**6. Conclusions**

This paper presents EconLedger, a lightweight and secure-by-design distributed ledger to enhance trust and security properties for smart IoVT systems at the edge. The EconLedger combines an efficient PoENF consensus mechanism with a deterministic voting-based chain finality in order to achieve safety and liveness. By using on-chain ledger and DDB enabled off-chain storage, the EconLedger network reduces storage overheads on validators and guarantees security and resilience of data sharing in a distributed IoVT network. The experimental results based on a prototype demonstrate that it achieves higher computation efficiency and *tx* throughput than benchmarks.

The experimental results on the prototype are encouraging, but there still are open issues to solve before developing a practical solution in real-world video surveillance systems. Using ENF signals for proof of work in consensus process is creative, however, whether ENF variation extracted from multimedia is reliable given attacks on ENF recordings such as synchronizing ENF and injecting into raw video/audio data or colluding among adversaries by sharing ENF data, is still an open question. Thus, our ongoing efforts include validating the proposed architecture in a real-world video streamscontext, simulating attack scenarios such as using AI enabled methods to generate fake ENF recordings, and ensuring overall efficiency and security.

In addition, validators in EconLedger system cannot directly obtain cryptocurrency rewards though PoENF consensus, but they can gain benefits from transaction fees. As a punishment strategy, slashing security deposits can increase financial cost if the adversary uses sybil nodes to disturb consensus protocol. However, there are open questions on the incentive mechanism. Our future work will use game theory to evaluate how incentive mechanisms can enhance system robustness and security.

Moreover, our EconLedger solution aims to provide a lightweight and security distributed ledger under a small-scale IoVT network, such as a campus. However, it still requires more investigation on how to apply EconLedger at a large-scale application scenario, such as smart cities or smart grids. Another future investigation for our team is designing scalable blockchain infrastructure that relies on a hierarchical framework in order to federate multiple privately distributed ledgers.

**Author Contributions:** Conceptualization, R.X., D.N., and Y.C.; methodology, R.X. and D.N.; software, R.X. and D.N.; validation, R.X., D.N., and Y.C.; formal analysis, R.X., D.N., and Y.C.; investigation, R.X.; resources, R.X. and D.N.; data curation, R.X.; writing—original draft preparation, R.X. and D.N.; writing—review and editing, R.X. and Y.C.; visualization, R.X.; supervision, Y.C.; project Administration, Y.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by National Science Foundation, gran<sup>t</sup> number CNS-2039342 and United States Air Force Office of Scientific Research, gran<sup>t</sup> number FA9550-21-1-0229.

**Data Availability Statement:** Not Applicable, the study does not report any data.

**Acknowledgments:** This work is supported by the U.S. National Science Foundation (NSF) via gran<sup>t</sup> CNS-2039342 and the U.S. Air Force Office of Scientific Research (AFOSR) Dynamic Data and Information Processing Program (DDIP) via gran<sup>t</sup> FA9550-21-1-0229. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the U.S. Air Force.

**Conflicts of Interest:** The authors declare no conflict of interest.
