arXiv subjects

Evangelos Pournaras

Publications and source records attributed to Evangelos Pournaras.

At least 55 records · Page 3Linked to original sources

arXiv2023

Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence

Collective privacy loss becomes a colossal problem, an emergency for personal freedoms and democracy. But, are we prepared to handle personal data as scarce resource and collectively share data under the doctrine: as little as possible, as much as necessary? We hypothesize a significant privacy recovery if a population of individuals, the data collective, coordinates to share minimum data for running online services with the required quality. Here we show how to automate and scale-up complex collective arrangements for privacy recovery using decentralized artificial intelligence. For this, we compare for first time attitudinal, intrinsic, rewarded and coordinated data sharing in a rigorous living-lab experiment of high realism involving >27,000 real data disclosures. Using causal inference and cluster analysis, we differentiate criteria predicting privacy and five key data-sharing behaviors. Strikingly, data-sharing coordination proves to be a win-win for all: remarkable privacy recovery for people with evident costs reduction for service providers.

cs.AI↗

arXiv2023

Improving City Life via Legitimate and Participatory Policy-making: A Data-driven Approach in Switzerland

This paper introduces a novel data-driven approach to address challenges faced by city policymakers concerning the distribution of public funds. Providing budgeting processes for improving quality of life based on objective (data-driven) evidence has been so far a missing element in policy-making. This paper focuses on a case study of 1,204 citizens in the city of Aarau, Switzerland, and analyzes survey data containing insightful indicators that can impact the legitimacy of decision-making. Our approach is twofold. On the one hand, we aim to optimize the legitimacy of policymakers' decisions by identifying the level of investment in neighborhoods and projects that offer the greatest return in legitimacy. To do so, we introduce a new context-independent legitimacy metric for policymakers. This metric allows us to distinguish decisive vs. indecisive collective preferences for neighborhoods or projects on which to invest, enabling policymakers to prioritize impactful bottom-up consultations and participatory initiatives (e.g., participatory budgeting). The metric also allows policymakers to identify the optimal number of investments in various project sectors and neighborhoods (in terms of legitimacy gain). On the other hand, we aim to offer guidance to policymakers concerning which satisfaction and participation factors influence citizens' quality of life through an accurate classification model and an evaluation of relocations. By doing so, policymakers may be able to further refine their strategy, making targeted investments with significant benefits to citizens' quality of life. These findings are expected to provide transformative insights for practicing direct democracy in Switzerland and a blueprint for policy-making to adopt worldwide.

cs.CY↗

arXiv2023

Discrete-choice Multi-agent Optimization: Decentralized Hard Constraint Satisfaction for Smart Cities

Making Smart Cities more sustainable, resilient and democratic is emerging as an endeavor of satisfying hard constraints, for instance meeting net-zero targets. Decentralized multi-agent methods for socio-technical optimization of large-scale complex infrastructures such as energy and transport networks are scalable and more privacy-preserving by design. However, they mainly focus on satisfying soft constraints to remain cost-effective. This paper introduces a new model for decentralized hard constraint satisfaction in discrete-choice combinatorial optimization problems. The model solves the cold start problem of partial information for coordination during initialization that can violate hard constraints. It also preserves a low-cost satisfaction of hard constraints in subsequent coordinated choices during which soft constraints optimization is performed. Strikingly, experimental results in real-world Smart City application scenarios demonstrate the required behavioral shift to preserve optimality when hard constraints are satisfied. These findings are significant for policymakers, system operators, designers and architects to create the missing social capital of running cities in more viable trajectories.

cs.DC↗

arXiv2023

Constructing Effective Customer Feedback Systems -- A Design Science Study Leveraging Blockchain Technology

Organizations have to adjust to changes in the ecosystem, and customer feedback systems (CFS) provide important information to adapt products and services to changing customer preferences. However, current systems are limited to single-dimensional rating scales and are subject to self-selection biases. This work contributes design principles for CFS and implements a CFS that advances current systems by means of contextualized feedback according to specific organizational objectives. We apply Design Science Research (DSR) methodology and report on a longitudinal DSR journey considering multiple stakeholder values by utilizing value-sensitive design methods. We conducted expert interviews, design workshops, demonstrations, and a four-day experiment in an organizational setup, involving 132 customers of a major Swiss library. In the process, we validated the identified design principles and the implemented software artifact both qualitatively and quantitatively and drew conclusions for their efficient instantiation. In particular, we found that i) blockchain technology can afford three design principles of effective CFS. Also, ii) combining DSR with value-sensitive design methods explicitly provides rationale for design principles in the form of identified important values. Moreover, iii) utilizing this methodology makes the construction of software artifacts more efficient it terms of design time by restricting the design space of a software artefact to those options that align with stakeholder values. Hence, the findings of this work advance the knowledge on the design of CFS and provides both, for researchers a theoretical contribution to reason about design principles and a guideline to managers and decision makers for designing software artefacts efficiently.

