networks ${\mathcal{FC}}_n$ are a class of hierarchical networks which enjoy the strengths of a constant vertex degree and good expansibility. In this paper, we determine ${\kappa }_t\left({\mathcal{FC}}_n\right)$, $\kappa ({\mathcal{FC}}_n;L)$ and ${\kappa }_t({\mathcal{FC}}_n;L)$ for $t=1$ and $L\in \{K_{1,1},K_{1,2},K_{1,3}\}$. We also establish the edge versions ${\lambda }_t\left({\mathcal{FC}}_n\right)$, $\lambda ({\mathcal{FC}}_n;L)$ and ${\lambda }_t({\mathcal{FC}}_n;L)$ for $t=1$ and $L\in \{K_{1,1},K_{1,2}\}$. Full article

Given a graph $G=\left(V\right(G),V(E\left)\right)$, a non-negative integer g and a set of faulty vertices $F\subseteq V\left(G\right)$, the g-extra connectivity of G, denoted by ${\kappa }_g\left(G\right)$, is the smallest cardinality of F, whose value of deletion, if exists, will disconnect G and give each remaining component at least $g+1$ vertices. The g-extra diagnosability of the graph G, denoted by $t_g\left(G\right)$, is the maximum cardinality of the set F of fault vertices that the graph can guarantee to identify under the condition that each fault-free component has more than g vertices. In this paper, we determine that g-extra connectivity of $(n,k)$-bubble-sort network $B_{n,k}$ is ${\kappa }_g\left(B_{n,k}\right)=n+g(k-2)-1$ for $4\le k\le n-1$ and $0\le g\le n-k$. Afterwards, we show that g-extra diagnosability of $B_{n,k}$ under the PMC model $(4\le k\le n-1$ and $0\le g\le n-k)$ and MM* model $(4\le k\le n-1$ and $0\le g\le min\{n-k-1,k-2\})$ is $t_g\left(B_{n,k}\right)=n+g(k-1)-1$, respectively. Full article

With the rapid development of 5G, UAV, and military communications, the data volume obtained by the non-cooperative perception system has increased exponentially, and the distributed system has become the development trend of the non-cooperative perception system. The data distribution service (DDS) produces a significant effect on the performance of distributed non-cooperative perception systems. However, the traditional DDS discovery protocol has problems such as false positive misjudgment and high flow overhead, so it can hardly adapt to a large multi-node distributed system. Therefore, the design of a DDS discovery protocol for large distributed system is technically challenging. In this paper, we proposed SDP-DCBF-SFF, a discovery protocol based on the Dynamic Counter Bloom Filter (DCBF) and Second Feedback Filter (SFF). The proposed discovery protocol coarsely filters the interested endpoints through DCBF and then accurately screens the uninterested endpoints through SFF to eliminate the connection requests of false positive endpoints and avoid extra flow overhead. The experimental results indicate that the proposed discovery protocol could effectively reduce the network overhead, and eliminate the false positive probability of endpoints in small, medium, large, and super large systems. In addition, it adopts the self-adaptive extension mechanism of BF to reduce the reconfiguration delay of BF and achieve the smallest system transmission delay. Therefore, the proposed discovery protocol has optimal comprehensive performance and system adaptability. Full article

Advancements in assisted driving technologies are expected to enable future passengers to use a wide range of multimedia applications in electric vehicles (EVs). To address the bandwidth demands for high-resolution and immersive videos during peak traffic, this study introduces a bandwidth-management algorithm to support differentiated streaming services in heterogeneous vehicle-to-everything (V2X) networks. By leveraging cellular 6G base stations, along with Cell-Free (CF) Massive Multi-Input Multi-Output (mMIMO) Wi-Fi 7 access points, the algorithm aims to provide a high-coverage, high-speed, and low-interference V2X network environment. Additionally, Li-Fi technology is employed to supply extra bandwidth to vehicles with limited connectivity via V2V communication. Importantly, the study addresses the urgency and prioritization of different applications to ensure the smooth execution of emergency applications and introduces a pre-downloading mechanism specifically for non-real-time applications. Through simulations, the algorithm’s effectiveness in meeting EV users’ bandwidth needs for various multimedia streaming applications is demonstrated. During peak-bandwidth-demand periods, users experienced an average increase in bandwidth of 47%. Furthermore, bandwidth utilization across the V2X landscape is significantly improved. Full article

