Fifth generation (5G) standalone (SA) mobile networks are rapidly gaining prominence worldwide, and becoming increasingly prevalent as the telecommunication industry standard. Most published work concerning 5G applications relies on open-source 5G radio access network (RAN) simulation and emulation tools to evaluate various concepts, algorithms, and use cases. However, these tools are not always accurate in conveying a realistic representation of real-world RAN performance and expected quality of service (QoS). This paper discusses the deployment of a 5G SA testbed supporting three different RAN scenarios of real and simulated deployments using open- source software, commercial-off-the-shelf (COTS) hardware, and software defined radios (SDRs). We experimentally evaluate the performance of these scenarios for the RAN and quantify their differences in terms of computational resource utilization, throughput, latency, coverage, and power consumption. Specifically, we explore the emulation and simulation tools’ ability to reflect realistic RAN performance and highlight the differences compared to the SDR-based deployment. Through this analysis, this paper provides insights into the performance of each approach and sheds light on the feasibility of using open- source software for 5G testing and experimentation.

Network slicing enables the provision of customized services in next-generation mobile networks. Accordingly, the network is divided into logically isolated networks that share underlying resources but are tailored to meet the distinct service requirements of their users. However, allocating the minimum necessary resources to satisfy slices’ requirements is challenging, particularly when the number of slices is variable or too large which is envisioned in Open RAN. State-of-the-art proposals leverage reinforcement learning (RL) algorithms; however, they suffer from over-provisioning and/or frequent violations of service-level agreement (SLA) due to the large and changing state and action spaces. This paper introduces a novel cooperative multi-agent RL algorithm for RAN slicing in Open RAN, designed to adapt to variable slice numbers and effectively scale as they grow. To train this model, we exploit a novel constrained RL algorithm that explicitly considers SLA constraints to maintain a decreasing SLA violation ratio during training. Our approach is compatible with the Open RAN architecture, allowing for feasible deployment in future mobile networks. CMARS surpasses RL methods in SLA satisfaction by 50% in large-scale slicing, using only 9% more resources. It has 8% fewer SLA violations and 19% lower resource consumption for a flexible number of slices.

In the rapidly evolving landscape of 5G and its successor technologies, the Next Generation Radio Access Network (NG-RAN) stands out as a transformative pillar. Functional splitting, a core concept in NG-RAN, splits the traditional base station into distinct functional entities, notably the Distributed Unit (DU), Centralized Unit (CU) and Radio Unit (RU). With flexible functional splitting, Infrastructure Providers (InPs) can dynamically allocate RAN resources to cater to each network slice's distinct throughput and latency demand. However, the problem of optimally selecting functional splits, placement of RAN functions in DU/CU with constrained computational capacities and determining routing paths present an NP-hard challenge. The coexistence of multiple slices on shared infrastructure may necessitate slice isolation for security, performance, and operational reasons, adding another layer of complexity. To address this multifaceted problem, we formulate an Integer Linear Programming (ILP) model that seeks to maximize the InP profit considering computation, virtual machine instantiation and routing costs. Using Gurobi optimizer, we show that optimal slice admission solutions directly impact InP profit and that enhanced computational capacities can increase the number of slices admitted.

We study queues with impatient customers and Processor Sharing (PS) discipline as well as other variants of PS discipline, namely, Discriminatory Processor Sharing (DPS) and Generalized Processor Sharing (GPS) disciplines, where customers have deadlines until the end of service (DES). Customers arrive according to a state-dependent Poisson process and have general impatience. Customers have exponential service times with state-dependent service rates. Analytical methods based on simple Markov chains are given for the performance analysis of such queues. The principal measures of performance are the steady-state probability of missing deadline and the steady-state probability of blocking. Similar results are obtained for related queues with Random Order Service (ROS) discipline where customers have deadlines until the beginning of service (DBS). In view of a lack of exact analytical results for First Come First Served (FCFS) queues with state-dependent rates, a highly accurate approximation method is also given for these latter queues. The efficacy and accuracy of the approach are illustrated by some numerical examples and simulation experiments.

Execution speed seriously bothers application developers and users for wearable devices such as Google Glass. Intensive applications like 3D games suffer from significant delays when CPU is busy. Energy is another concern when the devices are in low battery level, but users need them for urgency use. To ease such pains, one approach is to expand the computational power by cloud offloading. This paradigm works well when the available Internet access has enough bandwidth. Another way is to leverage nearby devices for computation-offloading, which is known as device-to-device (D2D) offloading. In this paper, we present Dandelion, a unified code offloading system for wearable computing. Such applications can leverage both the nearby devices and cloud for performance acceleration and energy efficiency. Dandelion is a novel generic code offloading system for wearable computing with a reference implementation on Google Glass. Dandelion includes a programmer-friendly framework based on Java annotation, a lightweight offloading service, and a runtime task scheduler to make offloading decisions. We design some wearable applications and several parallel execution benchmark methods for Dandelion performance evaluation. Extensive experiments on a testbed of Google Glass and Android phones demonstrate that Dandelion generally achieves over 5X execution speedup for local execution and can quickly recover from errors caused by network disruption.

We develop Hyperion a Wearable Augmented Reality (WAR) system based on Google Glass to access text information in the ambient environment. Hyperion is able to retrieve text content from users' current view and deliver the content to them in different ways according to their context. We design four work modalities for different situations that mobile users encounter in their daily activities. In addition, user interaction interfaces are provided to adapt to different application scenarios. Although Google Glass may be constrained by its poor computational capabilities and its limited battery capacity, we utilize code-level offloading to companion mobile devices to improve the runtime performance and the sustainability of WAR applications. System experiments show that Hyperion improves users ability to be aware of text information around them. Our prototype indicates promising potential of converging WAR technology and wearable devices such as Google Glass to improve people's daily activities.

Mobile ad hoc networks (MANETs) have been turned into very attractive area of research in the duration of recent years, whereas security is the most challenging point that they undergo. Cryptography is an essential solution for providing security within MANETs. However, storing all keys in every node, if practically possible, is inefficient in large scale MANETs due to some limitations such as memory or process capability. This paper extends our previous idea which was a novel probabilistic key management algorithm that stores only a few randomly chosen keys instead of all ones. In this paper, several different scenarios are proposed for key selection in which they are more practical, and then, the impact of each scenario on the performance and security metrics is analyzed. Results show that the proposed scenarios can reduce the path length in addition to keeping the network highly connected.