Pervasive computing applications deal with the intelligence surrounding users that can facilitate their activities. This intelligence is provided in the form of software components incorporated in embedded systems or devices in close... more
Pervasive computing applications deal with the intelligence surrounding users that can facilitate their activities. This intelligence is provided in the form of software components incorporated in embedded systems or devices in close distance with end users. One example of infrastructure that can host intelligent pervasive services is the Edge Computing (EC) ecosystem. EC nodes can execute a number of tasks for data collected by devices present in the Internet of Things (IoT). In this paper, we propose an intelligent, proactive tasks management model based on demand. Demand depicts the number of users or applications interested in using the available tasks in EC nodes, thus characterizing their popularity. We rely on a Deep Machine Learning (DML) model and more specifically on a Long Short Term Memory (LSTM) network to learn the distribution of demand indicators for each task and estimate the future interest in them. This information is combined with historical observations of and s...
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Lack of knowledge in the underlying data distribution in distributed large-scale data can be an obstacle when issuing analytics & predictive modelling queries. Analysts find themselves having a hard time finding analytics/exploration... more
Lack of knowledge in the underlying data distribution in distributed large-scale data can be an obstacle when issuing analytics & predictive modelling queries. Analysts find themselves having a hard time finding analytics/exploration queries that satisfy their needs. In this paper, we study how exploration query results can be predicted in order to avoid the execution of ‘bad’/non-informative queries that waste network, storage, financial resources, and time in a distributed computing environment. The proposed methodology involves clustering of a training set of exploration queries along with the cardinality of the results (score) they retrieved and then using query-centroid representatives to proceed with predictions. After the training phase, we propose a novel refinement process to increase the reliability of predicting the score of new unseen queries based on the refined query representatives. Comprehensive experimentation with real datasets shows that more reliable predictions ...
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Data management at the edge of the network can increase the performance of applications as the processing is realized close to end users limiting the observed latency in the provision of responses. A typical data processing involves the... more
Data management at the edge of the network can increase the performance of applications as the processing is realized close to end users limiting the observed latency in the provision of responses. A typical data processing involves the execution of queries/tasks defined by users or applications asking for responses in the form of analytics. Query/task execution can be realized at the edge nodes that can undertake the responsibility of delivering the desired analytics to the interested users or applications. In this paper, we deal with the problem of allocating queries to a number of edge nodes. The aim is to support the goal of eliminating further the latency by allocating queries to nodes that exhibit a low load and high processing speed, thus, they can respond in the minimum time. Before any allocation, we propose a method for estimating the computational burden that a query/task will add to a node and, afterwards, we proceed with the final assignment. The allocation is concluded...
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In recent years, there has been a significant increase in the use of mobile devices and their applications. Meanwhile, cloud computing has been considered as the latest generation of computing infrastructure. There has also been a... more
In recent years, there has been a significant increase in the use of mobile devices and their applications. Meanwhile, cloud computing has been considered as the latest generation of computing infrastructure. There has also been a transformation in cloud computing ideas and their implementation so as to meet the demand for the latest applications. mobile edge computing (MEC) is a computing paradigm that provides cloud services near to the users at the edge of the network. Given the movement of mobile nodes between different MEC servers, the main aim would be the connection to the best server and at the right time in terms of the load of the server in order to optimize the quality of service (QoS) of the mobile nodes. We tackle the offloading decision making problem by adopting the principles of optimal stopping theory (OST) to minimize the execution delay in a sequential decision manner. A performance evaluation is provided using real world data sets with baseline deterministic and ...
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The Pervasive Computing paradigm has raised issues such as conceptual semantic descriptions and management of ambient information resources. The probabilistic theory on the other hand provides knowledge representation schemes that model... more
The Pervasive Computing paradigm has raised issues such as conceptual semantic descriptions and management of ambient information resources. The probabilistic theory on the other hand provides knowledge representation schemes that model uncertainty. However, history models related to activities exploit both semantic and probabilistic modeling. Issues such as attack prediction and classification of activities and intentions are of high importance in ubiquitous environments. In this paper we propose a novel history context model that purifies the history of certain activity-oriented contexts in terms of inferring collaborative activities towards attacks. Therefore, we evaluate such model by focusing on attack context modeling, which is based upon a distributed honeynet Intrusion Detection System (IDS) architecture. Breadth and Depth Bayesian classifier and an inference probabilistic algorithm, over well defined fuzzy conceptual information, by means of ontology, is introduced regarding context histories.
