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Chapter 9 Light POW for Smart Grid Communication

  • Chigurubattula Jatin , Pankam Sasaank , Jyothi Rahul Enrique and Kunta Sai Siddartha
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Cybersecurity Unlocked
This chapter is in the book Cybersecurity Unlocked

Abstract

We introduce a novel, energy-frugal proof-of-work (PoW) protocol tailored for Internet of Things-enabled smart grids. Our approach harnesses ESP32 microcontrollers paired with ACS712 current and ZMPT101B voltage sensors to execute succinct hash microbursts, followed by a swift challenge-response verification. Local peer-to-peer exchanges leverage ESP-NOW to maintain end-to-end latencies below 10 ms, while remote reporting is secured via MQTT over TLS. In benchmark tests, our PoW scheme consumed 73% less energy than conventional Wi-Fi-based methods and lowered computational overhead by 40% compared to existing lightweight authentication frameworks. Communication throughput reached 1,200 packets/min over ESP-NOW, markedly surpassing BLE’s 400 packets/min rate under comparable conditions. Comprehensive security analyses validate robust defenses against Sybil and replay attacks through nonce-based freshness checks and mutual device authentication. Scalability trials exhibit linear performance degradation as network size grows, confirming the protocol’s viability for extensive deployments.

Our findings demonstrate that this architecture deftly balances stringent energy constraints, ultra-low latency, and strong tamper resistance – ensuring reliable, real-time monitoring in smart grid environments without undermining device longevity or network responsiveness.

Abstract

We introduce a novel, energy-frugal proof-of-work (PoW) protocol tailored for Internet of Things-enabled smart grids. Our approach harnesses ESP32 microcontrollers paired with ACS712 current and ZMPT101B voltage sensors to execute succinct hash microbursts, followed by a swift challenge-response verification. Local peer-to-peer exchanges leverage ESP-NOW to maintain end-to-end latencies below 10 ms, while remote reporting is secured via MQTT over TLS. In benchmark tests, our PoW scheme consumed 73% less energy than conventional Wi-Fi-based methods and lowered computational overhead by 40% compared to existing lightweight authentication frameworks. Communication throughput reached 1,200 packets/min over ESP-NOW, markedly surpassing BLE’s 400 packets/min rate under comparable conditions. Comprehensive security analyses validate robust defenses against Sybil and replay attacks through nonce-based freshness checks and mutual device authentication. Scalability trials exhibit linear performance degradation as network size grows, confirming the protocol’s viability for extensive deployments.

Our findings demonstrate that this architecture deftly balances stringent energy constraints, ultra-low latency, and strong tamper resistance – ensuring reliable, real-time monitoring in smart grid environments without undermining device longevity or network responsiveness.

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Chapter 1 Emerging Cyber Threats: Challenges, Impacts, and Proactive Defenses in the Digital Age 1
  4. Chapter 2 Silent Guardians: Proactive Approaches to Modern Cyber Threats 31
  5. Chapter 3 Data Science for Threat Detection and Analysis 59
  6. Chapter 4 An Integrated Approach: Merging Cybersecurity, AI, and Threat Detection 87
  7. Chapter 5 Cybersecurity Analytics: A Review of Challenges and the Role of Machine Learning and Deep Learning in Threat Detection 103
  8. Chapter 6 Hardware-Based Authentication Techniques for Secure Data Transmission in IoT Edge Computing 141
  9. Chapter 7 Securing the IoT Networks Using a Deep Learning Paradigm for Intrusion Detection 161
  10. Chapter 8 Hybrid Malware Detection and Classification Using Explainable Deep Neural Network 177
  11. Chapter 9 Light POW for Smart Grid Communication 201
  12. Chapter 10 Zero Trust Architecture – A Beginner’s Guide 227
  13. Chapter 11 Post-quantum Cryptography for Enhanced Authentication in Mobile Data Communication: Resilience Against Quantum Attacks 265
  14. Chapter 12 Two-Factor Authentication (2FA) and Multi-factor Authentication (MFA) Solutions for Secure Mobile Data Communication 287
  15. Chapter 13 Artificial Intelligence and Machine Learning in Cybersecurity 313
  16. Chapter 14 Enhancing IoT Security with Zero Trust Networking: Protecting Wireless Sensors, Edge Devices, and Cloud Environments 343
  17. Chapter 15 Biometric Authentication Methods for Mobile Devices: Exploring Fingerprint, Face Recognition, and Iris Scanning 365
  18. Chapter 16 Robust Dynamic Voice-Based Key Generation Using Novel Fuzzy Extraction, Averaged Thresholding, and Hamming Enhancement Techniques 385
  19. Chapter 17 Enhancing Cybersecurity with Artificial Intelligence and Machine Learning Techniques 413
  20. Chapter 18 Firewall and IDS in Cybersecurity 439
  21. Index
https://doi.org/10.1515/9783111712895-009

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Chapter 1 Emerging Cyber Threats: Challenges, Impacts, and Proactive Defenses in the Digital Age 1
  4. Chapter 2 Silent Guardians: Proactive Approaches to Modern Cyber Threats 31
  5. Chapter 3 Data Science for Threat Detection and Analysis 59
  6. Chapter 4 An Integrated Approach: Merging Cybersecurity, AI, and Threat Detection 87
  7. Chapter 5 Cybersecurity Analytics: A Review of Challenges and the Role of Machine Learning and Deep Learning in Threat Detection 103
  8. Chapter 6 Hardware-Based Authentication Techniques for Secure Data Transmission in IoT Edge Computing 141
  9. Chapter 7 Securing the IoT Networks Using a Deep Learning Paradigm for Intrusion Detection 161
  10. Chapter 8 Hybrid Malware Detection and Classification Using Explainable Deep Neural Network 177
  11. Chapter 9 Light POW for Smart Grid Communication 201
  12. Chapter 10 Zero Trust Architecture – A Beginner’s Guide 227
  13. Chapter 11 Post-quantum Cryptography for Enhanced Authentication in Mobile Data Communication: Resilience Against Quantum Attacks 265
  14. Chapter 12 Two-Factor Authentication (2FA) and Multi-factor Authentication (MFA) Solutions for Secure Mobile Data Communication 287
  15. Chapter 13 Artificial Intelligence and Machine Learning in Cybersecurity 313
  16. Chapter 14 Enhancing IoT Security with Zero Trust Networking: Protecting Wireless Sensors, Edge Devices, and Cloud Environments 343
  17. Chapter 15 Biometric Authentication Methods for Mobile Devices: Exploring Fingerprint, Face Recognition, and Iris Scanning 365
  18. Chapter 16 Robust Dynamic Voice-Based Key Generation Using Novel Fuzzy Extraction, Averaged Thresholding, and Hamming Enhancement Techniques 385
  19. Chapter 17 Enhancing Cybersecurity with Artificial Intelligence and Machine Learning Techniques 413
  20. Chapter 18 Firewall and IDS in Cybersecurity 439
  21. Index
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