Resource Allocation Techniques for extending the performance of Long-Range Network

Internet of Things (IoT) finds the applications in various domains such as transportation system, health monitoring, and home security. Most of the IoT devices are powered by the battery and do not support power hungry solutions. Long-Range (LoRa) is one of the networking protocols which support IoT because it covers the advantages of both short-range and long-range communication protocols, i.e., it consumes low energy and provides long distance communication. LoRa network consists of a large number of devices in the network and therefore it suffers from the interference problem in the network. Effective allocation of the resources in LoRa reduces the interference problem and enhances the throughput of the network. In this thesis, we address various issues in LoRa network. We propose resource allocation techniques for extending the performance of LoRa network. The experimental results showed that the proposed techniques can enhance the network performance.

[1]  Tanima Dutta,et al.  An Incentive Mechanism-Based Stackelberg Game for Scheduling of LoRa Spreading Factors , 2020, IEEE Transactions on Network and Service Management.

[2]  Abbas Bradai,et al.  Adaptive dynamic network slicing in LoRa networks , 2019, Future Gener. Comput. Syst..

[3]  Tanima Dutta,et al.  Estimation of Time Duration for Using the Allocated LoRa Spreading Factor: A Game-Theory Approach , 2020, IEEE Transactions on Vehicular Technology.

[4]  Tanima Dutta,et al.  A Nodes Scheduling Approach for Effective Use of Gateway in Dense LoRa Networks , 2020, ICC 2020 - 2020 IEEE International Conference on Communications (ICC).