Hierarchical Federated Learning (HFL) has emerged as a promising approach for scalable and communication-efficient model training in wireless networks. However, achieving energy efficiency while ensuring convergence remains challenging due to limited bandwidth resource and strict latency constraints. This paper addresses energy-efficient HFL under both statistical and system heterogeneity, aiming to minimize long-term energy consumption through adaptive and unbiased edge scheduling and resource allocation in dynamic environments. A convergence analysis is first conducted without relying on a convex assumption, explicitly characterizing the influences of the number of scheduled edges and scheduling probabilities. An iterative algorithm is then proposed to jointly optimize these variables: the scheduling probabilities are solved by using a Barrier Method (BM) with an Infeasible-Start Newton Method (ISNM), while the number of scheduled edges is derived in a closed form. To further enhance communication efficiency, Non-Orthogonal Multiple Access (NOMA) is employed at the user–edge layer. Then, a joint optimization of inter-edge bandwidth allocation and intra-edge local resource allocation is developed to balance computation and communication overhead. Extensive simulations demonstrate that the proposed framework significantly outperforms existing benchmarks in terms of energy consumption under Non-Independent and Identically Distributed (Non-IID) data and dynamic wireless environments.

Low earth orbit (LEO) satellite communication has been seen as a promising solution for achieving global ubiquitous connectivity and high data rates. However, the propagation delays between satellites and cells are time-varying and significantly different, resulting in asynchronous inter-cell interference. Most existing research assume that the signals from all satellites are received simultaneously, which oversimplifies the interference situation and limits their applicability. To address this challenge, a beam management approach with asynchronous interference mitigation is proposed to improve network throughput. Firstly, a primary serving duration allocation method is developed based on convex optimization, while asynchronous interference is ignored. Subsequently, considering that inter-cell interference can be mitigated by appropriately setting guard periods, a beam scheduling with guard period selection method is designed under given service duration constraints, where conflict graphs are constructed to characterize interference situations. Extensive simulation results validate that our proposed scheme can effectively mitigate asynchronous inter-cell interference and significantly enhance network throughput. Specifically, the network through-put is increased by 20% compared with baselines that ignore asynchronous interference.

Hierarchical Federated Learning (HFL) has been proposed to achieve large-scale model training and more efficient communication, surpassing conventional Federated Learning (FL). However, inappropriate aggregation frequency and edge association in HFL result in excessive energy consumption for users with poor channels or hinder its convergence performance due to stochastic gradient descent (SGD) and Non-Independent and Identical Distribution (NIID) data, which is particularly challenging for energy-limited users. Motivated by this, a joint aggregation frequency and edge association optimization problem is proposed to minimize the long-term energy consumption during HFL training process. The problem can be formulated by incorporating computation, communication model and convergence analysis together. Due to the coupling between control variables, we decompose it into two sub-problems and adopt an iterative algorithm to approximate their optimal solutions. Specifically, the aggregation frequency is optimized under a given edge association by convex optimization to trade-off the computation and communication energy consumption, considering the convergence characteristic and SGD noise. Then, Deep Reinforcement Learning (DRL) is adopted to optimize edge association based on data distribution, dynamic channels and the derived aggregation frequency. Simulation results demonstrate that our proposed strategy achieves the lowest energy consumption while attaining the required model accuracy, outperforming other benchmarks.

Hierarchical Federated Learning (HFL) has been introduced to enhance the communication efficiency and scalability of traditional Federated Learning (FL). In addition, the integration of Non-Orthogonal Multiple Access (NOMA) into the HFL framework serves to bolster system capacity and spectral efficiency. However, the formidable challenge of energy efficiency persists, particularly in energy-constrained scenarios, which can be further compounded by factors such as Non-Independent and Identical Distribution (NIID) data, varying channels across users, heterogeneous computation and communication resources, and the interference from weak users. Motivated by this, we aim to minimize the sum of the computation and communication energy consumption of all users in the NOMA-based HFL system. This is achieved through a joint optimization of User-Edge Association (UEA) and Resource Allocation (RA). Specifically, we utilize Deep Reinforcement Learning (DRL) to optimize UEA to achieve the objective from a long-term perspective. Subsequently, computation and communication resources are jointly optimized by Newton's Method to balance the computation and communication energy consumption while meeting a given latency requirement. Numerical results show that our strategy significantly improves energy efficiency of the system compared with other benchmarks.

