The Internet of Things (IoT) is rapidly becoming ubiquitous in our daily lives as more and more devices become connected to the internet. However, the use of IoT also brings new challenges, especially with regard to the security and reliability of IoT-based applications. This paper proposes a novel approach for ensuring the reliability of IoT-based applications using a Trusted Platform Module (TPM). The proposed method takes advantage of the TPM's hardware-based security to create a safe and trustworthy environment for IoT devices. The TPM is used to store and manage the cryptographic keys necessary for secure communication between IoT devices, as well as to authenticate and authorize access to IoT resources. This ensures that only authorized devices and applications are capable of accessing sensitive data and resources. The proposed approach is evaluated using a prototype implementation, and the results demonstrate the efficiency in ensuring the reliability of IoT-based applications. Specifically, the prototype implementation is capable of detecting unauthorized access attempts, as well as ensure that data is transmitted securely and reliably between IoT devices. Overall, this paper presents a promising approach for addressing the security and reliability challenges of IoT-based applications and highlights the important role that hardware-based security solutions like the TPM can play in ensuring the integrity and trustworthiness of IoT systems.

In the framework of the H2020 project INCLUDING (www.including-cluster.eu), the Piraeus port commercial terminal has hosted a field exercise on the identification and recovery of two orphan sources inside a cargo container. The primary objective of the field exercise was to test the integration of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) in the response plan coordinated by the CBRN experts of the Hellenic Ministry of Defence and the first operational verification of a platform under development in the project for the management of the mobilized resources on the incident scene. The activity marks a solid step ahead in introducing innovation in the exercise and training activities in the nuclear security domain and in improving sharing of resources at EU level. In this article is described the concept of operation of the INCLUDING platform, its architecture and its use in the different phase of the Piraeus port exercise, that is one of the eight Joint Action planned during the INCLUDING project duration.

The Mobile IoT domain has been significantly expanded with the proliferation of drones and unmanned robotic devices. In this new landscape, the communication between the resource-constrained device and the fixed infrastructure is similarly expanded to include new messages of varying importance, control, and monitoring. To efficiently and effectively control the exchange of such messages subject to the stochastic nature of the underlying wireless network, we design a time-optimized, dynamic, and distributed decision-making mechanism based on the principles of the Optimal Stopping and Change Detection theories. The findings from our experimentation platform are promising and solidly supportive to a vast spectrum of real-time and latency-sensitive applications with quality-of-service requirements in mobile IoT environments.

Nowadays, the increased amount of users' devices produce huge volumes of data that should be efficiently managed by modern applications. Streams are adopted to deliver data that, usually, are stored into a number of partitions. Splitting the data offers a lot of advantages as applications can process them in parallel, thus, they increase the speed of processing. Progressive analytics are also adopted to deliver partial responses, during processing, thus, saving time in the execution of applications. Data exploration and analytics queries are very significant for future applications. Usually, such queries demand for an ordered set of objects as a response and require intelligent predictive schemes to deliver the responses on top of the partial results retrieved by the distributed data partitions. A finite set of query processors are adopted to produce these partial results. Processors are placed in front of each partition and report progressive analytics to a central entity. In this paper, we envision the query controller (QC) as the central entity that collects progressive analytics and return the final response to users/applications. The QC receives partial ordered sets of objects and aggregates them to derive the final outcome. We focus on a QC that applies time-optimized techniques and aggregation operators to deliver every response, i.e., ordered sets, over streams of partial ordered subsets. We perform a comprehensive performance assessment with synthetic data and report on the performance of the QC. Our experimental evaluation reveals the pros and cons of the proposed model and a comparison assessment places this paper in the respective literature.

Mobile Ad Hoc Network (MANET) is a network with no fixed topology, no infrastructure and yet capable of performing tasks i.e., information diffusion. During the network operation the network nodes are moving, possibly in a stochastic manner, or following a certain pattern. The nodes interact and exchange information, thus spending energy which is usually limited and can not be replaced. In addition to these, the nodes must be aware of malicious nodes which will try to either intercept the node-to-node communications and/or drain the resources of the nodes. Two fundamental problems need to be addressed: The temporal extension of the network operation by uniformly exploiting the network resources and the accurate and timely detection of malicious nodes. In this paper, which is an extension of previous work of ours, we address both of these issues. 1) We propose a clustering scheme which produces clusters that are stable enough to allow for the trust information to diffuse, initially through the cluster and evidently through the entire network. 2) We propose a trust scheme which assigns and updates a trust value to every node of the network and thus reveals the malicious nodes and diffuses the information through the network.

