# Interoperable IoMT Approach for Remote Diagnosis with Privacy-Preservation Perspective in Edge Systems

^{1}

^{2}

^{3}

^{4}

^{5}

^{6}

^{*}

## Abstract

**:**

## 1. Introduction

- To achieve interoperability for heterogeneous IIoT environments, reliable network connectivity is essential.
- To attain the best data collection from IIoT devices, the environment must be known with precision, and the data must be captured with suitable granularity. In this case, fault data are identified.
- To efficiently find the adaptive threshold, the sensed data should be analyzed in real time.
- To improve the network scalability for incorporating a large number of nodes, energy-efficient clusters should be formed.

#### 1.1. Contributions

#### 1.2. Organization

## 2. Related Works

#### 2.1. Energy-Efficient Network Models

#### 2.2. Security Using Blockchain

#### 2.3. Security without Blockchain

## 3. The Methodology

#### 3.1. Conceptual Model

#### 3.2. Secure Credentials (SCs)-Based Authentication

Algorithm 1: TWINE |

INPUT: ID, password, PUF OUTPUT: Secret key ${\mathit{S}}_{\mathit{K}}$ ${\mathit{Y}}_{\mathbf{\left(}\mathbf{64}\mathbf{\right)}}^{\mathbf{1}}\leftarrow {\mathit{T}}_{\mathit{P}}$ ${{\mathit{R}}_{\mathit{k}}}_{\mathbf{\left(}\mathbf{32}\mathbf{\right)}}^{1}\Vert \dots .\Vert {{\mathit{R}}_{\mathit{k}}}_{\mathbf{\left(}\mathbf{32}\mathbf{\right)}}^{\mathbf{35}}\leftarrow {\mathit{R}}_{\mathit{k}\mathbf{(}\mathbf{32}\mathbf{\times}\mathbf{36}\mathbf{)}}$ for $\mathit{i}\leftarrow \mathbf{1}\text{}\mathit{t}\mathit{o}\text{}\mathbf{35}$ do ${\mathit{Y}}_{\mathbf{0}\mathbf{\left(}\mathbf{4}\mathbf{\right)}}^{\mathit{i}}\Vert {\mathit{Y}}_{\mathbf{1}\mathbf{\left(}\mathbf{4}\mathbf{\right)}}^{\mathit{i}}\Vert ..\Vert {\mathit{Y}}_{\mathbf{14}\mathbf{\left(}\mathbf{4}\mathbf{\right)}}^{\mathit{i}}\Vert {\mathit{Y}}_{\mathbf{15}\mathbf{\left(}\mathbf{4}\mathbf{\right)}}^{\mathit{i}}\leftarrow {\mathit{Y}}_{\mathbf{\left(}\mathbf{64}\mathbf{\right)}}^{\mathit{i}}$ ${{\mathit{R}}_{\mathit{k}}}_{\mathbf{0}\mathbf{\left(}\mathbf{4}\mathbf{\right)}}^{\mathit{i}}\Vert {{\mathit{R}}_{\mathit{k}}}_{\mathbf{1}\mathbf{\left(}\mathbf{4}\mathbf{\right)}}^{\mathit{i}}\Vert \dots \Vert {{\mathit{R}}_{\mathit{k}}}_{\mathbf{1}\mathbf{\left(}\mathbf{4}\mathbf{\right)}}^{\mathit{i}}\leftarrow {{\mathit{R}}_{\mathit{k}}}_{\mathbf{\left(}\mathbf{32}\mathbf{\right)}}^{\mathit{i}}$ for $\mathit{j}\leftarrow \mathbf{0}\text{}\mathit{t}\mathit{o}\text{}\mathbf{7}$ do ${\mathit{Y}}_{\mathbf{2}\mathit{j}\mathbf{+}\mathbf{1}}^{\mathit{i}}\leftarrow \mathit{S}\mathbf{(}{\mathit{Y}}_{\mathbf{2}\mathit{j}}^{\mathit{i}}\mathbf{\u2a01}{{\mathit{R}}_{\mathit{k}}}_{\mathit{j}}^{\mathit{i}}\mathbf{)}\mathbf{\u2a01}{\mathit{Y}}_{\mathbf{2}\mathit{j}\mathbf{+}\mathbf{1}}^{\mathit{i}}$ for $\mathit{k}\leftarrow \mathbf{0}\text{}\mathit{t}\mathit{o}\text{}\mathbf{15}$ ${\mathit{Y}}_{\mathit{\rho}\mathbf{\left[}\mathit{k}\mathbf{\right]}}^{\mathit{i}\mathbf{+}\mathbf{1}}\leftarrow {\mathit{Y}}_{\mathit{k}}^{\mathit{i}}$ ${\mathit{Y}}^{\mathit{i}\mathbf{+}\mathbf{1}}\leftarrow {\mathit{Y}}_{\mathbf{0}}^{\mathit{i}\mathbf{+}\mathbf{1}}\Vert {\mathit{Y}}_{\mathbf{1}}^{\mathit{i}\mathbf{+}\mathbf{1}}\Vert ..\Vert {\mathit{Y}}_{\mathbf{14}}^{\mathit{i}\mathbf{+}\mathbf{1}}\Vert {\mathit{Y}}_{\mathbf{15}}^{\mathit{i}\mathbf{+}\mathbf{1}}$ for $\mathit{j}\leftarrow \mathbf{0}\text{}\mathit{t}\mathit{o}\text{}\mathbf{7}$ do ${\mathit{Y}}_{\mathbf{2}\mathit{j}\mathbf{+}\mathbf{1}}^{\mathbf{36}}\leftarrow \mathit{S}\mathbf{(}{\mathit{Y}}_{\mathbf{2}\mathit{j}}^{\mathbf{36}}\mathbf{\u2a01}{{\mathit{R}}_{\mathit{k}}}_{\mathit{j}}^{\mathbf{36}}\mathbf{)}\mathbf{\u2a01}{\mathit{Y}}_{\mathbf{2}\mathit{j}\mathbf{+}\mathbf{1}}^{\mathbf{36}}$ ${\mathit{S}}_{\mathit{K}}\leftarrow {\mathit{Y}}^{\mathbf{36}}$ |

