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Abstract-The unrelenting increase in the population of mobile users and their traffic demands drive cellular network operators to densify their network infrastructure. Network densification shrinks the footprint of base stations (BSs) and reduces the number of users associated with each BS, leading to an improved spatial frequency reuse and spectral efficiency, and thus, higher network capacity. However, the densification gain come at the expense of higher handover rates and network control overhead. Hence, users mobility can diminish or even nullifies the foreseen densification gain. In this context, splitting the control plane (C-plane) and user plane (U-plane) is proposed as a potential solution to harvest densification gain with reduced cost in terms of handover rate and network control overhead. In this article, we use stochastic geometry to develop a tractable mobility-aware model for a two-tier downlink cellular network with ultra-dense small cells and C-plane/U-plane split architecture. The developed model is then used to quantify the effect of mobility on the foreseen densification gain with and without C-plane/U-plane split. To this end, we shed light on the handover problem in dense cellular environments, show scenarios where the network fails to support certain mobility profiles, and obtain network design insights.Index Terms-5G cellular networks, C-plane/U-plane split, lean carrier, network densification, phantom cells, handover, X2 interface handover, stochastic geometry.

The dramatic advancments on communication and networking technologies have led to the emergence of Internet-of-Things (IoT). IoT technology has opened the door for various applications. In particular, the home automation was one of the common applications that took the advantage of IoT. Several research efforts have addressed the home automation system using IoT covering wide range of functionalities. One of the concerning tasks is providing a secure system that can give alarms for suspicious activities within the house. This paper presents a secure house system based on IoT where several activities are being sensed and detected. Specifically, gas, humidity, body temperature and motion have been considered within the sensing based on two main types of micro-controller including Arduino and Raspberry Pi. Consequentially, an Android prototype has bene developed in order to give an interactive interface for warning the house owner regarding any suscpicious activities. Results of simulation demonstrated the efficancy of the proposed system

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Using stochastic geometry tools, we develop a systematic framework to characterize the meta distributions of the downlink SIR/SNR and data rate of the typical device in a cellular network with coexisting sub-6GHz and millimeter wave (mm-wave) spectrums. Macro base-stations (MBSs) transmit on sub-6GHz channels (which we term "microwave" channels), whereas small base-stations (SBSs) communicate with devices on mm-wave channels. The SBSs are connected to MBSs via a microwave (µwave) wireless backhaul. The µwave channels are interference limited and mm-wave channels are noise limited; therefore, we have the meta distribution of SIR and SNR in µwave and mm-wave channels, respectively. To model the line-of-sight (LOS) nature of mm-wave channels, we use Nakagami-m fading model. To derive the meta distribution of SIR/SNR, we characterize the conditional success probability (CSP) (or equivalently reliability) and its b th moment for the typical device (a) when it associates to a µwave MBS for direct transmission, and (b) when it associates to a mm-wave SBS for dual-hop transmission (backhaul and access transmission). Performance metrics such as the mean and variance of the local delay (network jitter), mean of the CSP (coverage probability), and variance of the CSP are derived. Closed-form expressions are presented for special scenarios. The extensions of the developed framework to the µwave-only network or mm-wave only networks where SBSs have mm-wave backhauls are discussed. Numerical results validate the analytical results. Insights are extracted related to the reliability, coverage probability, and latency of the considered network.