The emerging beyond fifth-generation (B5G) and envisioned sixth-generation (6G) wireless networks are considered as key enablers in supporting a diversified set of applications for industrial mobile robots (MRs). The scenario under investigation in this paper relates to mobile robots that autonomously roam on an industrial floor and assist in offloading tasks generated at different workstations. In such scenarios, the potential simultaneous task offloading during multiple MR movements may cause an excessive edge computing burden for offloaded tasks. To jointly consider MR path planning and efficient task offloading strategy, a novel weighted-sum multi-objective optimization problem is proposed where the robot total travel time and aggregated computation workload are optimized jointly to provide Pareto efficient optimal non-dominated solutions. An extensive set of numerical investigations reveal that compared with the time-only and workload-only optimization schemes, the proposed multi-objective optimization scheme can reduce the total travel time and aggregated computation workload by 39.2%, 89.2%, respectively, while achieving a decrease of the aggregated computation workload by an average of 85% compared to the Vehicle Routing Problem (VRP) solution.

With the emergence of the sixth generation (6G) era, ubiquitous sensing and machine intelligence have emerged as the primary motivation for the development of large-scale Internet of Things (IoT) systems. However, the limited communication capabilities of IoT devices are often constrained by costs and further inhibit their full potential. Nowadays sensor technologies are rapidly evolving, offering improved accuracy and update rates for environmental perception. Considering this, the challenge faced by 6G IoT lies in the increasing data payload from sensing and the constrained communication capacity. To this end, lite communication solutions for IoT are investigated in this paper. By integrating task-oriented communication concepts with sensing objectives, we extract and represent semantic information from sensor nodes. Subsequently, this information is transmitted to edge nodes via wireless transmission for analysis and processing of sensory data. This approach effectively reduces redundant data transmission, thus achieving lite communication in IoT. Based on these principles, a lite edge task-oriented communication system architecture tailored for IoT applications is proposed. Specifically, we implement and evaluate the IoT communication module and the edge task-oriented module, demonstrating that the proposed system reduces resource consumption by 99 % and significantly alleviates the transmission payload of IoT. Through leveraging low-speed IoT transmission protocols and high-precision sensors, our system effectively supports a wide range of sensing tasks.

Hierarchical Federated Learning (HFL) has been proposed to achieve larger-scale model training and more efficient communications compared to conventional Federated Learning (FL). However, both inappropriate edge association strategy and aggregation frequency may consume massive energy in users with poor channel conditions or degrade the HFL convergence performance due to Non Independent and Identical Distribution (NIID) data, which is challenging to energy-limited users. Motivated by this, a dynamically joint edge association and aggregation frequency optimization problem is proposed from the perspective of minimizing long-term energy consumption. By incorporating the communication model and convergence analysis, the problem can be formulated to strike a balance between HFL convergence rate and energy consumed by all users within one global communication round. Then, a Deep Reinforcement Learning (DRL) agent is designed to approximate the optimal solution. Simulation results verify the convergence analysis and the proposed DRL-assisted joint strategy can consume the least energy while reaching the required target model accuracy compared to other benchmarks.

Federated Learning (FL) is widely regarded as a leading distributed machine learning paradigm, owing to its outstanding performance in preserving privacy and conserving communication resources. To use it efficiently in wireless communication networks, novel transmission schemes that jointly consider the model propagation and training features are required. In this paper, a novel joint user scheduling and resource allocation scheme is proposed to reduce the communication cost in terms of the weighted sum of energy and time consumption while ensuring the convergence of FL. The time-varying channels and unpredictable model loss in the system make it difficult to use conventional optimization methods for this problem. Furthermore, considering optimal transmission policy in FL is to train a qualified model in the dynamic iterative process, a deep reinforcement learning based Proximal Policy Optimization (PPO) approach is employed to train an automatic policy maker. Specifically, the dynamic policy is decided in each training round based on the observed model accuracy and the time-varying channel gains, aiming at minimizing the total cost. Simulation results verify the proposed scheme can reduce the defined communication cost and improve the training efficiency compared with the traditional greedy and random benchmarks.

