Federated learning is increasingly adopted for traffic flow prediction (TFP) to enable privacy preserving collaboration across distributed sensors. However, real-world deployments are highly heterogeneous in computational capability, causing stragglers that dominate per-round latency and severely slow down model updates. Most existing approaches mitigate stragglers by suppressing or discarding slow clients, which reduce data representativeness and introduce training bias. It is a harmful trade-off for TFP where broad spatial coverage is crucial for accuracy. We propose a communication-efficient logical clustering federated learning framework (LCFed) that mitigates stragglers by logically balancing effective training time while preserving full client participation. LCFed combines a coarse-grained logical dynamic clustering algorithm ( LoDynClust ) to balance computational resources across clusters and reduce synchronization delays, with a fine-grained intra-cluster adaptive collaborative training mechanism ( ICACT ) to regulate aggregation intervals and mitigate training bias. We further provide a convergence analysis. Extensive experiments on three real-world traffic datasets show that LCFed significantly reduces training latency caused by stragglers while maintaining competitive prediction accuracy compared with state-of-the-art baselines.

Edge computing has recently emerged as a promising paradigm to support mobile access in Internet of Things (IoT) multinetworks, where heterogeneous wireless communication solutions coexist. Meanwhile, software-defined networking (SDN) presents a potential infrastructure to monitor and manage mobile edge computing. However, resilient access in the integrated IoT-Edge-SDN environment is a key challenge. In this article, we present location-aware spatio-temporal solution (LASTS) as an edge computing-empowered software-defined system to scalably control mobile IoT access and detect sequential anomaly. LASTS utilizes a Personal access point protocol to enable switching between multiple networks. In addition, it supports efficient control and transfer of the mobile device’s spatio-temporal context. This context information plays an important role in a deep learning model employed for sequential anomaly detection in the LASTS system. Realistic testbed experiments confirm that LASTS can successfully achieve scalable access control and sequential anomaly detection in mobile IoT.

Due to the blooming of Internet of Things (IoT), heterogeneous IoT mobile devices emerge to connect the network infrastructure. Traditional mobile access system faces several challenges arising from these IoT devices: 1) centralized controllers are distant from the end devices, 2) inefficient access control of heterogeneous IoT devices, and 3) insufficient authentication and monitoring for IoT devices. In order to tackle the challenges from IoT devices on mobile access control and scalable access monitoring, we present LEDGE, an agile and secured software-defined edge computing system for resilient access management of mobile IoT. In a nutshell, our LEDGE is a synergy of an efficient location authentication method to secure communication between each IoT mobile device and access point (AP) pair, an optimal AP assignment scheme to satisfy IoT flow requests, a Personal AP protocol for scalable access, and a deep learning model for anomaly detection. We prototype our system, and realistic testbed experiments demonstrate that LEDGE could achieve promising results in mobile IoT.

The growth of Internet of Things (IoT) devices with multiple radio interfaces has resulted in a number of urban-scale deployments of IoT multinetworks, where heterogeneous wireless communication solutions coexist (e.g., WiFi, Bluetooth, Cellular). Managing the multinetworks for seamless IoT access and handover, especially in mobile environments, is a key challenge. Software-defined networking (SDN) is emerging as a promising paradigm for quick and easy configuration of network devices, but its application in urban-scale multinetworks requiring heterogeneous and frequent IoT access is not well studied. In this paper we present UbiFlow, the first software-defined IoT system for combined ubiquitous flow control and mobility management in urban heterogeneous networks. UbiFlow adopts multiple controllers to divide urban-scale SDN into different geographic partitions (assigning one controller per partition) and achieve distributed control of IoT flows. A distributed hashing based overlay structure is proposed to maintain network scalability and consistency. Based on this UbiFlow overlay structure, the relevant issues pertaining to mobility management such as scalable control, fault tolerance, and load balancing have been carefully examined and studied. The UbiFlow controller differentiates flow scheduling based on per-device requirements and whole-partition capabilities. Therefore, it can present a network status view and optimized selection of access points in multinetworks to satisfy IoT flow requests, while guaranteeing network performance for each partition. Simulation and realistic testbed experiments confirm that UbiFlow can successfully achieve scalable mobility management and robust flow scheduling in IoT multinetworks; e.g., 67.21 percent throughput improvement, 72.99 percent reduced delay, and 69.59 percent jitter improvements, compared with alternative SDN systems.