## 6 Conclusions and Future Directions  
This study investigated the creation of an on-demand digital environmental ecosystem that relies on algorithms to analyze a huge amount of data, as well as the requirement that this data should be immutable, authenticated, and distributed. Using the MAM protocol, we demonstrated how encrypted environmental sensor data can be broadcasted, stored, and fetched from the IOTA Tangle to prove the data's integrity, security, and privacy. We also showed how granular access controls can be defined and updated by environmentalists. The MAM protocol proved to be a useful tool for encrypting and authenticating sensor  
Figure 12: Line graph displaying the average “fetch time”, with respect to payload size, for the MAM protocol and non-protocol methods.  
data, although it may be improved in terms of performance and design. Many application fields, such as healthcare, the supply chain, and data storage of many kinds of sensor data, could benefit from this way of storing and delivering encrypted sensor data.  
Based on the results of our extensive experimental evaluation of the proposed model, we can conclude that the MAM protocol performs better and provides better security than the non-protocol approach. The MAM protocol provided some additional features such as data encryption and granular access control, which provided better security and privacy, compared to the non-protocol method. Therefore, the MAM protocol can be seamlessly linked to various IoT devices to meet the scalability demands of these devices.  
For future work, we suggest that the MAM protocol must integrate a secure and efficient key-transmitting method that would exchange the authorization keys between different entities. Additionally, as the MAM protocol develops and matures, we will demonstrate how this protocol can be used to ensure data integrity, by developing a proof-of-concept across academic universities.  
Finally, we need to address how a huge dataset can be maintained across different stakeholders since the sensor data is widely distributed and the sensors are producing more data exponentially with time. IoT and embedded devices have the potential to generate massive amounts of data that will be incompatible with complete nodes, which cannot store the entire history of data. The complete nodes will keep track of the current state and prune the remaining data to make room for new transactions. An organization must have a complete record of all relevant transactions; nevertheless, the trimmed transactions will still have provable cryptographic links.  
[PERSON] created the proposed architecture and experimental scripts, and is the main writer. [PERSON] supervised the proposed model and is the secondary writer. [PERSON] contributed toward the writing of background technologies for this research. [PERSON]-[PERSON] and [PERSON] supervised the conducted research and reviewed the research article. Finally, [PERSON] provided the funding and the resources to perform this research. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the U.S Department of Energy National Energy Technology Laboratory (DOE NETL), grant number is DE-FE0031745.  
The dataset used for this research was publicly available and can be found here [[[https://www.kaggle.com/garystafford/environmental-sensor-data-132k](https://www.kaggle.com/garystafford/environmental-sensor-data-132k)]([https://www.kaggle.com/garystafford/environmental-sensor-data-132k](https://www.kaggle.com/garystafford/environmental-sensor-data-132k)) (accessed on 20 July 2020)].  
The authors declare no conflict of interest.