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Distributed Computing, Information Retrieval, Scheduling, Abstraction, Middleware, and 12 moreQuality Assurance, Consumer, Wireless Network, Information Quality, Optimal Stopping, Parallel & Distributed Computing, Secretary problem, Search Algorithm, Parallel, Ad hoc network, Sensor Array, and Wireless Sensor Network
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... 7. Introduction to Web Services (pages 134-154). C. Pennington (University of Georgia, USA) Sample PDF | More details... ... Semantic Web Service Discovery in the WSMO Framework (pages 281-316). U. Keller (Leopold-Franzens-Universität... more
... 7. Introduction to Web Services (pages 134-154). C. Pennington (University of Georgia, USA) Sample PDF | More details... ... Semantic Web Service Discovery in the WSMO Framework (pages 281-316). U. Keller (Leopold-Franzens-Universität Innsbruck, Austria), R. Lara (Grupo ...
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We propose a cross-layer scheme for regulating power consumption in energy-constrained ad hoc wireless networks where information is disseminated in an adaptive epidemic manner. A time-optimized mechanism based on the Optimal Stopping... more
We propose a cross-layer scheme for regulating power consumption in energy-constrained ad hoc wireless networks where information is disseminated in an adaptive epidemic manner. A time-optimized mechanism based on the Optimal Stopping Theory utilises noise fluctuations to trigger the adaptation of transmission characteristics in a fashion that a perceived net reward is maximized. This results in data delivery being enhanced while energy cost remains modest.
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Informatics, Decision Making, Situation awareness, Ubiquitous Computing, Fuzzy Logic, and 11 moreConceptual Modelling, Pervasive Computing, Fuzzy Systems, Communication Networks, Fuzzy Inference, Information Services, Ontologies, Conceptual Model, Information Service, Context Modeling, and Ontological Engineering
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ABSTRACT Location-based services (LBS) deliver location-dependent content to mobile users. Mobile users are equipped with devices, e.g., smartphones, which regularly report the position to a back-end system (BES). Users, with similar... more
ABSTRACT Location-based services (LBS) deliver location-dependent content to mobile users. Mobile users are equipped with devices, e.g., smartphones, which regularly report the position to a back-end system (BES). Users, with similar moving patterns, might also have requests for similar content delivery (e.g., museum/city-tour guidance, traffic conditions). Moreover, users can be formed into (temporal) groups, whose structure can vary over time (i.e., group-merge/split, group membership). The larger the number of users is, the higher the overall BES load becomes. In this paper, we propose a group management scheme (SCHISM) which exploits the users' formation into groups in order to tackle the BES-mobile device communication load. Specifically, we treat a group as a single user (the group leader; GL). In this case, the BES communicates only with the GL for certain time horizon. The GL can then disseminate the LBS content to the group members. We adopt an agglomerative clustering scheme for the group formation phase. The clustering gain is sequentially monitored corresponding to the transitions between consecutive merge-steps. A user group is classified as ‘suspicious’, i.e., candidate for fragmentation/split, when a decrease in the clustering gain is obtained during the group formation phase. Performance assessment reveals significant improvements due to the considered scheme for location-based systems.
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__________________________________... Future web business models involve virtual environments where entities interact in order to sell or buy information goods. Such environments are known as Information Markets (IMs). Intelligent agents... more
__________________________________... Future web business models involve virtual environments where entities interact in order to sell or buy information goods. Such environments are known as Information Markets (IMs). Intelligent agents are used in IMs ...
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ABSTRACT Contextual data collection is a major challenge in mobile sensor networks, in which sources generate quality-stamped context and mobile nodes (collectors) attempt to gather context of high quality. We deal with the context... more
ABSTRACT Contextual data collection is a major challenge in mobile sensor networks, in which sources generate quality-stamped context and mobile nodes (collectors) attempt to gather context of high quality. We deal with the context collection problem, in which collectors forage for high quality context and, then, deliver it to mobile context-aware applications. Collectors undergo a context collection process by exchanging contextual data with neighbouring collectors and/or sources in light of receiving context of better quality. The quality indicator of context normally decreases with time. Hence, the collectors cannot prolong such process forever, where the delivered context might be useless for the application. We propose a context collection scheme based on the optimal stopping theory (OST), which supports collectors with time-optimized context delivery decisions. We compare the performance of the proposed scheme with pre-existing OST-based context collection mechanism and quantify the benefits stemming for its adoption.
