The purpose of this article is to introduce a new strategy to identify areas with high human density and mobility, which are at risk for spreading COVID-19. Crowded regions with actively moving people (called at-risk regions) are susceptible to spreading the disease, especially if they contain asymptomatic infected people together with healthy people. Methods: Our scheme identifies at-risk regions using existing cellular network functionalities-handover and cell (re)selection-used to maintain seamless coverage for mobile end-user equipment (UE). The frequency of handover and cell (re)selection events is highly reflective of the density of mobile people in the area because virtually everyone carries UEs. Results: These measurements, which are accumulated over very many UEs, allow us to identify the atrisk regions without compromising the privacy and anonymity of individuals. Conclusions: The inferred at-risk regions can then be subjected to further monitoring and risk mitigation. Index Terms-COVID-19, infectious diseases, tracking. Impact Statement-Method to identify crowded regions with actively moving individuals, at risk for spreading COVID-19, by exploiting existing cellular-network functionalities. Requires no active participation by individuals and introduces no privacy concerns.

No abstract

No abstract

No abstract

Mobility management (MM) in Long-Term Evolution (LTE) networks is a vital process to keep an individual User Equipment (UE) connected while moving within the network coverage area. MM Entity (MME) is the LTE component responsible for tracking and paging procedures and controlling the corresponding signaling between the UE and its serving cell, which is necessary for data-packet exchange. Because of the massive increase in the density of mobile UEs, MME is burdened by the high volume signaling load, especially because most of that load comes from Tracking Area Update (TAU) and Paging messages, which are essential to exchange UE-specific information with the network. To achieve cost-efficient resource provisioning, many solutions have been proposed for TAU and Paging management to optimize not only UE experience (ie, battery power consumption) but also network resources (ie, bandwidth). In this paper, we discuss various solution schemes for TAU and Paging in terms of complexity, latency, and computation costs. Also, this review discusses the adverse effects of these solutions on the LTE Key Performance Indicators (KPIs). Furthermore, we present a new trend of MM solutions in LTE networks, called software-defined network (SDN) and software-defined virtualization (SDNV).To this end, we examine the existing schemes and challenges in the literature toward next-generation wireless networks (eg, 5G, Internet-of-Things [IoT], and machine to machine [M2M] communications), and we describe user mobility models that are used to analyze the network performance.Int J Network Mgmt. 2020;30:e2088.wileyonlinelibrary.com/journal/nem market in the United States reveals that most signaling loads on the MME are caused by TAU and Paging procedures, as shown in Figure 2; cost about 34% of the total signaling load on the MME. 3 Generally, LNOs use many LTE Key Performance Indicators (KPIs) measurements, as primary indicators, to evaluate and measure the network performance to satisfy the end-user requirements according to Service Level Agreement (SLA). 12 Basically, the Paging Success Rate and TAU Success Rate are defined as the two LTE KPIs that measure how well the TAU and Paging procedures are succeeded. 13,14 Having low KPI values of Paging Success Rate and TAU Success Rate can be caused by low UE and/or network performances. Many researchers and practitioners try to mitigate the overall signaling loads on the MME by improving both the TAU and Paging procedures and the end-user experience to maintain the related KPIs at optimal values.