The ongoing evolution of future generations of mobile systems and fixed wireless networks is primarily motivated to enable high-bandwidth and low-latency demanding services in different vertical sectors. This endeavor is not fueled by smartphones alone, but technologies like industrial internet of things (IIoT), extended reality (XR), and human-to-machine (H2M) collaborations for fostering industrial and social revolutions like Industry 4.0/5.0 and Society 5.0. To ensure an ideal immersive experience and avoid cyber-sickness for the users in all the aforementioned usage scenarios, it is typically challenging to synchronize XR content from a remote machine to a human operator (HO) according to the head movements in real-time over communication networks. Thus, we propose a novel H2M collaboration scheme where the HO’s head movements are predicted ahead with very high accuracy to orient the machine’s camera in advance. As XR frame size varies in accordance with the HO’s head movements, we predict the corresponding bandwidth requirements from the machine’s camera to propose a human-machine coordinated dynamic bandwidth allocation (HMC-DBA) scheme. Through extensive simulations, we show that end-to-end latency and jitter requirements of XR frames are satisfied over enterprise networks like Fiber-To-The-Room-Business. Furthermore, we show that better efficiency in network resource utilization is achieved by employing our proposed HMC-DBA over state-of-the-art schemes.

The operational security of latency-sensitive networked applications is increasingly threatened by evolving malicious attacks that compromise operational integrity and network performance. Human-to-machine (H2M) applications, which rely on seamless bidirectional control signals and haptic feedback transmission, exemplify such latency-sensitive use cases. Existing learning-based malicious attack detection frameworks suffer from their reliance on pre-trained datasets, making machine learning models within them ineffective against previously unseen attack patterns. As attack profiles dynamically evolve, static models become obsolete, necessitating adaptive mechanisms to maintain detection accuracy. In this context, concept drift adaptation will serve as a critical tool for enabling models to continuously adjust to changing traffic distributions and emerging attack patterns. However, real-world H2M applications lack access to accurately labeled malicious traffic data, making real-time adaptation of defense mechanisms infeasible. To address these challenges, we propose a Concept Drift Adaptation-facilitated malicious attack Defense framework (CDAD). Firstly, CDAD employs Adaptive Random Forest as an incremental learning approach, integrating an error-rate-based concept drift detection mechanism to dynamically identify evolving attack patterns and trigger adaptive model updates. Secondly, a haptic behavior classifier is introduced to classify expected human operator interactions and compare them with real-time haptic feedback from remote machines. This enables automated traffic relabeling, allowing CDAD to adapt to previously unseen attacks without relying on pre-labeled datasets. The superior performance of CDAD over existing state-of-the-art methods is demonstrated across various malicious attack scenarios through extensive simulations. Results show that with CDAD, the attack success rate can be limited to 3%, while maintaining an inference time below 1ms, thereby ensuring effective and efficient malicious attack defense in latency-sensitive H2M applications.

The open-radio access network (O-RAN) embraces cloudification and network function virtualization for base-band function processing by dis-aggregated radio units (RUs), distributed units (DUs), and centralized units (CUs). These enable the cloud-RAN vision in full, where multiple mobile network operators (MNOs) can install their proprietary or open RUs, but lease on-demand computational resources for DU-CU functions from commonly available open-clouds via open x-haul interfaces. In this paper, we propose and compare the performances ofmin-max fairnessandVickrey-Clarke-Groves (VCG) auction-based x-haul and DU-CU resource allocation mechanisms to create a multi-tenant O-RAN ecosystem that is sustainable for small, medium, and large MNOs. The min-max fair approachminimizes the maximum OPEX of RUsthrough cost-sharing proportional to their demands, whereas the VCG auction-based approachminimizes the total OPEX for all resources utilized while extracting truthful demands from RUs. We consider time-wavelength division multiplexed (TWDM) passive optical network (PON)-based x-haul interfaces where PON virtualization technique is used to flexibly provide optical connections among RUs and edge-clouds at macro-cell RU locations as well as open-clouds at the central office locations. Moreover, we design efficient heuristics that yield significantly better economic efficiency and network resource utilization than conventional greedy resource allocation algorithms and reinforcement learning-based algorithms.