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ABSTRACT We treat the problem of movement prediction as a classification task. We assume the existence of a (gradually populated/trained) knowledge base and try to compare the movement pattern of a certain object with stored information... more
ABSTRACT We treat the problem of movement prediction as a classification task. We assume the existence of a (gradually populated/trained) knowledge base and try to compare the movement pattern of a certain object with stored information in order to predict its future locations. A conventional prediction scheme would suffer from potential noise in movement patterns. Such noise (typically manifested as small-random deviations from previously seen patterns): 1) negatively impacts the prediction capability (accuracy) of the classification system and 2) oversizes the knowledge base (i.e., the storage needs become excessive). We try to alleviate such shortcomings through the use of optimal stopping theory (OST) and the introduction of a very specific movement prediction work-flow. OST relaxes the classification task so that slightly different patterns can be treated as similar. Moreover, the underlying knowledge base is kept as concise as possible by retaining those patterns with limited spatial variance. The performance assessment and comparison to other schemes reveals the superiority of the proposed system.
ABSTRACT We focus on Location Based Services (LBSs) which deliver information to groups of mobile users based on their spatial context. The existence (and non-trivial lifetime) of groups of mobile users can simplify the operation of LBS... more
ABSTRACT We focus on Location Based Services (LBSs) which deliver information to groups of mobile users based on their spatial context. The existence (and non-trivial lifetime) of groups of mobile users can simplify the operation of LBS and reduce network overhead (due to location updates and application content flow). We propose an incremental group formation algorithm and an optimally scheduled, adaptive group validation mechanism which detects and further exploits spontaneous formation of groups of mobile users. The advantages of application simplification and network load reduction are pursued. Through the proposed mechanism the LBS server (back-end system) monitors representatives of formed groups, i.e., group leader, and, in turn, disseminates location-dependent application content to group members. We first introduce an incremental group formation algorithm for group partitioning and identification of group leaders. We elaborate on the mechanism which adopts the Optimal Stopping Theory in order to assess the group persistence through evaluation of compactness and coherency metrics. We compare the performance of the proposed scheme with that of existing mechanisms for moving objects clustering and quantify the benefits stemming for its adoption.
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Conceptual modeling is viewed as a promising means to represent contextual knowledge, which may be enriched with semantics. Such modeling is capable of describing context, as well as, reasoning about it. Moreover, contextual reasoning is... more
Conceptual modeling is viewed as a promising means to represent contextual knowledge, which may be enriched with semantics. Such modeling is capable of describing context, as well as, reasoning about it. Moreover, contextual reasoning is attained taking into ...
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ABSTRACT Mobile applications are required to operate in ubiquitous environments of dynamic nature. Specifically, the availability of resources and services may vary significantly during a typical session of system operation. As a... more
ABSTRACT Mobile applications are required to operate in ubiquitous environments of dynamic nature. Specifically, the availability of resources and services may vary significantly during a typical session of system operation. As a consequence, mobile applications need to be ...
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ABSTRACT We study the performance improvement of Location Based Services through the identification and subsequent use of groups of mobile nodes. In our scheme we exploit the formation of nodes into groups in order to reduce the... more
ABSTRACT We study the performance improvement of Location Based Services through the identification and subsequent use of groups of mobile nodes. In our scheme we exploit the formation of nodes into groups in order to reduce the computation load incurred in back--end systems (e.g., Location Servers) and the associated network overhead. The back--end systems track the position and communicate with the Group Leader (GL). The GL, in turn, passes the received information to the members of the group (e.g., through short--range communications). The formation of mobile groups is validated over time to avoid misinterpreted temporary groupings which could endanger the adoption of the reduced load/overhead scheme. A time scheduling scheme based on the Optimal Stopping Theory assists in the finalization of the group validity. Metrics like group compactness are thoroughly assessed in line with the optimal stopping time scheme to increase confidence on group validity and persistence. Performance assessment reveals significant benefits for the considered location based services system.
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Abstract In certain settings, like for example a Wireless Sensor Network (WSN), contextual information (context) needs to be disseminated between nodes and then interpreted. Dissemination is typically performed through a wireless network... more
Abstract In certain settings, like for example a Wireless Sensor Network (WSN), contextual information (context) needs to be disseminated between nodes and then interpreted. Dissemination is typically performed through a wireless network infrastructure where ...
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One of the main problems in mobile ad-hoc networks (MANETs) is the efficient dissemination of data, typically from the different sources to destination nodes. Solutions that work efficient for a specific setup do not perform well on... more
One of the main problems in mobile ad-hoc networks (MANETs) is the efficient dissemination of data, typically from the different sources to destination nodes. Solutions that work efficient for a specific setup do not perform well on slightly different applications. Hence, careful ...
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Clustering and Big Data
Abstract Pervasive computing is a broad and compelling research topic in computer science that focuses on the applications of technology to assist users in everyday life situations. It seeks to provide proactive and self-tuning... more
Abstract Pervasive computing is a broad and compelling research topic in computer science that focuses on the applications of technology to assist users in everyday life situations. It seeks to provide proactive and self-tuning environments and devices to seamlessly ...