“Linearly many faults” is a phenomenon observed by Cheng and Lipták in which a specific structure emerges when a graph is disconnected and often occurs in various interconnection networks. This phenomenon means that if a certain number of vertices or edges are deleted from a graph, the remaining part either stays connected or breaks into one large component along with smaller components with just a few vertices. This phenomenon can be observed in many types of graphs and has important implications for network analysis and optimization. In this paper, we first validate the phenomenon of linearly many faults for surviving graph of a burnt pancake graph $B{P}_n$ when removing any edge subset with a size of approximately six times $\lambda \left(B{P}_n\right)$. For graph G, the ℓ-component edge connectivity denoted as ${\lambda }_{\ell }\left(G\right)$ (resp., the ℓ-extra edge connectivity denoted as ${\lambda }^{\left(\ell \right)}\left(G\right)$) is the cardinality of a minimum edge subset S such that $G-S$ is disconnected and has at least ℓ components (resp., each component of $G-S$ has at least $\ell +1$ vertices). Both ${\lambda }_{\ell }\left(G\right)$ and e${\lambda }^{\left(\ell \right)}\left(G\right)$ are essential metrics for network reliability assessment. Specifically, from the property of “linearly many faults”, we may further prove that ${\lambda }_5\left(B{P}_n\right)={\lambda }^{\left(3\right)}\left(B{P}_n\right)+3=4n-3$ for $n⩾5$; ${\lambda }_6\left(B{P}_n\right)={\lambda }^{\left(4\right)}\left(B{P}_n\right)+4=5n-4$ and ${\lambda }_7\left(B{P}_n\right)={\lambda }^{\left(5\right)}\left(B{P}_n\right)+5=6n-5$ for $n⩾6$. Full article

Cooperative intelligent transport systems (C-ITS) continue to be developed to enhance transportation safety and sustainability. However, the communication of vehicle-to-everything (V2X) systems is inherently open, leading to vulnerabilities that attackers can exploit. This represents a threat to all road users, as security failures can lead to privacy violations or even fatalities. Moreover, a high fatality rate is correlated with soft-mobility road users. Therefore, when developing C-ITS systems, it is important to broaden the focus beyond connected vehicles to include soft-mobility users and legacy vehicles. This work presents a new approach developed in the context of emerging hybrid networks, combining intelligent transport systems operating in 5.9 GHz (ITS-G5) and radio-mobile cellular technologies. Two protocols were implemented and evaluated to introduce security guarantees (such as privacy and integrity) in communications within the developed C-ITS hybrid environment. As a result, this work securely integrates G5-connected ITS stations and soft-mobility users through a smartphone application via cellular networks. Commercial equipment was used for this goal, including on-board and roadside units. Computational, transmission and end-to-end latency were used to assess the system’s performance. Implemented protocols introduce an additional 11% end-to-end latency in hybrid communications. Moreover, workflows employing hybrid communications impose, on average, an extra 28.29 ms of end-to-end latency. The proposal shows promise, as it reaches end-to-end times below the latency requirements imposed in most C-ITS use cases. Full article

In the present research work, an algorithm of artificial neural network (ANN) has been developed based on the processing of digital images of Persian lemons with the aim of optimizing the quality control of the product. For this purpose, the physical properties (weight, thickness of the peel, diameter, length, and color) of 90 lemons selected from the company Esperanza de San José Ornelas SPR de RL (Jalisco, Mexico) were studied, which were divided into three groups (Category “extra”, Category I, and Category II) according to their characteristics. The parameters of weight (26.50 ± 3.00 g), diameter/length (0.92 ± 0.08) and thickness of the peel (1.50 ± 0.29 mm) did not present significant differences between groups. On the other hand, the color (determined by the RGB and HSV models) presents statistically significant changes between groups. Due to the above, the proposed ANN correctly classifies 96.60% of the data obtained for each of the groups studied. Once the ANN was trained, its application was tested in an automatic classification process. For this purpose, a prototype based on the operation of a stepper motor was simulated using Simulink from Matlab, which is connected to three ideal switches powered by three variable pulse generators that receive the information from an ANN and provide the corresponding signal for the motor to turn to a specific position. Manual classification is a process that requires expert personnel and is prone to human error. The scientific development presented shows an alternative for the automation of the process using low-cost computational tools as a potential alternative. Full article

