Elaborative Investigation of Blockchain Technology in Intelligent Networks

The fifth generation (5G) network advancements focus to help mixed upright applications by associating heterogeneous gadgets and machines with extreme upgrades regarding high quality of administration, extended organization limit, and improved framework throughput regardless of significant difficulties like decentralization, straightforwardness, dangers of information interoperability, network protection, and security weaknesses. The challenges and limitations of other intelligent 5G intelligent internet of networks (5G IoTs) are also to be met by using blockchain technology with the integration of cloud computing and edge computing technologies. In this chapter, the authors render an elaborated analytics of the empowering of blockchain technology in intelligent networks that includes 5G networks and 5G-based IoT. The solutions for the spectrum management, data sharing, security, and privacy in 5G networks will also be analyzed. It is believed that the chapter would be useful for researchers in the field of blockchain in intelligent networks.

CSMC

The rise concern about mobile communication performance has driven the growing demand for the construction of mobile network signal maps which are widely utilized in network monitoring, spectrum management, and indoor/outdoor localization. Existing studies such as time-consuming and labor-intensive site surveys are difficult to maintain an update-to-date finegrained signal map within a large area. The mobile crowdsensing (MCS) paradigm is a promising approach for building signal maps because collecting large-scale MCS data is low-cost and with little extra-efforts. However, the dynamic environment and the mobility of the crowd cause spatio-temporal uncertainty and sparsity of MCS. In this work, we leverage MCS as an opportunity to conduct the city-wide mobile network signal map construction. We propose a fine-grained city-wide Cellular Signal Map Construction (CSMC) framework to address two challenges including (i) the problem of missing and unreliable MCS data; (ii) spatio-temporal uncertainty of signal propagation. In particular, CSMC captures spatio-temporal characteristics of signals from both inter- and intra- cellular base stations and conducts missing signal recovery with Bayesian tensor decomposition to build large-area fine-grained signal maps. Furthermore, CSMC develops a context-aware multi-view fusion network to make full use of external information and enhance signal map construction accuracy. To evaluate the performance of CSMC, we conduct extensive experiments and ablation studies on a large-scale dataset with over 200GB MCS signal records collected from Shanghai. Experimental results demonstrate that our model outperforms state-of-the-art baselines in the accuracy of signal estimation and user localization.

Dynamic spectrum management using frequency selection at licensed and unlicensed bands for efficient vehicle-to-vehicle communication

Background: The exponential increase in pattern of vehicles on the roads demands a need to develop a vehicular infrastructure that may not only ease congestions and provide a better experience but also pivot the levels of safety among users. The development of wireless technology has made it convenient for machines, devices and vehicles to interact with one another. The efficacy of this wireless communications relies on utilising current and available technology to enable information to be shared efficiently. In the wake of the available advancement in wireless technology, a new dynamic spectrum management (DSM) in vehicle-to-vehicle (V2V) communication that coexists with the existing Long-Term Evolution (LTE) network to increase the throughput in V2V communication is proposed. This will provide some solutions to enable a more efficient vehicular infrastructure. Methods: This paper focuses on the utilization of DSM in V2V communications by selecting an appropriate frequency band through the selection of available licensed and unlicensed frequency bands for vehicles. Further investigations are done to identify the effect of interference in the dynamic spectrum by observing the path loss, SINR, and the throughput with various interfering users. Results: The results show that the performance of the proposed DSM augments a significant improvement in the overall throughput and the signal-to-interference-plus-noise ratio (SINR) value is reduced by up to 60% when compared to the fixed spectrum allocation. Conclusions: Although the dynamic spectrum is still affected by the interference from the existing cellular users, the throughput of the dynamic spectrum remains sufficient to transmit the information to other vehicles.

