In Time Sensitive Networking (TSN), deterministic transmission of critical traffic (CT) is essential to real-time applications. Existing traffic control methods either rely on high-precision clock synchronization which is often costly and difficult to maintain in large-scale deployments, or fail to provide fine-grained control over latency and jitter. To address these issues, in this paper, we propose two novel traffic shapers including Ordered-CSQF (O-CSQF) and Transposed-CSQF (T-CSQF), that eliminate the need for network-wide sub-microsecond clock synchronization, thereby reducing per-switch hardware procurement and maintenance costs. O-CSQF employs a local PIFO-based packet scheduler-realizable on a single switch without any PTP modules, while T-CSQF further simplifies deployment via cyclic queue polling. Two traffic scheduling algorithms Per-Hop Injection (PHI) and Average Time Injection (ATI) are designed to match the two proposed shapers, respectively. The core idea of the proposed traffic shapers and scheduling algorithms is to control the sending time of each CT packet on switches according to the latency and jitter requirements. Extensive experimental results demonstrate that PHI and ATI with corresponding traffic shapers can meet the delay requirement with jitter constrained within the 10 μs bound while achieving schedulability up to 92.5% and 99.1%, respectively.

In the mobile edge computing (MEC)-enabled air-terrestrial integrated network, unmanned aerial vehicles (UAVs) serve as air edge nodes with the purpose to collaboratively train the high-availability prediction model by federated learning (FL). Nonetheless, in view of the significance of data freshness for an accurate training model, UAVs suffer from the stochastic and intermittent nature in energy harvesting (EH). This paper formulates an inter-UAV cooperative federated computing (IUCFC) problem to jointly optimize prediction accuracy, age of information (AoI) in flight region, and overall energy consumption of EH-enabled UAVs for edge data processing. To address the intricate IUCFC problem, a deep reinforcement learning (DRL) based cooperative UAV intelligent decision (CUID) algorithm is proposed, which leverages a dual Actor-Critic architecture, in pursuit of the collective tuning of hybird actions. Further, the Ornstein-Uhlenbeck (OU) noise is engaged in continuous action spaces to prompt exploration, while a conditional iteration dropout (CID) scheme mitigates the infeasible actions caused by the noise introduced, thereby bolstering the exploration efficiency and quality of CUID algorithm. Considering the non-stationary environments originated from UAV mobility, priority experience replay (PER) is adopted to dynamically modify experience priority. Extensive experiments show that CUID attains superior performance over those advanced algorithms, upgrading system utility by 8.79%, while augmenting FL model accuracy by 3.68% in dynamic scenarios with heterogeneous data distributions.

Time Sensitive Networking (TSN) provides deterministic transmission services for critical traffic (CT) through clock synchronization, traffic shaper, and traffic scheduling algorithms. However, time failures occurring at network nodes may cause some CT packets to be forwarded unscheduled, thereby destroying deterministic guarantees and affecting overall system stability. In this paper, we classify time failures into node-level and port-level failures, and present a counter-based mechanism called TSN-Counter for failure detection in TSN, which leverages periodic CT behavior to detect and classify timing faults with minimal overhead. First, a lightweight twostage detector flags any coarse-slot violation and immediately pinpoints the culprit port in a fine-slot pass. Next, a compact hash-tree structure with bounded fallback efficiently narrows down the set of affected flows, and a simple time-window check prunes spurious alarms. Finally, concise per-switch reports are aggregated at the controller for accurate fault tracing. Simulation results demonstrate that TSN-Counter can obtain much better performance than baseline methods, achieving 100% precision with over 98% recall and near-zero relative error. Compared with the state-of-the-art designs, TSN-Counter has the failure detection time decreased by 75.9% in various fault scenarios.

IEEE 802.1Qbv Time-Aware Shaper (TAS) is a cornerstone of Time-Sensitive Networking (TSN), offering bounded latency and near-zero jitter for critical traffic (CT). However, prior scheduling algorithms either rely on ideal clock synchronization or resource reservation, sacrificing bandwidth efficiency. In this paper, we present a unified framework for the analysis of stability for queueing process and delay bounds in TAS, in which clock drift, gate-switching jitter, and sync-message loss are modeled as a stochastic service-loss process in TAS scheduling. Then, a Lyapunov-drift analysis is used to derive both deterministic worst-case delay bounds under maximal service loss and probabilistic stability conditions when the average service rate exceeds the arrival rate. Finally, based on the theoretical analysis, the guard-band sizing and slot-provisioning guidelines are proposed for practical TSN deployment under non-ideal synchronization. Extensive simulations have been conducted to validate the tightness of our delay bounds, and the simulation results demonstrate that the proposed configuration rules can guarantee bounded latency and queue stability.

Mobile crowdsourcing has become an efficient paradigm for performing large scale tasks. The incentive mechanism is important for the mobile crowdsourcing system to stimulate participants, and to achieve good service quality. In this paper, we focus on solving the insufficient participation problem for the budget constrained online crowdsourcing system. We present a two-tiered social crowdsourcing architecture, which can enable the selected registered users to recruit their social neighbors by diffusing the tasks to their social circles. We present three system models for two-tiered social crowdsourcing system based on the arrival modes of registered users and social neighbors: offline model, semi-online model, and full-online model. We consider the tasks are associated with different end times. We present an incentive mechanism for each of three system models. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed incentive mechanisms achieve computational efficiency, individual rationality, budget feasibility, cost truthfulness, and time truthfulness. We further show that our incentive mechanisms for semi-online model and full-online model can obtain averagely 51.1$\%$and 39.7$\%$value of approximate optimal untruthful offline algorithm, respectively.