_{p}). This algorithm of 64 bit in length provides ciphertext (C

_{T}) of 64 bit in length. It also has a round key (R

_{k}) value of 80 to 128 bit in length that is derived from S

_{k}. The TWINE algorithm includes a non-linear layer using a 4-bit diffusion layer and S-Boxes, and it permutes the 16 blocks. The round function is executed 36 times for providing S

_{k}. The permutation of the block indexes is $\rho :\left\{\mathrm{0,1},\dots 15\right\}\to \left\{\mathrm{0,1}\dots .15\right\}$, where the sub-block is mapped with the $\rho \left[j\right]$th subblock. We form the clusters by the information sensed from the Environment. In CH election, we consider the six factors: link quality (RSS value) $F1$, residual energy $F2$, no. of rounds reached (expected count) $F3$, fairness score according to geographical area (0-1) $F4$, coverage ratio $F5$ and node degree $F6$.

_{i}and Q

_{i}are considered with membership grades ${M}_{fP}\left(y\right)\text{}and{\text{}M}_{fQ}\left(y\right)$, and the zSlices-induced fuzzy sets are represented as follows:

Algorithm 2: Twine-LiteNet |

INPUT: ${\mathit{D}}_{\mathit{P}}$ OUTPUT: ${\mathit{E}}_{\mathit{D}}$ Begin { Initialize ${\mathit{D}}_{\mathit{P}}$ // convolutional layer for i from 1 to n do for j from 1 to n do{ encrypt the data packets ${\mathit{D}}_{\mathit{P}}$ using TWINE ${\mathit{Y}}_{\mathbf{64}}^{\mathbf{1}}\leftarrow {\mathit{D}}_{\mathit{P}}$ for $\mathit{i}\leftarrow \mathbf{1}\text{}\mathit{t}\mathit{o}\text{}\mathbf{35}$ do ${\mathit{Y}}_{\mathbf{2}\mathit{j}\mathbf{+}\mathbf{1}}^{\mathbf{36}}\leftarrow \mathit{S}\mathbf{(}{\mathit{Y}}_{\mathbf{2}\mathit{j}}^{\mathbf{36}}\mathbf{\u2a01}{{\mathit{R}}_{\mathit{k}}}_{\mathit{j}}^{\mathbf{36}}\mathbf{)}\mathbf{\u2a01}{\mathit{Y}}_{\mathbf{2}\mathit{j}\mathbf{+}\mathbf{1}}^{\mathbf{36}}$ ${\mathit{E}}_{\mathit{D}}\mathbf{\leftarrow}{\mathit{Y}}^{\mathbf{36}}$ } // Fully connected layer (Lite module, 2 dense layers, and softmax layer) for i from t to n do temp = 0 for j from 1 to n do $\mathit{t}\mathit{e}\mathit{m}\mathit{p}=\mathit{t}\mathit{e}\mathit{m}\mathit{p}+{\mathit{w}}_{\mathit{i}\mathit{j}}\times \mathit{X}\left[\mathit{j}\right]$ end for ${\mathit{Y}}_{\mathit{i}}=\mathit{t}\mathit{e}\mathit{m}\mathit{p}$ end for end for end for end |

Algorithm 3: SAR |

Population initialization in the range $({{\mathit{A}}_{\mathit{k}}}^{\mathit{m}\mathit{a}\mathit{x}},{{\mathit{A}}_{\mathit{k}}}^{\mathit{m}\mathit{i}\mathit{n}})$ Perform sorting and determine the best solution The routing matrix $\mathit{A}$ takes the first half of the sorted solution and the remaining to matrix $\mathit{R}$ Initialize $\mathit{S}\mathit{E}$$,\text{}\mathit{M}\mathit{F},$ and $\mathit{F}\mathit{N}\mathbf{=}\mathbf{0}$ While the end criterion is not fulfilled do $\mathrm{For}\text{}\mathit{x}\mathbf{=}\mathbf{1}$ to N do $\mathrm{Update}\text{}\mathit{H}$ using Equation (15) If rand<0.5 do Computation of the position of ${\mathit{x}}^{\mathit{t}\mathit{h}}$ route using Equation (17) Else Computation of the position of ${\mathit{x}}^{\mathit{t}\mathit{h}}$ route using Equation (18) End If Perform boundary conditions of ${\mathit{x}}^{\mathit{t}\mathit{h}}$ route by Equation (19) Updation of matrix $\mathit{R}$ and position of ${\mathit{x}}^{\mathit{t}\mathit{h}}$ route by (20) Updation of $\mathit{F}\mathit{N}$ If $\mathit{F}\mathit{N}>\mathit{M}\mathit{F}$ do ${\mathit{A}}_{\mathit{x}}$ is replaced with a random solution using Equation (21) End if Perform restart strategy End for Compute the current best position and update the previous best End while Return the best solution |

## 4. Results and Discussion

#### 4.1. Simulation Study

^{2}area and simulated using NS3.26. The machine runs Ubuntu 14.04 and has the NS-3 simulator loaded onto it. Initially, nodes consist of limited energy and are exhausted for each communication. Table 3 portrays the obtained simulation values for the implementation of interoperable network operations, and it represents the simulation parameters and descriptions.

