The recently-developed reconfigurable intelligent surfaces (RISs) are capable of improving the coverage of spaceair-ground integrated networks (SAGINs), where the signals can be reflected in the desired direction without relying on powerthirsty radio-frequency (RF) chains. However, in the face of the substantially increased Doppler frequency, the classic orthogonal frequency-division multiplexing (OFDM) becomes inadequate in supporting RIS for the following reasons. Firstly, the detrimental doubly-selective fading leads to inter-symbol interference (ISI) and inter-carrier interference (ICI), which result in error floors for OFDM operating in the time-frequency (TF) domain. Secondly, it is far from trivial to configure RIS based on the time-varying fading channels. Thirdly, the interpolation-based TF-domain channel estimation methods become impractical for the high-Doppler and high-dimensional RIS systems. Against this background, in this paper, we propose the powerful twodimensional orthogonal time frequency space (OTFS) modulation for RIS-aided SAGINs, which transforms the time-varying fading encountered in the TF-domain to the time-invariant fading in the delay-Doppler (DD) domain. More explicitly, first of all, for the first time in the literature, we devise the DD-domain channel model of RIS assisted SAGINs in the face of doubly-selective fading. Secondly, in order to facilitate the RIS configuration in the DD-domain, we propose to create "virtual" Doppler frequencies that guide the phase changes at the RIS, even though the RIS phase rotations do not suffer from Doppler effects. Thirdly, we conceive an attractive DD-domain RIS channel estimation method that can support both OFDM and OTFS, where the TF-domain interpolation is eliminated. Our simulation results demonstrate that the proposed DD-domain RIS configuration and channel estimation methods for both OFDM and OTFS are capable of mitigating the error floors encountered in the TF-domain. Furthermore, our simulation results confirm that OTFS-based RIS-assisted SAGIN systems are capable of outperforming their OFDM counterparts and exhibit excellent performance across a wide range of SAGIN channel parameters including the Ricean

In this paper, based on the information entropy and spatio-temporal correlation of sensing nodes in the Internet of Things (IoT), a Spatio-temporal Scope Information Model (SSIM) is proposed to quantify the scope of the valuable information of sensor data. Specifically, the valuable information of sensor data decays with space and time, which can be used to guide the system to make efficient sensor activation scheduling decisions for regional sensing accuracy. A simple sensing and monitoring system with three sensor nodes is investigated in this paper, and a single-step scheduling decision mechanism is proposed for the optimization problem of maximizing valuable information acquisition and efficient sensor activation scheduling in the sensed region. Regarding the above mechanism, the scheduling results and approximate numerical bounds on the node layout between different scheduling results are obtained through theoretical analyses, which are consistent with simulation. In addition, a long-term decision mechanism is also proposed for the aforementioned optimization issues, where the scheduling results with different node layouts are derived by modeling as a Markov decision process and utilizing the Q-learning algorithm. Concerning the above two mechanisms, the performance of both is verified by conducting experiments using the relative humidity dataset; furthermore, the differences in performance and limitations of the model are discussed and summarized.

In the future smart cities, connected vehicles provide various infotainment and nonsafety services using vehicle to vehicle and vehicle to road side units (RSUs) communications. To enable these infotainment services, content providers cache their popular data on the RSU storage to make it quickly accessible for the vehicles. In this article, we propose a novel content caching protocol used by mobile network operators. The proposed protocol uses knapsack optimization algorithm to allocate the variable‐sized contents to the RSUs based on their utility. Results show that the proposed protocol provides 13% and 70% approximately higher download data than the market matching and random caching, respectively.

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