# Optimal Data Collection Time in LoRa Networks--A Time-Slotted Approach
## 8 Conclusions and Future Directions  
In this work, we studied the problem of bulk data collections in LoRa-based networks for situations where a gateway is not available to receive data at all times due to the lack of a permanent power source or the lack of coverage (e.g., in satellite IoT systems). We showed that traditional Aloha-based networks suffer from very long data collection times in order to achieve a satisfactory data delivery ratio. Here, we explored the possibility of using collision-free time-slotted LoRa communications together with SF and slot scheduling to accelerate the data collection. We mathematically formulated and solved the corresponding scheduling problem of finding the best SF and slot allocations that optimise the data collection time. The comparison with heuristics for small data sets showed only a slight performance gap in data collection time compared to the optimum approach. We implemented our methods using real deployments and we examined critical components of the bulk data collection such as the transmission of the schedule. Despite the duty cycle and power limitations, the results demonstrated the significant advantage of using the proposed synchronous approaches to achieve fast data collection especially with hundreds of nodes.  
For future work, we intend to investigate the possibility of using medium sensing techniques for data offloading in dense node situations and see their effect on the data collection time. More specifically, we intend to assess the use of the Channel Activity Detection (CAD) mechanism of LoRa transceivers in scenarios where a high packet rate is required, and also develop a feedback and re-transmission mechanism for those scenarios. Another interesting problem to explore is the transmission of the schedule using multiple gateways. We are planning to design an approach to divide the schedule in multiple pieces and transmit it to the nodes over different channels and SFs in order to accelerate the process and increase the levels of scalability--but also of energy efficiency--in large-scale networks. Finally, an open problem remains the development of a payload-agnostic time-slotted algorithm for data collection purposes.  
Figure 10: Experiment with 10 nodes: Data collection time, Packet Delivery Ratio, and active radio time with and without retransmissions (Collision-Free approaches, Slotted-Aloha, -ACK: the corresponding approach with retransmissions).  
**Author Contributions:** Conceptualization, D.Z.; formal analysis, D.Z. and C.C.; funding acquisition, D.P.; methodology, D.Z., C.C. and K.A.H.; resources, D.Z.; software, D.Z.; writing--original draft, D.Z.; writing--review & editing, D.Z., C.C., K.A.H. and D.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) and is co-funded under the European Regional Development Fund under Grant Number 13/RC/2077, the CONFIRM fund under Grant Number 16/RC/3918, and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 713567.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.