The system demonstrated scalable, distributed threat detection, dynamic role classification, and responsive semantic analysis. Particularly, it achieved these capabilities without introducing processing bottlenecks or significant latency overhead. C. Conclusion This paper presented NetMoniAI, a hybrid agentic AI framework for real-time, distributed network monitoring and threat detection. By combining decentralized sensing at node level with centralized semantic analysis using GPT -O3, the system detects both localized and coordinated attacks with low latency and high accuracy. Evaluated across a local micro-testbed and NS-3 simulations, NetMoniAI demonstrated timely anomaly detection, accurate DDoS classification, and clear operator feedback through structured reports and an interactive dashboard. Its scalable, asynchronous architecture supports interpretable, layered responses without sacrificing performance. Future work will extend the framework with adaptive mitigation, multi-agent coordination, and SDN-based policy enforcement.

Motivated by the proliferation of Internet-of-Thing (IoT) devices and the rapid advances in the field of deep learning, there is a growing interest in pushing deep learning computations, conventionally handled by the cloud, to the edge of the network to deliver faster responses to end users, reduce bandwidth consumption to the cloud, and address privacy concerns. However, to fully realize deep learning at the edge, two main challenges still need to be addressed: (i) how to meet the high resource requirements of deep learning on resource-constrained devices, and (ii) how to leverage the availability of multiple streams of spatially correlated data, to increase the effectiveness of deep learning and improve application-level performance. To address the above challenges, we explore collaborative inference at the edge, in which edge nodes and end devices share correlated data and the inference computational burden by leveraging different ways to split computation and fuse data. Besides traditional centralized and distributed schemes for edge-end device collaborative inference, we introduce selective schemes that decrease bandwidth resource consumption by effectively reducing data redundancy. As a reference scenario, we focus on multi-view classification in a networked system in which sensing nodes can capture overlapping fields of view. The proposed schemes are compared in terms of accuracy, computational expenditure at the nodes, communication overhead, inference latency, robustness, and noise sensitivity. Experimental results highlight that selective collaborative schemes can achieve different trade-offs between the above performance metrics, with some of them bringing substantial communication savings (from 18% to 74% of the transmitted data with respect to centralized inference) while still keeping the inference accuracy well above 90%.

In recent years, over-the-air aggregation has been widely considered in large-scale distributed learning, optimization, and sensing. In this paper, we propose the over-the-air federated policy gradient algorithm, where all agents simultaneously broadcast an analog signal carrying local information to a common wireless channel, and a central controller uses the received aggregated waveform to update the policy parameters. We investigate the effect of noise and channel distortion on the convergence of the proposed algorithm, and establish the complexities of communication and sampling for finding an $\epsilon$-approximate stationary point. Finally, we present some simulation results to show the effectiveness of the algorithm.

These days automation is being applied everywhere. In every environment, planning for the actions to be taken by the agents is an important aspect. In this paper, we plan to implement planning for multi-agents with a centralized controller. We compare three approaches: random policy, Q-learning, and Q-learning with Options Framework. We also show the effectiveness of planners by showing performance comparison between Q-Learning with Planner and without Planner.

In this paper, we introduce a semi-decentralized control technique for a swarm of robots transporting a fragile object to a destination in an uncertain occluded environment.The proposed approach has been split into two parts. The initial part (Phase 1) includes a centralized control strategy for creating a specific formation among the agents so that the object to be transported, can be positioned properly on the top of the system. We present a novel triangle packing scheme fused with a circular region-based shape control method for creating a rigid configuration among the robots. In the later part (Phase 2), the swarm system is required to convey the object to the destination in a decentralized way employing the region based shape control approach. The simulation result as well as the comparison study demonstrates the effectiveness of our proposed scheme.

Packet routing is a fundamental problem in communication networks that decides how the packets are directed from their source nodes to their destination nodes through some intermediate nodes. With the increasing complexity of network topology and highly dynamic traffic demand, conventional model-based and rule-based routing schemes show significant limitations, due to the simplified and unrealistic model assumptions, and lack of flexibility and adaption. Adding intelligence to the network control is becoming a trend and the key to achieving high-efficiency network operation. In this paper, we develop a model-free and data-driven routing strategy by leveraging reinforcement learning (RL), where routers interact with the network and learn from the experience to make some good routing configurations for the future. Considering the graph nature of the network topology, we design a multi-agent RL framework in combination with Graph Neural Network (GNN), tailored to the routing problem. Three deployment paradigms, centralized, federated, and cooperated learning, are explored respectively. Simulation results demonstrate that our algorithm outperforms some existing benchmark algorithms in terms of packet transmission delay and affordable load.

