Physical Unclonable Functions (PUFs) are widely researched in the field of security because of their unique, robust, and reliable nature, PUFs are considered device-specific root keys that are hard to duplicate. There are many variants of PUFs that are being studied and implemented including hardware and software PUFs. Though PUFs are believed to be secure and reliable, they are not without challenges of their own. The efficient performance of PUF depends on various environmental factors, which leads to inefficiency. Bit flipping is one such problem that can bring down the reliability of the PUF. Memory-based PUFs are prone to unavoidable bit flips occurring in the hardware, similarly, sensor-based PUFs are prone to bit flips occurring due to temperature variation. The number of errors in the PUF response must be minimized to improve the reliability of the PUF in security applications. In this research we explore the Machine Learning (ML) model based on K-mer sequencing to detect and correct the bit flips in the PUFs, hence fortifying the PUF-based secure authentication system for authentication and authorization of Edge Data Centers (EDC) in a Collaborative Edge Computing (CEC) Environment.

Physical Unclonable Functions (PUFs) are widely studied for the security of devices in the largely heterogenous Internet-of-Things ecosystem. The need for low-power and low-cost yet robust and reliable security systems is of prime importance in resource-constrained environments like smart villages. Using PUFs as a security primitive has the limitation of environmental effects that lead to bit flipping in the PUF response, the challenge in using PUFs is to overcome the bit errors without adding to the area overhead or computational overhead. This research proposes a novel bit error detection and correction algorithm implemented using Federated Learning (FL). The error detection and correction model uses the N-gram concept of Natural Language Processing (NLP). The FL model is implemented on Flower AI, the global model gets the locally trained model's parameters, updates itself, and shares the updated models with all the local models. At the edge, the use of FL for model training and updating enhances the efficiency of the authentication system that uses PUF Challenge-Response Pairs (CRPs), reduces the area overhead and power consumption, and improves the security of the PUF-based authentication system.

In the era of ubiquitous computing, efficient resource allocation is critical to managing the diverse and dynamic environments created by interconnected devices. This paper presents a comprehensive comparative analysis of greedy and auction-based resource allocation algorithms within ubiquitous computing systems. The study examines the performance of these algorithms under three implementation approaches: centralized, decentralized, and hierarchical. We use a process-based discrete-event simulation model to evaluate the algorithms based on key performance metrics, including throughput, average response time, and energy utilization. Our results demonstrate that auction-based algorithms, particularly in a hierarchical implementation, consistently outperform greedy algorithms in both throughput and energy efficiency while maintaining competitive response times. The findings highlight the advantages of auction-based strategies in dynamically adapting to changing conditions and optimizing resource use in complex, decentralized environments. This research contributes valuable insights into optimal resource management strategies for ubiquitous computing, guiding system designers and researchers toward more efficient and scalable solutions. Future work should address the limitations of simplified network and energy models to enhance the applicability of these findings in real-world scenarios.

Collaborative Edge Computing (CEC) works on the distributed model, and is established at the Fog layer that consists of multiple edge devices like Edge Data Centers (EDCs), Edge Routers etc. In the CEC environment, the Edge layer has the capability of storing and processing data. Since the processing capacity is limited, many edge devices collaborate with each other to offload the processing in a scheme called Load Balancing. CEC enables applications in smart villages through task offloading/sharing, which calls for a trusted security system to make the resource sharing and information safe. Since the Edge is a resource-constrained environment where not all data centers are resourceful enough to implement computation intensive security systems. Physically Unclonable Functions (PUF) are a robust, secure, and light-weight solution for providing hard- ware security. PUFs are used to authenticate the EDCs during load balancing in a collaborative edge computingenvironment. Though PUFs are secure and difficult to remodel, the drawback lies in the storage of Challenge-Response Pairs (CRP) in a CRP database. The storage space for the CRP database becomes a concern when many EDCs participate in dynamic load balancing and each EDC needs to store a copy of the database. This research proposes a PUF based certificate Authority protocol for authentication of EDCs which will eliminate the need for CRP database storage while harnessing the security feature of the PUF. Further, in this research the effised authentication system is evaluated through oretical analysis and experimental results.