How to effectively support massive access and data transmission in Internet of Things scenarios has been a long-standing and critical issue for various wireless communication networks. To address this issue, a flexible and efficient medium access control protocol is the key. In this paper, we propose Slotted Aloha with Successive Transmission (SAST) scheme, in which upon the successful transmission of the Head-of-Line (HoL) packet, the node delivers the remaining packets with probability 1 until the buffer is cleared or a collision occurs, thereby capitalizing on immediate channel availability. By formulating vacation queuing models of both node and channel, the access/data throughput and access/data delay are explicitly characterized and optimized by properly choosing the transmission probability of the HoL packet. Our analysis reveals that the maximum data throughput of SAST scheme is 0.5, higher than$e^{-1}$in classic slotted Aloha. The practical insights of the analysis are also demonstrated by taking the example of 2-step Small Data Transmission (SDT) random access in 5G. It is shown that the SAST scheme can be seamlessly implemented into 5G and the comparison with 2-step SDT random access reveals that SAST can improve the throughput performance while significantly reduce the signaling overhead, nearly halved in the saturated case and up to 70% reduction in the unsaturated case.

For the application of intelligent and green transportation systems (e.g., autonomous driving), traffic congestion is a severe challenge. So far, when traffic congestion is perceived for a route, a common solution is searching for another congestion-free route. However, it is observed that not all congestion should be tackled with rerouting since the extra overhead (e.g., travel time, fuel consumption, and CO2 emission) caused by specific congestion might be lower than that of rerouting. Against this backdrop, a prediction-based route guidance method (PRGM) is proposed for intelligent and green transportation systems. To begin with, PRGM involves a novel hybrid and dynamic system architecture based on the collaboration of vehicle clusters and the cloud platform. Notably, a backup mechanism between adjacent cluster heads is designed to avoid the problem that the data might be lost during dynamic clustering. Furthermore, PRGM involves a novel traffic congestion control strategy, which is based on four procedures: 1) perception about traffic congestion with three indexes (i.e., speed index, dense index, and acceleration index); 2) judgment about congestion type with four defined congestion types; 3) prediction about congestion duration considering the formation of congestion (i.e., why and how the congestion is formed); and 4) route planning about vehicles considering congestion duration and the extra time overhead of rerouting. Simulations are performed, and they show that the proposed PRGM not only can perceive traffic congestion more precisely and timely but also can reduce the travel time, fuel consumption, and CO2 emission of vehicles.

Traffic congestion is a severe challenge for intelligent transportation system (ITS). So far, if traffic congestion is perceived in a route, a common solution is searching for another congestion-free route. However, it is observed that not all congestions should be tackled with rerouting, since the extra overhead (e.g., extra travel time, extra fuel consumption, and extra CO2 emission) caused by certain congestions might be lower than that of re-routing. Against this backdrop, an intelligent route guidance strategy is proposed, in which vehicles will trade off the extra overhead of re-routing and that of waiting in congestion. Firstly, the perceived congestion is divided into four basic types. Then, the prediction method about the duration of each congestion type is formulated. Finally, the intelligent route planning mechanism is developed. Simulations demonstrate that the proposed strategy can reduce the travel time, fuel consumption, and CO2 emission for vehicles.