Federated Learning (FL) is an emerging distributed learning paradigm with the privacy-preserving advantage of collaboratively training a shared model across multiple participants. Considering the prevailing device heterogeneity circumstance in practice, asynchronous interaction is introduced into FL to break the straggler barrier of synchronization, at the cost of significant accuracy degradation derived from model staleness. Although quite a few works attempt to partially mitigate the detrimental impact after occurring staleness issue, they neglect to control the overall staleness degree of clients-side local models from the whole training perspective, resulting in highly-stale models for aggregation and slow convergence speed. To this end, we propose a Staleness-Controlled Asynchronous Federated Learning (SC-AFL) method, which enables to restrict staleness degree of local models within a certain bound via dynamically tuning the aggregated strategy of each round, aiming to strike a good balance between accuracy guarantee and convergence acceleration. Specifically, we leverage the Lyapunov optimization framework to decouple the troublesome round-coupling problem into the single-round sequential solving problem, and further develop a deterministic algorithm that selects the aggregated number of clients to minimize training time under the constraint of maintaining staleness queue stability. Besides, we derive the theoretical convergence analysis of SC-AFL and also present the upper bound of the performance gap with the optimum. Extensive experiments on three datasets demonstrate the superiority of SC-AFL in terms of time-to-accuracy speedup on both IID and Non-IID data distributions, achieving a good balance between model accuracy and convergence efficiency in AFL system.

Industrial cognitive radio sensor networks (ICRSNs) extend channel resources by occupying the vacant licensed channels in the absence of licensed users. In ICRSNs, industrial devices should switch to a common available channel to set up a communication link. However, channel switching leads to severe energy consumption. As the energy resources of battery-powered industrial devices are limited, it is crucial to carefully allocate channels to prolong the network lifetime of multi-hop ICRSNs. This paper is the first work that studies the channel allocation problem to optimize the network lifetime by considering the channel-switching (CS) energy consumption and the time-critical requirements of industrial applications. The problem is formulated to maximize the minimum residual energy at each round of data transmission, which is linearized as integer linear programming. As the channel allocation results will affect the residual energy at subsequent rounds, we propose a switching distance-optimized channel allocation (SDOCA) scheme that shortens the CS distances to improve the residual energy of each device. Moreover, we analyze the characteristics of SDOCA, i.e., convergent CS distance and guaranteed end-to-end delay. Extensive simulation results show that SDOCA can adaptively allocate channels according to the end-to-end delay requirement and significantly prolong the network lifetime.

In cognitive radio networks (CRNs), due to the external interference from primary users, secondary users (SUs) cannot reserve a common control channel (CCC). Hence, it is essential to consider the impact of channel rendezvous on the end-to-end delay in multi-hop CRNs. For this reason, we propose a High Probabilistic Transmission Efficiency Multi-hop Routing (HPTEMR) protocol without utilizing a CCC. In HPTEMR, we design an efficient waiting channel hopping sequence to achieve fast channel rendezvous between neighborhood SUs. We then propose a novel link metric, i.e., transmission efficiency, which characterizes the transmission distance and channel-rendezvous delay. Based on the link metric, a sender SU transmits data packets to the receiver SU with the highest probability that data packets can be forwarded to the destination SU with the shortest end-to-end delay. Evaluation results verify the effectiveness of HPTEMR and show its superiority in end-to-end delay and ratio of effective packets.

In distributed Internet of Things (IoTs), channel hopping (CH) is an effective scheme for neighbor nodes to achieve blind rendezvous over common available channels and to establish communication links. When nodes are unaware of each other's local clocks and the global channels and have no pre-assigned CH strategies or identifiers (IDs), it is particularly challenging to guarantee blind rendezvous within a finite period of time, which has not been solved yet by using only one radio. In this paper, we propose a novel quaternary-encoding-based CH (QECH) algorithm to tackle the above issue. The QECH algorithm encodes a randomly selected channel into a quaternary string according to the 6B/8B encoding. We also append a common prefix string as well as the randomly selected channel before the quaternary string to guarantee overlaps in the asynchronous scenario. For all kinds of quaternary digits, we construct four mutually co-prime numbers to enumerate all possible combinations of the common available channels. We theoretically analyze the deterministic rendezvous principle and the upper bounded rendezvous latency of the QECH algorithm. We also verify the effectiveness of the QECH algorithm through extensive simulations. Evaluation results show the superiority of the QECH algorithm in terms of rendezvous latency.