#### 4.1.1. Impact of Throughput

#### 4.1.2. Impact of Energy Consumption

#### 4.1.3. Impact of Delay

#### 4.1.4. Impact of Packet Delivery Ratio

#### 4.1.5. Impact of Network Lifetime

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Ahmed, A.; Kleiner, M.; Roucoules, L. Model-Based Interoperability IoT Hub for the Supervision of Smart Gas Distribution Networks. IEEE Syst. J.
**2019**, 13, 1526–1533. [Google Scholar] [CrossRef] - Chen, S.; Wang, Z.; Zhang, H.; Yang, G.; Wang, K. Fog-based Optimized Kronecker-Supported Compression Design for Industrial IoT. IEEE Trans. Sustain. Comput.
**2020**, 5, 95–106. [Google Scholar] [CrossRef] - Ray, P.; Thapa, N.; Dash, D. Implementation and Performance Analysis of Interoperable and Heterogeneous IoT-Edge Gateway for Pervasive Wellness Care. IEEE Trans. Consum. Electron.
**2019**, 65, 464–473. [Google Scholar] [CrossRef] - Jaleel, A.; Mahmood, T.; Hassan, M.; Bano, G.; Khurshid, S.K. Towards Medical Data Interoperability Through Collaboration of Healthcare Devices. IEEE Access
**2020**, 8, 132302–132319. [Google Scholar] [CrossRef] - Jiang, D.; Wang, Y.; Lv, Z.; Wang, W.; Wang, H. An Energy-Efficient Networking Approach in Cloud Services for IIoT Networks. IEEE J. Sel. Areas Commun.
**2020**, 38, 928–941. [Google Scholar] [CrossRef] - Wang, J.; Jiang, C.; Zhang, K.; Hou, X.; Ren, Y.; Qian, Y. Distributed Q-Learning Aided Heterogeneous Network Association for Energy-Efficient IIoT. IEEE Trans. Ind. Inform.
**2020**, 16, 2756–2764. [Google Scholar] [CrossRef] - Awan, K.A.; Din, I.U.; Almogren, A.S.; Guizani, M.; Khan, S. StabTrust—A Stable and Centralized Trust-Based Clustering Mechanism for IoT Enabled Vehicular Ad-Hoc Networks. IEEE Access
**2020**, 8, 21159–21177. [Google Scholar] [CrossRef] - Alami, H.E.; Najid, A. ECH: An Enhanced Clustering Hierarchy Approach to Maximize Lifetime of Wireless Sensor Networks. IEEE Access
**2020**, 7, 107142–107153. [Google Scholar] [CrossRef] - Yu, R.; Xue, G.; Zhang, X. Provisioning QoS-Aware and Robust Applications in Internet of Things: A Network Perspective. IEEE/ACM Trans. Netw.
**2019**, 27, 1931–1944. [Google Scholar] [CrossRef] - Viriyasitavat, W.; Xu, L.D.; Bi, Z.; Hoonsopon, D.; Charoenruk, N. Managing QoS of Internet-of-Things Services Using Blockchain. IEEE Trans. Comput. Soc. Syst.
**2019**, 6, 1357–1368. [Google Scholar] [CrossRef] - Memon, R.A.; Li, J.; Nazeer, M.I.; Khan, A.; Ahmed, J. DualFog-IoT: Additional Fog Layer for Solving Blockchain Integration Problem in Internet of Things. IEEE Access
**2019**, 7, 169073–169093. [Google Scholar] [CrossRef] - Zheng, J.; Dong, X.; Liu, Q.; Zhu, X.; Tong, W. Blockchain-based secure digital asset exchange scheme with QoS-aware incentive mechanism. In Proceedings of the 2019 IEEE 20th International Conference on High Performance Switching and Routing (HPSR), Xi’an, China, 26–29 May 2019; IEEE: New York, NY, USA, 2019; pp. 1–6. [Google Scholar]
- Kafle, V.P.; Muktadir, A.H. Intelligent and Agile Control of Edge Resources for Latency-Sensitive IoT Services. IEEE Access