The Internet of Things involves a huge number of devices that collect data and deliver them to the Cloud. The processing of data at the Cloud is characterized by increased latency in providing responses to analytics queries defined by analysts or applications. Hence, Edge Computing (EC) comes into the scene to provide data processing close to the source. The collected data can be stored in edge devices and queries can be executed there to reduce latency. In this paper, we envision a case where entities located in the Cloud undertake the responsibility of receiving analytics queries and decide on the most appropriate edge nodes for queries execution. The decision is based on statistical signatures of the datasets of nodes and the statistical matching between statistics and analytics queries. Edge nodes regularly update their statistical signatures to support such decision process. Our performance evaluation shows the advantages and the shortcomings of our proposed schema in edge computing environments.

Properly clustering a Mobile Ad Hoc Network (MANET) is an efficient method to ensure optimal network resources exploitation. A lot of clustering schemes have been proposed, most of them suffering from the same drawback i.e., cluster lifetime instability due to the mobility of the nodes. In order to cope with this particular problem, along with trust issues that arise and have been partially investigated previously, we propose a time-optimized clustering scheme which improves the lifespan of the network clusters, thus leading to increased trust efficiency, since malicious nodes are easier to locate. The malicious node spotting is achieved by combining individual node observations, as well as the collective cluster trust estimation regarding the reputation vector of a specific node.

Nowadays, the domain of robotics experiences a significant growth. We focus on Unmanned Vehicles intended for the air, sea and ground (UxV). Such devices are typically equipped with numerous sensors that detect contextual parameters from the broader environment, e.g., obstacles, temperature. Sensors report their findings (telemetry) to other systems, e.g., back-end systems, that further process the captured information while the UxV receives control inputs, such as navigation commands from other systems, e.g., commanding stations. We investigate a framework that monitors network condition parameters including signal strength and prioritizes the transmission of control messages and telemetry. This framework relies on the Theory of Optimal Stopping to assess in real-time the trade-off between the delivery of the messages and the network quality statistics and optimally schedules critical information delivery to back-end systems.

Event management in sensor networks is a multidisciplinary field involving several steps across the processing chain. In this paper, we discuss the major steps that should be performed in real- or near real-time event handling including event detection, correlation, prediction and filtering. We explain the rationale behind each considered step and we focus on an event correlation algorithm based on a variable-order Markov model. The proposed theory is applied on the maritime domain and is validated through extensive experimentation with real sensor streams originating from large-scale sensor networks deployed in a maritime fleet of ships.

Software Defined Networking (SDN) is the key technology for combining networking and Cloud solutions to provide novel applications. SDN offers a number of advantages as the existing resources can be virtualized and orchestrated to provide new services to the end users. Such a technology should be accompanied by powerful mechanisms that ensure the end-to-end quality of service at high levels, thus, enabling support for complex applications that satisfy end users needs. In this paper, we propose an intelligent mechanism that agglomerates the benefits of SDNs with real-time “Big Data” forecasting analytics. The proposed mechanism, as part of the SDN controller, supports predictive intelligence by monitoring a set of network performance parameters, forecasting their future values, and deriving indications on potential service quality violations. By treating the performance measurements as time-series, our mechanism employs a novel ensemble forecasting methodology to estimate their future values. Such predictions are fed to a Type-2 Fuzzy Logic system to deliver, in real-time, decisions related to service quality violations. Such decisions proactively assist the SDN controller for providing the best possible orchestration of the virtualized resources. We evaluate the proposed mechanism w.r.t. precision and recall metrics over synthetic data.

We focus on Internet of Things (IoT) environments where sensing and computing devices (nodes) are responsible to observe, reason, report, and react to a specific phenomenon. Each node (e.g., an unmanned vehicle or an autonomous device) captures context from data streams and reasons on the presence of an event. We propose a distributed predictive analytics scheme for localized context reasoning under uncertainty. Such reasoning is achieved through a contextualized, knowledge-driven clustering process, where the clusters of nodes are formed according to their belief on the presence of the phenomenon. Each cluster enhances its localized opinion about the presence of an event through consensus realized under the principles of fuzzy logic (FL). The proposed FL-driven consensus process is further enhanced with semantics adopting type-2 fuzzy sets to handle the uncertainty related to the identification of an event. We provide a comprehensive experimental evaluation and comparison assessment with other schemes over real data and report on the benefits stemmed from its adoption in IoT environments.

