Signaling storms threaten cellular core networks when synchronized reconnection attempts from massive numbers of devices trigger cascading, metastable overloads. Existing defenses rely on manual, static configurations of local overload controls, which ignore serial dependencies among heterogeneous network elements. We present Spillway, a digital-twin-driven system that automates global signaling-flood mitigation. Spillway introduces a hierarchical defense architecture that enforces altruistic throttling, allowing upstream nodes to shed load before downstream bottlenecks collapse. To evaluate candidate configurations, Spillway uses CN-DES, a domain-specific discrete-event simulator with a vectorized kernel. By aggregating users that share protocol states, CN-DES decouples simulation cost from user count and simulates regional-scale storms involving tens of millions of users in minutes, achieving a 60× speedup over traditional simulation while preserving fidelity. Spillway then uses heteroscedastic evolutionary Bayesian optimization to search a large, non-convex parameter space. We report on a five-year deployment in the world's largest 5G Standalone network. During real incidents, including application anomalies and RAN failures, networks using Spillway-optimized configurations experienced substantially fewer user fallbacks than predicted under legacy configurations; post-incident analysis confirms that pre-deployed parameters kept all network elements within safe operating bounds.

Against the backdrop of the rapid proliferation of Industrial Internet of Things, due to the rapid increase in edge devices and multi-modal data, Multi-modal Federated Learning (MFL)—an extension of Federated Learning (FL)—has become the mainstream solution for fusing heterogeneous sensor data in edge computing. Although MFL is inherently vulnerable to backdoor attacks similar to FL, its cross-modal fusion techniques for verifying semantic consistency gradually may cause single-modal triggers to be faded during training. To address the aforementioned issue, we propose a cross-modality bi-trigger backdoor attack against MFL, named Bad-MFL, which is the first backdoor attack specifically targeting MFL. Bad-MFL employs two trigger generation modes to randomly implant logically correlated, invisible bi-trigger in two modalities. By maintaining semantic consistency between the triggers, Bad-MFL bypasses cross-modal fusion validation and successfully implants a backdoor into the global model. Moreover, it ensures the backdoor will not disappear as training progresses. Our experiments indicate that, compared to traditional backdoor attack methods, Bad-MFL achieving an Attack Success Rate up to 89.04%, which is 56% higher than the baseline attack, and its backdoor remains effective in high heterogeneous environments throughout training.

Robot-assisted vascular interventional surgery can isolate interventionists and X-ray radiation, and improve surgical accuracy. However, the leader side outside the operating room still has problems such as incomplete collection of operating information and unrealistic tactile feedback. The main objective of this paper is to design a haptic interface that can simultaneously capture the force-position information of the interventionists and generate force to assist the interventionists in performing surgeries on the leader side. It can capture the interventionists’ delivery displacement, twisting angle, clamping force, and provide real-time force feedback. A leader-follower bidirectional force feedback control strategy was proposed. Based on this strategy, on the one hand, the interventionist perceives the multi-modal information fed back from the follower side, makes judgments, and actively adjusts the surgical operation. On the other hand, the interventionist controls the grasping state of the instruments remotely to control the safety operating force threshold. Finally, the experimental setup was built and a series of evaluation experiments were performed. The experimental results verified the feasibility of the designed haptic interface. It can generate dynamic and accurate force feedback and realize leader-follower grasping force control.

Real-time applications that require timely data delivery over wireless multi-hop networks within specified deadlines are growing increasingly. Effective routing protocols that can guarantee real-time QoS are crucial, yet challenging, due to the unpredictable variations in end-to-end delay caused by unreliable wireless channels. In such conditions, the upper bound on the end-to-end delay, i.e., worst-case end-to-end delay, should be guaranteed within the deadline. However, existing routing protocols with guaranteed delay bounds cannot strictly guarantee real-time QoS because they assume that the worst-case end-to-end delay is known and ignore the impact of routing policies on the worst-case end-to-end delay determination. In this paper, we relax this assumption and propose DDRL-ARGB, an Adaptive Routing with Guaranteed delay Bounds using Deep Distributional Reinforcement Learning (DDRL). DDRL-ARGB adopts DDRL to jointly determine the worst-case end-to-end delay and learn routing policies. To accurately determine worst-case end-to-end delay, DDRL-ARGB employs a quantile regression deep Q-network to learn the end-to-end delay cumulative distribution. To guarantee real-time QoS, DDRL-ARGB optimizes routing decisions under the constraint of worst-case end-to-end delay within the deadline. To improve traffic congestion, DDRL-ARGB considers the network congestion status when making routing decisions. Extensive results show that DDRL-ARGB can accurately calculate worst-case end-to-end delay, and can strictly guarantee real-time QoS under a small tolerant violation probability against two state-of-the-art routing protocols.

