Device-to-device (D2D) communication is considered as a promising technology for improving both the spectral and energy efficiencies of cellular networks by reusing the resources of conventional cellular users (CUs) for direct communication of two nearby devices in a spatial manner. When the channel between the two D2D devices is highly attenuated, it is necessary to use an intermediate relay to achieve reliable and flexible relay-aided D2D communication. In order to motivate the cooperative relays to participate, it is assumed that they can harvest energy from radio frequency (RF) signals based on the power splitting (PS) protocol as well as renewable energy (RE) sources. However, resource sharing between the cellular and relay-aided D2D links leads to mutual interference that degrades their sum rate. Considering the energyharvesting relays (EHRs) and downlink (DL) resource sharing, this paper aims to maximize the sum rate of both the links without degrading the quality of service (QoS) requirements of all users. Our maximization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem that cannot be solved in a straightforward manner. Therefore, we propose a low complexity algorithm, namely the resource and power allocation with relay selection EH-aided algorithm (RPRS-EH), which determines the reuse partners, the PS factor sub-optimal value with optimal links power allocation, and provides two different strategies for optimal relay selection. The numerical results show the behavior of the proposed algorithm under various parameters as well as its considerable performance when compared to one of the most recent algorithms in terms of the links sum rate and relay energy efficiency.

No abstract

An earthquake early warning system (EEWS) should be included in smart cities to preserve human lives by providing a reliable and efficient disaster management system. This system can alter how different entities communicate with one another using an Internet of Things (IoT) network where observed data are handled based on machine learning (ML) technology. On one hand, IoT is employed in observing the different measures of EEWS entities. On the other hand, ML can be exploited to analyze these measures to reach the best action to be taken for disaster management and risk mitigation in smart cities. This paper provides a survey on the different aspects required for that EEWS. First, the IoT system is generally discussed to provide the role it can play for EEWS. Second, ML models are classified into linear and non-linear ones. Third, the evaluation metrics of ML models are addressed by focusing on seismology. Fourth, this paper exhibits a taxonomy that includes the emerging ML and IoT efforts for EEWS. Fifth, it proposes a generic EEWS architecture based on IoT and ML. Finally, the paper addresses the application of ML for earthquake parameters’ observations leading to an efficient EEWS.

No abstract