**2019**, 8, 207991–208002. [Google Scholar] [CrossRef] - Zhou, Z.; Yu, S.; Chen, W.; Chen, X. CE-IoT: Cost-Effective Cloud-Edge Resource Provisioning for Heterogeneous IoT Applications. IEEE Internet Things J.
**2020**, 7, 8600–8614. [Google Scholar] [CrossRef] - Xu, G.; Zhao, Y.; Jiao, L.; Feng, M.; Ji, Z.; Panaousis, E.; Chen, S.; Zheng, X. TT-SVD: An Efficient Sparse Decision Making Model with Two-way Trust Recommendation in the AI Enabled IoT Systems. IEEE Internet Things J.
**2020**, 8, 9559–9567. [Google Scholar] [CrossRef] - Firouzi, F.; Farahani, B.; Barzegari, M.; Daneshmand, M. AI-Driven Data Monetization: The other Face of Data in IoT-based Smart and Connected Health. IEEE Internet Things J.
**2019**, 9, 5581–5599. [Google Scholar] [CrossRef] - Lin, X.; Li, J.; Wu, J.; Liang, H.; Yang, W. Making Knowledge Tradable in Edge-AI Enabled IoT: A Consortium Blockchain-Based Efficient and Incentive Approach. IEEE Trans. Ind. Inform.
**2019**, 15, 6367–6378. [Google Scholar] [CrossRef] - García-Magariño, I.; Muttukrishnan, R.; Lloret, J. Human-Centric AI for Trustworthy IoT Systems with Explainable Multilayer Perceptrons. IEEE Access
**2019**, 7, 125562–125574. [Google Scholar] [CrossRef] - Shen, M.; Tang, X.; Zhu, L.; Du, X.; Guizani, M. Privacy-Preserving Support Vector Machine Training Over Blockchain-Based Encrypted IoT Data in Smart Cities. IEEE Internet Things J.
**2019**, 6, 7702–7712. [Google Scholar] [CrossRef] - Poloju, N.; Rajaram, A. Data mining techniques for patients healthcare analysis during COVID-19 pandemic conditions. J. Environ. Prot. Ecol.
**2022**, 23, 2105–2112. [Google Scholar] - Kalaivani, K.; Kshirsagarr, P.R.; Sirisha Devi, J.; Bandela, S.R.; Colak, I.; Nageswara Rao, J.; Rajaram, A. Prediction of biomedical signals using deep learning techniques. J. Intell. Fuzzy Syst.
**2023**, preprint. [Google Scholar] [CrossRef] - Andrew, J.; Mathew, S.S.; Mohit, B. A comprehensive analysis of privacy-preserving techniques in deep learning based disease prediction systems. J. Phys. Conf. Ser.
**2019**, 1362, 012070. [Google Scholar] [CrossRef] - Haseeb, K.; Abbas, N.; Saleem, M.Q.; Sheta, O.E.; Awan, K.; Islam, N. RCER: Reliable Cluster-based Energy-aware Routing protocol for heterogeneous Wireless Sensor Networks. PLoS ONE
**2019**, 14, e0222009. [Google Scholar] - Thangaramya, K.; Kulothungan, K.; Logambigai, R.; Selvi, M.; Ganapathy, S.; Kannan, A. Energy Aware Cluster and Neuro-Fuzzy Based Routing Algorithm for Wireless Sensor Networks in IoT. Comput. Netw.
**2019**, 151, 211–223. [Google Scholar] [CrossRef] - Verma, S.; Sood, N.; Sharma, A.K. Genetic Algorithm-based Optimized Cluster Head selection for single and multiple data sinks in Heterogeneous Wireless Sensor Network. Appl. Soft Comput.
**2019**, 85, 105788. [Google Scholar] [CrossRef] - Xiang, X.; Liu, W.; Wang, T.; Xie, M.; Li, X.; Song, H.; Liu, A.; Zhang, G. Delay and energy-efficient data collection scheme-based matrix filling theory for dynamic traffic IoT. EURASIP J. Wirel. Commun. Netw.
