ДЕЦЕНТРАЛИЗАЦИЯ В ЦИФРОВОМ ОБЩЕСТВЕ: ПАРАДОКС ДИЗАЙНА

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?Цифровая трансформация основывается на автоматизированных процессах и инвестициях в новые технологии: искусственный интеллект, блокчейн, анализ данных и интернет вещей. Но в центре успешной стратегии цифровой трансформации все равно находится человек. Цифровая трансформация порождает парадоксы новых моделей: с одной стороны, распространяются повсеместно технологии, такие, как интернет вещей, большие данные позволяют улучшить продукты и услуги для потребителей, предложить им новую ценность и т. д. Но, с другой стороны, аналитика данных и их хранение управляются высокоцентрализованным способом, приводящим к вторжению в частную жизнь людей, контролю за их действиями, к дискриминационным и сегрегационным социальным явлениям. В статье рассматриваются вопросы: каково различие между распределенным и децентрализованным системным проектированием? Как возможна организация «децентрализованной» обработки персональных  данных в наше время? Подрывают ли централизованный сбор и обработка данных автономию? Может ли децентрализация во внедренных технологиях влиять на этические и социальные параметры, такие, как социальная справедливость? Ведет ли децентрализация к  устойчивости функционирования систем? Есть ли взаимосвязь между децентрализацией цифровых технологий и децентрализацией городского развития?В статье делается вывод о том, что децентрализаванные системы имеют гораздо большую эффективность в современных условиях и являются альтернативой или естественной адаптацией к сложившимся условиям. Например, децентрализованное производство электроэнергии делает людей одновременно производителями и потребителями, что приводит к повышению энергоэффективности. Точно так же аналитика данных не является монополией систем больших данных. Анализ может также быть выполнен полностью децентрализованным способом как общественное благо с использованием коллективного разума

Appliance-Level Flexible Scheduling for Socio-Technical Smart Grid Optimization

Participation in residential energy demand response programs requires an active role by 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

Optimization of privacy-utility trade-offs under informational self-determination

The pervasiveness of Internet of Things results in vast volumes of personal data generated by smart devices of users (data producers) such as smart phones, wearables and other embedded sensors. It is a common requirement, especially for Big Data analytics systems, to transfer these large in scale and distributed data to centralized computational systems for analysis. Nevertheless, third parties that run and manage these systems (data consumers) do not always guarantee users’ privacy. Their primary interest is to improve utility that is usually a metric related to the performance, costs and the quality of service. There are several techniques that mask user-generated data to ensure privacy, e.g. differential privacy. Setting up a process for masking data, referred to in this paper as a ‘privacy setting’, decreases on the one hand the utility of data analytics, while, on the other hand, increases privacy. This paper studies parameterizations of privacy settings that regulate the trade-off between maximum utility, minimum privacy and minimum utility, maximum privacy, where utility refers to the accuracy in the estimations of aggregation functions. Privacy settings can be universally applied as system-wide parameterizations and policies (homogeneous data sharing). Nonetheless they can also be applied autonomously by each user or decided under the influence of (monetary) incentives (heterogeneous data sharing). This latter diversity in data sharing by informational self-determination plays a key role on the privacy-utility trajectories as shown in this paper both theoretically and empirically. A generic and novel computational framework is introduced for measuring privacy-utility trade-offs and their Pareto optimization. The framework computes a broad spectrum of such trade-offs that form privacy-utility trajectories under homogeneous and heterogeneous data sharing. The practical use of the framework is experimentally evaluated using real-world data from a Smart Grid pilot project in which energy consumers protect their privacy by regulating the quality of the shared power demand data, while utility companies make accurate estimations of the aggregate load in the network to manage the power grid. Over 20,000 differential privacy settings are applied to shape the computational trajectories that in turn provide a vast potential for data consumers and producers to participate in viable participatory data sharing systems

Self-Corrective Dynamic Networks via Decentralized Reverse Computations

The feasibility of large-scale decentralized networks for local computations, as an alternative to big data systems that are often privacy-intrusive, expensive and serve exclusively corporate interests, is usually questioned by network dynamics such as node leaves, failures and rejoins in the network. This is especially the case when decentralized computations performed in a network, such as the estimation of aggregation functions, e.g. summation, are linked to the actual nodes connected in the network, for instance, counting the sum using input values from only connected nodes. Reverse computations are required to maintain a high aggregation accuracy when nodes leave or fail. This paper introduces an autonomic agent-based model for highly dynamic self-corrective networks using decentralized reverse computations. The model is generic and equips the nodes with the capability to disseminate connectivity status updates in the network. Highly resilient agents to the dynamic network migrate to remote nodes and orchestrate reverse computations for each node leave or failure. In contrast to related work, no other computational resources or redundancy are introduced. The self-corrective model is experimentally evaluated using real-world data from a smart grid pilot project under highly dynamic network adjustments that correspond to catastrophic events with up to 50% of the nodes leaving the network. The model is highly agile and modular and is applied to the large-scale decentralized aggregation network of DIAS, the Dynamic Intelligent Aggregation Service, without major structural changes in its design and operations. Results confirm the outstanding improvement in the aggregation accuracy when self-corrective actions are employed with a minimal increase in communication overhead

