LoRaWAN is a compelling low-cost solution for large-scale indoor Internet of Things (IoT) data backhaul, owing to its strong penetration capability and low power consumption. However, its default pure ALOHA access mechanism leads to severe channel contention, substantial packet loss, and reduced throughput under dense, concurrent transmissions. To overcome this, we propose a lightweight out-of-band (OOB) synchronization scheme that integrates a time division multiple access (TDMA) mechanism into commercial LoRaWAN Class A networks. Unlike approaches requiring gateway scheduling, frequent downlink signaling, or custom hardware, our method introduces a single low-cost node providing millisecond-level alignment via a dedicated OOB synchronization channel. End devices seamlessly access this channel by briefly retuning their existing LoRa transceivers. Consequently, the scheme imposes zero downlink overhead during the steady-state reporting phase, requires no hardware modifications to gateways or end devices, and remains fully backward-compatible. This design enables collision-free scheduled channel access within the configured nominal resource capacity, thereby improving throughput and reducing contention. Real-world experiments using an indoor positioning prototype demonstrate that the proposed TDMA-LoRaWAN architecture improves system throughput by over 30% and reduces the packet loss rate from 25.8% to 5.02% in a 20-node indoor deployment. Furthermore, large-scale simulations corroborate these empirical findings, support the scalability analysis under larger network sizes, and indicate improved energy efficiency per successful packet in dense network settings. These combined results demonstrate the effectiveness of the proposed approach for dense indoor IoT data collection and indicate its practical potential under high uplink reporting demands.

In future space-air-ground integrated networks, time synchronization technology serves as the cornerstone for realizing network communication. In this network, the self-synchronization within the network achieved via communication links has advantages such as autonomy and higher synchronization accuracy compared to the timekeeping of the Global Navigation Satellite System (GNSS), and it is applied in various complex environments. Therefore, in the time synchronization technology of integrated networks, more attention should be paid to the issue that space-air/ground links are affected by dynamic interference. Such interference can lead to a decline in time synchronization accuracy and fluctuations in network performance. To address these challenges, this paper proposes a Neighbor Aware Reinforcement Learning (NARL) time synchronization framework. A lightweight neighbor node interaction mechanism is designed to control synchronization overhead and effectively prevent broadcast information explosion among nodes. Specifically, for partial space-air/ground link interference, we develop the NARL master-slave synchronization (NARL-MSS) algorithm, which constructs an online scoring model to enable rapid synchronization response while enhancing long-term performance through RL. Under full space-air/ground link interference, an NARL distribution synchronization (NARL-DS) algorithm is employed to identify optimal synchronization link sets, jointly optimizing synchronization accuracy and network overhead. Simulations demonstrate that NARL-MSS can achieve an 80% improvement in synchronization errors, while NARL-DS exhibits excellent synchronization convergence and significantly reduces synchronization costs.

Wireless energy transfer (WET) technology enables Internet of Things (IoT) networks to have longer lifetime without the need of frequent battery replacements. This article proposes a payload-adaptive hybrid MAC (PAH-MAC) protocol by considering both contention and time-slot allocation among sensors for large-scale data collection scenarios within WET-enhanced IoT networks. The PAH-MAC protocol employs different access strategies based on the payload size of data packets. Furthermore, leveraging synchronization mechanisms, the coordinator periodically dispatches energy packets to replenish the battery of all sensors. The stationary performance of PAH-MAC protocol is analyzed by invoking Markov chains. Considering the traffic variations caused by changes in the number of sensors in the network, a slot adaptive adjustment algorithm is proposed to maximize the throughput and energy performance. Therefore, the coordinator adaptively adjusts the duration of the contention period and the energy transmission period within the next superframe according to access conditions observed in current superframe. Another transmission power adjustment algorithm is proposed for sensors to improve their energy efficiency. According to our simulation, our proposed protocol consumes less energy in low-traffic scenarios than the classic carrier-sense multiple access/CA and another baseline protocol. Furthermore, in high-traffic scenarios, our proposed protocol achieves higher throughput, lower latency, and lower energy consumption than its counterpart.

Radio-frequency (RF) signals are capable of simultaneously transferring data and energy from a hybrid access point (HAP) toward battery-powered and batteryless wireless devices (WDs). Battery-powered and batteryless WDs with the capability of RF energy harvesting need a distributed access control protocol with collision avoidance to achieve higher energy efficiency. We study the performance of a data and energy integrated network (DEIN) that adopts an enhanced carrier sensing multiple access with collision avoidance (CSMA/CA) protocol. Each device in this network can switch to RF energy harvesting mode or data reception mode according to HAP’s instruction, and freezes its backoff counter when energy storage is insufficient. By invoking a 3-D Markov chain, we model the operating behaviors of batteryless WDs and an HAP in a DEIN. Apart from backoff operations of devices, the 3-D Markov chain also depicts their dynamic energy changes, including RF energy harvesting and energy consumption. WDs consume energy harvested from the HAP’s downlink transmissions for powering their data upload and random backoff. With the aid of the 3-D Markov chain, the upload throughput of devices can be obtained in semi-closed-form. Moreover, a decoupling method is proposed to approximate throughput performance with low complexity. The accuracy of our theoretical model is validated by simulation results. By characterizing the impact of various parameters on throughput performance, a design guideline for a DEIN with a distributed batteryless access protocol is provided.