Agent-controlled intelligent subnetworks are envisioned as an integral part of Beyond 5G (B5G) communications for automatically selecting distinct policies based on dynamic performance constraints. In the B5G era, energy efficiency will be a prominent feature of Radio Access Network (RAN) slicing for achieving sustainable networks and lowering operational costs while multiplexing users with heterogeneous Quality-of-Service (QoS) requirements. Considering the importance of Intelligent Transportation Systems (ITS) within the realm of B5G applications, in this paper, we propose the LazyRAN framework as an energy-efficient Radio Resource Block (RRB) allocation approach for multi-policy RAN slicing in B5G ITS edge networks. Initially, we focus on resource utilization efficiency and define Lazy Skip Markov Decision Process (LS-MDP) formulation for spectrum agents to individually perform fine and coarse stochastic control depending on performance requirements incorporating varying levels of laziness. Followingly, we propose LazyRAN framework that uses Bayesian Optimization (BO) based Offline Policy Selection (OPS) for optimality calculations in case of multiple slicing policies. Our framework enables efficient multi-policy evaluation employing both exploration and exploitation in the agent policy space. The OPS method utilizes a Gaussian Process (GP) surrogate function combining logged data with online agent interactions before searching for the best slicing policy with BO. Using an energy-aware approach, hybrid QoS reward per energy consumption (HQEC), we compare the performance of LazyRAN framework in centralized and decentralized settings considering diverse agent policies. Our results show that the proposed scheme can significantly improve energy utilization with greater HQEC and higher throughput measurements while satisfying hybrid QoS demands.

The ongoing shift towards electric vehicles and the simultaneous integration of sensor, computing and communication systems for automated driving pose challenges for the interplay between batteries and onboard autonomous mobility functions and services. Research on autonomous test vehicle (ATV) power consumption does not match the current realities of industry solutions brought to early markets worldwide. Current research predicts high power demands of commercial AVs, reflected in extrapolating data points of underlying hardware data sheets, simulations, or the piloting of ATVs. Although we can relay to significant power demands in ATVs, we identified different realities and higher efficiency in emerging industry vehicles. We show a counteracting reality in how AVs power demand is perceived due to a mismatch between ATVs and early market-ready AVs. To bridge the gap between research and practice we pose a power-demand model and core indicators to be considered in estimating power demands.

A limitation for collaborative robots (cobots) is their lack of ability to adapt to human partners, who typically exhibit an immense diversity of behaviors. We present an autonomous framework as a cobot’s real-time decision-making mechanism to anticipate a variety of human characteristics and behaviors, including human errors, toward a personalized collaboration. Our framework handles such behaviors in two levels: (1) short-term human behaviors are adapted through our novel Anticipatory Partially Observable Markov Decision Process (A-POMDP) models, covering a human’s changing intent (motivation), availability, and capability; (2) long-term changing human characteristics are adapted by our novel Adaptive Bayesian Policy Selection (ABPS) mechanism that selects a short-term decision model, e.g., an A-POMDP, according to an estimate of a human’s workplace characteristics, such as her expertise and collaboration preferences. To design and evaluate our framework over a diversity of human behaviors, we propose a pipeline where we first train and rigorously test the framework in simulation over novel human models. Then, we deploy and evaluate it on our novel physical experiment setup that induces cognitive load on humans to observe their dynamic behaviors, including their mistakes, and their changing characteristics such as their expertise. We conduct user studies and show that our framework effectively collaborates non-stop for hours and adapts to various changing human behaviors and characteristics in real-time. That increases the efficiency and naturalness of the collaboration with a higher perceived collaboration, positive teammate traits, and human trust. We believe that such an extended human-adaptation is a key to the long-term use of cobots.

Intelligent Transportation Systems (ITSs) are expected to have a profound impact on the quality of experience in future smart cities. Anomaly detection is an imperative for urban ITS applications to alleviate vulnerabilities that may cause accidents and fatal causalities. Previously proposed anomaly detection methods mostly require prior knowledge and domain specific training and/or optimization procedures. Therefore, in this work, we propose Competitive Learning based Anomaly Detection (CLAD) as a generic end-to-end approach for unsupervised anomaly detection using Auto Regressive Integrated Moving Average (ARIMA) forecasting model, data imaging and Centroid Neural Networks (CentNNs). Utilizing multi-dimensional time-series data obtained from diverse sensory measurements in the DIGINET-PS smart city infrastructure of TU Berlin, we compare performance of CLAD with unsupervised competitive learning as well as deep learning based anomaly detection techniques. Experimental results show that proposed approach results in higher detection accuracy and precision compared to other methods when multiple degrees of anomalies are considered.

Abstract This paper proposes a new family of algorithms for the online optimisation of composite objectives. The algorithms can be interpreted as the combination of the exponentiated gradient and p-norm algorithm. Combined with algorithmic ideas of adaptivity and optimism, the proposed algorithms achieve a sequence-dependent regret upper bound, matching the best-known bounds for sparse target decision variables. Furthermore, the algorithms have efficient implementations for popular composite objectives and constraints and can be converted to stochastic optimisation algorithms with the optimal accelerated rate for smooth objectives.

