The convergence of Federated Learning (FL), a nascent decentralized machine learning paradigm, with the Internet of Things (IoT) presents unprecedented opportunities for promoting sustainable agricultural development. However, this synergy faces severe challenges from untargetd poisoning attacks which undermine the performance of global models. Due to factors such as seasonality and location, agricultural data exhibit a high degree of non-IID characteristics which further exacerbates this risk. In this context, the model updates submitted by benign clients tend to become dispersed, allowing malicious updates to blend in and go undetected. This dispersion renders traditional defense frameworks based on global anomaly detection ineffective, preventing FL from realizing its full potential in the agricultural domain. In this article, we propose FedCoguard, aimed at ensuring the integrity of FL in smart agriculture. FedCoguard shifts the perspective of defense from the server to the client. By leveraging the intrinsic differences in training objectives between benign and malicious clients, it excludes malicious updates spontaneously, thereby protecting the global model. Experiments on five benchmark datasets demonstrate that even in scenarios of highly non-IID data and a substantial presence of malicious clients, FedCoguard can achieve high accuracy and robust performance. By addressing security issues in the collaborative training process, this research alleviates the challenge of data silos in agricultural data sharing, unlocking the potential of collaborative intelligence and promoting the development of sustainable agricultural systems.

Partial Differential Equations (PDEs) are fundamental for modeling physical systems, yet solving them in a generic and efficient manner using machine learning-based approaches remains challenging due to limited multi-input and multi-scale generalization capabilities, as well as high computational costs. This paper proposes the Multi-input and Multi-scale Efficient Transformer (MMET), a novel framework designed to address the above challenges. MMET decouples mesh and query points as two sequences and feeds them into the encoder and decoder, respectively, and uses a Gated Condition Embedding (GCE) layer to embed input variables or functions with varying dimensions, enabling effective solutions for multi-scale and multi-input problems. Additionally, a Hilbert curve-based reserialization and patch embedding mechanism decrease the input length. This significantly reduces the computational cost when dealing with large-scale geometric models. These innovations enable efficient representations and support multi-scale resolution queries for large-scale and multi-input PDE problems. Experimental evaluations on diverse benchmarks spanning different physical fields demonstrate that MMET outperforms SOTA methods in both accuracy and computational efficiency. This work highlights the potential of MMET as a robust and scalable solution for real-time PDE solving in engineering and physics-based applications, paving the way for future explorations into pre-trained large-scale models in specific domains. This work is open-sourced at https://github.com/YichenLuo-0/MMET.

As traditional analog industrial automation transitions toward digitalization, the efficient deployment of control systems becomes increasingly critical. Open programmable logic controllers (OpenPLC) and the Modbus communication protocol, leveraging the widespread adoption of containerization technologies, are gradually migrating to native cloud architectures. This paper investigates the deployment of OpenPLC in local environments, Docker containers, and Kubernetes clusters, evaluating several key performance indicators, including resource consumption, Modbus communication response time, throughput, multi-user handling, stability, and fault recovery. By designing experimental platforms tailored to different environments and conducting validation across multiple platforms, the experimental results indicate that, although local deployment performs better in terms of low latency and resource consumption, deployments on the Kubernetes platform exhibit significant advantages in fault recovery, throughput, and system stability. In contrast, Docker-based deployments demonstrate more balanced performance, but lag behind Kubernetes in scalability and fault tolerance. Based on the experimental findings, this study provides valuable insights for selecting the optimal deployment strategy for industrial automation systems, taking into account OpenPLC, containerization technologies, performance requirements, and the integration of cloud-fog automation for enhanced flexibility and scalability.

This article presents a Kubernetes-based database benchmarking framework for Cloud-Fog Automation (CFA) in industrial systems, integrating Locust (dynamic load simulation) and Prometheus (resource monitoring) to evaluate four databases under industrial workloads: write-heavy , query-intensive (real-time analytics), and 6:4 read-write hybrid (control-logic scenarios). Kubernetes, as a representative of cloud-native technologies, is a crucial support for industrial CPS, but the database performance for industrial data management is yet to be fully determined. In 1k-concurrent-user tests (emulating distributed CFA edge nodes), ReductStore delivered 649.63 req/s throughput with <6 ms latency, ideal for fog-level real-time control, while OpenGauss achieved P99 latency <1 ms (critical for PLC synchronization) at 4.69 Gi memory cost—quantifying trade-offs for resource-constrained fog deployments. The framework demonstrates Kubernetes’ role in elastic cloud-fog orchestration, aligning with CFA’s industrial demands: InfluxDB suits massive IIoT data aggregation, while OpenGauss optimizes mission-critical latency. Our results bridge cloud-native scalability with deterministic industrial performance, enabling cost-efficient DBMS selection for smart factories.

