Device-to-Device (D2D) relaying is considered a promising technology to increase the data rates in next generation networks. We consider the D2D relay selection problem in which cell-edge mobile devices (CMDs), having bad channel conditions to the access point (AP), may forward their data to the AP via relaying mobile devices (RMDs) with better channel conditions. For this purpose, the RMDs sacrifice a fraction of their communication bandwidth and energy to relay the data of the CMDs. A key challenge in D2D relaying is to increase the willingness of RMDs to act as relays to CMDs. To overcome this challenge, considering the technical perspective of bandwidth allocation and transmit power optimization is not enough. In addition, the social perspective is important with the users' different individual motivations to participate, such as strong social relationships between CMDs and RMDs and an altruistic motivation to help CMDs. In this paper, we address the D2D relay selection problem with a socio-technical approach, i.e., we consider the RMDs as individual decision makers whose participation decision is influenced by its preferences regarding technical and social motivations. Furthermore, we formulate a relay selection problem to maximize the expected capacity under the a priori unknown decisions of the RMDs regarding their participation. To solve this problem, we propose a novel decentralized, preference-aware D2D relay selection algorithm, termed DPA-D2D, which is based on game theory. We show that the CMDs' capacity gain is more than 40 % higher compared to state-of-the-art D2D relay selection algorithms.

Multi-access edge computing (MEC) refers to deploying computation resources, known as cloudlets or edge servers, near the edge of the mobile network. Services like augmented reality (AR) benefit from MEC by service placement, which refers to installing service-specific software and allocating resources on cloudlets. Service placement in MEC improves service quality and potentially reduces costs compared to centralized cloud computing approaches. The main stakeholders in MEC are infrastructure providers (IPs), who manage the MEC infrastructure, and service providers (SPs), who offer services to users. Both have unique technical and economic perspectives, such as resource demands, resource availability, and costs. Information asymmetries exist as only IPs have access to information about their resources, and only SPs have information about service usage and resource demands. This work addresses challenges of service placement in MEC from a multi-stakeholder, techno-economic perspective. We introduce a model including the stakeholders’ technical and economic goals and information asymmetries. To solve this problem efficiently, we propose a multi-stakeholder bargaining mechanism, termed Nash Backward Induction with Linear Equilibrium Strategies (NBI-LES). In a case study with 544 users and 16 SPs, we achieve$\text{79}{\%}$of the optimal reduction in traffic given by a centralized optimal service placement strategy.

In Mobile Crowdsensing (MCS) a mobile crowd-sensing platform (MCSP) collects sensing data from mobile units (MUs) in exchange for payment. The MCSP broadcasts a list of available sensing tasks. Based on this list, each MU solves a task proposal problem to decide which task it is willing to perform and sends a proposal to the MCSP. Based on the MUs' proposals, the MCSP solves a task assignment problem. There are two challenges when finding efficient task proposal strategies for the MUs and an efficient task assignment strategy for the MCSP (i) The techno-economic perspective of MCS: From the technical perspective, MCS should maximize the data quality while minimizing time and energy consumption. From the economic perspective, there are two sides, the MUs and the MCSP which act as selfish decision-makers, who aim at maximizing their own income. (ii) Incomplete information at two sides: Initially, the MCSP does not know the expected data quality and the MUs do not know the expected effort required for task completion. To overcome these challenges, we propose a novel Two-Sided Learning (TSL) approach. At the MU side, TSL is based on an innovative gradient-based multi-armed bandit solution to maximize the MUs' utility under incomplete information about the strategies of other MUs. At the MCSP side, a learning strategy is used to find the task assignment strategy that maximizes its utility. Simulation results show that TSL achieves near-optimal social welfare, which is the sum of MUs' and MCSP's utilities, and a near-optimal energy efficiency.

The problem of coordinated data collection is studied for a mobile crowdsensing (MCS) system. A mobile crowdsensing platform (MCSP) sequentially publishes sensing tasks to the available mobile units (MUs) that signal their willingness to participate in a task by sending sensing offers back to the MCSP. From the received offers, the MCSP decides the task assignment. A stable task assignment must address two challenges: the MCSP’s and MUs’ conflicting goals, and the uncertainty about the MUs’ required efforts and preferences. To overcome these challenges a novel decentralized approach combining matching theory and online learning, called collision-avoidance multi-armed bandit with strategic free sensing (CA-MAB-SFS), is proposed. The task assignment problem is modeled as a matching game considering the MCSP’s and MUs’ individual goals while the MUs learn their efforts online. Our innovative “free-sensing” mechanism significantly improves the MU’s learning process while reducing collisions during task allocation. The stable regret of CA-MAB-SFS, i.e., the loss of learning, is analytically shown to be bounded by a sublinear function, ensuring the convergence to a stable optimal solution. Simulation results show that CA-MAB-SFS increases the MUs’ and the MCSP’s satisfaction compared to state-of-the-art methods while reducing the average task completion time by at least 16%.

