The digital divide remains one of the pressing challenges, as it persists in both developed and developing regions. Recent events that required a sudden and radical shift from offline to online have brought the digital divide issue sharply into focus. Prior research has investigated how differences in mobile ICT availability and use relate to individuals’ income opportunities or lack thereof. In this research, we take a new perspective on digital divides. We show that besides the commonly investigated digital divide in economic and opportunity-related terms, the unavailability of mobile ICT can give rise to a digital divide regarding public safety. In detail, we leverage publicly available spatial crime data and the random occurrence of mobile ICT outages to investigate the link between ICT use and safety for distinctly wealthy population sub-groups. Based on the results of a difference-in- differences analysis, we find that the effects of mobile ICT discontinuations are non- uniform across socioeconomic groups with socioeconomically disadvantaged areas suffering disproportionally higher increases in property crimes than their wealthier counterparts. Overall, our research supports the idea of mobile ICT infrastructure as a public utility by showing the importance of fail-safe mobile ICT infrastructures for all, but particularly for socioeconomically disadvantaged areas.

Mobile crowdsensing (MCS) is a distributed sensing architecture that utilizes existing sensors on mobile units (MUs) to perform sensing tasks. A mobile crowdsensing platform (MCSP) publishes the sensing tasks and the MUs decide if they want to participate in their execution in exchange for money. The MCS system is characterized by its dynamic nature in which the task requirements, the MUs’ availability, and their available resources change over time. The MUs aim to find an efficient task participation strategy to maximize their income while the MCSP focuses on maximizing the number of completed tasks. As optimal task participation strategies require perfect non-causal information about the MCS system, which is unavailable in realistic scenarios, the main challenge in MCS is to find an efficient task participation strategy for the MUs under incomplete information. To this aim, a novel fully decentralized federated deep reinforcement learning algorithm, termed FDRL-PPO is proposed. FDRL-PPO enables every MU to learn its own task participation strategy based on its experiences, available resources, and preferences, without relying on perfect non-causal information about the MCS system. To replenish their batteries, the MUs rely on energy harvesting. As a result, their available energy varies over time, leading to varying availability and fragmented learning experiences. To mitigate these challenges, the proposed approach leverages federated learning, enabling MUs to collaboratively improve their models without having to share private raw data like their own experiences. By exchanging only learned models, MUs collectively compensate for individual limitations, and find more scalable, robust, and efficient task participation strategies. Comprehensive evaluations on both synthetic and real-world datasets show that FDRL-PPO consistently outperforms benchmark algorithms in terms of task completion ratio, fairness in task completion, energy consumption, and number of conflicting proposals.

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.

Machine learning can provide predictions with disparate outcomes, in which subgroups of the population (e.g., defined by age, gender, or other sensitive attributes) are systematically disadvantaged. In order to comply with upcoming legislation, practitioners need to locate such disparate outcomes. However, previous literature typically detects disparities through statistical procedures for when the sensitive attribute is specified a priori. This limits applicability in real-world settings where datasets are high dimensional and, on top of that, sensitive attributes may be unknown. As a remedy, we propose a data-driven framework called Automatic Location of Disparities (ALD) which aims at locating disparities in machine learning. ALD meets several demands from industry: ALD (1) is applicable to arbitrary machine learning classifiers; (2) operates on different definitions of disparities (e.g., statistical parity or equalized odds); (3) deals with both categorical and continuous predictors even if disparities arise from complex and multi-way interactions known as intersectionality (e.g., age above 60 and female). ALD produces interpretable audit reports as output. We demonstrate the effectiveness of ALD based on both synthetic and real-world datasets. As a result, we empower practitioners to effectively locate and mitigate disparities in machine learning algorithms, conduct algorithmic audits, and protect individuals from discrimination.

