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Various mobile devices are developing rapidly in contemporary society, such as smart phones and tablet PCs. Users are able to acquire different multimedia services through wireless communication anytime and anywhere. However, the increased demand also gives rise to a problem of insufficient bandwidth. Therefore, a fourth generation mobile telecommunications (4G) technology was proposed and widely investigated. One of the popular technologies is Long Term Evolution Advanced (LTE-Advanced), which was proposed by the Third Generation Project Partnership (3GPP). The Evolved Node B (eNB) and Relay Node (RN) are the major components in an LTE-Advanced network. How best to deploy these two components to extend network coverage and expand performance is a vital issue. In this paper, we utilize an integer linear programming model (ILP) to formulate the coverage problem, and refer to a well-known problem called the Set International Journal of Electronic Commerce Studies 182Cover problem. Then we propose a heuristic algorithm named as the Set Covering algorithm to solve it. The ultimate object is achieving the highest network coverage and capacity with the least uncovered mobile user. In the simulation result, we use MATLAB to simulate a network deployment, and evaluate the planning results. According to the simulation results, we accomplished better network capacity and a higher number of covered users.

The demand for satisfying service requests, effectively allocating computing resources, and providing service on-demand application continuously increases along with the rapid development of the Internet. Edge computing is used to satisfy the low latency, network connection, and local data processing requirements and to alleviate the workload in the cloud. This paper proposes a gateway-based edge computing service model to reduce the latency of data transmission and the network bandwidth from and to the cloud. An on-demand computing resource allocation can be achieved by adjusting the task schedule of the edge gateway via the lightweight virtualization technology, Docker. The edge gateway can also process the service requests in the local network. The proposed edge computing service model not only eliminates the computation burden of the traditional cloud service model but also improves the operation efficiency of the edge computing nodes. This model can also be used for various innovation applications in the cloud-edge computing environment for 5G and beyond.

The aims of this study are to examine the effect of crowdsourced employer ratings and employee recommendations of an employer as an employer of choice, to examine which employer ratings that represent different employee value propositions can predict the overall employer rating through crowdsourcing, to examine whether the Fortune 500 ranking can also influence overall employer ratings, and to mine which keywords are popularly used when employees post a comment about the pros and cons of their employers on a crowdsourced employer branding platform. The study collected crowdsourced employer review data from Glassdoor based on 2019 Fortune 500 companies, and the results found that crowdsourced employer ratings are positively associated with “recommend to a friend,” while culture and values predominantly influence overall employer ratings. The rank of Fortune 500 has less predictive power for overall employer ratings than for other specific employer ratings, except for business outlook. The most popular keywords of Pros on Glassdoor are work–life balance and pay and benefits, whereas the most popular keywords of Cons on Glassdoor are work–life balance and upper management.