Enabling quantum switches (QSs) to serve requests submitted by quantum end nodes in quantum communication networks (QCNs) is a challenging problem due to the heterogeneous fidelity requirements of the submitted requests and the limited resources of the QCN. Effectively determining which requests are served by a given QS is fundamental to foster developments in practical QCN applications, like quantum data centers. However, the state-of-the-art on QS operation has overlooked this association problem, and it mainly focused on QCNs with a single QS. In this paper, the request-QS association problem in QCNs is formulated as a matching game that captures the limited QCN resources, heterogeneous application-specific fidelity requirements, and scheduling of the different QS operations. To solve this game, a swap-stable request-QS association (RQSA) algorithm is proposed while considering partial QCN information availability. Extensive simulations are conducted to validate the effectiveness of the proposed RQSA algorithm. Simulation results show that the proposed RQSA algorithm achieves a near-optimal (within 5%) performance in terms of the percentage of served requests and overall achieved fidelity, while outperforming benchmark greedy solutions by over 13%. Moreover, the proposed RQSA algorithm is shown to be scalable and maintain its near-optimal performance even when the size of the QCN increases.

In multi-access edge computing (MEC), mobile users (MUs) can offload computation tasks to nearby computational resources, which are owned by a mobile network operator (MNO), to save energy. In this work, we investigate two important challenges of task offloading in MEC: (i) The techno-economic interactions of the MNO and the MUs. The MNO faces a profit maximization problem, whereas the MUs face an energy minimization problem. (ii) Limited information at the MUs about the MNO's communication and computation resources and the task offloading strategies of other MUs. To overcome these challenges, we model the task offloading problem as a matching game between the MUs and the MNO including their techno-economic interactions. Furthermore, we propose a novel Collision-Avoidance Task Offloading Multi-Armed-Bandit (CA-TO-MAB) algorithm, that allows the MUs to learn the amount of available resources at the MNO and the task offloading strategies of other MUs in an online, fully decentralized way. We show that by using CA-TO-MAB, the cumulative revenue of the MNO can be increased by 25% and, at the same time the energy consumption of the MUs can be reduced by 6% compared to state-of-the-art online learning algorithms for task offloading. Furthermore, the communication overhead can be reduced by 55% compared to a non-learning game-theoretic approach.

We present a novel multi-hop data dissemination protocol for wireless networks that minimizes the total energy consumption across an entire network by minimizing the transmission power at each hop. It is based on a game-theoretic model, constructs a spanning tree topology in a decentralized manner, and is usable in practice. We evaluate the protocol via simulation and a pratical implementation on a testbed of 75 Raspberry Pis, demonstrating that a total energy reduction of up to 90% can be achieved compared to a simple broadcast protocol.

Mobile crowdsensing (MCS) is a novel approach to increase the coverage, lower the costs, and increase the accuracy of sensing data. Its main idea is to collect sensor data using mobile units (MUs). The sensing is controlled by a mobile crowdsensing platform (MCSP) through the assignment of delay-sensitive sensing tasks to the MUs. Although promising, research effort in MCS is still needed to find task assignment solutions that maximize the coverage while considering the cost incurred by the MCSPs, the preferences of the MUs and the limited communication resources available. Specifically, we identify two main challenges: (i) A task assignment problem which incorporates the MCSP’s utility and the preferences of the MUs. (ii) An underlying communication resource allocation problem formulating the requirement of the timely transmission of sensing results given the limited communication resources. To address these challenges, we propose a novel two-stage matching algorithm. In the first stage, potential MU-task pairs are constructed considering the preferences of the MUs and the utility of the MCSP. In the second stage, the communication resource allocation is done based on potential MU-task pairs from the first stage. Through numerical simulations, we show that our proposed approach outperforms state-of-the-art methods in terms of the MCSP’s utility, coverage and MU’s satisfaction.