The g-extra connectivity is a very important index to evaluate the fault tolerance, reliability of interconnection networks. Let g be a non-negative integer, G be a connected graph with vertex set V and edge set E, a subset $S\subseteq V$ is called a g-extra cut of G if the graph induced by the set $G-S$ is disconnected and each component of $G-S$ has at least $g+1$ vertices. The g-extra connectivity of G, denoted as ${\kappa }_g\left(G\right)$, is the cardinality of the minimum g-extra cut of G. Mycielski introduced a graph transformation to discover chromatic numbers of triangle-free graphs that can be arbitrarily large. This transformation converts a graph G into a new compound graph called $\mu \left(G\right)$, also known as the Mycielskian graph of G. In this paper, we study the relationship on g-extra connectivity between the Mycielskian graph $\mu \left(G\right)$ and the graph G. In addition, we show that ${\kappa }_3\left(\mu \left(G\right)\right)=2{\kappa }_1\left(G\right)+1$ for ${\kappa }_1\left(G\right)\le min\{4,\lfloor \frac{n}{2}\rfloor \}$, and prove the bounds of ${\kappa }_{2g+1}\left(\mu \left(G\right)\right)$ for $g\ge 2$. Full article

Olive tree growing (Olea europaea L.) has considerably increased in the last decades, as has the consumption of extra virgin olive oil in the world. Precision agriculture is increasingly being applied in olive orchards as a new method to manage agronomic variability with the aim of providing individual plants with the right input amount, limiting waste or excess. The objective of this study was to develop a methodology on a GIS platform using GEOBIA algorithms in order to build prescription maps for variable rate (VRT) nitrogen fertilizers application in an olive orchard. The fertilization plan was determined for each tree by applying its own nitrogen balance, taking into account the variability of nitrogen in soil, leaf, production, and actual biometric and spectral conditions. Each olive tree was georeferenced using the S7-G Stonex instrument with real-time kinematic RTK positioning correction and the trunk cross section area (TCSA) was measured. Soil and leaves were sampled to study nutrient variability. Soil and plant samples were analyzed for all major physical and chemical properties. Spectral data were obtained using a multispectral camera (DJI multispectral) carried by an unmanned aerial vehicle (UAV) platform (DJI Phantom4). The biometric characteristics of the plants were extracted from the achieved normalized vegetation index (NDVI) map. The obtained prescription map can be used for variable rate fertilization with a tractor and fertilizer spreader connected via the ISOBUS system. Using the proposed methodology, the variable rate application of nitrogen fertilizer resulted in a 31% reduction in the amount to be applied in the olive orchard compared to the standard dose. Full article

 The cascaded droop-voltage-current controller plays a key role in the effective operation of microgrids, where the controller performance is critically impacted by the desigheme, a constant value n of the droop controller. Moreover, in critical loading (e.g.: connection/disconnection of large inductive load), the pre-set value of the droop coefficient brings asymmetry in transient performance leading to instability. Hence, to improve symmetry by reducing the trade-off between transient response and stability margin, this paper proposes a state machine-based droop control method (SMDCM) aided with droop coefficients’ tuning through in-feasible range detection. Here, to realize the issues and the role of the droop controller’s dynamics on the microgrid’s stability, a small-signal stability analysis is conducted, thereby, an in-feasible range of droop values is identified. Accordingly, safe values for droop coefficients are implemented using the state machine concept. This proposed SMDCM is compared with the conventional constant droop control method (CDCM) and fuzzy logic-based droop control method (FLDCM) in terms of frequency/power/voltage characteristics subjected to different power factor (PF) loading conditions. From the results, it is seen that CDCM failed in many metrics under moderate and poor PF loadings. FLDCM is satisfactory under moderate PF loading, but, showed 54 Hz/48 Hz as maximum/minimum frequency values during poor PF loading. These violate the standard limit of ±2%, but SMDCM satisfactorily showed 50.02 Hz and 49.8 Hz, respectively. Besides, FLDCM levied an extra burden of 860 W on the system while it is 550 W with SMDCM. System recovery has taken 0.04 s with SMDCM, which completely failed with FLDCM. Similarly, voltage THD with FLDCM is 58.9% while with SMDCM is 3.08%. Peak voltage due to capacitive load switching is 340V with FLDCM and 150 V with SMDCM. These findings confirm that the proposed SMDCM considerably improved the transient performance of microgrids.  Full article

