What does this research mean for the field?

Jointly optimizing dynamic Elliptic Curve Cryptography (ECC) curve selection with latency and resource allocation using a greedy heuristic drastically reduces replay attack vulnerability while maintaining near-optimal task success rates in vehicular edge networks. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To jointly optimize task offloading scheduling and dynamic cryptographic curve selection in vehicular edge networks while satisfying strict latency bounds and mitigating replay attacks.

September 10, 2026IoTOpen Access

NP-Hard Joint Latency and Security Optimization for Task Offloading in IoT-Enabled Vehicular Networks

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Authors

AAAshraf Alkhresheh

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Overview

Simulation study shows dynamic cryptographic curve selection cuts replay attack success to 8.93% in vehicular networks, indicating robust real-time security without sacrificing latency.

Key Points

To jointly optimize task offloading scheduling and dynamic cryptographic curve selection in vehicular edge networks while satisfying strict latency bounds and mitigating replay attacks.
Proved that the joint optimization of node assignment and security curve selection reduces to an NP-hard generalized assignment problem.
Formulated a unified scoring function combining latency, CPU load, bandwidth, and Elliptic Curve Cryptography (ECC) selection, solved using a polynomial-time greedy heuristic.
Benchmarked the heuristic against an exact integer linear programming (ILP) bound, latency-only scheduling, and static ECC baselines in a Python 3.9 simulation.
Dynamic ECC achieved an 85.26% task success rate compared to 85.58% for latency-only scheduling, while reducing replay attack success from 100% to 8.93%.
Static ECC produced an inferior 40.29% task success rate and an 84.54% replay attack success rate due to unboundedly accumulated staleness.
Cryptographic overhead ranged from 0.35 to 1.54 ms per task, with the greedy heuristic operating within approximately 2.34% of the exact ILP bound.

Cite This Study

Ashraf Alkhresheh (2026) studied this question.

synapsesocial.com/papers/6aa27a2a58559d80afc72d45 https://doi.org/10.3390/iot7030074

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September 10, 2026IoTOpen Access

NP-Hard Joint Latency and Security Optimization for Task Offloading in IoT-Enabled Vehicular Networks

View Full Paper

Ask AI

Bookmark

Authors

AAAshraf Alkhresheh

Discussion

Member takes

Overview

Simulation study shows dynamic cryptographic curve selection cuts replay attack success to 8.93% in vehicular networks, indicating robust real-time security without sacrificing latency.

Key Points

To jointly optimize task offloading scheduling and dynamic cryptographic curve selection in vehicular edge networks while satisfying strict latency bounds and mitigating replay attacks.
Proved that the joint optimization of node assignment and security curve selection reduces to an NP-hard generalized assignment problem.
Formulated a unified scoring function combining latency, CPU load, bandwidth, and Elliptic Curve Cryptography (ECC) selection, solved using a polynomial-time greedy heuristic.
Benchmarked the heuristic against an exact integer linear programming (ILP) bound, latency-only scheduling, and static ECC baselines in a Python 3.9 simulation.
Dynamic ECC achieved an 85.26% task success rate compared to 85.58% for latency-only scheduling, while reducing replay attack success from 100% to 8.93%.
Static ECC produced an inferior 40.29% task success rate and an 84.54% replay attack success rate due to unboundedly accumulated staleness.
Cryptographic overhead ranged from 0.35 to 1.54 ms per task, with the greedy heuristic operating within approximately 2.34% of the exact ILP bound.

Cite This Study

Ashraf Alkhresheh (2026) studied this question.

synapsesocial.com/papers/6aa27a2a58559d80afc72d45 https://doi.org/10.3390/iot7030074

View Full Paper

Ask AI

Bookmark