Load balancing during resource allocation (RA) in multicell multi-input multi-output non-orthogonal multiple access (MC-MIMO-NOMA) poses significant challenges for the deployment of massive MIMO-NOMA in next-generation networks. This paper introduces load-balancing algorithms to address RA challenges in massive MIMO-NOMA systems. First, we formulate the general RA problem as a load minimization problem, involving the joint optimization of resource allocation and user clustering. To minimize computation complexity, this problem is then reduced to a suboptimal RA problem within each cell. However, due to the non-convex nature of the problem, obtaining an optimal solution is not straightforward. To overcome this, we propose the power to targeted load optimization (PTLO) approach to solve the RA problem in each cell based on underload and overload scenarios. By leveraging the PTLO approach, we develop a user clustering strategy using branch-and-bound and weighted matching theory to optimize RA in each cell. Through simulations, we evaluate the performance of the proposed load-balancing algorithms (LB-BB-MIMO-NOMA and LB-M2O-MIMO-NOMA) against benchmark algorithms such as Correlated-MIMO-NOMA, Coalition-MIMO-NOMA, Gain-Diff-MIMO-NOMA, Greedy-MIMO-NOMA, and MIMO-OMA. The results demonstrate that the proposed algorithms consistently outperform the benchmarks. Furthermore, we provide a detailed analysis highlighting the efficiency of the proposed algorithms in the MC-MIMO-NOMA networks.

With the growing demand for low-altitude transportation, the application of the Internet of drones (IoD) in urban logistics has become increasingly significant. However, the complex obstacles present in urban environments, such as tall buildings and no-fly zones, pose numerous challenges for the IoD, including low data-driven efficiency and difficulties in representing implicit knowledge. To address these challenges, this paper proposes an IoD swarm collaborative scheduling method based on distributed knowledge-enhanced multi-agent reinforcement learning (DKEMARL). The method leverages prior environmental knowledge to design a knowledge embedding and expansion module, which provides comprehensive and detailed environmental observation data during training and introduces a reward mechanism that balances task timeliness with flight safety. Within a centralized training and decentralized execution framework, the multi-agent deep deterministic policy gradient (MADDPG) approach is employed to facilitate efficient cooperation among the IoD. Specifically, we develop a scenario model for low-altitude transportation tasks involving an IoD swarm, considering factors such as task timeliness, flight distance cost, and safety constraints, and propose an optimization objective to balance timely task completion with flight safety. Simulation results demonstrate that the proposed DKEMARL algorithm can significantly enhance task completion efficiency compared to baseline methods that do not incorporate knowledge enhancement.

The popular multiagent reinforcement learning (MARL) methods primarily focus on exploring the capability of value functions to facilitate multiagent coordination. These MARL methods, following the centralized training with decentralized execution (CTDE) paradigm, tend to design ingenious network architectures while overlooking the impact of coordination through expanding local observation information. To tackle this deficiency, we model the multiagent systems (MASs) as a graph and use a graph neural network (GNN) to extract rich information between one agent and the others efficiently. Moreover, we propose a multigraph-neural-network information representation (MGIR) method that uses the power of GNN in local observation information extraction, enabling the acquisition of higher quality information. Specifically, multiple GNNs are used during centralized training to characterize the MAS from different perspectives and extract representations of latent variables. During distributed execution, these latent variables are leveraged to expand local observation information. Extensive comparative experiments substantiate that our proposed MGIR demonstrates superior coordination performance when compared with baseline methods. In addition, it can be flexibly integrated into various value function decomposition methods of MARL.

Deep reinforcement learning (DRL) has superior autonomous decision-making capabilities, combining deep learning and reinforcement learning (RL). Unlike DRL employs deep neural networks (DNNs), broad RL (BRL) adopts the broad learning system (BLS) that is established with flat networks to generate the strategy. This article proposes the multiagent adaptive broad-DRL (ABDRL) approach for traffic light control (TLC), which combines the broad network with the deep network structure. Specifically, the structure of ABDRL first expands in the form of flatted broad networks. Then, the feature representation module that contains DNNs is employed to extract the critical traffic information. In addition, experiences sampled randomly by the experience replay mechanism cannot reflect the current training status of the agent effectively. In order to alleviate the impacts caused by random sampling, the forgetful experience mechanism (FEM) is incorporated into ABDRL. The FEM enables the agent to discriminate the importance of experiences stored in the experience reply buffer to improve robustness and adaptability. We validate the effectiveness of ABDRL in TLC, and the results illustrate the optimality and robustness of ABDRL over the state-of-the-art multiagent DRL (MADRL) algorithms.

