arXiv subjects

Mohammad Reza Heidarpour

Publications and source records attributed to Mohammad Reza Heidarpour.

6 recordsLinked to original sources

arXiv2026

A Dynamic UPF Fault Recovery Mechanism for Enhanced Resilience in 5G Core Networks

Ensuring fault tolerance in the User Plane Function is essential to maintain service continuity in 5G networks, particularly as latency-sensitive applications become more prevalent. This paper presents a novel mechanism for dynamic UPF failure detection and recovery within an OpenAirInterface-based 5G core environment. The approach introduces an application-layer session restoration mechanism to ensure service continuity and minimize disruptions during UPF failures. Experimental results demonstrate that the proposed mechanism reduces UPF downtime while maintaining low packet loss and enabling rapid service recovery. In tests with TCP and UDP traffic, connectivity was restored within a few seconds, minimizing service degradation. Furthermore, our failure-aware UPF selection mechanism improves long-term system resilience by considering recent failure history. The results highlight the effectiveness of our lightweight framework in improving the overall reliability of 5G networks, making it a valuable contribution to network robustness.

cs.NI↗

arXiv2026

CN2F: A Cloud-Native Cellular Network Framework

Upcoming cellular networks aim to improve the efficiency and flexibility of mobile networks by incorporating various technologies, such as Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Network Slicing (NS). There exist open-source projects that implement components of different cellular generations. In this paper, we elaborate on how to use these open-source projects to realize a flexible and extendable testbed for conducting experiments on the future generation of cellular networks. In particular, a Cloud-Native Cellular Network Framework (CN2F) is presented, which uses OpenAirInterface's codebase to generate cellular Virtual Network Functions (VNFs) and deploys Kubernetes to disperse and manage them among multiple worker nodes. Moreover, CN2F leverages ONOS and Mininet to emulate the effect of the IP transport networks in the fronthaul and backhaul of real-world cellular networks. Using CN2F, we implement different network scenarios, including Edge Computing (EC), Cloud Computing (CC), and Radio Access Network (RAN) slicing, to showcase the effectiveness of the proposed testbed for academia and industrial Research and Development (R&D) activities.

cs.NI↗

arXiv2025

Beyond Dedicated-Active: A General Reliability Provisioning Framework for SFC Placement in Fog Computing

The explosive growth of Internet of Things (IoT) devices has strained traditional cloud infrastructures, highlighting the need for low-latency and energy-efficient alternatives. Fog computing addresses this by placing computation near the network edge. However, limited and heterogeneous fog resources pose reliability challenges, especially for mission-critical applications. On the other hand, to improve flexibility, applications are deployed as Service Function Chains (SFCs), where each function runs as a Virtual Network Function (VNF). While scalable, this approach is more failure-prone than monolithic deployments, necessitating intelligent redundancy and placement strategies. This paper addresses the reliability-aware SFC placement problem over heterogeneous fog servers through the lens of reliability theory. We explore four redundancy strategies, combining shared vs. dedicated and active vs. standby modes, and propose a general framework to minimize latency and cost while meeting reliability and deadline constraints. The problem is formulated as an Integer Non-Linear Program (INLP), and two genetic algorithm (GA)-based solutions are developed. Simulation results show that shared-standby redundancy outperforms the conventional dedicated-active approach by up to 84%.

cs.NI↗

arXiv2022

A Branch-and-Price Approach to a Variant of the Cognitive Radio Resource Allocation Problem

Radio-frequency portion of the electromagnetic spectrum is a scarce resource. Cognitive radio technology has emerged as a promising solution to overcome the spectrum scarcity bottleneck. Through this technology, secondary users (SUs) sense the spectrum opportunities free from primary users (PUs), and opportunistically take advantage of these (temporarily) idle portions, known as spectrum holes. In this correspondence, we consider a variant of the cognitive radio resource allocation problem posed by Martinovic et al. in 2017. The distinguishing feature of this version of the problem is that each SU, due to its hardware limitations, imposes the requirement that the to-be-aggregated spectrum holes cannot be arbitrarily far from each other. We call this restriction as the Maximal Aggregation Range (MAR) constraint, and refer to this variant of the problem as the MAR-constrained hole assignment problem. The problem can be formalized as an NP-hard combinatorial optimization problem. We propose a novel binary integer linear programming (ILP) formulation to the problem. The number of constraints in this formulation is the number of spectrum holes plus the number of SUs. On the other hand, the number of binary decision variables in the formulation can be prohibitively large, as for each legitimate spectrum allocation to each SU, one variable is needed. We propose a branch-and-price (B&P) framework to tackle this challenge. This framework is in fact a branch-and-bound procedure in which at each node of the search tree, we utilize the so-called (delayed) column generation technique for solving the LP relaxation of the corresponding subproblem. As evidenced by the numerical results, the LP relaxation bounds are very tight. This allows for a very effective pruning of the search space. Compared to the previously suggested formulations, the proposed technique can require much less computational effort.

cs.DS↗

arXiv2021

Optimal Association Strategy of Multi-gateway Wireless Sensor Networks Against Smart Jammers

Engineers have numerous low-power wireless sensor devices in the current network setup for the Internet of Things, such as ZigBee, LoRaWAN, ANT, or Bluetooth. These low-power wireless sensors are the best candidates to transfer and collect data. But they are all vulnerable to the physical jamming attack since it is not costly for the attackers to run low power jammer sources in these networks. Having multiple gateways and providing alternative connections to sensors would help these networks to mitigate successful jamming. In this paper, we propose an analytical model to solve the problem of gateway selection and association based on a Stackelberg game, where the jammer is the follower. We first formulate the payoffs of both sensor network and attacker and then establish and prove the conditions leading to NASH equilibrium. With numerical investigation, we also present how our model can capture the performance of sensor networks under jamming with a varying number of gateways. Our results show that compared to the single gateway scenario, the network's throughput will improve by 26% and 60% when we deploy two and four gateways in the presence of a single jammer.

cs.GT↗

arXiv2021

Wide-AdGraph: Detecting Ad Trackers with a Wide Dependency Chain Graph

Websites use third-party ads and tracking services to deliver targeted ads and collect information about users that visit them. These services put users' privacy at risk, and that is why users' demand for blocking these services is growing. Most of the blocking solutions rely on crowd-sourced filter lists manually maintained by a large community of users. In this work, we seek to simplify the update of these filter lists by combining different websites through a large-scale graph connecting all resource requests made over a large set of sites. The features of this graph are extracted and used to train a machine learning algorithm with the aim of detecting ads and tracking resources. As our approach combines different information sources, it is more robust toward evasion techniques that use obfuscation or changing the usage patterns. We evaluate our work over the Alexa top-10K websites and find its accuracy to be 96.1% biased and 90.9% unbiased with high precision and recall. It can also block new ads and tracking services, which would necessitate being blocked by further crowd-sourced existing filter lists. Moreover, the approach followed in this paper sheds light on the ecosystem of third-party tracking and advertising.

cs.CR↗