Distributed Edge Resilience for Proactive Jammer Defense
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Solution Overview
Problem
Existing edge computing systems face challenges in protecting against adversarial jamming attacks, which disrupt communication between legitimate transceivers and can severely hamper distributed artificial intelligence systems, particularly in cellular wireless networks, leading to service denial and model training disruptions.
Innovation Solution
A distributed edge resilience enhancement program that incorporates reinforcement learning-based joint optimization of wireless physical layer parameters and artificial intelligence system parameters to provide proactive jammer-resilient training and smart defense scheduling, including techniques like frequency hopping and frequency band switching, to counter adversarial jamming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If edge computing systems operate in wireless environments, then processing speed and data analysis capability are improved, but vulnerability to adversarial jamming attacks increases
Solution Approach 1:
The system performs preliminary detection of jamming conditions and proactively switches to alternative communication channels before complete communication failure occurs. The edge computing system monitors wireless channel quality and pre-empts jamming attacks by having backup channels ready, thus maintaining processing capabilities while mitigating the harmful effect of jamming.
Solution Approach 2:
The system dynamically changes communication parameters such as frequency, modulation scheme, and transmission power in response to detected jamming conditions. By adapting these parameters, the edge computing system maintains its processing speed and productivity even in the presence of adversarial jamming attacks.
2Reliability
If distributed artificial intelligence systems are deployed in cellular wireless networks, then computational power and data storage are brought closer to users, but service denial and model training disruptions occur
Solution Approach 1:
The system implements continuous feedback loops that monitor the health of distributed AI computations and wireless communication channels. When jamming is detected or communication degradation occurs, the system receives feedback and automatically adjusts computation offloading strategies, switching between edge and cloud processing to maintain model training efficiency and computational availability.
Solution Approach 2:
The distributed AI system dynamically adapts its architecture and computation placement in response to changing wireless conditions. The system can flexibly shift computational tasks between different edge nodes, cloud servers, or local devices based on real-time channel quality and jamming conditions, thus maintaining reliability and productivity.
3Reliability
If proactive defense mechanisms are deployed against jamming, then resilience is improved, but system complexity and deployment time increase
Solution Approach 1:
The edge computing system performs self-diagnosis and self-healing in response to jamming attacks. The system automatically detects communication degradation, identifies jamming conditions, and executes corrective actions such as channel switching or computation rescheduling without requiring complex external control systems, thus reducing overall system complexity while maintaining resilience.
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for distributed edge resilience enhancement is provided. The embodiment may include identifying an adversarial jammer is causing an impact on a wireless system. The embodiment may also include generating a risk assessment of impact caused by the adversarial jammer to a user. The embodiment may further include identifying an action to apply based on the risk assessment. The embodiment may also include performing the identified action.