Information Dissemination Mechanism Based on Cloud Computing Cross-Media Public Opinion Network Environment

As an important expression of social public opinion, network public opinion develops rapidly with the popularization of the internet and then affects the real society. Therefore, the use of computer technology to study the network public opinion information transmission mechanism has strong practical significance. The purpose of this paper is to use cloud computing to realize the research of information dissemination mechanism in the context of cross-media public opinion network. Researched from three aspects of operator supervision, number of media, and user density, the hotspot propagation mechanism of Storm platform given in this paper can solve the efficiency problems of traditional algorithms while ensuring accuracy, improve efficiency, and lay the foundation for the research on the monitoring of Internet public opinion propagation.

Dynamic Coverage Optimization for 5G Ultra-Dense Cellular Networks Based on Their User Densities

This paper has proposed a user-density-based coverage optimization technique for ultra-dense cellular networks. Antenna tilting is a promising coverage optimization technique to be used in 5G networks that significantly improve the signal to interference plus noise ratio (SINR) by choosing the appropriate angle of tilt. In this paper, the cellular coverage has been optimized for scattered user densities/user-hotspots using an adaptive antenna tilting mechanism that steers the beams towards the temporal hot spot in the coverage area. The proposed method has the competence to improve the desired SINR level and coverage area for a group of the user rather than a single user. In this work, a reinforcement learning (RL) algorithm has been implemented to optimize the tilt angle. The performance of the proposed technique has been evaluated in the simulation platform considering a three-sectored multicellular mobile network where the groups of user clusters are distributed randomly. The result confirms the improvement in RSS and SINR value in the group of users having high density with maximum user satisfaction.

Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service

This paper formulates a new problem for the optimal placement of Unmanned Aerial Vehicles (UAVs) geared towards wireless coverage provision for Voice over WiFi (VoWiFi) service to a set of ground users confined in an open area. Our objective function is constrained by coverage and by VoIP speech quality and minimizes the ratio between the number of UAVs deployed and energy efficiency in UAVs, hence providing the layout that requires fewer UAVs per hour of service. Solutions provide the number and position of UAVs to be deployed, and are found using well-known heuristic search methods such as genetic algorithms (used for the initial deployment of UAVs), or particle swarm optimization (used for the periodical update of the positions). We examine two communication services: (a) one bidirectional VoWiFi channel per user; (b) single broadcast VoWiFi channel for announcements. For these services, we study the results obtained for an increasing number of users confined in a small area of 100 m2 as well as in a large area of 10,000 m2. Results show that the drone turnover rate is related to both users’ sparsity and the number of users served by each UAV. For the unicast service, the ratio of UAVs per hour of service tends to increase with user sparsity and the power of radio communication represents 14–16% of the total UAV energy consumption depending on ground user density. In large areas, solutions tend to locate UAVs at higher altitudes seeking increased coverage, which increases energy consumption due to hovering. However, in the VoWiFi broadcast communication service, the traffic is scarce, and solutions are mostly constrained only by coverage. This results in fewer UAVs deployed, less total power consumption (between 20% and 75%), and less sensitivity to the number of served users.

Density Estimation System of Space Users Using High Frequencies of Speaker

In this paper, we propose a density estimation system of user density at the closed space using high frequencies of speaker and microphone of smart device. High frequencies are sent to the closed space by the server speaker of the density estimation system, and smart devices located at the space detect the high frequencies via the microphone of each device. The smart devices detecting the high frequencies send data to the server system, and the system calculates data from the smart devices. To evaluate performance of the proposed system, we did some experiments with the density estimation system and 20 smart devices. According to the test results, the proposed system showed 96.5% accuracy, and we confirm that the system is very useful for density estimation. Therefore, this system can precisely estimate user density at the closed space, and it could be useful technology for the density estimation of space users and measurement of space using state at indoor space.

Performance Analysis in Heterogeneous Networks with Spatiotemporal Traffic and Scheduling

In this work, we consider interference performance under direct data transmission in a heterogeneous network. The heterogeneous network consists of K-tier base stations and users, whose locations follow independent Poisson point processes (PPPs). Packet arrivals of users follow independent Bernoulli processes. Two different scheduling policies, round-robin (RR) and random scheduling (RS), are employed to all the Base Stations (BS). The universal frequency reuse mode is adopted to reveal actual spectrum reuse. By leveraging stochastic geometry and queueing theory, the interference interactions of the proposed network are accurately modelled. Accurate expressions for the mean packet throughputs of the network under universal frequency reuse mode are derived. The simulation results explore the optical bias factors in heterogeneous networks to maximize the mean packet throughput. Under a given user density, by changing BS densities, we achieved a certain mean packet throughput level.

An Approach Based on Customized Robust Cloaked Region for Geographic Location Information Privacy Protection

Location-based services (LBS) have gained huge popularity because of the easy availability of modern mobile devices and the fast development of geographical information science (GIS). However, the lack of protection for private user positions might give rise to privacy concerns. This kind of problem is especially serious in mobile application environment because many mobile applications tend to use LBS. In this paper, we propose a new privacy preserving approach using customized robust cloaked region (RCR), depending on a peer-to-peer structure and the premise that users do not trust each other when sharing their geographical locations. Two algorithms are used to generate the RCR with high user density. The area of the RCR is controlled by the user’s demanded degree of protection. To enhance the resistance to regional background knowledge attack, we incorporate a location semantic value into each unit of the user map. According to extensive simulations, our method can effectively obfuscate a user’s geographical location into a highly indistinguishable region because of the disturbance of nearby users and different equally possible locations.

Study on the User Density Identification via Improved Whale Optimization Algorithm in Device-to-Device Communication

The present study proposes a new algorithm for device-to-device (D2D) user density identification in a 5G network based on resource allocation. The method initially established a multiobjective optimization function that calculates system throughput and quality of service (QoS) of D2D users. The optimal resource allocation result of the multiobjective function is obtained via the improved whale optimization algorithm (IWOA). System throughput after resource allocation exhibits a linear relationship with the number of users. Therefore, the D2D user density areas are accurately identified via the throughput value. The simulation result reveals that the accuracy of D2D user density identification reaches 95%.