As robot swarms leave the lab and enter our daily lives, it is important that we find ways by which we can effectively communicate with robot swarms, especially ones that contain a high number of robots. In our lab, we are thinking of ways to make swarms for people that are easy and intuitive to interact with. By making robots expressive, we will be able to understand their state and therefore, we will be able to make decisions accordingly. To that extent, we have created a system where humans can build a canvas with robots and create shapes with up to 300 real robots and up to 1000 simulated robots. In a system we created called Robotic Canvas, we project an image onto a robot swarm via an overhead projector, and the swarm replicates the image using their LEDs by sensing the colour of light projected. If a GIF or a video is projected onto the robots, the robots appear to be showing a video.

Following the metric-based between human and artificial intelligence is the ability to methods, MetaDelta firstly adopts pretrained convolutional learn from small samples, e.g., learning to recognize objects networks as backbones to project images to latent vectors from limited examples. Inspired by human's ability of learning and trains the backbones with linear classifiers in a nonepisodic to learn from experience, meta-learning (Vanschoren way on the training classes. To improve the system's 2018) aims to transfer the generic experience learned from generalization capacity to any unknown datasets under multiple tasks of limited data to efficiently complete new time and memory budgets, we employ multiple metalearning tasks. As one of the most successful applications for metalearning, models with multi-processing, while managing the few-shot learning targets at learning from a limited time and resources with a central controller in the main number of labeled examples, which has become a research process at the same time. Moreover, we implement a latefusion trend recently. Few-shot image classification is a task where meta-ensemble mechanism to improve the generalization the classifier must learn to accommodate new classes not ability by taking the prediction from each model seen during training with limited examples.

This paper proposes a reinforcement learning approach for nightly offline rebalancing operations in free-floating electric vehicle sharing systems (FFEVSS). Due to sparse demand in a network, FFEVSS require relocation of electrical vehicles (EVs) to charging stations and demander nodes, which is typically done by a group of drivers. A shuttle is used to pick up and drop off drivers throughout the network. The objective of this study is to solve the shuttle routing problem to finish the rebalancing work in the minimal time. We consider a reinforcement learning framework for the problem, in which a central controller determines the routing policies of a fleet of multiple shuttles. We deploy a policy gradient method for training recurrent neural networks and compare the obtained policy results with heuristic solutions. Our numerical studies show that unlike the existing solutions in the literature, the proposed methods allow to solve the general version of the problem with no restrictions on the urban EV network structure and charging requirements of EVs. Moreover, the learned policies offer a wide range of flexibility resulting in a significant reduction in the time needed to rebalance the network.

Resilient Coverage: Exploring the Local-to-Global Tradeoff Ragesh K. Ramachandran 1, Lifeng Zhou 2 and Gaurav S. Sukhatme 1 Abstract -- We propose a centralized control framework to select suitable robots from a heterogeneous pool and place them at appropriate locations to monitor a region for events of interest. In the event of a robot failure, the framework repositions robots in a user-defined local neighborhood of the failed robot to compensate for the coverage loss. The central controller augments the team with additional robots from the robot pool when simply repositioning robots fails to attain a user-specified level of desired coverage. The size of the local neighborhood around the failed robot and the desired coverage over the region are two settings that can be manipulated to achieve a user-specified balance. We investigate the tradeoff between the coverage compensation achieved through local repositioning and the computation required to plan the new robot locations. We also study the relationship between the size of the local neighborhood and the number of additional robots added to the team for a given user-specified level of desired coverage. The computational complexity of our resilient strategy (tunable resilient coordination), is quadratic in both neighborhood size and number of robots in the team. At first glance, it seems that any desired level of coverage can be efficiently achieved by augmenting the robot team with more robots while keeping the neighborhood size fixed. However, we show that to reach a high level of coverage in a neighborhood with a large robot population, it is more efficient to enlarge the neighborhood size, instead of adding additional robots and repositioning them.