cs.SE↗

arXiv2022

3, 2, 1, Drones Go! A Testbed to Take off UAV Swarm Intelligence for Distributed Sensing

This paper introduces a testbed to study distributed sensing problems of Unmanned Aerial Vehicles (UAVs) exhibiting swarm intelligence. Several Smart City applications, such as transport and disaster response, require efficient collection of sensor data by a swarm of intelligent and cooperative UAVs. This often proves to be too complex and costly to study systematically and rigorously without compromising scale, realism and external validity. With the proposed testbed, this paper sets a stepping stone to emulate, within small laboratory spaces, large sensing areas of interest originated from empirical data and simulation models. Over this sensing map, a swarm of low-cost drones can fly allowing the study of a large spectrum of problems such as energy consumption, charging control, navigation and collision avoidance. The applicability of a decentralized multi-agent collective learning algorithm (EPOS) for UAV swarm intelligence along with the assessment of power consumption measurements provide a proof-of-concept and validate the accuracy of the proposed testbed.

cs.RO↗

arXiv2021

Crowd Sensing and Living Lab Outdoor Experimentation Made Easy

Living lab outdoor experimentation using pervasive computing provides new opportunities: higher realism, external validity and socio-spatio-temporal observations in large scale. However, experimentation `in the wild' is complex and costly. Noise, biases, privacy concerns, compliance with standards of ethical review boards, remote moderation, control of experimental conditions and equipment perplex the collection of high-quality data for causal inference. This article introduces Smart Agora, a novel open-source software platform for rigorous systematic outdoor experimentation. Without writing a single line of code, highly complex experimental scenarios are visually designed and automatically deployed to smart phones. Novel geolocated survey and sensor data are collected subject of participants verifying desired experimental conditions, for instance, their localization at certain urban spots. This new approach drastically improves the quality and purposefulness of crowd sensing, tailored to conditions that confirm/reject hypotheses. The features that support this innovative functionality and the broad spectrum of its applicability are demonstrated.

cs.HC↗

arXiv2021

Self-healing Dilemmas in Distributed Systems: Fault Correction vs. Fault Tolerance

Large-scale decentralized systems of autonomous agents interacting via asynchronous communication often experience the following self-healing dilemma: fault detection inherits network uncertainties making a remote faulty process indistinguishable from a slow process. In the case of a slow process without fault, fault correction is undesirable as it can trigger new faults that could be prevented with fault tolerance that is a more proactive system maintenance. But in the case of an actual faulty process, fault tolerance alone without eventually correcting persistent faults can make systems underperforming. Measuring, understanding and resolving such self-healing dilemmas is a timely challenge and critical requirement given the rise of distributed ledgers, edge computing, the Internet of Things in several energy, transport and health applications. This paper contributes a novel and general-purpose modeling of fault scenarios during system runtime. They are used to accurately measure and predict inconsistencies generated by the undesirable outcomes of fault correction and fault tolerance as the means to improve self-healing of large-scale decentralized systems at the design phase. A rigorous experimental methodology is designed that evaluates 696 experimental settings of different fault scales, fault profiles and fault detection thresholds in a prototyped decentralized network of 3000 nodes. Almost 9 million measurements of inconsistencies were collected in a network, where each node monitors the health status of another node, while both can defect. The prediction performance of the modeled fault scenarios is validated in a challenging application scenario of decentralized and dynamic in-network data aggregation using real-world data from a Smart Grid pilot project. Findings confirm the origin of inconsistencies at design phase.

cs.DC↗

arXiv2021

Decentralized Edge-to-Cloud Load-balancing: Service Placement for the Internet of Things

