This cross-country survey research investigated the relationship between screen addiction and media multitasking and examined factors that mediate and moderate this relationship. The web survey recruited Internet users owning multiple devices in the United States (N = 798) and Taiwan (N = 834). Although American users spent longer time on screen devices and engaged more in media multitasking more, Taiwanese respondents had higher screen addiction. Results showed that media multitasking and screen device use were positively related to screen addiction. Country of respondents’ residence moderated the relationship between media multitasking and screen addiction. Additionally, screen device usage mediated the positive effects of media multitasking on screen addiction. Finally, leisure boredom moderated screen device usage’s mediating effects on the relationship between media multitasking and screen addiction, which differed in two countries. Young people and females tended to have high levels of screen addiction. Implications of screen addiction and media multitasking were discussed.

Dense deployments of small cells in heterogeneous networks (HetNets) massively increase spectral efficiency and capacity. Since ubiquitous fiber availability is prohibitively expensive, wireless backhaul technique via millimeter wave (mmWave) is regarded as a practical solution. Additionally, mmWave with huge under-utilized bandwidth is able to provide multi-Gbps data rate comparable to fiber lines. In this paper, we propose an mmWave hierarchical small cell base station backhauling (mw-HierBack) system to organize densely deployed base stations (BSs) in HetNet. A group of Super-BSs (S-BSs) are selected to minimize the resource cost on the gateways and to robustly relay backhaul traffic of the remaining BSs to the core network against any blockage or link failure. Under this network structure, we present a path protection strategy to balance backhaul traffic among S-BSs and to minimize the fluctuations incurred by rerouting. The simulation results show our solution can efficiently scale with the growth of BSs and properly route the backhaul traffic without overloading any one of the S-BSs.

As the application of WSNs for long-term monitoring purposes becomes real, the issue of WSN system health monitoring grows increasingly important. Manually understanding the root causes of an observed behavior is time-consuming and difficult, often knowledge of prior behavior is necessary for understanding the potential risk on the long-term system performance. The challenges lie in the balance between the amount of system data collected and the level of detail in which state can be inferred from this data. In this paper, we propose a lightweight runtime logging and corresponding network state inference mechanism that enables scalable WSN health monitoring. Concretely, we propose that nodes only report their internal state on the occurrence of important events. Having a very low computational complexity and message overhead within the sensor network, reported events are analyzed at a less constrained network sink.