The evaluation of the fault diagnosis capability of a data center network (DCN) is important research in measuring network reliability. The g-extra diagnosability is defined under the condition that every component except the fault vertex set contains at least g+1 vertices. The t/k diagnosis strategy is that the number of fault nodes does not exceed t, and all fault nodes can be isolated into a set containing up to k fault-free nodes. As an important data center network, BCube Connected Crossbars (BCCC) has many excellent properties that have been widely studied. In this paper, we first determine that the g-extra connectivity of $B{C}_{n,k}$ for $0\le g\le n-1$. Based on this, we establish the g-extra conditional diagnosability of $B{C}_{n,k}$ under the MM* model for $1\le g\le n-1$. Next, based on the conclusion of the largest connected component in g-extra connectivity, we prove that the t/k-diagnosability of $B{C}_{n,k}$ under the MM* model for $1\le k\le n-1$. Finally, we present a t/k diagnosis algorithm on BCCC under the MM* model. The algorithm can correctly identify all nodes at most k nodes undiagnosed. So far, t/k-diagnosability and diagnosis algorithms for most networks in the MM* model have not been studied. Full article

Recently, the local climate zone (LCZ) system has been presented to establish the connection between urban landscape and local thermal environment. However, LCZ entities are very difficult to be identified by pixel-based classifiers or object-oriented image analysis, as they are often a complicated combination of multiple ground objects (e.g., buildings, roads, grassland, etc.). Scene classifiers, especially deep learning methods can exploit the structure or contextual information of image scenes and then improve the performance of LCZ classification. However, the square and uniform-sized image patches often bring about extra challenges, as they cannot exactly match LCZ entities of diverse sizes and shapes in most cases. In this study, a sequential virtual scene method is presented to identify LCZ entities of diverse shapes and sizes, which consists of a small “core patch” for scanning diverse entities and sequential virtual scenes for providing abundant context. Specifically, the Bidirectional Long Short-Term Memory (Bi-LSTM) were used to learn the spatial relationship among virtual scenes, respectively. Importantly, a “self-attention” mechanism is designed to weigh the contribution of every virtual scene for alleviating the influences of mixed patches, according to the similarity between its hidden state and the final hidden state. Experiments prove SVS achieves better accuracies than random forest and ResNet and has the outstanding capacity of identifying irregular LCZ entities. It is a promising way to carry out LCZ mapping in cities of different types due to its flexibility and adaptability. Full article

Lithium-ion batteries on electric vehicles have been increasingly deployed for the enhancement of grid reliability and integration of renewable energy, while users are concerned about extra battery degradation caused by vehicle-to-grid (V2G) operations. This paper details a multi-year cycling study of commercial 24 Ah pouch batteries with Li(NiMnCo)O₂ (NCM) cathode, varying the average state of charge (SOC), depth of discharge (DOD), and charging rate by 33 groups of experiment matrix. Based on the reduced freedom voltage parameter reconstruction (RF-VPR), a more efficient non-intrusive diagnosis is combined with incremental capacity (IC) analysis to evaluate the aging mechanisms including loss of lithium-ion inventory and loss of active material on the cathode and anode. By analyzing the evolution of indicator parameters and the cumulative degradation function (CDF) of the battery capacity, a non-linear degradation model with calendar and cyclic aging is established to evaluate the battery aging cost under different unmanaged charging (V0G) and V2G scenarios. The result shows that, although the extra energy throughput would cause cyclic degradation, discharging from SOC 90 to 65% by V2G will surprisingly alleviate the battery decaying by 0.95% compared to the EV charged within 90–100% SOC, due to the improvement of calendar life. By optimal charging strategies, the connection to the smart grid can potentially extend the EV battery life beyond the scenarios without V2G. Full article