Understanding of RF Cloud Interference Measurement and Modeling

Importance of spectrum regulation and management was first revealed on May of 1985 after the release of unlicensed ISM bands resulting in emergence of Wi-Fi, Bluetooth and many other wireless technologies that has affected our daily lives by enabling the emergence of the smart world and IoT era. Today, the idea of a liberated spectrum is circulating around, which can potentially direct wireless networking industry into another revolution by enabling a new paradigm in intelligent spectrum regulation and management. The RF signal radiated from IoT devices as well as other wireless technologies create an RF cloud causing co- and cross-interference to each other. Lack of a science and technology for understanding, measurement, and modeling of the RF cloud interference in near real-time results in inefficient utilization of the precious spectrum, a unique natural resource shared among all wireless devices of the universe in frequency, time, and space. Near real time forecasting of the RF cloud interference is essential to pursue the path to the optimal utilization of spectrum and a liberated spectrum management. This paper presents a historical perspective on the evolution of spectrum regulation and management, explains the diversified meanings of interference for different sectors of the wireless industry, and presents a path for implementing a theoretical foundation for interference monitoring and forecasting to enable the emergence of a liberated spectrum industry and a new paradigm in spectrum management and regulations.

DAHP–TOPSIS-Based Channel Decision Model for Co-Operative CR-Enabled Internet on Vehicle (CR-IoV)

The Internet of Vehicles (IoV) is an important idea in developing intelligent transportation systems and self-driving cars. Vehicles with various wireless networking options can communicate both inside and outside the vehicles. IoVs with cognitive radio (CR) enable communication between vehicles in a variety of communication scenarios, increasing the rate of data transfer and bandwidth. The use of CR can meet the future need for quicker data transport between vehicles and infrastructure (V2I). Vehicles with CR capabilities on VANET have a different appearance than regular VANET vehicles. This paper aims to develop effective spectrum management for CR-equipped automobiles. An improved channel decision model has been proposed with proven outcomes to boost the pace of transmission, eliminate end-to-end delays, and minimize the number of handoffs. Many high-bandwidth channels will be used in the near future to communicate large-sized multimedia content between vehicles and roadside units (RSU) for both entertainment and safety purposes. Co-operative sensing promotes energy-constrained CR vehicles for sensing a wide spectrum, resulting in high-quality communication channels for requesting vehicles. Our research on the CR-VANET focuses on channel decision instead of spectrum sensing and it differs from previous studies. We used the DAHP–TOPSIS model under multi-criteria decision analysis (MCDA), a sub-domain of operations research, to boost profits, i.e., transmission rate with less computing time. We constructed a test-bed in MATLAB and carried out several analyses to demonstrate that the suggested model performs better than other parallel MCDA models because there has been a limited amount of research work conducted with CR-VANET

An Experimental Study on Transient Response of a Hybrid Thermoelectric–Photovoltaic System with Beam Splitter

In the current study, the electrical responses of a thermoelectric (TE) module and a photovoltaic (PV) cell are investigated in three different systems, namely, a PV-only system, TE-only system, and hybrid TE-PV system with a beam splitter (TE-PV-BS), under variable solar irradiations demonstrating partly cloudy weather conditions. To enhance the deployment of solar energy, a predesigned beam splitter combined with the amorphous silicon TE and PV system is used in the experiments. The impact of the spectral beam splitting technology on the conversion performance of the TE module and PV cell in the hybrid system is studied and compared to the performance of the TE-only and PV-only systems. The electrical output parameters of the TE module and PV cell are obtained for the studied systems, and they are discussed in detail. The results of this work show that the power generated by the PV cell has a stepwise fluctuation similar to the variation in the concentrated solar radiation. Affected by its heat capacity, the power variation is monotonous with the TE module. The results moreover indicate that there is more power generated by the PV cell in the TE-PV-BS hybrid system than by the PV-only system. In comparison, the TE-only system produces more power than the TE module in the hybrid system. Furthermore, the TE-PV-BS hybrid system generates higher and more stable electrical power than the TE-only and PV-only systems, showing a significant advantage of the spectrum management concept.