Virtual Programmable Logic Controllers (vPLCs), as a newborn technology, are becoming increasingly important in modern industrial automation due to their flexibility and scalability. There is lack of researches on data synchronization and redundancy mechanisms for vPLCs, limiting applications of vPLCs in critical industrial scenarios. This paper designs and implements a data synchronization and redundancy mechanism between vPLCs based on heartbeat detection to enhance the reliability of vPLC systems. The mechanism continuously monitors for failures and synchronizes data between vPLCs to ensure seamless control task takeover in the event of a failure. Experimental results demonstrate the mechanism’s high effectiveness in fault detection and recovery, achieving a redundancy switchover time that meets industrial application requirements.

With the rapid development of the industrial internet of things and automation control systems, supervisory control and data acquisition (SCADA) systems have been widely adopted in industrial manufacturing due to their flexibility and scalability. The cloud-fog automation (CFA) paradigm is emerging to address higher real-time and computing demands in complex industrial environments. To fully leverage the efficiency, flexibility, and scalability of Kubernetes, an open-source container orchestration platform Kubernetes in managing containerized applications, this article investigates methods for deploying SCADA systems on the Kubernetes platform. This approach aims to capitalize on Kubernetes’ benefits, such as automated deployment, elastic scaling, and high availability, to optimize resource management and enhance system performance. To validate the proposed solution, we employs testing tools such as wrk and tc, along with monitoring tools like Prometheus and Grafana, to conduct a comprehensive evaluation of Kubernetes’ advantages in various scenarios. We focus on three key aspects: reliability, scalability, and security. The results demonstrate that Kubernetes can significantly improve the scalability, fault recovery capabilities, and stability of SCADA systems.

Accurate detection of aero-engine blades is critical for aviation safety and maintenance. While industrial endoscopes enable efficient inspection of complex engine interiors, existing systems struggle with blade detection due to geometric variations, lighting changes, reflections, and occlusions. This paper proposes a novel spatial-temporal transformer model that enhances small blade detection via multi-scale feature extraction and improves edge robustness with an enhanced deformable DETR module, addressing defects like erosion or fractures. For video sequences, a unified spatial-temporal representation resolves counting errors (e.g., omissions/duplicates). Experiments on a custom inspection platform demonstrate superior accuracy and reliability over state-of-the-art methods.

Currently, robots face significant challenges in independently completing tasks within dynamic and unstructured environments. Teleoperation systems that utilize exoskeletons as input devices present an effective solution to this issue. This paper introduces an ergonomic 7-degree-of-freedom (7-DOF) exoskeleton device and develops an immersive teleoperation system integrated with a virtual reality (VR) head-mounted display (HMD). In this system, the operator, serving as the master side, dons the exoskeleton to issue control commands to the slave-side robot while leveraging feedback from both the exoskeleton and the VR HMD for cognitive decision-making. This closed-loop teleoperation system provides a multi-sensory feedback experience that integrates visual and haptic sensations, significantly enhancing operational stability and accuracy. Furthermore, for force feedback control, we propose a strategy based on environmental parameter estimation in conjunction with Weber’s law, allowing for self-adaptive adjustments of force feedback mapping in response to varying environmental conditions. Experimental results indicate that operators experience a high level of immersion with this system and successfully complete tasks such as remote ultrasound detection. The system demonstrates superior performance in terms of stability, accuracy, and user adaptability, highlighting its potential for complex remote operations in dynamic and unstructured environments.