The complex interactions in advanced CCAM use cases that utilize C-V2X infrastructure require novel means to develop and test the solutions, especially when it comes to cross-border or cross-domain environments that consist of an overlay of multiple stakeholders. Accordingly this paper proposes a concept for a simulative / emulative platform for C-V2X applications in order to facilitate the development of advanced CCAM use cases. For this purpose we combine different simulation approaches with the in-vehicle C-V2X infrastructure, which allows us, based on realistic traffic conditions, to create additional emulated road users and test the integration of multi-operator C-V2X platforms.

Radio Access Network (RAN) slicing is getting increasing attention as a resource allocation technique for satisfying diverse Quality-of-Service (QoS) requirements in 5G vehicular networks. Hierarchical Reinforcement Learning (HRL), such as hierarchical-DQN (h-DQN), is a promising slice management approach that decomposes performance constraints into a subroutine hierarchy and uses Deep Reinforcement Learning (DRL) at different temporal scales for online-learning an optimal policy of bandwidth allocation. In this paper, we tackle RAN slicing problem in 5G vehicle-to-everything (V2X) communications and present h-DQN based Soft Slicing (HSS) method for model-free opportunistic slice management. HSS consists of a multi-controller learning framework where a high-level meta-controller takes state input for determining a subgoal and a low-level controller decides on the action based on the given subgoal and the state. We compare performance of HSS with model-free and model-based Reinforcement Learning (RL) methods in terms of Age of Information (AoI), service delay and network throughput. Our results show that proposed scheme improves sample-efficiency and outperforms traditional and RL-based V2X RAN slice management methods in terms of network utility maximization.

Connected and autonomous driving is one of the prominent vertical applications to showcase the capabilities of the 5G mobile network to transform and disrupt numerous industrial sectors and service chains. Cooperative autonomous mobility applications require ultra low latency communication, utilize massive broadband connections for vast numbers of connected devices, all of this under high mobility settings. Cloud and Edge Computing, artificial intelligence (AI), and the Internet of things (IoT) pave the path for the most important role of 5G: to attain an integration platform for the challenges of vertical applications. In this work, we first review these enabling features of 5G for autonomous driving and their integration in our autonomous driving testbed on urban public roads. We then propose an enhancement to the 5G vehicle to everything (V2X) architecture by incorporating the informationcentric communication paradigm. Our simulations of a proposed approach for integrated service discovery and placement in the high mobility settings of autonomous driving applications show improved service continuity and latency measured by hop counts to service endpoints and number of cache nodes. The results serve as a base line performance indicator for the practical implementation of the proposed approach.

The multicast communication concept offers a scalable and efficient method for many classes of applications; however, its potential remains largely unexploited when it comes to link-layer multicasting in wireless local area networks. The fundamental lacking feature for this is a transmission rate control mechanism that offers higher transmission performance and lower channel utilization, while ensuring the reliability of wireless multicast transmissions. This is much harder to achieve in a scalable manner for multicast when compared with unicast transmissions, which employs explicit acknowledgment mechanisms for rate control. This article introduces EWRiM, a reliable multicast transmission rate control protocol for IEEE 802.11 networks. It adapts the transmission rate sampling concept to multicast through an aggregated receiver feedback scheme and combines it with a sliding window forward error correction (FEC) mechanism for ensuring reliability at the link layer. An inherent novelty of EWRiM is the close interaction of its FEC and transmission rate selection components to address the performance-reliability tradeoff in multicast communications. The performance of EWRiM was tested in three scenarios with intrinsically different traffic patterns; namely, music streaming scenario, large data frame delivery scenario, and an IoT scenario with frequent distribution of small data packets. Evaluation results demonstrate that the proposed approach adapts well to all of these realistic multicast traffic scenarios and provides significant improvements over the legacy multicast- and unicast-based transmissions.

Recommender systems have strongly attracted the attention of the machine learning research community with prosperous real-life deployments in the last few decades. The performance and success of most applications developed in this domain highly depend on an elaborate selection of models and configuration of their hyperparameters. The international MoST-Rec 2019 workshop addresses the issues of algorithm selection and parameter tuning for recommender systems. The workshop aims to bring together researchers from the model selection and hyperparameter tuning community in the general scope of machine learning with researchers from the recommender systems community for discussing and exchanging recent advances and open challenges in the field.

We propose an architecture as a robot»s decision-making mechanism to anticipate a human»s state of mind, and so plan accordingly during a human-robot collaboration task. At the core of the architecture lies a novel stochastic decision-making mechanism that implements a partially observable Markov decision process anticipating a human»s state of mind in two-stages. In the first stage it anticipates the human»s task related availability, intent (motivation), and capability during the collaboration. In the second, it further reasons about these states to anticipate the human»s true need for help. Our contribution lies in the ability of our model to handle these unexpected conditions: 1) when the human»s intention is estimated to be irrelevant to the assigned task and may be unknown to the robot, e.g., motivation is lost, another assignment is received, onset of tiredness, and 2) when the human»s intention is relevant but the human doesn»t want the robot»s assistance in the given context, e.g., because of the human»s changing emotional states or the human»s task-relevant distrust for the robot. Our results show that integrating this model into a robot»s decision-making process increases the efficiency and naturalness of the collaboration.