The emerging beyond fifth-generation (B5G) and envisioned sixth-generation (6G) wireless networks are considered as key enablers in supporting a diversified set of applications for industrial mobile robots (MRs). In this paper, we consider mobile robots that autonomously roam on an industrial floor and perform a variety of tasks at different locations, whilst utilizing high directivity beamformers in millimeter wave (mmWave) small cells for joint communication and sensing. In such scenarios, the potential close proximity of mobile robots connected to different base stations, may cause excessive levels of interference having as a net result a decrease in the overall achievable data rate and service degradation in the network. To mitigate this effect an interference aware path planning algorithm is proposed by explicitly taking into account the achievable performance of both communication and sensing. More specifically, the proposed heuristic scheme aims to find paths with minimal interfering time for each mobile robot whilst improving the overall communication and sensing performance. A wide set of numerical investigations reveal that the proposed heuristic path planning scheme for the mmWave connected mobile robots can improve the overall achievable communication throughput and the sensing mutual information by up to 35.6% and 23.6% respectively compared to an interference oblivious scheme. Most importantly, those gains are attained without penalizing noticeably the total travel time of the MRs.

Promotions are commonly used by e-commerce merchants to boost sales. The efficacy of different promotion strategies can help sellers adapt their offering to customer demand in order to survive and thrive. Current approaches to designing promotion strategies are either based on econometrics, which may not scale to large amounts of sales data, or are spontaneous and provide little explanation of sales volume. Moreover, accurately measuring the effects of promotion designs and making bootstrappable adjustments accordingly remains a challenge due to the incompleteness and complexity of the information describing promotion strategies and their market environments. We present PromotionLens, a visual analytics system for exploring, comparing, and modeling the impact of various promotion strategies. Our approach combines representative multivariant time-series forecasting models and well-designed visualizations to demonstrate and explain the impact of sales and promotional factors, and to support "what-if" analysis of promotions. Two case studies, expert feedback, and a qualitative user study demonstrate the efficacy of PromotionLens.

The emerging beyond 5G and envisioned 6G wire-less networks are considered as key enablers in supporting a diversified set of applications for industrial mobile robots (MRs). The scenario under investigation in this paper relates to mobile robots that autonomously roam on an industrial floor and perform a variety of tasks at different locations whilst utilizing high directivity beamformers in mmWave small cells. In such scenarios, the potential close proximity of mobile robots connected to different base stations, may cause excessive levels of interference having as a net result a decrease in the overall achievable data rate in the network. To resolve this issue, a novel mixed integer linear programming formulation is proposed where the trajectory of the mobile robots is considered jointly with the interference level at different beam sectors. Therefore, creating a low interference path for each mobile robot on the industrial floor. A wide set of numerical investigations reveal that the proposed path planning optimization approach for the mmWave connected mobile robots can improve the overall achievable throughput by up to 31% compared to an interference oblivious scheme, without penalizing the overall travelling time.

Fog computing is seen as a key enabler to meet the stringent requirements of industrial Internet of Things (IIoT). Specifically, lower latency and IIoT devices’ energy consumption can be achieved by offloading computation-intensive tasks to fog access points (F-APs). However, traditional computation offloading optimization methods often possess high complexity, making them inapplicable in practical IIoT. To overcome this issue, this article proposes a deep reinforcement learning (DRL) based approach to minimize long-term system energy consumption in a computation offloading scenario with multiple IIoT devices and multiple F-APs. The proposal features a multi-agent setting to deal with the curse of dimensionality of the action space by creating a DRL model for each IIoT device, which identifies its serving F-AP based on network and device states. After F-AP selection is finished, a low complexity greedy algorithm is executed at each F-AP under a computation capability constraint to determine which offloading requests are further forwarded to the cloud. By conducting offline training in the cloud and then making decisions online, iterative online optimization procedures are avoided and, hence, F-APs can quickly adjust F-AP selection for each device with trained DRL models. Via simulation, the impact of batch size on system performance is demonstrated and the proposed DRL-based approach shows competitive performance compared to various baselines including exhaustive search and genetic algorithm based approaches. In addition, the generalization capability of the proposal is verified as well.