Pest recognition is of great significance for achieving sustainable development in agriculture. Nevertheless, due to the wide variety of pest species, subtle interspecies differences, and significant intraspecies variations, existing artificial intelligence and Internet of Things (IoT) technologies can only recognize a small number of known pests effectively. In this article, we propose a zero-shot learning pest recognition framework based on ensemble hierarchical attribute prompting, termed EHAPZero. EHAPZero can identify pest images collected by IoT devices, and then transmit the recognition results to the IoT platform for terminal display. Specifically, the image recognition function is implemented by an attribute generation module (AGM), a hierarchical prompting module (HPM), and a semantic-visual interaction module (SVIM). AGM utilizes large language models to construct a knowledge graph of pests. It employs both node importance evaluation algorithms and manual methods to perform dual filtering on attribute nodes within the graph. Inspired by human knowledge reasoning, HPM dynamically predicts different hierarchical attributes of input images within the Transformer intermediate blocks. These predicted attributes are subsequently injected into the intermediate layer features of the Transformer as prompts. To achieve semantic disambiguation and knowledge transfer, SVIM employs a visual-guided semantic representation method and a semantic-guided visual representation method to strengthen cross-domain interaction between semantics and vision. Finally, the final prediction score is derived through ensemble of prediction results across different levels. Extensive experiments show that EHAPZero achieves the new state-of-the-art results on the real-word pest recognition benchmark. The codes are available at:https://github.com/jinqiwen/EHAPZero.

Time-Sensitive Networking (TSN), renowned for its deterministic properties, has become a pivotal technology under-pinning real-time industrial control in Cyber-Physical Systems. Existing research emphasizes enhancing the transmission services of TSN networks for control applications by improving flow schedulability and minimizing end-to-end delay. However, these studies abstract the performance requirements of control applications into rigid, impractical constraints for flow scheduling, disrupting the connection between control optimization and transmission enhancement, and eventually undermining genuine progress in industrial control. Within a co-optimization framework of communication and control, this paper proposes AFS-RT, an Adaptive TSN Flow Scheduling method for Robotic arm Teleoperation, a representative industrial control application. Specifically, through a comprehensive analysis of the teleoperation case, we first integrate slot allocation-based flow scheduling with remote control to formulate a control-driven co-optimization model. To tackle the complexities arising from the implicit mapping between communication and control, we augment the Deep Reinforcement Learning agent responsible for slot allocation with slot-correlation-guided feature extraction, improving feature comprehension by leveraging inherent correlations between slots and thereby boosting the agent’s decision-making capabilities. Extensive testbed and simulation experiments demonstrate that AFS-RT significantly improves teleoperation performance under diverse network conditions compared to SOTA algorithms.

The advancement of large models has initiated a transformation in the field of time-series forecasting. Both the repurposing of existing large models and the development of large models tailored for time-series analysis have exhibited impressive performance. In industrial applications, challenges, such as limited data availability and constrained computational resources, render the first approach viable. However, it is important to note that this approach is still in its infancy and lacks both a thorough technical analysis and a unified effective framework. Meanwhile, as large models become a mainstream artificial intelligence paradigm, it is urgent to discuss typical industrial scenarios, such as how automated systems can transition from intelligent to collaborative operation and maintenance. In light of this premise, this article endeavors to advance a generalized technical framework for large model-driven time-series forecasting, under which existing methods can be subsumed. Then, within this overarching technical paradigm, the technical advancements facilitated by diverse methods will be systematically elucidated and analyzed, along with a comparative evaluation conducted across seven benchmark datasets. Concluding this analysis, the implementation pathway for the industrial automation system is delineated that integrates operator action commands to forecast post-action trends to assess action correctness in advance. Finally, the challenges and future directions of large model-based time-series forecasting are outlined.

In 6G-enabled vehicle-to-edge networks, through deploying computing power resources closer to mobile vehicles for providing low latency and highly reliable services, Mobile Edge Computing (MEC) as an emerging paradigm has promoting mobile vehicles with limited capacities to come with diversified artificial intelligence (AI) applications. Nevertheless, the computing power of MEC server is still finite, seeking the optimal resource pricing and allocation strategies for MEC servers, and determining the optimal computation offloading for intelligent vehicle applications, still remain challenging issues that are necessary to be reasonably solved to improve the service experience. To address this issue, a scenario that multiple intelligent vehicle applications cooperatively initiate computation offloading requests in 6G-enabled multi-server multi-access vehicle-to-edge computing systems is considered in this paper, and with the defined reasonable utilities for MEC servers and intelligent vehicle applications, a solution of bidding-enabled dynamic resource pricing for computation offloading is proposed. Concretely, through considering the relationship of resource supply and demand, and the bidding among MEC servers, a dynamic resource pricing scheme is designed for MEC servers, meanwhile, with the complete consideration of dynamic resource pricing and time-varying wireless channel interference in multi-cell networks, a Q-learning based offloading decision algorithm is proposed for intelligent vehicle applications. Simulation experiments finally are conducted to demonstrate the efficiency in achieving the win-win situation with guaranteed utilities for both MEC servers and intelligent vehicle applications.