Ubiquitous connectivity in smart city and smart home scenarios gives rise to new uses cases for local distribution of data to multiple devices. In wireless local area networks, the common mode of data transport even for such scenarios is unicast, despite the broadcast nature of wireless medium. This is mainly due to lack of efficient mechanisms for high-throughput reliable multicast transmissions. In this paper we present an adaptive multicast rate control mechanism for IEEE 802.11 wireless networks, which can effectively improve the performance of multicast transfers while tolerating an adjustable level of packet loss at the MAC layer. Then we adapt and employ NORM as the transport layer protocol to complement the proposed multicast transmission rate control, as a proof of concept for cross-layer flexible handling of rate control and reliability. We compare the performance of this multicast approach to the common practice of TCP-based unicast data distribution as well as to the NORM-based multicast distribution at the basic transmission rate. The proposed approach is shown to scale well and improve data distribution performance for any given network topology and multicast receiver group size.

Information and communication technologies (ICTs) have a profound impact on the current state and envisioned future of automobiles. This paper presents an overview of research on ICT-based support and assistance services for the safety of future connected vehicles. A general classification and a brief description of the focus areas for research and development in this direction are given under the titles of vehicle detection, road detection, lane detection, pedestrian detection, drowsiness detection, and collision avoidance. Following an overview and taxonomy of the reviewed research articles, a categorized literature survey of safety critical applications is presented in detail. Future research directions are also highlighted.

Attack graphs show possible paths that an attacker can use to intrude into a target network and gain privileges through series of vulnerability exploits. The computation of attack graphs suffers from the state explosion problem occurring most notably when the number of vulnerabilities in the target network grows large. Parallel computation of attack graphs can be utilized to attenuate this problem. When employed in online network security evaluation, the computation of attack graphs can be triggered with the correlated intrusion alerts received from sensors scattered throughout the target network. In such cases, distributed computation of attack graphs becomes valuable. This article introduces a parallel and distributed memory-based algorithm that builds vulnerability-based attack graphs on a distributed multi-agent platform. A virtual shared memory abstraction is proposed to be used over such a platform, whose memory pages are initialized by partitioning the network reachability information. We demonstrate the feasibility of parallel distributed computation of attack graphs and show that even a small degree of parallelism can effectively speed up the generation process as the problem size grows. We also introduce a rich attack template and network model in order to form chains of vulnerability exploits in attack graphs more precisely.

Future wireless and Internet paradigm are envisioned to be more intelligent, adaptive, recovering from transient faults and problems without service interruption, and contineously optimizing the use of resources. In this work, we present agent based network control and management framework that enables domain based dynamic policy formulation. The article discusses the proposed architecture that adapts the hierarchical interactions in the typical telecommunication chain in defining different types of cognitive agents running on specific network elements for realizing the end-to-end goals. We also discuss the communication model, different behaviors of the proposed framework, the developed software tools, and the experimental demonstrator that we developed for realizing this framework. We believe that the proposed framework can meet the requirements of adaptability, self-x functionalities, sustainability, etc. The paper also provide details on how the proposed framework may be applied for a representative case-study. The performance of the proposed approach is evaluated for different criteria and results are discussed.

User Centric Networks (UCN) empowers the endusers as providers rather than just consumers of the network. Cooperation incentives based on reputation and crediting mechanisms are certain core enabling factors of UCN among which ensuring a certain level of QoS plays a great role for the maintenance of the UCN environment. In this paper we propose a QoS-based crediting mechanism, providing incentives for access points (APs) to share the load, i.e. the clients, in a fair way, thereby ensuring a homogenous level of QoS throughout the UCN community. We discuss a utility-based decision making framework for the incentivized cooperation based load balancing mechanism in UCN environment.

With rapid developments in the community mesh networks and wireless sensor networks research, the need for more efficient channel access techniques for multi-hop wireless networks has become eminent. In this work, we propose a novel hybrid channel access scheme that spatially limits the contention in the network such that 2-hop neighbors access the channel contention-free among each other whereas only the immediate neighbors may contend among each other. The contention among neighbors can be handled much more efficiently by a basic CSMA protocol as if operating in a single-hop network. We provide a general framework for the SLICON scheme and compare its performance to the conventional RTS/CTS-based collision avoidance scheme. By a case study using the IEEE 802.11 protocol as the underlying CSMA protocol, the proposed scheme pleads itself as a more efficient alternative to the RTS/CTS based collision avoidance scheme for large and dense multi-hop ad hoc networks with stationary nodes, such as wireless mesh and sensor networks.

In this paper we define a new metric for quantifying the degree of anonymity collectively afforded to users of an anonymous communication system. We show how our metric, based on the permanent of a matrix, can be useful in evaluating the amount of information needed by an observer to reveal the communication pattern as a whole. We also show how our model can be extended to include probabilistic information learned by an attacker about possible sender-recipient relationships. Our work is intended to serve as a complementary tool to existing information-theoretic metrics, which typically consider the anonymity of the system from the perspective of a single user or message.