Despite the extensive deployment of digital instruments in modern times, their stability is challenging to maintain in adverse environmental conditions such as extreme temperatures, pressure, or powerful electromagnetic radiation. Analog meters, owing to their mechanical resilience and electromagnetic impedance, persistently find usage across nuclear power plants, petroleum, and chemical industries. However, under these harsh conditions, manual reading of the instruments may prove to be difficult and dangerous while failing to meet the requirements of real-time monitoring. In recent years, several machine vision-based meter reading systems have been proposed, however, achieving high accuracy through camera-based methods under varying angles and lighting conditions poses significant challenges. Cloud deployment may compromise plant privacy, while edge computing faces limitations in real-time meter reading due to limited computing power. To address these issues, we propose a real-time reading system based on the YolactEdge instance segmentation framework for single-point analog meters. Our system is more accurate than previous studies and is implemented and deployed on the Jetson Xavier NX edge computing device. Our performance evaluation shows that our model outperforms other baselines, with low reference values and relative errors of 0.0237% and 0.0300%, respectively, and an average inference speed of 10.26 FPS with INIT 8 linear acceleration on Nvidia Jetson NX.

The ongoing roll-out of cloud–edge computing and Internet of Things (IoT) has been simulating the boom of new advance reservation (AR) services, such as bulk-data migration and virtual machine backup, driving the development of substrate elastic optical networks (EONs). These AR requests are initial-delay-insensitive if they are guaranteed to be completed before a predefined deadline. Therefore, the routing, modulation, and spectrum assignment (RMSA) problem is extended to the time-spectrum domain rather than the single spectrum domain. Traditional heuristic RMSA algorithms follow static procedures under handcrafted rules and assumptions, and thus cannot be optimized automatically. To solve this problem, we propose a deep reinforced deadline-driven allocation (DRDA) algorithm. To the best of our knowledge, this work is the first to leverage deep reinforcement learning (DRL) methods to solve the AR resource allocation problem. Moreover, compared with the single experiment scenario of many existing works, the DRDA algorithm is evaluated in both static and dynamic scenarios. Simulation results show that our DRDA algorithm outperforms the other leading algorithms in both static scenario and dynamic scenario.

Efficient traffic light control is a critical part of realizing smart transportation. In particular, deep reinforcement learning (DRL) algorithms that use deep neural networks (DNNs) have superior autonomous decision-making ability. Most existing work has applied DRL to control traffic lights intelligently. In this article, we propose a novel context-aware multiagent broad reinforcement learning (CAMABRL) approach based on broad reinforcement learning (BRL) for mixed pedestrian-vehicle adaptive traffic light control (ATLC). CAMABRL exploits the broad learning system (BLS) established in a flat network structure to make decisions instead of a deep network structure. Unlike previous works that consider the attributes of vehicles, CAMABRL also takes the states of pedestrians waiting at the intersection into consideration. Combining with the context-aware mechanism that utilizes the states of adjacent agents and potential state information captured by the long short-term memory (LSTM) network, agents can make farsighted decisions to alleviate traffic congestion. The experimental results show that CAMABRL is superior to several state-of-the-art multiagent reinforcement learning (MARL) methods.

