Bluetooth Low Energy (BLE) is a key wireless technology for the Internet of Things (IoT), supporting low-power, multi-device communication. However, managing and scheduling multiple BLE connections under tight timing constraints remains a significant challenge. The Bluetooth specification lacks explicit strategies for efficient multi-device coordination, often leading to unfair resource allocation and connection event interruptions due toresource overlapamong peripherals. To address this problem, we proposeEMBLEM, a novel scheduling scheme that treats multiple connections as a unified resource list and dynamically adjusts allocations based on traffic intensity. By leveraging event preemption and connection subrating,EMBLEMenables flexible resource allocation that accommodates more peripherals while ensuring fairness. To further improve efficiency, we introduce a bitmap-based state tree for memory-efficient resource tracking, and also connection event extension to reduce resource wastage and improve throughput. We evaluateEMBLEMon a 51-node testbed against state-of-the-art scheduling schemes and popular BLE stacks. Experiment results show thatEMBLEMeliminates resource overlap, improves stability, and significantly enhances system fairness, throughput, and scalability under diverse operating conditions.

Cross technology communication (CTC) facilitates direct communication between heterogeneous wireless technologies in the overlapped frequencies such as the 2.4 GHz ISM band. In this poster, we propose a novel method named WBMC for direct multicast communication from a Wi-Fi device to multiple BLE transceivers. This is achieved by making a Wi-Fi signal appear like BLE's Gaussian frequency shift keying (GFSK) signal over multiple subcarrier groups of Wi-Fi. Uniqueness of WBMC compared to prior Wi-Fi-to-BLE CTC studies is that one Wi-Fi transmission can deliver distinct data to multiple BLE receivers simultaneously. The potential and feasibility of the newly suggested approach is demonstrated through implementation on GNURadio.

Bluetooth Low Energy (BLE) is a representative wireless technology for Internet of Things (IoT) that enables concurrent communication with multiple devices at low power. However, the connection establishment mechanism of BLE is susceptible to resource overlap problem among connected peripherals, leading to resource unfairness. To address problem and improve resource utilization, we propose a "Subrating-based Connection Scheduling (SCS)". It schedules and periodically manages connections by considering the service requirements of the connected devices. Preliminary experiments demonstrate that SCS achieves higher throughput and fairness compared to popular commercial BLE stacks while satisfying the requirements of peripherals.

To guarantee real-time performance and quality-of-service (QoS) of time-critical industrial systems, time-aware shaper (TAS) in time-sensitive networking (TSN) controls frame transmission times in a bridged network using a scheduled gate control mechanism. However, most TAS scheduling methods generate schedules based on pre-configured routes without exploring alternatives for better schedulability, and methods that jointly consider routing and scheduling require enormous runtime and computing resources. To address this problem, we propose a TSN Scheduler with Reinforcement Learning-based Routing (TSLR) that identifies improved load balanced routes for higher schedulability with acceptable complexity using distributional reinforcement learning. We evaluate TSLR through TSN simulations and compare it against state-of-the-art algorithms to demonstrate that TSLR effectively improves TAS schedulability and link utilization in TSN with lower complexity. Specifically, TSLR shows a more than 66% increase in schedulability compared to the other algorithms, and TSLR’s scheduling time is reduced by more than 1 h. It also shows flows’ transmission latency is less than 25% of their latency deadline requirement and reduces maximum link utilization by approximately 50%.

To guarantee stringent real-time requirements of time-critical traffic in industrial systems, the IEEE time-sensitive networking (TSN) task group has standardized time-aware shaping (TAS) in IEEE 802.1Qbv, which schedules precise and periodic transmission times using preassigned traffic information. However, nonperiodic/unexpected but time-critical traffic, such as emergency events or alarms, may occur in real industrial scenarios, and TAS does not provision for performance of traffic that are unknowna priori, nor the impact thereof on prescheduled traffic. Moreover, recalculating the schedule for every sporadic, nonisochronous event traffic is extremely difficult, complex, and costly. To address these challenges, we propose a novel enhancement to TAS, referred to aseTAS, which defines a new scheduling rule for immediate forwarding of emergency traffic to guarantee real-time performance, while dynamically extending the scheduled time windows to protect scheduled time-critical traffic from the interference of emergency traffic. We evaluateeTASthrough extensive simulations on OMNeT++ under an advanced driver assistance system (ADAS) scenario for autonomous driving to show thateTASeffectively allows rapid transmission of event traffic with minimal impact on scheduled traffic, even for highly congested networks.