In this paper, we employ a user-centric (UC) cell-free massive MIMO (CFmMIMO) network for providing ultra reliable low latency communication (URLLC) when traditional ground users (GUs) coexist with unmanned aerial vehicles (UAVs). We study power control in both the downlink and the uplink when partial zero-forcing (PZF) transmit/receive beamforming and maximum ratio transmission/combining are utilized. We consider optimization problems where the objective is to maximize either the users’ sum URLLC rate or the minimum user’s URLLC rate. The URLLC rate function is both complicated and nonconvex rendering the considered optimization problems nonconvex. Thus, we propose two approximations for the complicated URLLC rate function and employ successive convex optimization (SCO) to tackle the considered optimization problems. Specifically, we propose the SCO with iterative concave lower bound approximation (SCO-ICBA) and the SCO with iterative interference approximation (SCO-IIA). We provide extensive simulations to evaluate SCO-ICBA and SCO-IIA and compare UC CFmMIMO deployment with traditional colocated massive MIMO (COmMIMO) systems. The obtained results reveal that employing the SCO-IIA scheme to optimize the minimum user’s rate for CFmMIMO with MRT in the downlink, and PZF reception in the uplink can provide the best corresponding URLLC rate performances.

Earthquake early-warning system (EEWS) is inevitable for saving human lives. The fast determination of the Earthquake’s (EQ’s) magnitude and its location is significant in disaster management and EQ risk mitigation. These parameters can be conveyed over the Internet-of-Things (IoT) network to alleviate an EQ disaster. In this article, a deep learning model based on integrating autoencoder (AE) and convolutional neural network (CNN) for a swift pinpointing of EQ magnitude and location after 3 s from the onset of the P-wave is proposed. Thus, we name it 3 s AE and CNN (3S-AE-CNN). The employed data set is observed by three stations from the Japanese Hi-net seismic network. We have trained our model on 12200 events (109.80 thousand 3-s-three-component seismic windows). The model facilitates the extraction of waveforms’ significant features leading to robust estimation of the EQ parameters. The proposed model predicts the magnitude and location of EQ with errors in magnitude, latitude, and longitude that reach 0.000028, 0.0000033, and 0.0001, respectively. The EQ’s parameters calculated by the proposed 3S-AE-CNN model are swiftly sent to a centralized IoT system that in turn directs the involved entity to take suitable action. The obtained results of the 3S-AE-CNN are compared to the conventional manual solution method, which represents the optimum solution mean. The 3S-AE-CNN shows an enhanced performance for the magnitude and location determination as compared with the benchmark method, which proves its effectiveness for EEWS.

In the era of internet‐of‐things (IoT), many applications utilise wireless sensor networks (WSNs)including smart grids (SGs). Designing WSNs to fulfill the SGs requirementsimposes some challenges such as limited power and signal propagationimpairments, especially, in harsh environments. Consequently, saving powerconsumption in WSNs‐based SGs is among the most significant challenges. Thetotal power required at a certain sensor depends on two main parameters: thepacket length and inter‐node distance. This paper investigates the optimalpacket length and inter‐node distance to be utilised in a SG over six differentenvironments aiming at maximising the network lifetime. The investigation isbased on a link‐layer model using Tmote Sky nodes taking into consideration thesix environments impact. A mixed‐integer programming (MIP) model is utilised todetermine the best packet length and number of nodes for maximising the networklifetime. This model analyses the performance of maximum SG network lifetimeover those environments and addresses the inter‐node distance effect on thenetwork lifetime maximisation. Simulation results show that decreasing thenumber of nodes covering a certain area is preferable to prolonging the networklifetime. Furthermore, for the considered models, the longer the packet lengthis, the longer the network lifetime will be.