A Mechanism that Provides Incentives for Truthful Feedback in Peer-to-Peer Systems

We propose a mechanism for providing the incentives for reporting truthful feedback in a peer-to-peer system for exchanging services (or content). This mechanism is to complement reputation mechanisms that employ ratings' feedback on the various transactions in order to provide incentives to peers for offering better services to others. Under our approach, each of the transacting peers (rather than just the client) submits a rating on the performance of their mutual transaction. If these are in disagreement, then both transacting peers are punished, since such an occasion is a sign that one of them is lying. The severity of each peer's punishment is determined by his corresponding non- credibility metric; this is maintained by the mechanism and evolves according to the peer's record. When under punishment, a peer does not transact with others. We model the punishment effect of the mechanism in a peer-to-peer system as a Markov chain that is experimentally proved to be very accurate. According to this model, the credibility mechanism leads the peer-to-peer system to a desirable steady state isolating liars. Then, we define a procedure for the optimization of the punishment parameters of the mechanism for peer-to-peer systems of various characteristics. We experimentally prove that this optimization procedure is effective and necessary for the successful employment of the mechanism in real peer-to-peer systems. Then, the optimized credibility mechanism is combined with reputation-based policies to provide a complete solution for high performance and truthful rating in peer-to-peer systems. The combined mechanism was experimentally proved to deal very effectively with large fractions of collaborated liar peers that follow static or dynamic rational lying strategies in peer-to-peer systems with dynamically renewed population, while the efficiency loss induced to sincere peers by the presence of liars is diminished. Finally, we describe the potential implementation of the mechanism in real peer-to-peer systems

An Exploration Of Parameters Affecting Employee Energy Conversation Behaviour At The Workplace, Towards IOT-Enabled Behavioural Interventions

Energy conservation is one of the widely recognised important means towards addressing CO2 emissions and the resulting global issue of climate change. Furthermore, public buildings have been recognised as contributing significantly to the consumption of energy worldwide. More importantly, occupant behaviour, a factor that needs to be studied further, can have a high impact on the energy consumed within public buildings. Through our study, we have conducted an exploratory study on the parameters affecting employee energy conservation behaviour in public buildings, towards constructing a behavioural model that can be employed in IoT-enabled personalised energy disaggregation initiatives. We propose an extension to an existing model of employee energy behaviour based on Values Beliefs Norms (VBN) theory, with the addition of five parameters – comfort levels, burnout, locus of control, personal disadvantages and energy awareness. In addition, we discriminate between two groups of inter-related energy conservation behaviours at work – popular and unpopular energy conservation behaviours – and explain our resulting behavioural models’ utility towards IoT-enabled energy conservation, within workplaces. We find that promoting employees’ energy awareness levels, as well as positively affecting their environmental worldviews and personal norms are important factors that should be considered in behavioural interventions toward energy conservation at the workplace

Model-View Sensor Data Management in the Cloud

Infinite nature of sensor data poses a serious challenge for query processing even in a cloud infrastructure. Model-based sensor data approximation reduces the amount of data for query processing, but all modeled segments need to be scanned, in the worst case. In this paper, we propose an innovative index for modeled segments in key-value stores, namely KVI-index. KVI-index has an in-memory tree component and a secondary structure materialized in the key-value store that maps the tree nodes to the modeled data segments. Then, we introduce a KVI-index-Scan-MapReduce hybrid approach to perform efficient query processing. As proved by a series of experiments in a real private cloud infrastructure, our approach outperforms in query response time and index updating efficiency both Hadoop-based parallel processing of the raw sensor data and multiple alternative indexing approaches of model-view data

Gamification at Work: Employee Motivations to Participate and Preference for Energy Conservation

Energy wastage, especially in public buildings, is one of the widely acknowledged issues that have to be addressed towards protecting the environment. Furthermore, affecting the occupants’ behaviour has been identified in the literature as an under-investigated means of conserving energy. In this research paper we report on the results from an investigation we conducted in three different workplaces, situated in different EU countries. In a survey of N=119 employees, we explore Employee Motivations to Participate in Gamification at work (EMPG) and identify the needs for (i)Self- Actualisation, (ii)Self-Regulation, (iii)Rewards & Recognition and (iv)Affiliation as most prominent. Additionally we examine the employees’ profiles, specific needs and preferences in game elements, towards participating in gamification aimed at conserving energy at the workplace. Correlations of the four types of EMPG with basic game elements and energy-saving actions at work are consequently explored and discussed. Ultimately, taking into consideration employees’ motivations and preferences, we derive and propose design guidelines for gamified applications providing personalised feedback towards saving energy at work