**2019**, 2019, 168. [Google Scholar] [CrossRef] - Zeng, M.; Huang, X.; Zheng, B.; Fan, X. A Heterogeneous Energy Wireless Sensor Network Clustering Protocol. Wirel. Commun. Mob. Comput.
**2019**, 2019, 7367281. [Google Scholar] [CrossRef] - Zhao, S.; Li, S.; Yao, Y. Blockchain Enabled Industrial Internet of Things Technology. IEEE Trans. Comput. Soc. Syst.
**2019**, 6, 1442–1453. [Google Scholar] [CrossRef] - Jang, J.; Jung, I.; Park, J.H. An effective handling of secure data stream in IoT. Appl. Soft Comput.
**2018**, 68, 811–820. [Google Scholar] [CrossRef] - Liu, Y.N.; Wang, Y.P.; Wang, X.F.; Xia, Z.; Xu, J.F. Privacy-preserving raw data collection without a trusted authority for IoT. Computer Networks. Comput. Netw.
**2018**, 148, 340–348. [Google Scholar] [CrossRef] - Nawaratne, R.; Alahakoon, D.; De Silva, D.; Chhetri, P.; Chilamkurti, N. Self-evolving intelligent algorithms for facilitating data interoperability in IoT environments. Future Gener. Comput. Syst.
**2018**, 86, 421–432. [Google Scholar] [CrossRef] - Mohanty, S.N.; Ramya, K.C.; Rani, S.S.; Gupta, D.; Shankar, K.; Lakshmanaprabu, S.K.; Khanna, A. An efficient Lightweight integrated Blockchain (ELIB) model for IoT security and privacy. Future Gener. Comput. Syst.
**2020**, 102, 1027–1037. [Google Scholar] [CrossRef] - Mughal, M.A.; Shi, P.; Ullah, A.; Mahmood, K.; Abid, M.; Luo, X. Logical Tree Based Secure Rekeying Management for Smart Devices Groups in IoT Enabled WSN. IEEE Access
**2019**, 7, 76699–76711. [Google Scholar] [CrossRef] - Iqbal, A.; Ullah, F.; Anwar, H.; Kwak, K.S.; Imran, M.; Jamal, W.; ur Rahman, A. Interoperable Internet-of-Things platform for smart home system using Web-of-Objects and cloud. Sustain. Cities Soc.
**2018**, 38, 636–646. [Google Scholar] [CrossRef] - Abbasi, M.A.; Memon, Z.A.; Durrani, N.M.; Haider, W.; Laeeq, K.; Mallah, G.A. A multi-layer trust-based middleware framework for handling interoperability issues in heterogeneous IoTs. Clust. Comput.
**2021**, 24, 2133–2160. [Google Scholar] [CrossRef] - Abou-Nassar, E.M.; Iliyasu, A.M.; El-Kafrawy, P.M.; Song, O.Y.; Bashir, A.K.; Abd El-Latif, A.A. DITrust chain: Towards blockchain-based trust models for sustainable healthcare IoT systems. IEEE Access
**2020**, 8, 111223–111238. [Google Scholar] [CrossRef] - Qaisar, S.M.; Khan, S.I.; Srinivasan, K.; Krichen, M. Arrhythmia classification using multirate processing metaheuristic optimization and variational mode decomposition. J. King Saud Univ. Comput. Inf. Sci.
**2023**, 35, 26–37. [Google Scholar] - Qaisar, S.M.; Aljefri, R. Event-driven time-domain elucidation of the power quality disturbances. Procedia Comput. Sci.
**2020**, 168, 217–223. [Google Scholar] [CrossRef] - Qaisar, S.M. Efficient mobile systems based on adaptive rate signal processing. Comput. Electr. Eng.
**2019**, 79, 106462. [Google Scholar] [CrossRef]