The Industrial Internet of Things (IIoT) has revolutionized industrial sectors with enhanced connectivity, data exchange, and predictive maintenance. However, it faces various challenges from non-IID data distributions and communication overheads, to the consistency and privacy of prediction models for maintenance. Federated Learning (FL) has been considered as a prevalent technique to address privacy concerns. On the other hand, Edge computing (EC) is being increasingly introduced to ensure low-latency data processing in IIoT systems, especially those with time-critical requirements, e.g., industrial robotics and motion control systems. Moreover, the complexity and design dimensions of today’s IIoT systems has lead to the development of large Machine Learning (ML) models with millions of parameters, e.g., using computer vision for field management. This introduces computational and privacy challenges in IIoT scenarios. Innovative FL optimization approaches such as pruning are aimed to tackle these challenges by reducing the number of training parameters, while they may impact accuracy. In this paper, we propose a new technique, called Adaptive Mean aBsolute devIaTion (AMbit), which is an innovative pruning approach optimizing data transmission without compromising model accuracy and inducing additional computation overhead. By dynamically comparing the difference between the current and the previous weight value, AMbit adapts better to parameter fluctuations at different stages, thereby accurately locating those parameters that have less impact on convergence. AMbit’s generality and efficiency are illustrated using MNIST and CIFAR-10 datasets, outperforming traditional Magnitude pruning in FL. For MNIST, AMbit reduces data uploads up to 43.75%, with an increase of 0.62% in accuracy. While for For CIFAR-10, AMbit achieved a 63.44% decrease with a 5.22% drop in accuracy.

The increasing prevalence of electric vehicles de-mands efficient and sustainable management of end-of-life lithium-ion batteries. This paper examines the use of Pretrained Foundation Models and Cloud-Fog Automation to improve robotic disassembly of these batteries. We evaluate the performance of two Vision Transformer Models, in tasks involving deformed, rusty, contaminated, and worn batteries. Our proposed architecture, utilizing cloud and fog computing, balances performance with resource efficiency, providing a scalable solution for electric vehicles battery recycling.

Abstract To solve the security problems of the moving robot system in the fog network of the Industrial Internet of Things (IIoT), this paper presents a privacy‐preserving data integration scheme in the moving robot system. First, a novel data collection enhancement algorithm is proposed to enhance the image effects, and a k ‐anonymous location and data privacy protection protocol based on Ad hoc network (Ad hoc‐based KLDPP protocol) is designed in secure data collection phase to protect the privacy of location and network data. Second, the secure multiparty computation with verifiable key sharing is introduced to realize the valid computation against share cheating in the robot system. Third, the ciphertext classification method in a neural network is considered in the secure data storage process to realize the special application. Finally, experiments and simulations are conducted on the robot system of fog computing in the IIoT. The results demonstrate that the proposed scheme can improve the security and efficiency of the said robot system.

With recent advents of industrial Internet of Things (IIoT), the connectivity and data collection capabilities of industrial equipment have be significantly enhanced, yet bringing new challenges for the remaining useful life (RUL) prediction. To fulfill the RUL predicting demand in multivariate time series, this work proposes an encoder-decoder model termed as dual-scale transformer model (DSFormer), built upon the Transformer architecture. First, in the encoder part, a dual-attention module is designed for the weight feature extraction from both dimensions of the sensor and time series, aiming to compensate for the diverse impacts of different sensors on the prediction. Next, a temporal convolutional network (TCN) module is introduced to capture sequence features and alleviate the loss of positional information incurred by stacking blocks. Then, the feature decomposition module is integrated into the decoder for trend feature extraction from sequences, providing the model with additional sequence information. Finally, compared to existing models, the proposed method can obtain the superior performance in terms of the root mean square error (RMSE) and Score metrics on the FD001, FD002 and FD003 subsets of the C-MAPSS dataset, with an average improvement of 3.2% and 2.5% respectively. In particular, the ablation experiment further validates the effectiveness of proposed modules in handling multivariate time series and extracting features.