Mobile edge computing based on cloudlets is an emerging paradigm to improve service quality by bringing computation and storage facilities closer to end users and reducing operating cost for infrastructure providers (IPs) and service providers (SPs). To maximize their individual benefits, IP and SP have to reach an agreement about placing and executing services on particular cloudlets. We show that a Nash Bargaining Solution (NBS) yields the optimal solution with respect to social cost and fairness if IP and SP have complete information about the parameters of their mutual cost functions. However, IP and SP might not be willing or able to share all information due to business secrets or technical limitations. Therefore, we present a novel iterative bargaining approach without complete mutual information to achieve substantial cost reductions for both IP and SP. Furthermore, we investigate how different degrees of information sharing impact social cost and fairness of the different approaches. Our evaluation based on the mobile augmented reality game Ingress shows that our approach achieves up to about 82% of the cost reduction that the NBS achieves and a cost reduction of up to 147% compared to traditional Take-it-or-Leave-it approaches, despite incomplete information.

Ultra-Reliable Low-Latency Communications (URLLC) is an important part of emerging 5G and 6G networks which enables mission-critical applications like autonomous driving. These novel applications depend on the error-free delivery of short messages before an application-specific deadline, which is challenging in a fast-changing environment. In this work, we consider a wireless fading downlink channel shared for the transmission of periodically arriving messages for multiple mobile units (MUs). The message sizes, deadlines and the period of message arrival are MU-specific. The message for a MU can be split into smaller data packets, so that multiple unreliable transmissions can be combined to achieve a reliable transmission. We formulate an infinite time horizon Markov Decision Process (MDP) for the average timely throughput, and show that the MDP is periodic. We propose a novel two-timescale scheduling solution, which incorporates the uncertainty of the channel in an inter-frame problem and errors caused by short-packet coding in an intra-frame problem. Through numerical simulations, we show that the proposed approach outperforms State-of-the-Art scheduling algorithms in terms of timely throughput.

While many innovations in Technology Enhanced Learning (TEL) have emerged over the last two decades, the uptake of these innovations has not always been very successful, particularly in schools. The transition from proof of concept to integration into learning activities has been recognized as a bottleneck for quite some time. This major problem, which is affecting many TEL stakeholders, is the focus of the four year iTEC project that is developing a comprehensive approach.

In this paper, we introduce use cases and standards towards a new approach of learning that is driven by learning outcomes to be achieved by learners. This leads to higher employability of learners.

In this article, we describe a Smart Space for Learning™ (SS4L) framework and infrastructure that enables personalized access to distributed heterogeneous knowledge repositories. Helping a learner to choose an appropriate learning resource or activity is a key problem which we address in this framework, enabling personalized access to federated learning repositories with a vast number of learning offers. Our infrastructure includes personalization strategies both at the query and the query results level. Query rewriting is based on learning and language preferences; rule-based and ranking-based personalization improves these results further. Rule-based reasoning techniques are supported by formal ontologies we have developed based on standard information models for learning domains; ranking-based recommendations are supported through ensuring minimal sets of predicates appearing in query results. Our evaluation studies show that the implemented solution enables learners to find relevant learning resources in a distributed environment and through goal-based personalization improves relevancy of results.

This case study summarizes the demonstration of a semantic network of interoperable educational systems referred to as Smart Space for Learningtrade. We started connecting several educational nodes in projects such as Elena, Prolearn, and Icamp. Integration was achieved by using the interaction standard SQI, common schemas for querying and results presentation, and query exchange language, e.g. QEL. The paper particularly focuses on how heterogeneous nodes can be made interoperable by reusing generalizations of mediating components - building blocks for a Smart Space for Learningtrade

The concept of an electronic educational market, which is an "open" system for the exchange and brokerage of electronic learning resources between institutions of higher education is introduced and its value and feasibility are demonstrated within the paradigm of the EducaNext portal. The brokerage system can deal with highly heterogeneous learning resources, ranging from asynchronous educational to educational activities like computer-mediated lectures and courses. The main aim of such an endeavour is to develop and validate a scalable exchange model, which embraces offers, enquiries, booking and controlled delivery of learning resources. The key innovation is to create and manage an open electronic educational market with a standardized way of describing the pedagogical, administrative and technical characteristics of learning resources. Electronic educational markets enable institutions to enrich their curricula with remotely sourced material. The emphasis here is placed on the quality management of electronic educational markets.

The paper illustrates the design and implementation of a highly flexible, electronic market-place for learning resources called UNIVERSAL. Integrating learning resource related data in a (semi-)automated way demands a flexible data model. The paper elaborates on components of an educational market-place model such as learning resources, agents, rights and delivery systems. The central data formats of UNIVERSAL are based on RDF. We argue that the flexibility of RDF provides a high level of adaptability to future changes in the data model and a maximum level of openness. UNIVERSAL aims at contributing to the idea of a semantic Web of universities, pursuing the vision of having data on the Web defined and linked in such a way that it can be used by machines not just for display purposes, but for automation, integration and reuse of data across various applications.