The Internet of Things (IoT) requires a new processing paradigm that inherits the scalability of the cloud while minimizing network latency using resources closer to the network edge. Building up such flexibility within the edge-to-cloud continuum consisting of a distributed networked ecosystem of heterogeneous computing resources is challenging. Furthermore, IoT traffic dynamics and the rising demand for low-latency services foster the need for minimizing the response time and balanced service placement. Load-balancing for fog computing becomes a cornerstone for cost-effective system management and operations. This paper studies two optimization objectives and formulates a decentralized load-balancing problem for IoT service placement: (global) IoT workload balance and (local) quality of service (QoS), in terms of minimizing the cost of deadline violation, service deployment, and unhosted services. The proposed solution, EPOS Fog, introduces a decentralized multi-agent system for collective learning that utilizes edge-to-cloud nodes to jointly balance the input workload across the network and minimize the costs involved in service execution. The agents locally generate possible assignments of requests to resources and then cooperatively select an assignment such that their combination maximizes edge utilization while minimizes service execution cost. Extensive experimental evaluation with realistic Google cluster workloads on various networks demonstrates the superior performance of EPOS Fog in terms of workload balance and QoS, compared to approaches such as First Fit and exclusively Cloud-based. The results confirm that EPOS Fog reduces service execution delay up to 25% and the load-balance of network nodes up to 90%. The findings also demonstrate how distributed computational resources on the edge can be utilized more cost-effectively by harvesting collective intelligence.

cs.DC↗

arXiv2021

Containing Future Epidemics with Trustworthy Federated Systems for Ubiquitous Warning and Response

In this paper, we propose a global digital platform to avoid and combat epidemics by providing relevant real-time information to support selective lockdowns. It leverages the pervasiveness of wireless connectivity while being trustworthy and secure. The proposed system is conceptualized to be decentralized yet federated, based on ubiquitous public systems and active citizen participation. Its foundations lie on the principle of informational self-determination. We argue that only in this way it can become a trustworthy and legitimate public good infrastructure for citizens by balancing the asymmetry of the different hierarchical levels within the federated organization while providing highly effective detection and guiding mitigation measures towards graceful lockdown of the society. To exemplify the proposed system, we choose the remote patient monitoring as use case. In which, the integration of distributed ledger technologies with narrowband IoT technology is evaluated considering different number of endorsed peers. An experimental proof of concept setup is used to evaluate the performance of this integration, in which the end-to-end latency is slightly increased when a new endorsed element is added. However, the system reliability, privacy, and interoperability are guaranteed. In this sense, we expect active participation of empowered citizens to supplement the more usual top-down management of epidemics.

cs.DC↗

arXiv2020

How Value-Sensitive Design Can Empower Sustainable Consumption

In a so-called overpopulated world, sustainable consumption is of existential importance.However, the expanding spectrum of product choices and their production complexity challenge consumers to make informed and value-sensitive decisions. Recent approaches based on (personalized) psychological manipulation are often intransparent, potentially privacy-invasive and inconsistent with (informational) self-determination. In contrast, responsible consumption based on informed choices currently requires reasoning to an extent that tends to overwhelm human cognitive capacity. As a result, a collective shift towards sustainable consumption remains a grand challenge. Here we demonstrate a novel personal shopping assistant implemented as a smart phone app that supports a value-sensitive design and leverages sustainability awareness, using experts' knowledge and "wisdom of the crowd" for transparent product information and explainable product ratings. Real-world field experiments in two supermarkets confirm higher sustainability awareness and a bottom-up behavioral shift towards more sustainable consumption. These results encourage novel business models for retailers and producers, ethically aligned with consumer preferences and with higher sustainability.

cs.CY↗

arXiv2020

Learning to Learn in Collective Adaptive Systems: Mining Design Patterns for Data-driven Reasoning

Engineering collective adaptive systems (CAS) with learning capabilities is a challenging task due to their multi-dimensional and complex design space. Data-driven approaches for CAS design could introduce new insights enabling system engineers to manage the CAS complexity more cost-effectively at the design-phase. This paper introduces a systematic approach to reason about design choices and patterns of learning-based CAS. Using data from a systematic literature review, reasoning is performed with a novel application of data-driven methodologies such as clustering, multiple correspondence analysis and decision trees. The reasoning based on past experience as well as supporting novel and innovative design choices are demonstrated.