In the paradigm of ubiquitous edge computing, with those advantages, e.g., high mobility, fast response, flexibility and controllability, and low cost of use, Unmanned Aerial Vehicles (UAVs) could be used not only as relays to assist with data collection, but also as computing power nodes to process uncomplicated computational workloads from ground users. Especially, UAVs could be employed to provide alternative computing power resources in field, lake, post-disaster and other complex regional environments. In this paper, to address the issue of computing power scheduling in UAVs empowered aerial computing systems, a scenario where multiple UAVs from the same departure station cooperatively fly over hovering points and achieve the data collection and computation in a decentralized manner is investigated. Nevertheless, due to limited onboard battery capacities of UAVs and diverse service requests of ground users, it is necessary to optimize energy efficiency and service fairness for improving mission execution capabilities of UAVs and the quality of service (QoS) experienced by ground users, and a joint optimization problem of energy efficiency and service fairness is formulated. Through considering complex coupling associations among the departure station, flight paths and hovering points of UAVs, the problem is investigated from the trajectory planning of UAVs and the location planning for both the departure station and hovering points. Proving investigations to be Markov decision processes (MDP), multi-agent cooperation approaches are proposed as promising solutions, and simulation results have been shown to demonstrate that the performance achieved by the proposal outperforms that achieved by schemes commonly used in literatures.

Vehicular Edge Computing (VEC) is enjoying a surge in research interest due to the remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the ever-growing service applications in VEC, how to effectively aggregate and flexibly schedule ubiquitous network resources for implementing diverse tasks and meeting differentiated demands from numerous vehicular users remains haunting. Toward this end, we investigate collaborative task computing and on-demand resource allocation. The collaborative computing framework in VEC is provided to support deep collaboration and intelligent management of heterogeneous resources widely distributed in vehicles, edge servers and cloud. Based on this framework, the joint optimization problem of distributed task offloading and multi-resource management is formulated with the aim to maximize the system utility by making the optimal task and resource scheduling policy, the novelty of which lies in the exploration of available vehicle resources and the consideration of service migration. In view of the dynamics, randomness and time-variant of vehicular networks, the asynchronous deep reinforcement algorithm is leveraged to find the optimal solution. Extensive simulation experiments are implemented to demonstrate the superiority of our proposed algorithm in terms of response latency compared with full offloading and random offloading.

Vehicular Edge Computing (VEC) has gained increasing interest due to its potential to provide low latency and reduce the load in backhaul networks. In order to meet drastically increasing computation demands from emerging ever-growing vehicular applications, e.g., autonomous driving, abundant computation resources of individual vehicles can play a crucial role in task execution in a VEC scenario, that can further contribute in considerably improving user experience. This is however an extremely challenging task due to high mobility of vehicles that can easily lead to intermittent connectivity, thereby disrupting on-going task processing. In this paper, we propose a task offloading scheme by exploiting multi-hop vehicle computation resources in VEC based on mobility analysis of vehicles. In addition to the vehicles within one hop from the task vehicle that generates computation tasks, certain multi-hop vehicles that meet the given requirements in terms of link connectivity and computation capacity, are also leveraged to carry out the tasks offloaded by the task vehicle. An optimization problem is formulated for the task vehicle to minimize the weighted sum of execution time and computation cost of all tasks. A semidefinite relaxation approach with an adaptive adjustment procedure is proposed to solve the formulated optimization problem for obtaining the corresponding offloading decisions. The simulation results show that our proposed offloading scheme can achieve significant improvement in terms of response delay by at least 34% compared with the other algorithms (e.g., local processing and random offloading).

