An Energy–Distance-Balanced Cluster Routing Protocol for Smart Microgrid IoT Sensing Networks

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper attempts to introduce a novel routing protocol, LEAD-RP (Link Efficiency and Available-Energy Driven Routing Protocol), for static-node IoT sensor networks in smart microgrids. Its first contribution is the incorporation of residual energy and distance factors in the selection of cluster heads and dynamic adjustment of cluster radius and head ratio to improve energy efficiency, stability, and lifetime of the network. Its strength lies in the wide exploration of energy consumption models and their testing using simulations against baseline protocols such as LEACH and HEED with better performance on important parameters such as data transmission, energy consumption, and cluster stability.
General observations
The approach, is not testbed-based or empirically validated and does not account for communication noise, environment heterogeneity, or heterogenous behavior of nodes. The debate on related work lacks a clear comparison of why current adaptive protocols will not work under microgrid constraints, which is absent, and the comparison among LEACH variants lacks a rigorous comparison beyond text description. There is also a lack of proper consideration for the limitations or failure modes of the proposed model, for instance, asymmetric node failure or time-varying interference.
The sources given do not have any recent empirical studies or implementation-focused studies on microgrids, especially those with actual sensor platforms or field testing. None of these topics are discussed or referred to by the authors when talking about reinforcement learning or federated approaches more suitable in contemporary WSNs. A rigorous critique of these sources would have been of assistance to reinforce the novelty aspect and place the contribution in a superior position compared to the state-of-the-art.
Specific comments
Lines 13–24: It would be useful to broaden the description of real-world practical concerns in microgrid settings in an effort to better situate the technical proposal.
Lines 39–50: A comparison of LEACH and its extensions within a summary table would be useful in enhancing clarity in noting their respective limitations.
Lines 67–75: The value and application of information-centric networking would be more substantiated with simulation results or case-specific demonstrations.
Lines 109–114: Include more detail on how LEAD-RP attains scalability through local knowledge—especially in dense deployments.
Lines 205–213: It would be preferable to substantiate the selection of energy model parameters with references to experimental or real-world sensor data.
Lines 223–230: Including an explicit statement about assumptions over spatial uniformity of nodes would also benefit displaying the dynamic cluster radius formula.
Lines 294–305: Explain possible trade-offs in computational complexity due to recalculating dynamic p in actual deployments.
Lines 355–357: Explain how the simulation handles stochastic variability—is there more than one trial per configuration averaged, etc.?
Lines 386–392: Add more explanation of how the protocol scales from 100 to 300 nodes beyond mere recounting of results.
Lines 439–443: Consider adding statistical tests of significance of performance differences discovered among protocols to add the power of comparative analysis.

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Dear authors, considering the proposed work, it presents contributions to sensor networks. However, there are some points that should be clarified, as well as results that need to be better discussed, and others that should be presented to truly demonstrate that the protocol achieves its objective of improving energy efficiency based on the allocation of head nodes by distance. I highlight below the points that should be analyzed:

1. Equation 1: the total energy Etx​ required by a node to transmit information — shouldn’t the transmission rate be taken into account, or am I mistaken?

2. The expression for the energy cost of transmitting information seems a bit simplistic. There are many wireless transmission parameters that should be considered, such as sensitivity, channel communication rate, transmission power, packet error rate, among others.

3. Figure 4 is missing (line 350), as well as another figure on line 393 — I believe this refers to Figure 5.

4. Lines 444–450: this paragraph is a conclusion rather than a result discussion, and should be moved to Section 5.

5. In the abstract, the authors state that the proposed protocol is capable of reducing energy consumption and improving energy efficiency in IoT applications. Through the results, it was shown that the method determines the ideal number of cluster heads; however, I felt a lack of more consistent results regarding energy efficiency/minimization.

6. Line 93: please present or reference works that demonstrate that LEACH-based clustering still dominates the design of Wireless Sensor Network protocols for the Internet of Things.

7. Line 289: The LEACH protocol defines a static proportion p of cluster heads relative to the total number of nodes, determined prior to network activation based on several factors. It is not sufficient to mention "several factors" — the authors should list the main ones and provide a brief explanation.

8. Equation 11: define what Kopt​ represents — is it the constant of reevaluation?

9. Equation 5: according to line 219, mEPX relates to the energy used to transmit m bits. Wouldn’t it also depend on the cluster node radius Ri, as implied by placing it within the integral?

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

1. The paper's core innovation lies in its energy-distance-balanced approach for cluster head selection, yet it fails to sufficiently differentiate LEAD-RP from contemporary protocols like CCIC-WSN [15] or EDsHEED [20]. While the dynamic cluster radius in Eq. 10 and adaptive CH ratio in Eq. 12 are theoretically justified, their superiority over machine learning-based alternatives e.g., reinforcement learning in [16] remains unquantified. Crucially, the RBAC-IoT-inspired security features mentioned in Section 3.3 lack implementation details and empirical validation, weakening the claimed novelty. To address this, the authors should conduct a side-by-side feature comparison with at least five recent protocols including SEP, DEEC, and AI-based methods and quantify the computational overhead of dynamic calculations versus energy savings.
2. A significant contradiction exists between the claimed "fully distributed operation" in Section 1 and the centralized computation of Ri​ in Eq. 10 and p in Eq. 12. This undermines the protocol’s applicability in large-scale microgrid deployments. The weight functions ω1​ and ω2​ in Eq. 16–17 using tanh lack empirical justification and sensitivity analysis. Furthermore, the calculation of Elocal​(i) in Eq. 14 requires global energy knowledge, which is unrealistic in distributed systems. The energy model also overlooks critical real-world factors like duty cycling, packet collisions, and retransmission costs. The authors should propose distributed alternatives for key calculations, validate weight functions through parameter sweeps, and extend the energy model to include MAC-layer behavior.
3. While LEAD-RP demonstrates 35–45% lifetime extension over LEACH in Table 4, its microgrid-specific value remains underdeveloped. Energy parameters in Table 1 lack alignment with real microgrid sensors, and there is no discussion of compatibility with industry standards like IEEE 2030.5. The impact on grid stability metrics e.g., frequency regulation during islanded transitions is unquantified. The authors should map energy parameters to commercial sensor specifications, include a case study with grid-connected/islanded transitions, and analyze how extended network lifetime improves control stability.

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The Authors have improved the paper significantly. It can be published.

Reviewer 2 Report

Comments and Suggestions for Authors

The observations and questions were met, article suitable for acceptance,

Reviewer 3 Report

Comments and Suggestions for Authors

I have no more comments.