This paper investigates cloud-based mobile robotics, with a use case focusing on the flexible transportation in modern factories. The feasibility and performance of Autonomous Mobile Robots (AMRs) fully controlled from cloud over wireless connection are studied. We show that completely decoupling the closed-loop control of the robot from the robot’s embedded system and placing it into an edge cloud execution environment while sustaining the necessary KPIs is feasible. Moving the control logic into the cloud benefits from ease of maintenance of the control software and improved resiliency to software and hardware failures. Furthermore, it enables the physical platform and control intelligence to evolve separately from each other.

Resource allocation in wireless access networks has been an intensively researched topic recently: many proposed solutions tackle radio channel access and dynamic spectrum allocation, but traditional issues of queuing, bandwidth sharing and packet processing at wireless access points have been targeted as well. In most of the related work the competition for high quality of service is usually solved by central coordination among users via optimizing a specific target aspect of the overall communication. In this paper we take a turn and provide users with the possibility of resource allocation suggestions. We propose a wireless access sharing framework, in which users have a say in optimizing their quality of service on the long term, and we tackle its analysis with the tool set of stochastic game theory. Our findings show that greedy users become polite against their counterparts when the load is relatively low with the goal of preparing for situations with high load.

Safe and efficient Human-Robot Collaboration (HRC) is an essential feature of future Industry 4.0 production systems which requires sophisticated collision avoidance mechanisms with intense computation need. Digital twins provide a novel way to test the impact of different control decisions in a simulated virtual environment even in parallel. In addition, Virtual/Augmented Reality (VR/AR) applications can revolutionize future industry environments. Each component requires extreme computational power which can be provided by cloud platforms but at the cost of higher delay and jitter. Moreover, clouds bring a versatile set of novel techniques easing the life of both developers and operators. Can these applications be realized and operated on today's systems? In this demonstration, we give answers to this question via real experiments.

This paper investigates the possibilities of relaxing the wireless resource consumption of a remote robot cell control use case. We propose a Quality of Control (QoC)-aware wireless resource allocation strategy that is based on the categorization of the robotic arm movement phases into high and low QoC requiring movements. We evaluate our proposed method in a simulation environment against the local controlled based solution of the Agile Robotics for Industrial Automation Competition (ARIAC) [1]. The evaluation shows that 54% of the radio time on average can be saved without affecting the productivity of the robot cell.

In this paper, we propose an architecture for network traffic prioritization. This architecture is application and protocol agnostic. We also give an interface for this architecture which enables QoE triggered fast network interface switching without interrupting the communication sessions. We give an algorithm in order to minimize the cost of the frequent network changes (reordering and retransmissions) and evaluate its performance in simulations and demonstrating it on real Wi-Fi and cellular access network. To demonstrating the effectiveness of our QoE triggered switching approach we eliminate the stalling events on YouTube video playback.

Governing how network resources are shared among various traffic flows is not a completely solved problem. The ideal system can implement a wide variety of detailed and flexible policies; enforces those policies under all possible offered traffic combinations and scales well with the number of flows. In this paper we introduce a framework that approximates the ideal system well. It consists of flow-aware Packet Value marking at the edges and flow-unaware operation inside the network. We show that inside the network relatively simple scheduling and AQM algorithms operating solely on Packet Value are suitable to implement the policies set at the network edge. We also discuss, how the framework can be used to support resource sharing over radio networks (where the cost of transmission varies) and how it enables load balancing, resource balancing and optimal network-wide load distribution.

We propose an app agnostic QoE deduction method that can be used for on-line traffic prioritization. Our implemented method extracts QoE information directly from the screen of mobile devices and this information is utilized for video traffic prioritization to improve user QoE. We evaluate our method and show that it can realize 80-100% of the achievable gain of the state-of-the-art methods with 50% less traffic that needs to be prioritized to achieve this. We provide deployment alternatives for traffic prioritization and evaluate performance impact on achievable gain.

In this paper we propose evaluation models and calculate range of potential gains for enhancing the average web-browsing QoE of all the users in a mobile network with given capacity. We investigate a method that takes capacity from users during QoE insensitive periods. The gained capacity is distributed among users being in QoE affecting state. Our models of web page network prioritization show that the potential gain for QoE affecting periods highly depends on the correlation between data downloaded during QoE affecting and QoE insensitive periods. The numerical evaluation of wide range of scenarios with web traffic shows that the achievable gain is up to 104% i.e., halving the download time of QoE affecting part. Network prioritization of web pages has good potential to improve web-browsing QoE.

The introduction of High Speed Downlink Packet Access (HSDPA) presents new challenges to be solved in the UMTS Terrestrial Radio Access Network. Bandwidth reservation for HSDPA is not efficient and TCP cannot efficiently resolve a congestion situation because lower layer retransmissions hide the congestion situations from TCP. An HSDPA Flow Control algorithm was introduced by 3GPP to control congestion. This HSDPA Flow Control algorithm was originally intended to control radio scheduler queues in Node B. In this paper we propose an algorithm that also provides congestion control in the transport network. The performance analysis concentrates on transport network limited scenarios and shows that the algorithm can achieve high end-user perceived throughput, while maintaining low delay and loss in the transport network. The analysis also shows the advantages of the newly introduced congestion detection functionality.

Admission control algorithms used in access networks for multiplexed voice sources are typically based on aggregated system characteristics, such as aggregate loss probability. Even though the relation of these aggregate performance measures to the performance of specific flows is not trivial, it has gained limited attention so far. We propose an admission control method that provides flow level QoS guarantees. The proposed method is based on Gaussian approximation applied for the bufferless multiplexing model. We show that the flow level packet loss violation probability can be approximated as the quantile of a normal distribution and we give a method to calculate its mean and variance. The obtained admission control formula includes the moments of the activity factor distribution.

This paper considers the analysis of second-order Markov reward models. In these systems the reward accumulation during state sojourns is not deterministic, but follows a Brownian motion with a state dependent drift and variance parameter. We give the differential equations that describe the density function and the moments of the accumulated reward, and show the similarities compared to the first-order (ordinary) case. A randomization based numerical method is also presented which is numerically stable, has an error bound to control the precision, and allows the efficient analysis of large models. The computational cost of the proposed procedure is practically the same as the one of the analysis of first-order reward models, while the modeling power of second-order models is clearly larger.

We develop a call level model of UMTS core networks where calls belonging to one of the four UMTS service classes arrive randomly. Arriving calls are granted service depending on the call's service class, the required maximum and minimum-bandwidth, and the available network resources at the arrival instance. We use a Markov model of transmission links to derive GoS (blocking probability) and QoS (throughput) measures under two reasonable and technologically feasible bandwidth sharing policies. We conclude that one of these policies is able to provide GoS/QoS guarantees for a wide range of traffic mixes. We argue that the results are applicable to the all IP/MPLS based new UMTS architecture.