Mayank Pandey

Akhil Dev Mishra, Mayank Pandey

The 5G Core User Plane Function is responsible for packet forwarding, GTP-U decapsulation, and quality of service enforcement for every user data session. How the UPF behaves under simultaneous multi-slice workloads remains empirically uncharacterised in the open literature. Specifically, how its forwarding latency responds to load, how well it isolates one slice from another, and what timing budgets remain available for intelligent control are all open questions. This paper presents a measurement study conducted on a containerised open5GS deployment with three concurrent network slices. We design and implement a namespace-aware TC-BPF instrumentation framework that resolves the fundamental obstacle preventing existing tools from attributing latency observations to individual containerised network functions. We deploy eMBB, URLLC, and mMTC slices with realistic application traffic under light, medium, and heavy load conditions and collect approximately 28 million matched N3 to N6 forwarding delay pairs. The gathered results reveal that eMBB forwarding delay is load-sensitive with the 99th percentile growing from 574 to 1,243 microseconds across load conditions. URLLC delay is load-insensitive, confirming per-UPF process isolation. mMTC exhibits wide-tail TCP behaviour. On this platform, N4 PFCP session modification latency remains consistently below 200 microseconds regardless of data-plane load, suggesting substantial timing headroom within the two-millisecond budget assumed by AI-driven UPF orchestration designs. The instrumentation framework, experiment scripts, and dataset schema are released at https://github.com/MP-Akhil-5G/open5gs-slice-measurement.

Mayank Pandey, Rachit Agarwal, Sandeep Kumar Shukla et al.

In cryptocurrency-based permissionless blockchain networks, the decentralized structure enables any user to join and operate across different regions. The criminal entities exploit it by using cryptocurrency transactions on the blockchain to facilitate activities such as money laundering, gambling, and ransomware attacks. In recent times, different machine learning-based techniques can detect such criminal elements based on blockchain transaction data. However, there is no provision within the blockchain to deal with such elements. We propose a reputation-based methodology for response to the users detected carrying out the aforementioned illicit activities. We select Algorand blockchain to implement our methodology by incorporating it within the consensus protocol. The theoretical results obtained prove the restriction and exclusion of criminal elements through block proposal rejection and attenuation of the voting power as a validator for such entities. Further, we analyze the efficacy of our method and show that it puts no additional strain on the communication resources.

Mayank Pandey, Rachit Agarwal, Sandeep K. Shukla et al.

The advancement in the healthcare sector is entering into a new era in the form of Health 4.0. The integration of innovative technologies like Cyber-Physical Systems (CPS), Big Data, Cloud Computing, Machine Learning, and Blockchain with Healthcare services has led to improved performance and efficiency through data-based learning and interconnection of systems. On the other hand, it has also increased complexities and has brought its own share of vulnerabilities due to the heavy influx, sharing, and storage of healthcare data. The protection of the same from cyber-attacks along with privacy preservation through authenticated access is one of the significant challenges for the healthcare sector. For this purpose, the use of blockchain-based networks can lead to a considerable reduction in the vulnerabilities of the healthcare systems and secure their data. This chapter explores blockchain's role in strengthening healthcare data security by answering the questions related to what data use, when we need, why we need, who needs, and how state-of-the-art techniques use blockchains to secure healthcare data. As a case study, we also explore and analyze the state-of-the-art implementations for blockchain in healthcare data security for the COVID-19 pandemic. In order to provide a path to future research directions, we identify and discuss the technical limitations and regulatory challenges associated with blockchain-based healthcare data security implementation.