F1 | F2 | F3 | F4 | F5 | F6 | CH |
---|---|---|---|---|---|---|

Low | Low | Low | Low | Low | Low | No |

Low | Low | Medium | Medium | High | Medium | Yes |

Low | Medium | High | High | Medium | High | Yes |

Low | High | Medium | High | Low | High | Yes |

Low | Low | Low | Low | Medium | Low | No |

Low | Medium | Low | High | High | High | Yes |

Medium | Medium | Low | Low | Medium | High | Yes |

Medium | Low | Medium | High | High | High | Yes |

Medium | Low | Low | Low | Low | Low | No |

Medium | High | Medium | Low | Low | Low | No |

Medium | High | High | Medium | High | Medium | Yes |

Medium | Low | Medium | Low | Low | Medium | No |

High | High | High | High | High | High | Yes |

High | Low | Low | Low | Low | Low | No |

High | High | Medium | Medium | Low | Medium | Yes |

High | High | Low | Medium | Medium | High | Yes |

High | Low | Medium | Low | High | Low | No |

High | Low | Medium | Low | Low | Low | No |

Shuffle Values of Block | Hexadecimal Values of S-Box | |||
---|---|---|---|---|

$\mathit{j}$ | $\mathit{\rho}\left[\mathit{j}\right]$ | ${\mathit{\rho}}^{-1}\left[\mathit{j}\right]$ | y | S(y) |