Cloud-fog computing emerges to satisfy the low latency and high computation requirements of Internet of Things (IoT) services. Elastic optical networks (EONs) are excellent substrate communication networks between fog datacenters and cloud datacenters. However, the uneven traffic of massive cloud-fog services incurs many spectrum fragments, leading to high extra energy consumption. To solve this problem, we propose an energy-efficient deep reinforced traffic grooming (EDTG) algorithm based on deep reinforcement learning. Unlike existing manually network features extracting methods, we convert the traditional network modal and the service routing path into colored network images to represent their states and extract the features automatically by MobilenetV3 according to these images. With the extracted features, we implement an advantage actor-critic (A2C) algorithm, whose actor module and critic module share an artificial neural network (ANN) to get optimal grooming actions. Additionally, after repeated attempts and experiments, we set up an objective reward and punishment mechanism to evaluate the grooming actions. We conduct extensive simulations for performance evaluation, and the results have shown that EDTG can significantly reduce energy consumption compared with two well-performed traffic grooming algorithms.

Numerous research efforts have been conducted to simulate the crowd movements, while relatively few of them are specifically focused on multihazard situations. In this paper, we propose a novel crowd simulation method by modeling the generation and contagion of panic emotion under multihazard circumstances. In order to depict the effect from hazards and other agents to crowd movement, we first classify hazards into different types (transient and persistent, concurrent and nonconcurrent, and static and dynamic) based on their inherent characteristics. Second, we introduce the concept of perilous field for each hazard and further transform the critical level of the field to its invoked-panic emotion. After that, we propose an emotional contagion model to simulate the evolving process of panic emotion caused by multiple hazards. Finally, we introduce an emotional reciprocal velocity obstacles (RVOs) model to simulate the crowd behaviors by augmenting the traditional RVO model with emotional contagion, which for the first time combines the emotional impact and local avoidance together. Our experimental results demonstrate that the overall approach is robust, can better generate realistic crowds and the panic emotion dynamics in a crowd. Furthermore, it is recommended that our method can be applied to various complex multihazard environments.

People in crowd scenes often exhibit the characteristic of imbalanced distribution. On the one hand, people size varies largely due to the camera perspective. People far away from the camera look smaller and are likely to occlude each other, whereas people near to the camera look larger and are relatively sparse. On the other hand, the number of people also varies greatly in the same or different scenes. This article aims to develop a novel model that can accurately estimate the crowd count from a given scene with imbalanced people distribution. To this end, we have proposed an effective multi-level convolutional neural network (MLCNN) architecture that first adaptively learns multi-level density maps and then fuses them to predict the final output. Density map of each level focuses on dealing with people of certain sizes. As a result, the fusion of multi-level density maps is able to tackle the large variation in people size. In addition, we introduce a new loss function named balanced loss (BL) to impose relatively BL feedback during training, which helps further improve the performance of the proposed network. Furthermore, we introduce a new data set including 1111 images with a total of 49 061 head annotations. MLCNN is easy to train with only one end-to-end training stage. Experimental results demonstrate that our MLCNN achieves state-of-the-art performance. In particular, our MLCNN reaches a mean absolute error (MAE) of 242.4 on the UCF_CC_50 data set, which is 37.2 lower than the second-best result.

In this paper, we study the survivable routing, modulation, and spectrum assignment (S-RMSA) problem for bulk data- flow transfer in elastic optical networks. We design a two- dimensional resource model for specifying resource state information that spans both the spectrum and time dimensions, and we develop a dynamic heuristic RMSA algorithm, Max Resource Utilization (MRU), to efficiently transfer data-flows while meeting deadline constraints and surviving single link failures. Furthermore, we provide a protection resource optimization strategy to progressively release the scheduled protection resources so as to increase spectrum utilization. We examine the performance of our proposed algorithm, and numerical results show that our proposed heuristic algorithm can reduce blocking probability and obtain high spectrum utilization while minimizing fragmentation.

We propose a multi-path fragmentation-aware routing, modulation and spectrum assignment algorithm (RMSA) for advance reservation (AR) and immediate reservation (IR) requests in elastic optical networks. To decrease fragmentation, we propose splitting requests into different parts and transferring each of these parts along a single-path or multi-paths utilizing sliceable bandwidth variable transponders. We first introduce a model to solve the problem and propose a two-dimensional fragmentation occurrence measurement in spectrum and time domains. Then we propose a multi-path fragmentation-aware RMSA algorithm (MPFA). Simulation results show that MPFA can achieve better performance than existing algorithms in terms of blocking probability and spectrum utilization.