This paper considers a time-critical task allocation problem in a distributed multi-UAV system. Existing distributed task allocation algorithms trend to increase communication overhead due to the resolution of numerous task-bundle conflicts between UAVs and easily trap into local optimum with greedy strategy. In this work, we propose a novel distributed task allocation method. First, tasks are divided into multiple clusters, and then UAVs build their task bundles from separate task clusters to avoid conflicts between them, thereby reducing communication overhead. Second, to increase the exploratory ability, an improved ant colony optimization algorithm is proposed to achieve task allocation of UAVs from their corresponding task clusters, instead of using greedy-based strategy. Moreover, an inter-cluster adjustment mechanism is proposed to solve unassigned tasks in task clusters to improve task assignment ratio with low communication overhead, which has been verified in our simulations. Extensive simulation results confirm that our method can achieve efficient task allocation solution with high task assignment ratio and low communication overhead when compared with the state-of-the-art algorithms.

In this paper, we address the problem of building adaptive network coding coefficients under dynamic network conditions (e.g., varying link quality and changing number of relays). In existing linear network coding solutions including deterministic network coding and random linear network coding, coding coefficients are set by a heuristic or randomly chosen from a Galois field with equal probability, which can not adapt to dynamic network conditions with good decoding performance. We propose INCdeep, an adaptive Intelligent Network Coding with Deep Reinforcement Learning. Specifically, we formulate a coding coefficients selection problem where network variations can be automatically and continuously expressed as the state transitions of a Markov decision process (MDP). The key advantage is that INCdeep is able to learn and dynamically adjust the coding coefficients for the source node and each relay node according to ongoing network conditions, instead of randomly. The results show that INCdeep has generalization ability that adapts well in dynamic scenarios where link quality is changing fast, and it converges fast in the training process. Compared with the benchmark coding algorithms, INCdeep shows superior performance, including higher decoding probability and lower coding overhead through simulations and experiments.

This paper addresses the problem of UAV positioning and resource allocation under dynamic network conditions and under instantaneous communication demands in FANETs. We propose MPRdeep, an adaptive, deep reinforcement learning (DRL) approach considering several QoS requirements concurrently. MPRdeep learns to optimize relay UAVs’ positions and forwarding probabilities to minimize reliability-achieving delay and reliability-achieving energy consumption. The key advantage is that MPRdeep is able to learn and dynamically adjust the node positioning and resource allocation according to ongoing network conditions. The results show that MPRdeep converges fast and has generalization ability that adapts well under dynamic network conditions and dynamic locations of users. Compared with baseline methods, MPRdeep shows superior performance in terms of lower reliability-achieving delay and lower reliability-achieving energy consumption via simulations and experiments.

The mobile robotic network consisting multiple robotic devices such as unmanned aerial vehicles (UAVs) is a high-speed mobile wireless network. Existing mobile ad hoc protocols cannot meet the demands of mobile robotic networks due to intermittently connected links and frequent topology changes. This paper proposes a deep reinforcement learning based adaptive and reliable routing protocol, ARdeep. We formulate routing decisions with a Markov Decision Process model to automatically characterize the network variations. To better infer network environment, the link status is considered when making routing decisions. Simulation results demonstrate that ARdeep outperforms the existing good performing QGeo and conventional GPSR.

A network with reliable and rapid communication is critical for Unmanned Aerial Vehicles (UAVs). Flying Ad Hoc Networks (FANETs) consisting of UAVs is a new paradigm of wireless communication . However, the highly dynamic topology of FANETs and limited energy of UAVs have brought great challenges to the routing design of FANETs. It is difficult for existing routing protocols for Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs) to adapt the high dynamics of FANETs. Moreover, few of existing routing protocols simultaneously meet the requirement of low delay and low energy consumption of FANETs. This paper proposes a novel Q-learning based Multi-objective optimization Routing protocol for FANETs to provide low-delay and low-energy service guarantees. Most of existing Q-learning based protocols use a fixed value for the Q-learning parameters. In contrast, Q-learning parameters can be adaptively adjusted in the proposed protocol to adapt to the high dynamics of FANETs. In addition, a new exploration and exploitation mechanism is also proposed to explore some undiscovered potential optimal routing path while exploiting the acquired knowledge. Instead of using past neighbor relationships, the proposed method re-estimates neighbor relationships in the routing decision process to select the more reliable next hop. Simulation results show that the proposed method can provide higher packet arrival ratio, lower delay and energy consumption than existing good performing Q-learning based routing method.

Communications in industrial applications like wireless factory automation demands different timing requirements. Providing timely medium access of the critical traffic and its prioritization over regular traffic is a significant challenge in industrial wireless networks. Successive Interference Cancellation (SIC) technique is an effective way to decrease access delay by allowing multiple transmissions concurrently. A series of novel Medium Access Control (MAC) protocols are proposed to differentiate access delay for various traffic types or only exploit SIC for unique traffic type. However, to the best of our knowledge, this work is the first priority-based distributed MAC protocol that employs SIC (PDMAC-SIC) to provide low delay and accommodate different types of traffic for industrial wireless networks. There are two major contributions of our work: first, an extra power contention procedure other than traditional RTS/CTS contention in CSMA/CA is introduced in our PDMAC-SIC. This power contention procedure allows multiple transmitters to access the same channel simultaneously and thus access delay is decreased. Second, PDMAC-SIC is modeled by Markov chain and then the access delay is minimized by optimizing the size of power contention window. Our analytical model is verified through simulation. Results reveal that PDMAC-SIC performs better on access delay and packet loss rate than the existing good performing priority based CSMA/CA.