Latency-sensitive services across the end-edge-cloud continuum require not only low mean latency but explicit control of tail latency and data freshness. We propose Control-Loop Freshness-aware Service Function Chain orchestration (CLFSFC), a freshness-aware orchestration framework for Service Function Chains (SFCs) that jointly selects model variants and function placements. We define a Control-Loop Freshness (CLF) objective that combines 95th/99th-percentile (P95/P99) end-toend latency with an Age of Information (AoI) proxy. To make this objective operational under uncertainty, we allocate per-stage risk budgets via the union bound and convert mean/variance profiles into percentile constraints using Cantelli's inequality, yielding a two-stage greedy solver with interpretable quotas. We implement CLF-SFC with offline profiling of YOLOv5 n/s/m variants across end/edge/cloud devices, and evaluate it with profiling-driven measurements on COCO 2017 under synthesized network regimes. Across bandwidth and round-trip time settings, CLF-SFC reduces P95/P99 latency and Service Level Objective (SLO) violations relative to Edge-only, Cloud-only, and a riskagnostic heuristic; at high bandwidth it remains comparable to the Shortest-Latency-Path (SLP) baseline. The proposed CLF-SFC framework naturally fits embodied-AI pipelines where perception, fusion, and policy modules operate in a closed loop. By explicitly incorporating the Age-of-Information (AoI), our orchestration ties data freshness to control stability, complementing tail-latency minimization.

To address the challenge of data acquisition in unknown disaster environments — such as earthquake zones, flood-affected regions, or industrial accident sites — this paper proposes a Hybrid Centralized - Distributed Collaborative Coverage (HCC) strategy for UAV swarms. The HCC framework integrates centralized trajectory planning with distributed obstacle avoidance to achieve rapid, efficient, and safe coverage of Points of Interest (PoIs). Specifically, a Centralized Collaborative Optimization (CCO) strategy is designed to compute secure cooperative coverage schemes on an edge server, while a Distributed Obstacle-avoidance Coverage Strategy (DOCS) enables each UAV to perform safety-aware navigation based on local sensing. Unlike sequential exploration, the proposed method supports parallel and synchronized coverage execution, ensuring that data acquisition across the entire region occurs concurrently. Simulation results demonstrate that the proposed method outperforms benchmark algorithms in terms of coverage rate, safety, energy efficiency, and network lifetime.

Blockchain is increasingly integrated in Multiaccess Edge Computing (MEC) to coordinate secure and lowlatency resource provisioning and service orchestration among resource-constrained embodied AI devices. However, conventional blockchains perform costly transaction verification during propagation, which can be exploited by spam transaction attacks and overload resource-limited edge devices. To mitigate the substantial overhead of verification, we propose a two-stage BLS multi-signature backed transaction propagation mechanism for blockchain-enabled MEC: a small-scope random-walk phase with deep verification and signing, followed by a large-scope propagation phase with probabilistic verification. In the first stage, nodes conduct deep verification and sign valid transactions using the BLS multi-signature, then forward the signed transaction to a small and randomly sampled subset of neighbors to rapidly accumulate valid signatures. In the second stage, transactions whose aggregated signature count exceeds a threshold will be broadcast throughout the entire blockchain network and undergo deep verification with a specific probability, relieving edge nodes' verification burden. Moreover, verifiers record signers associated with failed deep verifications. Signers whose failures exceed a system threshold are quarantined to restrain the spread of spam transactions. Experimental results demonstrate that the proposed mechanism reduces energy consumption by at least 60% and 18.6% compared with the original and benchmark mechanisms respectively, while maintaining nearly identical transmission performance and ensuring that the proportion of invalid transactions propagated to honest nodes does not exceed 14%.

Blockchain enhances trust and collaboration among entities through its inherent features of transparency, immutability, and traceability, leading to its extensive integration into Multi-access Edge Computing (MEC). However, existing transaction propagation mechanisms require MEC devices to consume significant computing resources for complex transaction verification, increasing their vulnerability to malicious attacks. Adversaries can exploit this by flooding the blockchain network with spam transactions, aiming to deplete device energy and disrupt system performance. To cope with these issues, this paper proposes a reputation-based energy-efficient transaction propagation mechanism that alleviates spam transaction attacks while reducing computing resources and energy consumption. Firstly, we design a subjective logic-based reputation scheme that assesses node trust by integrating local and recommended opinions and incorporates opinion acceptance to counteract false evidence. Then, we optimize the transaction verification method by adjusting transaction discard and verification probabilities based on the proposed reputation scheme to curb the propagation of spam transactions and reduce verification consumption. Finally, we enhance the transaction transmission strategy by prioritizing nodes with higher reputations, enhancing both resilience to spam transactions and transmission reliability. A series of simulations demonstrate the effectiveness of the proposed mechanism.