cs.SE↗

arXiv2020

A Self-Integration Testbed for Decentralized Socio-technical Systems

The Internet of Things comes along with new challenges for experimenting, testing, and operating decentralized socio-technical systems at large-scale. In such systems, autonomous agents interact locally with their users, and remotely with other agents to make intelligent collective choices. Via these interactions they self-regulate the consumption and production of distributed resources. While such complex systems are often deployed and operated using centralized computing infrastructures, the socio-technical nature of these decentralized systems requires new value-sensitive design paradigms; empowering trust, transparency, and alignment with citizens' social values, such as privacy preservation, autonomy, and fairness among citizens' choices. Currently, instruments and tools to study such systems and guide the prototyping process from simulation to live deployment are missing, or not practical in this distributed socio-technical context. This paper bridges this gap by introducing a novel testbed architecture for decentralized socio-technical systems running on IoT. This new architecture is designed for a seamless reusability of (i) application-independent decentralized services by an IoT application, and (ii) different IoT applications by the same decentralized service. This dual self-integration promises IoT applications that are simpler to prototype, and can interoperate with decentralized services during runtime to self-integrate more complex functionality. Such integration provides stronger validation of IoT applications, and improves resource utilization. Pressure and crash tests during continuous operations of several weeks, with more than 80K network joining and leaving of agents, 2.4M parameter changes, and 100M communicated messages, confirm the robustness and practicality of the testbed architecture.

cs.MA↗

arXiv2020

Proof of Witness Presence: Blockchain Consensus for Augmented Democracy in Smart Cities

Smart Cities evolve into complex and pervasive urban environments with a citizens' mandate to meet sustainable development goals. Repositioning democratic values of citizens' choices in these complex ecosystems has turned out to be imperative in an era of social media filter bubbles, fake news and opportunities for manipulating electoral results with such means. This paper introduces a new paradigm of augmented democracy that promises actively engaging citizens in a more informed decision-making augmented into public urban space. The proposed concept is inspired by a digital revive of the Ancient Agora of Athens, an arena of public discourse, a Polis where citizens assemble to actively deliberate and collectively decide about public matters. The core contribution of the proposed paradigm is the concept of proving witness presence: making decision-making subject of providing secure evidence and testifying for choices made in the physical space. This paper shows how the challenge of proving witness presence can be tackled with blockchain consensus to empower citizens' trust and overcome security vulnerabilities of GPS localization. Moreover, a novel platform for collective decision-making and crowd-sensing in urban space is introduced: Smart Agora. It is shown how real-time collective measurements over citizens' choices can be made in a fully decentralized and privacy-preserving way. Witness presence is tested by deploying a decentralized system for crowd-sensing the sustainable use of transport means. Furthermore, witness presence of cycling risk is validated using official accident data from public authorities, which are compared against wisdom of the crowd. The paramount role of dynamic consensus, self-governance and ethically aligned artificial intelligence in the augmented democracy paradigm is outlined.

cs.CY↗

arXiv2020

Mobile Link Prediction: Automated Creation and Crowd-sourced Validation of Knowledge Graphs

Building trustworthy knowledge graphs for cyber-physical social systems (CPSS) is a challenge. In particular, current approaches relying on human experts have limited scalability, while automated approaches are often not accountable to users resulting in knowledge graphs of questionable quality. This paper introduces a novel pervasive knowledge graph builder that brings together automation, experts' and crowd-sourced citizens' knowledge. The knowledge graph grows via automated link predictions using genetic programming that are validated by humans for improving transparency and calibrating accuracy. The knowledge graph builder is designed for pervasive devices such as smartphones and preserves privacy by localizing all computations. The accuracy, practicality, and usability of the knowledge graph builder is evaluated in a real-world social experiment that involves a smartphone implementation and a Smart City application scenario. The proposed knowledge graph building methodology outperforms the baseline method in terms of accuracy while demonstrating its efficient calculations on smartphones and the feasibility of the pervasive human supervision process in terms of high interactions throughput. These findings promise new opportunities to crowd-source and operate pervasive reasoning systems for cyber-physical social systems in Smart Cities.

