In brief, we present a novel framework that counts the number of people based on the readings of the sensors inside closed rooms. These sensors are connected using LoRa network. First, we identify the transmission failures that cause missing values in the dataset and handle such missing values. In addition, we validate the efficiency of handling the missing values using a long short-term memory (LSTM) architecture that forecast future values based on the handled missing values. Finally, we propose a neural network that formulate the number of people based on the readings. Simulation results show our model achieves an accuracy of 95% in predicting the number of people. We believe that our work would have a significant impact on the road of IoT and wide area network research in wireless communications. Our work could benefit lockdown restrictions during pandemic eras and smart air ventilation systems in smart residual building.

The age-of-information (AoI) is used to measure the freshness of the data. In IoT networks, the traditional resource management schemes rely on message exchange between the devices and the base station (BS) prior to communication which causes high AoI, high energy consumption, and low reliability. Unmanned aerial vehicles (UAVs) as flying BSs have many advantages in minimizing the AoI, energy-saving and throughput improvement. In this paper, we present a novel learning-based framework that estimates the traffic arrival of the IoT devices and optimizes the trajectory of multiple UAVs and their scheduling policy. First, the BS predicts the future traffic of the devices. We compare two traffic predictors: 1) the forward algorithm (FA) and 2) the long short-term memory (LSTM). Afterwards, we propose a deep reinforcement learning (DRL) approach to optimize the optimal policy of each UAV. Finally, we manipulate the optimum reward function for the proposed DRL approach. Simulation results show that the proposed algorithm outperforms the random-walk (RW) baseline model regarding the AoI, scheduling accuracy, and transmission power.

In many emerging Internet of Things (IoT) applications, the freshness of the data collected by the sensor nodes is an important design criterion. Age of Information (AoI) quantifies the freshness of the received information or status update. This work considers a setup of deployed IoT devices in an IoT network; multiple unmanned aerial vehicles (UAVs) serve as mobile relay nodes between the sensors and the base station. We formulate an optimization problem to jointly plan the UAVs’ trajectory, while minimizing the AoI of the received messages and the devices’ energy consumption. The solution accounts for the UAVs’ battery lifetime and flight time to recharging depots to ensure the UAVs’ green operation. The complex optimization problem is efficiently solved using a deep reinforcement learning algorithm. In particular, we propose a deep Q-network, which works as a function approximation to estimate the state-action value function. The proposed scheme is quick to converge and results in a lower ergodic age and ergodic energy consumption when compared with benchmark algorithms such as greedy algorithm (GA), nearest neighbour (NN), and random-walk (RW).

The road to extreme low latency urges the invention of intelligent access schemes that depart from the shortcomings associated with the classic ones. In this work, we present a novel traffic prediction and fast uplink framework for IoT devices activated by binary Markovian events. First, we apply the forward algorithm in the context of hidden Markov models (HMM) to predict the hidden states, which are used to schedule the available resources to the devices with maximum likelihood activation probabilities via fast uplink grant. In addition, we evaluate the regret metric as the number of missed resource allocation opportunities to evaluate the performance of the prediction. Next, we formulate a fairness optimization problem to minimize the age of information while keeping the regret as minimum as possible. Finally, we propose an iterative algorithm to estimate the model hyperparameters (activation probabilities) in a real-time application and apply an online-learning version of the proposed traffic prediction scheme. Simulation results show that the proposed algorithms outperform baseline models such as time division multiple access (TDMA) and grant-free (GF) random-access in terms of regret, the efficiency of system usage, and age of information.