Running computer vision applications, such as 3-D simultaneous localization and mapping (SLAM), on mobile devices requires low-latency responses and a massive amount of computation. Edge computing has been introduced to move Cloud features closer to end users, providing necessary computing and network resources for end devices. The heterogeneous edge devices, with different hardware architectures (e.g., CPUs and GPUs) and runtime environments, provide diverse resources to support processing tasks from end devices, resulting in different costs and quality of services. How to partition these computing tasks and distribute them over these heterogeneous hardware nodes is still an open research question. Considering these inherently heterogeneous hardware architectures, new approaches for service orchestration and task scheduling are required to meet the service-level agreement and reduce the overall cost of the system (e.g., facility utilization cost). This article presents a system framework, EDGE VISION, for computer vision applications partitioning and orchestration on heterogeneous edge computing platforms considering both CPUs and GPUs. EDGE VISION abstracts the heterogeneous hardware resources and the task runtime environments and divides the application into separate tasks to be orchestrated and deployed into the heterogeneous edge nodes. We also propose two scheduling algorithms in our framework, minimum latency task scheduling and minimum cost task scheduling, aiming to minimize the processing latency and the overall system cost. We evaluate our framework by implementing the edge-based 3-D SLAM application in our real testbed with ten heterogeneous edge devices. Evaluations show that EdgeVision can efficiently minimize the processing latency and the system overall cost and achieve up to 30% decrease in task processing latency and 15% more cost saving compared to the State-of-the-Art baselines.

Blockchain constructs a distributed point-to-point system, which is a secure and verifiable mechanism for decentralized transaction validation and is widely used in financial economy, Internet of Things, large data, cloud computing, and edge computing. On the other hand, artificial intelligence technology is gradually promoting the intelligent development of various industries. As two promising technologies today, there is a natural advantage in the convergence between blockchain and artificial intelligence technologies. Blockchain makes artificial intelligence more autonomous and credible, and artificial intelligence can prompt blockchain toward intelligence. In this paper, we analyze the combination of blockchain and artificial intelligence from a more comprehensive and three-dimensional point of view. We first introduce the background of artificial intelligence and the concept, characteristics, and key technologies of blockchain and subsequently analyze the feasibility of combining blockchain with artificial intelligence. Next, we summarize the research work on the convergence of blockchain and artificial intelligence in home and overseas within this category. After that, we list some related application scenarios about the convergence of both technologies and also point out existing problems and challenges. Finally, we discuss the future work.

The role of fog computing in future vehicular networks is becoming significant, enabling a variety of applications that demand high computing resources and low latency, such as augmented reality and autonomous driving. Fog-based computation offloading and service caching are considered two key factors in efficient execution of resource-demanding services in such applications. While some efforts have been made on computation offloading in fog computing, a limited amount of work has considered joint optimization of computation offloading and service caching. As fog platforms are usually equipped with moderate computing and storage resources, we need to judiciously decide which services to be cached when offloading computation tasks to maximize the system performance. The heterogeneity, dynamicity, and stochastic properties of vehicular networks also pose challenges on optimal offloading and resource allocation. In this paper, we propose an intelligent computation offloading architecture with service caching, considering both peer-pool and fog-pool computation offloading. An optimization problem of joint computation offloading and service caching is formulated to minimize the task processing time and long-term energy utilization. Finally, we propose an algorithm based on deep reinforcement learning to solve this complex optimization problem. Extensive simulations are undertaken to verify the feasibility of our proposed scheme. The results show that our proposed scheme exhibits an effective performance improvement in computation latency and energy consumption compared to the chosen baseline.

The need for energy efficiency in wireless communication is prevalent in all areas, but to an even greater extent in low-power and lossy networks that rely on resource-constrained devices. This paper seeks to address the problem of modeling the battery lifetime of a duty-cycled node, participating in a wireless sensor network that is typically used in smart home and building applications. Modeling in MATLAB and experimentation with prototype testing are employed to predict and validate the battery lifetime. Various scenarios including sleepy end devices in a wireless sensor network are modeled and validated. They range from variable wake-up frequency and packet payload transmission to increasing network contention with the addition of network load. A comprehensive analysis of the main factors contributing to wasteful energy usage is provided. It can be concluded that the model can estimate the battery lifetime under different testing scenarios with an error rate less than 5%.

Motivated by the proliferation of wireless building automation systems (BAS) and increasing security-awareness among BAS operators, in this paper, we propose a taxonomy for the security assessment of BASs. We apply the proposed taxonomy to Thread, an emerging native IP-based protocol for BAS. Our analysis reveals a number of potential weaknesses in the design of Thread. We propose potential solutions for mitigating several identified weaknesses and discuss their efficacy. We also provide suggestions for improvements in future versions of the standard. Overall, our analysis shows that Thread has a well-designed security control for the targeted use case, making it a promising candidate for communication in next generation BASs.