Data stream monitoring provides the basis for building intelligent context-aware applications over contextual data streams. A number of wireless sensors could be spread in a specific area and monitor contextual parameters for identifying various phenomena, e.g., fire or flood. A back-end system receives measurements and derives decisions for possible abnormalities related to negative effects. We propose a mechanism which, based on multivariate sensors data streams, provides real-time identification of phenomena. The proposed framework performs contextual information fusion over consensus theory for the efficient measurements aggregation while time-series prediction is adopted to result future insights on the aggregated values. The unanimous fused and predicted pieces of context are fed into a type-2 fuzzy inference system to derive highly accurate identification of events. The type-2 inference process offers reasoning capabilities under the uncertainty of the phenomena identification. We provide comprehensive experimental evaluation over real contextual data and report on the advantages and disadvantages of the proposed mechanism. Our mechanism is further compared with type-1 fuzzy inference and other mechanisms to demonstrate its false alarms minimization capability.

The new era of Web and Internet of Things (IoT) paradigm is being enabled by the proliferation of various devices like RFIDs, sensors, and actuators. Smart devices (devices having significant computational capabilities, transforming them to `smart things') are embedded in the environment to monitor and collect ambient information. In a city, this leads to Smart City frameworks. Intelligent services could be offered on top of such information related to any aspect of humans' activities. A typical example of services offered in the framework of Smart Cities is IoT-enabled waste management. Waste management involves not only the collection of the waste in the field but also the transport and disposal to the appropriate locations. In this paper, we present a comprehensive and thorough survey of ICT-enabled waste management models. Specifically, we focus on the adoption of smart devices as a key enabling technology in contemporary waste management. We report on the strengths and weaknesses of various models to reveal their characteristics. This survey sets up the basis for delivering new models in the domain as it reveals the needs for defining novel frameworks for waste management.

We establish a framework that can be used by Origin Servers (content-generating organizations) for claiming Content Delivery Network (CDN) resources in a fine-grained way. The basis of our work lies in the use of Stocks as well as a Secondary Market for the stock trading, tools and products commonly used in modern capital markets. Network and disk resources are being monitored through well-established techniques (Kernel Regression Estimators) in a given time frame (typically in the order to an hour). This information is dynamically updated to reflect transient behavior and longer-term trends. The model that we establish relies initially on the resource use forecasting that the kernels can provide but also on other complementary techniques. To cope with inaccuracies in the predictive capability of the prediction mechanism (that would imply inefficient use of the claimed resources) we establish a Secondary Market for the stock trading. This scheme allows the fast offloading of unused resources. Thus, all the involved players (including the CDN itself) benefit from the rationalized use of CDN resources.

We present a robust, dynamic scheme for the automatic self-deployment and relocation of mobile sensor nodes (e.g., unmanned ground vehicles, robots) around areas where phenomena take place. Our scheme aims (i) to sense environmental contextual parameters and accurately capture the spatiotemporal evolution of a certain phenomenon (e.g., fire, air contamination) and (ii) to fully automate the deployment process by letting nodes relocate, self-organize (and self-reorganize), and optimally cover the focus area. Our intention is to “opportunistically” modify the previous placement of nodes to attain high-quality phenomenon monitoring. The required intelligence is fully distributed within the mobile sensor network so the deployment algorithm is executed incrementally by different nodes. The presented algorithm adopts the Particle Swarm Optimization technique, which yields very promising results as reported in the article (performance assessment). Our findings show that the proposed algorithm captures a certain phenomenon with very high accuracy while maintaining the networkwide energy expenditure at low levels. Random occurrences of similar phenomena put stress upon the algorithm which manages to react promptly and efficiently manage the available sensing resources in the broader setting.

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 utilizes 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.

This article proposes two models that improve the Principal Component-based Context Compression (PC3) model for contextual information forwarding among sensor nodes in a Wireless Sensor Network (WSN). The proposed models (referred to as iPC3 and oPC3) address issues associated with the control of multivariate contextual information transmission in a stationary WSN. Because WSN nodes are typically battery equipped, the primary design goal of the models is to optimize the amount of energy used for data transmission while retaining data accuracy at high levels. The proposed energy conservation techniques and algorithms are based on incremental principal component analysis and optimal stopping theory. iPC3 and oPC3 models are presented and compared with PC3 and other models found in the literature through simulations. The proposed models manage to extend the lifetime of a WSN application by improving energy efficiency within WSN.