0 | 5 | 1 | 0 | C |

1 | 0 | 2 | 1 | 0 |

2 | 1 | 11 | 2 | F |

3 | 4 | 6 | 3 | A |

4 | 7 | 3 | 4 | 2 |

5 | 12 | 0 | 5 | B |

6 | 3 | 9 | 6 | 9 |

7 | 8 | 4 | 7 | 5 |

8 | 13 | 7 | 8 | 8 |

9 | 6 | 10 | 9 | 3 |

10 | 9 | 13 | A | D |

11 | 2 | 14 | B | 7 |

12 | 15 | 5 | C | 1 |

13 | 10 | 8 | D | E |

14 | 11 | 15 | E | 6 |

15 | 14 | 12 | F | 4 |

Parameter | Value | |
---|---|---|

Imitation zone | $1000\times 1000$ m | |

Quantity of radar node | 100 | |

Deployment | Random | |

MAC layer | IEEE 802.15.4 | |

Control message | 20 bits | |

Original oomph of node | 750 J | |

Packet amount | 400 | |

Retransmission amount | 7 (Max) | |

Size of packet | 12 KB | |

Interval of packet | 10 µS | |

Communication range in sensor | 200 m | |

Rate of data | 88 Mbps (Max) | |

Slots amount | 16 | |

Slot period | 10 µS | |

SRO | $SE$ | 0.05 |

$MU$ | 70 D | |

R | 5 | |

Number of iterations | 100 | |

Number of rounds | 100 | |

Simulation time | 100 s |

Performance | EIR-CIoT | BDCS-IoMT | Scenario-1 | Scenario-2 | |
---|---|---|---|---|---|

Throughput (Kbps) | $307\pm 5.0$ | $346.4\pm 3.0$ | $464\pm 1.0$ | $530\pm 1.0$ | |

Energy consumption (J) | Number of nodes | $0.384\pm 0.05$ | $0.356\pm 0.03$ | $0.322\pm 0.01$ | $0.162\pm 0.01$ |

Simulation rounds | $30.33\pm 1.5$ | $28.166\pm 1.0$ | $25.833\pm 0.5$ | $12.5\pm 0.5$ | |

Delay (s) | $3.6\pm 0.5$ | $3.41\pm 0.3$ | $2.61\pm 0.1$ | $1.8\pm 0.01$ | |

Packet delivery ratio (%) | $59.8\pm 1.5$ | $61.6\pm 1.0$ | $78\pm 0.5$ | $89.4\pm 0.5$ | |

Network lifetime (s) | $2224\pm 5.0$ | $2561.8\pm 3.0$ | $4620\pm 1.0$ | $5700\pm 1.0$ |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Subramaniam, E.V.D.; Srinivasan, K.; Qaisar, S.M.; Pławiak, P.
Interoperable IoMT Approach for Remote Diagnosis with Privacy-Preservation Perspective in Edge Systems. *Sensors* **2023**, *23*, 7474.
https://doi.org/10.3390/s23177474

**AMA Style**

Subramaniam EVD, Srinivasan K, Qaisar SM, Pławiak P.
Interoperable IoMT Approach for Remote Diagnosis with Privacy-Preservation Perspective in Edge Systems. *Sensors*. 2023; 23(17):7474.
https://doi.org/10.3390/s23177474

**Chicago/Turabian Style**

Subramaniam, Erana Veerappa Dinesh, Kathiravan Srinivasan, Saeed Mian Qaisar, and Paweł Pławiak.
2023. "Interoperable IoMT Approach for Remote Diagnosis with Privacy-Preservation Perspective in Edge Systems" *Sensors* 23, no. 17: 7474.
https://doi.org/10.3390/s23177474