• Energy Research

The contributions of this paper are twofold. Firstly, we analyze the decision making problem of caching contents by the network players of Information Centric Networking (ICN) from a game theoretic perspective. We also mention a possible content distribution model for ICN. By our proposed game theory, different network players can find the optimality taking into consideration which benefits them with optimum revenue. Secondly, we present a solution for Live Streaming Media broadcast in ICN and analyze the economic part with a decision tree. We believe, our paper clearly shows the economic incentives for major network players which can stand as a motivation to achieve the faster accommodation of ICN architecture. Besides, we identify some standardization issue in ICN architecture and we emphasize on the need for a common standard for content routers (CR) so that as a node in the ICN, CR ensure scalable content delivery as well as its functionalities match with the Internet open standard philosophy.

As the first legally binding global agreement on climate change, the Paris Agreement aims to limit long-term global warming to less than 2 °C, preferably below 1.5 °C compared to pre-industrial levels. Road transport, as one of the primary sources of energy consumption and carbon emissions, has great potential for energy conservation and emission reduction in achieving the climate goals. However, current approaches cannot efficiently reduce road travel, making it challenging to decarbonize in the transport sector. Therefore, in this paper, we propose STRICTs, a blockchain-enabled motor vehicle restrictive and trading system based on the carbon emissions cap. STRICTs allows automated carbon emission auditing and carbon emission violation punishment without relying on third-party escrow. Through a decentralized enforcer, STRICTs also enables carbon permit trading reliably and transparently. Finally, we implement a proof-of-concept prototype of the system based on Hyperledger Fabric and conduct experiments for comprehensive performance evaluation through CO2 Emission by Vehicles dataset. The experiment results show the practically affordable performance of STRICTs.

Satellite communication system is expected to play a vital role for realizing various remote Internet-of-Things (IoT) applications in sixth-generation vision. Due to unique characteristics of satellite environment, one of the main challenges in this system is to accommodate massive random access (RA) requests of IoT devices while minimizing their energy consumptions. In this article, we focus on the reliable design and detection of RA preamble to effectively enhance the access efficiency in high-dynamic low-earth-orbit (LEO) scenarios. To avoid additional signaling overhead and detection process, a long preamble sequence is constructed by concatenating the conjugated and circularly shifted replicas of a single root Zadoff–Chu (ZC) sequence in RA procedure. Moreover, we propose a novel impulse-like timing metric based on length-alterable differential cross-correlation (LDCC), that is immune to carrier frequency offset (CFO) and capable of mitigating the impact of noise on timing estimation. Statistical analysis of the proposed metric reveals that increasing correlation length can obviously promote the output signal-to-noise power ratio, and the first-path detection threshold is independent of noise statistics. Simulation results in different LEO scenarios validate the robustness of the proposed method to severe channel distortion, and show that our method can achieve significant performance enhancement in terms of timing estimation accuracy, success probability of first access, and mean normalized access energy, compared with the existing RA methods.

With the growing public attention on sustainable development and green ecosystems, the efficient management of fuzzy sewage treatment processes (FSTPs) has been a major concern in academia. Characterized by strong abstraction and analysis abilities, data mining technologies provide a novel perspective to solve this problem. In recent years, data-driven management for FSTP has been widely investigated, resulting in a number of typical approaches. However, almost all existing technical approaches consider FSTP a unidirectional, sequential process, ignoring the bidirectional temporality caused by backflow operations. Therefore, we propose a data-driven management mechanism for FSTP based on hybrid neural computing (IM-HNC for short). This mechanism attempts to capture the bidirectional time-series features of FSTP with the aid of a bidirectional long short-term memory model, and further introduces a convolutional neural network to construct feature spaces with a stronger expression capability. Empirically, we implement a series of experiments on three datasets under different parameter settings to test the efficiency and robustness of the proposed IM-HNC. The experimental results manifest that the IM-HNC has an average performance improvement of approximately 5% compared to the baselines.

The rapid advancement of wireless communication and artificial intelligence (AI) has led to a plethora of emerging applications that require exceptional connectivity, minimal latency, and substantial computing resources. The widespread adoption of cloud-edge intelligence is propelling the development of future networks capable of supporting intelligent computing. Mobile edge computing (MEC) technology facilitates the movement of computing resources and storage to the network’s edge, enabling cost-effective offloading of computational tasks for related applications which needs for reduced latency and improved energy efficiency. However, the offloading efficiency is hindered by limitations of wireless transmission capacity. This paper aims to address this issue by integrating reconfigurable intelligent surfaces (RISs) into a cell-free network within an intelligent cloud-edge system. The core idea is to strategically deploy passive RISs around base stations (BSs) to reconstruct the transmission channel and improve the corresponding capacity. Subsequently, we formulate an optimal problem involving joint beamforming for RISs and BSs, which is characterized by non-convexity and complexity. To tackle this challenge, we employ an alternating optimization scheme to ensure the effectiveness of joint beamforming. In particular, deep reinforcement learning (DRL) is leveraged to reduce the computational complexity involved in optimizing task offloading. Additionally, Lyapunov optimization is utilized to model the latency queue and improve the learning efficiency of the offloading framework. We conduct comprehensive evaluations on the wireless system’s capacity, average latency, and energy consumption, considering the integration of RIS with the DRL offloading framework. Experimental results demonstrate that our proposed scheme achieves superior efficiency and robustness.