Self-Repairable Smart Grids Via Online Coordination of Smart Transformers

The introduction of active devices in Smart Grids, such as smart transformers, powered by intelligent software and networking capabilities, brings paramount opportunities for online automated control and regulation. However, online mitigation of disruptive events, such as cascading failures, is challenging. Local intelligence by itself cannot tackle such complex collective phenomena with domino effects. Collective intelligence coordinating rapid mitigation actions is required. This paper introduces analytical results from which two optimization strategies for self-repairable Smart Grids are derived. These strategies build a coordination mechanism for smart transformers that runs in three healing modes and performs collective decision-making of the phase angles in the lines of a transmission system to improve reliability under disruptive events, i.e., line failures causing cascading failures. Experimental evaluation using self-repairability envelopes in different case networks, ac power flows, and varying number of smart transformers confirms that the higher the number of smart transformers participating in the coordination, the higher the reliability and the capability of a network to self-repair

Decentralized Collective Learning for Self-managed Sharing Economies

The Internet of Things equips citizens with a phenomenal new means for online participation in sharing economies. When agents self-determine options from which they choose, for instance, their resource consumption and production, while these choices have a collective systemwide impact, optimal decision-making turns into a combinatorial optimization problem known as NP-hard. In such challenging computational problems, centrally managed (deep) learning systems often require personal data with implications on privacy and citizens’ autonomy. This article envisions an alternative unsupervised and decentralized collective learning approach that preserves privacy, autonomy, and participation of multi-agent systems self-organized into a hierarchical tree structure. Remote interactions orchestrate a highly efficient process for decentralized collective learning. This disruptive concept is realized by I-EPOS, the Iterative Economic Planning and Optimized Selections, accompanied by a paradigmatic software artifact. Strikingly, I-EPOS outperforms related algorithms that involve non-local brute-force operations or exchange full information. This article contributes new experimental findings about the influence of network topology and planning on learning efficiency as well as findings on techno-socio-economic tradeoffs and global optimality. Experimental evaluation with real-world data from energy and bike sharing pilots demonstrates the grand potential of collective learning to design ethically and socially responsible participatory sharing economies

On cycling risk and discomfort: urban safety mapping and bike route recommendations

Bike usage in Smart Cities is paramount for sustainable urban development: cycling promotes healthier lifestyles, lowers energy consumption, lowers carbon emissions, and reduces urban traffic. However, the expansion and increased use of bike infrastructure has been accompanied by a glut of bike accidents, a trend jeopardizing the urban bike movement. This paper leverages data from a diverse spectrum of sources to characterise geolocated bike accident severity and, ultimately, study cycling risk and discomfort. Kernel density estimation generates a continuous, empirical, spatial risk estimate which is mapped in a case study of Zürich city. The roles of weather, time, accident type, and severity are illustrated. A predominance of self-caused accidents motivates an open-source software artifact for personalized route recommendations. This software is used to collect open baseline route data that are compared with alternative routes minimizing risk and discomfort. These contributions have the potential to provide invaluable infrastructure improvement insights to urban planners, and may also improve the awareness of risk in the urban environment among experienced and novice cyclists alike

How to Coordinate Decisions at Large Scale? A Hands-on Tutorial on Collective Learning for Smart Cities and beyond

This 1.5-hour tutorial will provide an introduction to the theory and practice of multi-agent collective learning for coordinating distributed decisions at large scale. You will develop the required skills to work with the EPOS software artifact to solve distributed optimization problems in Smart Cities. The tutorial will also promote collaborations within the ACSOS community. PhD students and more senior colleagues are particularly encouraged to participate. No programming experience is required. You are also encouraged to bring in your own multi-agent optimization problem to explore a potential solution using collective learning

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

Temporal Self-Regulation of Energy Demand

The increase in the deployment of smart meters has enabled collection of fine-grained energy consumption data at consumer premises. Analysis of this real-time energy consumption data bestows new opportunities for better demand-response (DR) programs. This paper offers a new perspective to study energy demand and helps in designing novel mechanisms for decentralized demand-side management. Specifically, a new concept of finding the demand states using energy consumption of consumers over time and feasible transitions therein is introduced. It is shown that the orchestration of temporal transitions between the demand states can meet broad range of smart grid objectives. An online demand regulation model is developed that captures the temporal dynamics of energy demand to identify target consumers for different DR programs. This methodology is empirically evaluated and validated using data from more than 4000 households, which were part of a real-world smart grid project. This paper is the first one to comprehensively analyze the temporal dynamics of demands