Seamless security apprise method for improving the reliability of sustainable energy-based smart home applications

Smart homes rely on sustainable energy sources and information technologies for providing ambient assisted living (AAL) support for humans. The significance of automation and energy optimization is prominent in providing secure and reliable control over in-home applications. Security is one of the drawbacks in smart home scenarios where the connected network of applications and sustainable energy sources are exposed to risk. This article introduces the seamless security apprises method (SSAM) to monitor the connected network's administered security measure. The proposed method monitors the lifetime of the administered security measures by verifying its exploitation and vulnerable levels in a periodic manner. In this verification process, the distributed security is classified for different operating and communicating states of the connected applications. For this state-based analysis, Q-learning is adapted to identify and update the state and its associated security measure. It reduces the early termination of the security measure due to identity thefts and provides continuous support for the operating applications. Therefore, sustainable energy resources are efficiently exploited without facing convergence in application functions. The cyber security features of the connected network are exploited in administering the security from a centralized platform. The proposed method's performance is verified using the verification time, security level, energy efficiency, and security loss.

Automated summary

Smart homes require sustainable energy and information technologies to provide ambient assisted living, with automation and energy optimization being crucial for secure control over in-home applications. This article presents a method for monitoring security measures in connected networks, verifying their lifespan and vulnerability levels periodically to ensure continuous support for operating applications. Through state-based analysis and Q-learning adaptation, the method aims to efficiently utilize sustainable energy resources and cybersecurity features for centralized security administration.