By customizing smart assistance systems to customer needs, car manufacturers can improve their systems and create additional benefits for users. However, it is still unclear which characteristics car drivers perceive as favorable and useful. To examine this, we employ a mixed-method approach. In our first study, we conduct a survey (N$=$301) to investigate general user perception antecedents of smart assistant systems in cars. We analyze the indirect effects of different system quality characteristics mediated by user perception on their usage intention. In our second study (N$=$270), we use a discrete choice experiment to measure the effects of concrete system attributes on user acceptance of IT-based parking systems representing a concrete instantiation of a smart assistance system. Consistent with our first study, we observe that the system quality factors user interface intuitiveness, full language flexibility, and system error occurrence significantly influence the consumers’ intention to use the technology. Accordingly, car manufacturers should put a particular focus on these factors when developing and implementing their smart assistance systems in cars. For IT-based parking systems, in particular, consumers are very price-sensitive. However, by implementing additional technical features, manufacturers can significantly increase the systems’ price value and thus the purchase probability.

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.

Multipath TCP (MPTCP) promises improvements in Quality of Service through connection bundling. This leads to the belief that it will inevitably improve the Quality of Experience (QoE), especially, for mobile applications running on top. The networking and transport layer improvements stem from bundling multiple paths, e.g., WiFi and LTE, as well as increasing the connection reliability through redundancy. For example, a smartphone running an application over WiFi may switch to the cellular network without service interruption upon user movement that gets the device out of the WiFi range, thus avoiding outage events. However, the impact of MPTCP on QoE for different applications has not yet been fully understood.

In future, smart home and smart living applications will enrich daily life. These applications are aware of their context, use artificial intelligence (AI) and are therefore able to recognize common use cases reliably and adapt these use cases individually with the current user in mind. This paper describes a concept for such an AI-based platform. The presented platform approach considers different stakeholders, e.g. the housing industry, service providers and tenants.

Augmented Reality (AR) is an approach to enrich the real world with additional information. It allows users to interact with virtual objects that are linked to locations in the real world. In the area of mobile computer games, AR is quite demanding for managing resources in communication networks, since in-game points of interest typically lead to high network loads, whereas network utilization is otherwise below the average. Predicting the number of in-game actions of mobile AR games can help to scale the game back-end reasonably without over- or under-provisioning of resources. Context information like weather or available Wi-Fi access points can play a key role in estimating or predicting the number of in-game actions. In this paper, we analyze a comprehensive dataset that contains players' actions of one of the most popular mobile AR games, Ingress. The dataset entails more than 23.9 million player actions over a period of 17 months, as well as additional contextual information. Our analysis shows a highly significant relationship between context factors and in-game actions, which explains large parts of users' behavior in terms of when, where and how often they play the game. By combining four different context factors (i.e., time, user, physical and computing context), our analysis can explain up to 84.44% of the variation in the number of in-game actions.

Video traffic is the main driver of Internet traffic volume. Thus, content providers and Content Delivery Networks (CDNs) are searching for ways to provide reliable video transmission at a low cost. Hybrid CDN/Peer-to-Peer (P2P) deployments like Akamai NetSession have been shown to combine the high reliability of a CDN backbone and the low cost of P2P networks. In the near future, the biggest barrier for user adoption will fall: the installation of a dedicated P2P client software will be replaced by website embedded browser-to-browser communication logic. However, this requires the explicit consent of users, and, since users need to share their upload capacity, their willingness to participate in such a system. In this work, the efficiency of incentive mechanisms trading a higher Quality of Experience (QoE) of video transmission for user's consent to utilize their upload capacity are investigated. This is the first study to investigate the question of incentives in distributed, adaptive streaming systems from a user perspective using a crowd working approach. The work presents results from 192 test subjects. We identify three classes of users and show how behavioral economics can be utilized to increase the impact of an incentive scheme.

Electronic services such as virtual communities or electronic commerce demand user authentication. Several more or less successful federated identity management systems have emerged to support authentication across diverse service domains in recent years. In this paper, we explore the determinants for success and failure of such systems with a focus on Germany representing one of the largest markets in Europe. To achieve this goal, we analyze the preferences and willingness to pay of prospective users by conducting a choice-based conjoint analysis. Our results indicate that users prefer simple systems where an intermediary takes care of their data. An additional market analyses confirms these findings and contradicts the assumptions of many researchers, especially in the fields of engineering and computer science, supporting systems with higher and higher levels of privacy and security.