Distributed Selfish Coaching

Although cooperation generally increases the amount of resources available to a community of nodes, thus improving individual and collective performance, it also allows for the appearance of potential mistreatment problems through the exposition of one node's resources to others. We study such concerns by considering a group of independent, rational, self-aware nodes that cooperate using on-line caching algorithms, where the exposed resource is the storage at each node. Motivated by content networking applications -- including web caching, CDNs, and P2P -- this paper extends our previous work on the on-line version of the problem, which was conducted under a game-theoretic framework, and limited to object replication. We identify and investigate two causes of mistreatment: (1) cache state interactions (due to the cooperative servicing of requests) and (2) the adoption of a common scheme for cache management policies. Using analytic models, numerical solutions of these models, as well as simulation experiments, we show that on-line cooperation schemes using caching are fairly robust to mistreatment caused by state interactions. To appear in a substantial manner, the interaction through the exchange of miss-streams has to be very intense, making it feasible for the mistreated nodes to detect and react to exploitation. This robustness ceases to exist when nodes fetch and store objects in response to remote requests, i.e., when they operate as Level-2 caches (or proxies) for other nodes. Regarding mistreatment due to a common scheme, we show that this can easily take place when the "outlier" characteristics of some of the nodes get overlooked. This finding underscores the importance of allowing cooperative caching nodes the flexibility of choosing from a diverse set of schemes to fit the peculiarities of individual nodes. To that end, we outline an emulation-based framework for the development of mistreatment-resilient distributed selfish caching schemes. Our framework utilizes a simple control-theoretic approach to dynamically parameterize the cache management scheme. We show performance evaluation results that quantify the benefits from instantiating such a framework, which could be substantial under skewed demand profiles.National Science Foundation (CNS Cybertrust 0524477, CNS NeTS 0520166, CNS ITR 0205294, EIA RI 0202067); EU IST (CASCADAS and E-NEXT); Marie Curie Outgoing International Fellowship of the EU (MOIF-CT-2005-007230

Multiservice UAVs for Emergency Tasks in Post-disaster Scenarios

UAVs are increasingly being employed to carry out surveillance, parcel delivery, communication-support and other specific tasks. Their equipment and mission plan are carefully selected to minimize the carried load an overall resource consumption. Typically, several single task UAVs are dispatched to perform different missions. In certain cases, (part of) the geographical area of operation may be common to these single task missions (such as those supporting post-disaster recovery) and it may be more efficient to have multiple tasks carried out as part of a single UAV mission using common or even additional specialized equipment. In this paper, we propose and investigate a joint planning of multitask missions leveraging a fleet of UAVs equipped with a standard set of accessories enabling heterogeneous tasks. To this end, an optimization problem is formulated yielding the optimal joint planning and deriving the resulting quality of the delivered tasks. In addition, a heuristic solution is developed for large-scale environments to cope with the increased complexity of the optimization framework. The developed joint planning of multitask missions is applied to a specific post-disaster recovery scenario of a flooding in the San Francisco area. The results show the effectiveness of the proposed solutions and the potential savings in the number of UAVs needed to carry out all the tasks with the required level of quality

Leveraging information in vehicular parking games

Our paper approaches the parking assistance service in urban environments as an instance of service provision in non-cooperative network environments. We propose normative abstractions for the way drivers pursue parking space and the way they respond to partial or complete information for parking demand and supply as well as specific pricing policies on public and private parking facilities. The drivers are viewed as strategic agents who make rational decisions attempting to minimize the cost of the acquired parking spot. We formulate the resulting games as resource selection games and derive their equilibria under different expressions of uncertainty about the overall parking demand. The efficiency of the equilibrium states is compared against the optimal assignment that could be determined by a centralized entity and conditions are derived for minimizing the related price of anarchy value. Our results provide useful hints for the pricing and practical management of on-street and private parking resources. More importantly, they exemplify counterintuitive less-is-more effects about the way information availability modulates the service cost, which underpin general competitive service provision settings and contribute to the better understanding of effective information mechanisms

Populism, Ethnic Nationalism and Xenophobia

In this paper, we study a set of new indices, which are based on the answers of citizens to certain batteries of items included in a CSES module 5 pilot study conducted in Greece after the parliamentary election of September 2015. The first index is used to capture attitudes of citizens towards the political elites and is related to the increasing number of recent publications focusing on the study of populist attitudes. Likewise, the second index is based on items related to a demand for more power to the poor people. Another index developed here is built to measure attitudes towards out-groups. The use of this index is motivated by the increasing power of radical right-wing anti-immigrant parties, especially in Europe and due, to a certain extent, to the recent immigrant crisis. In addition to the aforementioned indices, we also identify the characteristics respondents think to be the most important for someone to be considered as a "Real Greek", i.e. we present what are the most important lines that according to Greek citizens separate the in-group from the out-groups

Populism, Ethnic Nationalism and Xenophobia

In this paper, we study a set of new indices, which are based on the answers of citizens to certain batteries of items included in a CSES module 5 pilot study conducted in Greece after the parliamentary election of September 2015. The first index is used to capture attitudes of citizens towards the political elites and is related to the increasing number of recent publications focusing on the study of populist attitudes. Likewise, the second index is based on items related to a demand for more power to the poor people. Another index developed here is built to measure attitudes towards out-groups. The use of this index is motivated by the increasing power of radical right-wing anti-immigrant parties, especially in Europe and due, to a certain extent, to the recent immigrant crisis. In addition to the aforementioned indices, we also identify the characteristics respondents think to be the most important for someone to be considered as a "Real Greek", i.e. we present what are the most important lines that according to Greek citizens separate the in-group from the out-groups

AI-driven, Context-Aware Profiling for 5G and Beyond Networks

In the era of Industrial Internet of Things (IIoT) and Industry 4.0, an immense volume of heterogeneous network devices will coexist and contend for shared network resources, in order to satisfy the very challenging IIoT applications, requiring ultra-reliable and ultra-low latency communications. Although novel key enablers, such as Network Slicing, Software Defined Networking (SDN) and Network Function Virtualization (NFV) have already offered significant advantages towards more efficient and flexible network and resource management approaches, the particular characteristics of IIoT applications pose additional burdens, mainly due to the complex wireless environments, high number of heterogeneous network devices, sensors, user equipments (UEs), etc., which may stochastically demand and contend for the -often scarce -computing and communication resources of industrial environments. To this end, this paper introduces PRIMATE, a novel, Artificial Intelligence (AI)-driven framework for the profiling of the networking behavior of such UEs, devices, users and things, which is able to operate in conjunction with already standardized or forthcoming, AI-based network resource management processes towards further gains. The novelty and potential of the proposed work lies on the fact that instead of attempting to either predict raw network metrics in a reactive manner, or predict the behavior of specific network entities/devices in an isolated manner, a big data-driven classification approach is introduced, which models the behavior of any network device/user from both a macroscopic, as well as service-specific perspective. The extended evaluation at the last part of this work shows the validity and viability of the proposed framework.This work has been partially supported by EC H2020 5GPPP 5Growth project (Grant 856709)