cs.SI↗

arXiv2020

Appliance-level Flexible Scheduling for Socio-technical Smart Grid Optimisation

Participation in residential energy demand response programs requires an active role by the consumers. They contribute flexibility in how they use their appliances as the means to adjust energy consumption, and reduce demand peaks, possibly at the expense of their own comfort (e.g., thermal). Understanding the collective potential of appliance-level flexibility for reducing demand peaks is challenging and complex. For instance, physical characteristics of appliances, usage preferences, and comfort requirements all influence consumer flexibility, adoption, and effectiveness of demand response programs. To capture and study such socio-technical factors and trade-offs, this paper contributes a novel appliance-level flexible scheduling framework based on consumers' self-determined flexibility and comfort requirements. By utilizing this framework, this paper studies (i) consumers usage preferences across various appliances, as well as their voluntary contribution of flexibility and willingness to sacrifice comfort for improving grid stability, (ii) impact of individual appliances on the collective goal of reducing demand peaks, and (iii) the effect of variable levels of flexibility, cooperation, and participation on the outcome of coordinated appliance scheduling. Experimental evaluation using a novel dataset collected via a smartphone app shows that higher consumer flexibility can significantly reduce demand peaks, with the oven having the highest system-wide potential for this. Overall, the cooperative approach allows for higher peak-shaving compared to non-cooperative schemes that focus entirely on the efficiency of individual appliances. The findings of this study can be used to design more cost-effective and granular (appliance-level) demand response programs in participatory and decentralized Smart Grids.

eess.SY↗

arXiv2020

Decrypting Distributed Ledger Design -- Taxonomy, Classification and Blockchain Community Evaluation

More than 1000 distributed ledger technology (DLT) systems raising $600 billion in investment in 2016 feature the unprecedented and disruptive potential of blockchain technology. A systematic and data-driven analysis, comparison and rigorous evaluation of the different design choices of distributed ledgers and their implications is a challenge. The rapidly evolving nature of the blockchain landscape hinders reaching a common understanding of the techno-socio-economic design space of distributed ledgers and the cryptoeconomies they support. To fill this gap, this paper makes the following contributions: (i) A conceptual architecture of DLT systems with which (ii) a taxonomy is designed and (iii) a rigorous classification of DLT systems is made using real-world data and wisdom of the crowd. (iv) A DLT design guideline is the end result of applying machine learning methodologies on the classification data. Compared to related work and as defined in earlier taxonomy theory, the proposed taxonomy is highly comprehensive, robust, explanatory and extensible. The findings of this paper can provide new insights and better understanding of the key design choices evolving the modeling complexity of DLT systems, while identifying opportunities for new research contributions and business innovation.

cs.CY↗

arXiv2020

Decentralized Optimization of Vehicle Route Planning -- A Cross-City Comparative Study

New mobility concepts are at the forefront of research and innovation in smart cities. The introduction of connected and autonomous vehicles enables new possibilities in vehicle routing. Specifically, knowing the origin and destination of each agent in the network can allow for real-time routing of the vehicles to optimize network performance. However, this relies on individual vehicles being "altruistic" i.e., being willing to accept an alternative non-preferred route in order to achieve a network-level performance goal. In this work, we conduct a study to compare different levels of agent altruism and the resulting effect on the network-level traffic performance. Specifically, this study compares the effects of different underlying urban structures on the overall network performance, and investigates which characteristics of the network make it possible to realize routing improvements using a decentralized optimization router. The main finding is that, with increased vehicle altruism, it is possible to balance traffic flow among the links of the network. We show evidence that the decentralized optimization router is more effective with networks of high load while we study the influence of cities characteristics, in particular: networks with a higher number of nodes (intersections) or edges (roads) per unit area allow for more possible alternate routes, and thus higher potential to improve network performance.

eess.SY↗

arXiv2020

Decentralization in Digital Societies -- A Design Paradox

Digital societies come with a design paradox: On the one hand, technologies, such as Internet of Things, pervasive and ubiquitous systems, allow a distributed local intelligence in interconnected devices of our everyday life such as smart phones, smart thermostats, self-driving cars, etc. On the other hand, Big Data collection and storage is managed in a highly centralized fashion, resulting in privacy-intrusion, surveillance actions, discriminatory and segregation social phenomena. What is the difference between a distributed and a decentralized system design? How "decentralized" is the processing of our data nowadays? Does centralized design undermine autonomy? Can the level of decentralization in the implemented technologies influence ethical and social dimensions, such as social justice? Can decentralization convey sustainability? Are there parallelisms between the decentralization of digital technology and the decentralization of urban development?

cs.CY↗