Optimizing Resources and Increasing the Coverage of Internet-of-Things (IoT) Networks: An Approach Based on LoRaWAN

Abstract

A resource optimization methodology is proposed for application in long range wide area networks (LoRaWANs). Using variable neighborhood search (VNS) and a minimum-cost spanning tree algorithm, it reduces the implementation and the maintenance costs of such low power networks. Performance evaluations were conducted in LoRaWANs with LoRa repeaters to increase coverage, in scenario where the number and the location of the repeaters are determined by the VNS metaheuristic. Parameters such as spread factor (SF), bandwidth and transmission power were adjusted to minimize the network’s total energy per useful bit (Ebit) and the total data collection time. The importance of the SF in the trade-off between (Ebit) and time on-air is evaluated, considering a device scaling factor. Simulation results, obtained after model adjustments with experimental data, show that, in networks with few associated devices, there is a preference for small values of SF aiming at reduction of Ebit. The usage of large SF’s becomes relevant when reach extensions are required. The results also demonstrate that, for networks with high number of nodes, the scaling of devices over time become relevant in the fitness function, forcing an equal distribution of time slots per SF to avoid discrepancies in the time data collection.

Energy efficient slotted synchronization approach in LoRaWAN

“…To achieve the study's objective, we analyzed two transmission approaches, slotted and random, using Network Simulator 3 (NS3). The random synchronization approach was adopted from a work by [12]. The simulation of the two algorithms was carried out by varying the number of devices while recording the time and energy consumed to synchronize all devices in each transmission round.…”

Enhancement in Optimal Resource-Based Data Transmission Over LPWAN Using a Deep Adaptive Reinforcement Learning Model Aided by Novel Remora With Lotus Effect Optimization Algorithm

“…However, challenges persist in improving and optimizing resource allocation and managing interference and hardware costs. Variable Neighbourhood Search (VNS) and minimum spanning tree (MST) [21] provide low convergence rates and high packet reception ratio. Also, it improves the quality of service of the LoRa networks.…”

“…And Lotus Effect Optimization Algorithm (LEA) [30]. And various methods like Reinforcement Learning (RL) [19], MST [21], Game Theory [26]. and MIX-MAB [28].…”

Application of Artificial Neural Networks for Prediction of Received Signal Strength Indication and Signal-to-Noise Ratio in Amazonian Wooded Environments

“…In summary, the Internet of Things is an idea that has already impacted and will continue to impact all aspects of human life significantly [ 2 ]. Moreover, most devices used in IoT will employ non-cellular wireless technologies, such as Bluetooth ® , Wi-Fi, ZigBee, THREAD, EnOcean, Sigfox ® , and LoRa™, among others [ 3 ].…”