PANACEA: Tunable Privacy for Access Controlled Data in Peer-to-Peer Systems

Peer-to-peer paradigm is increasingly employed for organizing distributed resources for various applications, e.g. content distribution, open storage grid etc. In open environments, even when proper access control mechanisms supervise the access to the resources, privacy issues may arise depending on the application. In this paper, we introduce, PANACEA, a system that offers high and tunable privacy based on an innovative resource indexing approach. In our case, privacy has two aspects: the deduceability of a resource's existence/non-existence and the discovery of the provider of the resource. We systematically study the privacy that can be provided by the proposed system and compare its effectiveness as related to conventional P2P systems. Employing both probabilistic and information-theoretic approaches, we analytically derive that PANACEA can offer high privacy, while preserving high search efficiency for authorized users. Our analysis and the effectiveness of the approach have been experimentally verified. Moreover, the privacy offered by the proposed system can be tuned according to the specific application needs which is illustrated with detailed simulation study

Tunable Privacy for Access Controlled Data in Peer-to-Peer Systems

Peer-to-peer paradigm is increasingly employed for organizing distributed resources for various applications, e.g. content distribution, open storage grid etc. In open environments, even when proper access control mechanisms supervise the access to the resources, privacy issues may arise depending on the application. In this paper, we introduce, PANACEA, a system that offers high and tunable privacy based on an innovative resource indexing approach. In our case, privacy has two aspects: the deducibility of a resource's existence/non-existence and the discovery of the provider of the resource. We systematically study the privacy that can be provided by the proposed system and compare its effectiveness as related to conventional P2P systems. Employing both probabilistic and information-theoretic approaches, we analytically derive that PANACEA can offer high privacy, while preserving high search efficiency for authorized users. Our analysis and the effectiveness of the approach have been experimentally verified. Moreover, the privacy offered by the proposed system can be tuned according to the specific application needs which is illustrated with detailed simulation study

A Survey of Model-based Sensor Data Acquisition and Management

In recent years, due to the proliferation of sensor networks, there has been a genuine need of researching techniques for sensor data acquisition and management. To this end, a large number of techniques have emerged that advocate model-based sensor data acquisition and management. These techniques use mathematical models for performing various, day-to-day tasks involved in managing sensor data. In this chapter, we survey the state-of-the-art techniques for model-based sensor data acquisition and management. We start by discussing the techniques for acquiring sensor data. We, then, discuss the application of models in sensor data cleaning; followed by a discussion on model-based methods for querying sensor data. Lastly, we survey model-based methods proposed for data compression and synopsis generation

Analyzing the Emergence of Semantic Agreement among Rational Agents

Todays complex online applications often require the interaction of multiple services that potentially belong to different business entities. Interoperability is a core element of such an environment, yet not a straightforward one. In this paper, we argue that the emergence of interoperability is an economic process among rational agents and, although interoperability can be mutually beneficial for the involved parties, it is also costly and may fail to emerge. As a sample scenario, we consider the emergence of semantic interoperability among rational service agents in the service-oriented architectures (SOA) and analyze their individual economic incentives with respect to utility, risk and cost. We model this process as a positive-sum game and study its equilibrium and evolutionary dynamics. According to our analysis, which is also experimentally verified, certain conditions on the communication cost, the cost of technological adaptation, the expected mutual benefit from interoperability as well as the expected loss from isolation drive the process

A Decentralized Recommender System for Effective Web Credibility Assessment

An overwhelming and growing amount of data is available online. The problem of untrustworthy online information is augmented by its high economic potential and its dynamic nature, e.g. transient domain names, dynamic content, etc. In this paper, we address the problem of assessing the credibility of web pages by a decentralized social recommender system. Specifically, we concurrently employ i) item-based collaborative filtering (CF) based on specific web page features, ii) user-based CF based on friend ratings and iii) the ranking of the page in search results. These factors are appropriately combined into a single assessment based on adaptive weights that depend on their effectiveness for different topics and different fractions of malicious ratings. Simulation experiments with real traces of web page credibility evaluations suggest that our hybrid approach outperforms both its constituent components and classical content-based classification approaches