The recently proposed open-radio access network (O-RAN) architecture embraces cloudification and network function virtualization techniques to perform the base-band function processing by dis-aggregated radio units (RUs), distributed units (DUs), and centralized units (CUs). This enables the cloud-RAN vision in full, where mobile network operators (MNOs) could install their proprietary RUs, but then lease on-demand computational resources for the processing of DU and CU functions from commonly available open-cloud (O-Cloud) servers via open x-haul interfaces due to variation of load over the day. This creates a multi-tenant scenario where multiple MNOs share networking as well as computational resources. In this paper, we propose a framework that dynamically allocates x-haul and DU/CU resources in a multi-tenant O-RAN ecosystem with min-max fairness guarantees. This framework ensures that a maximum number of RUs get sufficient resources while minimizing the OPEX for their MNOs. Moreover, in order to provide an access network architecture capable of sustaining low-latency and high capacity between RUs and edge-computing devices, we consider time-wavelength division multiplexed (TWDM) passive optical network (PON)-based x-haul interfaces where the PON virtualization technique is used to provide a direct optical connection between end-points. This creates a virtual mesh interconnection among all the nodes such that the RUs can be connected to the Edge-Clouds at macro-cell RU locations as well as to the O-Cloud servers at the central office locations. Furthermore, we analyze the system performance with our proposed framework and show that MNOs can operate with a better cost-efficiency than baseline greedy resource allocation with uniform cost-sharing.

The fifth-generation of mobile radio technologies is expected to be agile, flexible, and scalable while provisioning ultra-reliable and low-latency communication (uRLLC), enhanced mobile broadband (eMBB), and massive machine type communication (mMTC) applications. An efficient way of implementing these is by adopting cloudification, network function virtualization, and network slicing techniques with open-radio access network (O-RAN) architecture where the base-band processing functions are disaggregated into virtualized radio unit (RU), distributed unit (DU), and centralized unit (CU) over front/mid-haul interfaces. However, cost-efficient solutions are required for designing front/mid-haul interfaces and time-wavelength division multiplexed (TWDM) passive optical network (PON) appears as a potential candidate. Therefore, in this paper, we propose a framework for the optimal placement of RUs based on long-term network statistics and connecting them to open access-edge servers for hosting the corresponding DUs and CUs over front/mid-haul interfaces while satisfying the diverse QoS requirements of uRLLC, eMBB, and mMTC slices. In turn, we formulate a two-stage integer programming problem and time-efficient heuristics for users to RU association and flexible deployment of the corresponding DUs and CUs. We evaluate the O-RAN deployment cost and latency requirements with our TWDM-PON-based framework against urban, rural, and industrial areas and show its efficiency over the optical transport network (OTN)-based framework.

Cloud-based computing technology is one of the most significant technical advents of the last decade and extension of this facility towards access networks by aggregation of cloudlets is a step further. To fulfill the ravenous demand for computational resources entangled with the stringent latency requirements of computationally-heavy applications related to augmented reality, cognitive assistance and context-aware computation, installation of cloudlets near the access segment is a very promising solution because of its support for wide geographical network distribution, low latency, mobility and heterogeneity. In this paper, we propose a novel framework, Cloudlet Cost OptiMization over PASSIve Optical Network (CCOMPASSION), and formulate a nonlinear mixed-integer program to identify optimal cloudlet placement locations such that installation cost is minimized whilst meeting the capacity and latency constraints. Considering urban, suburban and rural scenarios as commonly-used network deployment models, we investigate the feasibility of the proposed model over them and provide guidance on the overall cloudlet facility installation over optical access network. We also study the percentage of incremental energy budget in the presence of cloudlets of the existing network. The final results from our proposed model can be considered as fundamental cornerstones for network planning with hybrid cloudlet network architectures.

In the post-4G era, "low latency" has become one of the most important network requirements along with support for ultra-high capacity and ultra-high reliability. This has led to the evolution of "cloudlets" to support similar services provided by legacy cloud technology viz., storage capacity and computational capability. In this paper, we propose a novel framework for cloudlet-empowered-cloud network design and planning, based on optical access infrastructures. Our focus is on network planning to optimize network infrastructure cost by formulating a nonlinear programming model to identify placement locations of cloudlet servers subjected to capacity and latency constraints. We demonstrate the feasibility of the proposed model against urban, suburban and rural scenarios, providing guidance on the installation and maintenance costs. Furthermore, we assess the percentage of incremental energy arising from the presence of cloudlets in the optical access network. The proposed framework is a first in yielding insights that will serve as a foundation for further cloudlet network planning strategies.