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.

Electronic markets are virtual frameworks where entities not known in advance have the opportunity to interact for the trading of products or services. Usually, a negotiation is necessary for the conclusion of the transaction. The conclusion is either positive (agreement) or negative (conflict). An efficient reasoning mechanism is necessary for players participating in negotiations. In this paper, we focus on the buyer side and propose two decision models based on the optimal stopping theory (OST). OST is proved to be very efficient in cases where an entity tries to find the time to stop a process with the aim of maximizing her utility. The outcome of the proposed decision method indicates whether the buyer stops a negotiation either by accepting the offer or continuing in the negotiation by rejecting it. In our models, we assume zero knowledge on the players' characteristics. Our proposed decision models do not require any complex modeling or any information provided by experts. Experimental results reveal the efficiency of each model and provide a comparison assessment with other research efforts.

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.

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.

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.

The vision of autonomic computing involves distributed nodes capable of managing and preserving themselves. In practice, autonomic computing is also strongly connected to the concepts of mobility and platform heterogeneity since next generation networks assume different types of mobile nodes that operate inside ad hoc environments. In such cases where the context changes can be frequent, the capability of capturing the environmental changes is crucial. However, it cannot be assumed that all the nodes are equipped with all possible types of sensors in order to locally retrieve the required contextual information. Sensor information exchange between the nodes through efficient data dissemination mechanisms can further improve the overall awareness of the network. Another challenging task for autonomic computing concerns the self-reconfiguration of the autonomous nodes according to the sensed context. This paper presents the overall architecture design, the implementation and the evaluation of a middleware developed in the context of the Integrated Platform for Autonomic Computing (IPAC). This middleware is targeted to embedded devices and supports mobile context-aware applications that render the aforementioned desired behavior. The paper focuses on two aspects of autonomic computing: collaborative context-awareness and self-reconfiguration.

The use of network tomography has been proposed as a means to infer network topology in wireline networks. In what regards wireless sensor networks, the methods suggested for topology discovery make explicit use of neighborhood information sent by internal network nodes. We propose an algorithm for inferring the tree topology of a wireless sensor network based on loss measurements collected at the sink of the network. The proposed method does not rely on the active cooperation of internal network nodes, but is capable of inferring the sought topology, only through the received sensor readings (passive method), without undertaking energy-intensive tasks within the network.

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 for representing buyers or information providers (sellers). We focus on the decisions taken by the buyer in the purchase negotiation process with sellers. We propose a reasoning mechanism on the offers (prices of information goods) issued by sellers based on fuzzy logic. The buyer’s knowledge on the negotiation process is modeled through fuzzy sets. We propose a fuzzy inference engine dealing with the decisions that the buyer takes on each stage of the negotiation process. The outcome of the proposed reasoning method indicates whether the buyer should accept or reject the sellers’ offers. Our findings are very promising for the efficiency of automated transactions undertaken by intelligent agents.

Ad hoc networks such as Wireless Sensor Networks (WSN) are characterized by the scarcity of energy resources (among other resource types). Their intelligent design can extend their lifetime without compromising the operation of network nodes and the applications running on them. To this end we propose an adaptive epidemic scheme that helps reduce energy expenditure through intelligent tuning of the infection (forwarding) rate. Redundant communications and experienced error rate drive the adaptation of the forwarding rate. Simulation results show that significant energy gains can be obtained through the proposed scheme.

Mobile applications are required to operate in highly dynamic pervasive computing environments of dynamic nature and predict the location of mobile users in order to act proactively. We focus on the location prediction and propose a new model/framework. Our model is used for the classification of the spatial trajectories through the adoption of Machine Learning (ML) techniques. Predicting location is treated as a classification problem through supervised learning. We perform the performance assessment of our model through synthetic and real-world data. We monitor the important metrics of prediction accuracy and training sample size.

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 resources are scarce. Our focus is on the design/implementation of a context compression scheme that tries to minimize the pieces of information exchanged over the network. Our scheme heavily relies on the multi-value(vectorial) nature of context dissemination messages that flow throughout the network. We adopt the Principal Component Analysis and determine the statistical dependencies between the context vector components. We manage to reduce (compress)the transmitted contextual information down to the identified principal components. A comparative assessment with other energy efficient models is reported indicating the capability of the proposed model to minimize resource consumption.