In recent years, the number of network applications is constantly increasing, and network congestion often occurs. To ensure the network Quality of Service (QoS), different types of traffic are classified according to their requirements, and similar traffic is transmitted to the same queue for scheduling. The switch generally uses fair queuing and its extension schemes to schedule traffic. These schemes achieve different bandwidth allocation by configuring different queue weights, so as to obtain a lower packet loss rate. However, the switch can provide us with very few statistical parameters, so using a large number of statistical parameters for adaptive weight adjustment is challenging in implementation. At the same time, the weight range supported by the switch is large, but the action space supported by reinforcement learning is limited, which cannot represent the entire queue weight space. Although deep reinforcement learning can solve the problem with large space, the existing switches can not well support the calculation of neural network model. In this paper, we propose a scheduling optimization mechanism combining Q-learning and genetic algorithm, called QGSO, which is used to schedule traffic in real switches. Firstly, we model the scheduling optimization problem as a Markov decision process (MDP) and use Q-learning to solve it in order to select the optimal queue weights according to the state of the environment. Secondly, we use genetic algorithm to filter out a group of optimal queue weights from the weight space, achieving compression of the solution space. Finally, we use a hardware testbed to test and verify the effectiveness of the algorithm. The experimental results show that our algorithm can effectively schedule traffic and achieve a lower packet loss rate.

In the data center network, mixed flows which have contradictory service requirements are transmitted simultaneously. Switches usually aggregate similar flows to the same queue after flow classification and schedule them using fair queuing and its extension schemes capable of flow isolation. These schemes implement diverse bandwidth allocation by assigning different weights to queues. Existing solutions rely on rich statistics such as packet arrival rate and delay to realize dynamic bandwidth allocation. However, many statistics are difficult to accurately measure or even obtain in real switches due to resource limitations. Providing differentiated services for mixed flows under such restrictions is still a challenge. To solve this issue, this paper proposed a reinforcement learning-based scheduling optimization (RLSO) mechanism. First, mixed flows scheduling is modeled as the Markov decision process (MDP) and Q-learning is used to find the approximate optimal solution with a few statistics. Second, the solution space is compressed to reduce the complexity of the algorithm and adapt to the limited performance of switches. Finally, the performance of the proposed mechanism is evaluated on a hardware testbed with workloads that include coarsegrained and fine-grained flows. The results show that RLSO can effectively schedule mixed flows.

Multiple types of flows with contradictory service requirements, namely mixed flows, coexist in the data center network. Similar flows will be aggregated into the same queue after flow classification and are scheduled in switches by using fair queueing and its extension schemes which are capable of flow isolation. These schemes allocate different bandwidths by adjusting weights to realize differentiated services. However, existing solutions only focus on the requirements of some flows, which leads to the failure to satisfy the requirements of other flows. Therefore, it is necessary to make a trade-off between the service requirements of different flows when allocating bandwidth. In this paper, a max-min fairness based scheduling optimization (MMFSO) mechanism is proposed to schedule mixed flows. First, the bandwidth requirements of each queue are calculated by statistics of the switch. To reduce the influence of sampled statistics while forecasting bandwidth requirements, we introduce the exponentially weighted moving average for bandwidth requirements computation. Second, the bandwidth is allocated to each queue according to the max-mix fairness. The queue weight is determined by the allocated bandwidth of the queue. Finally, the performance of the proposed mechanism is evaluated on the hardware testbed in which workloads include coarse-grained flows and fine-grained flows. The results show that MMFSO can effectively schedule mixed flows.

Competitive opinion maximization (COM) in signed social networks targets at selecting a subset of influential individuals (i.e., seed nodes), spreading the desired opinions of the product to their neighbors against its opponents, and eventually achieving the maximum opinion propagation. Current studies mainly focus on competitive influence maximization and opinion maximization. However, COM in signed social networks has not been studied in depth. In this article, we study the COM in signed social networks and propose a novel reinforcement-learning-based opinion maximization framework (RLOM) to solve the COM problem. The proposed RLOM is composed of two phases: the activated dynamic opinion model and the reinforcement-learning-based seeding process. We theoretically prove the COM problem to be NP-hard. To model the opinion propagation process, we propose the activated dynamic opinion model based on a stateless Q-learning approach. Moreover, we propose the reinforcement-learning-based seeding scheme, which is leveraged in an unknown opponent strategy. Experiment results verify the effectiveness of our method in terms of effective opinions on three signed datasets.