In our everyday life, we frequently experience cases where persons group together. In those cases, context-aware systems capture and process identical context. Therefore, the need to collaboratively address Context Awareness (CA) emerges. In the considered setting, ad hoc networking between mobile nodes enables the exchange of information, thus, CA is facilitated. The synergy between mobile nodes materializes the Collaborative CA (CCA) paradigm. We advance the general CCA concept by performing communication between nodes probabilistically in a way similar to virus (epidemic) spreading. Nodes feature a hierarchical information model, which can be exploited by an information diffusion process. Multiple pieces of information, exchanged as epidemics, can complete the information present at a certain node, which in turn infers and spreads new information. We study this novel scheme extensively through an information model for context and an analytical framework (Markov process) with simulations. Our findings show that the information spreading large benefits from the mobility of nodes and semantic processing of the information model.

Software Agents can assume the responsibility of finding and negotiating products on behalf of their owners in an electronic marketplace. In such cases, Fuzzy Logic can provide an efficient reasoning mechanism especially for the buyer side. Agents representing buyers can rely on a fuzzy rule base in order to reason for their next action at every round of the interaction process with sellers. In this paper, we describe a model where the buyer builds its fuzzy knowledge base using algorithms for automatic fuzzy rules generation based on data provided by experts and compare a set of such algorithms. Owing to such algorithms, agent developers spend less time and effort for the definition of the underlying rule base. Moreover, the rule base is efficiently created through the use of the dataset indicating the behaviour of the buyer and, thus, representing its line of actions in the electronic marketplace. In our work, we use such algorithms for the definition of the buyer behaviour and we provide critical insides for every algorithm describing their advantages and disadvantages. Moreover, we present numerical results for every basic parameter of the interaction process, such as the time required for the rule base generation, the Joint Utility of the interaction process or the value of the acceptance degree that each algorithm results.

Electronic markets are places where entities not known in advance can negotiate and agree upon the exchange of products. Intelligent agents can be proved very advantageous when representing entities in markets. Mostly, such entities are based on reputation models in order to conclude a transaction. However, reputation is not the only parameter that they could be based on. In this work, we deal with the problem of how and on which entity a buyer should be rely upon in order to conclude a transaction. Reinforcement learning techniques are used for these purposes. More specifically, the Q-learning algorithm is used for the calculation of the reward that the buyer will take for every action in the market environment. Actions represent the selection of specific entities for the negotiation of products. The most important is that the reward values are calculated based on a number of parameters such as the price, the delivery time, etc. The result is a more efficient model that is not based only on the reputation of each entity. Finally, we extend the Q-learning algorithm and propose a methodology for the dynamic Q-table creation which results reduced time for its construction and respectively limited time for the purchase action. Simulations show that this model indicates a significant time reduction in the purchase process in conjunction with the best solution according to the characteristics of products.

The exploitation of modern sensing technologies constitutes an essential issue when targeting to context-aware applications in mobile environments. However, most of the existing solutions (commercial products, research pilots) assume proprietary sensor platforms, thus restricting the deployment of these applications only to computing nodes that are compatible with these platforms. This paper discusses how this topic is addressed in the context of the EU ICT project IPAC. The architecture and design of the proposed sensor framework exhibit a significant degree of portability across different platforms as they are based on the IEEE 1451 family of standards.

Intelligent agents can help users in finding and retrieving goods from electronic marketplaces. Additionally, agents can represent providers in such places facilitating the automatic negotiation about the purchase of products. In this paper, we describe a finite horizon bargaining model between buyers and sellers and we focus on the seller's side. Seller agents are a good example of an autonomous decentralized system. We present a method for the dasiabargainingpsila deadline calculation based on fuzzy-logic (FL). Such deadline indicates the time for which it is profitable for a seller to participate in the bargaining procedure. We provide methods for automatic fuzzy rules generation. These rules result the deadline values at each interaction and are based on data provided by experts. We compare results taken from a fuzzy controller based on such automatic methods with results taken by previous research efforts.

Content distribution networks are a very important part of today's Internet and the Web. They enable the scalable provision of content and yield significant saving for the information provides (the clients of the CDN). In this paper we propose a scheme that tries to optimize the use of disk space in the CDN. The basic idea involves the partial replication of the contents of a Web site. Site objects are transferred to the CDN in bundles. The site partitioning technique that produces bundles can be based on the site structure or the popularity of the site contents. We propose two schemes for site partitioning: the static partitioning and a technique based on Markov graphs (mostly intended for Web prefetching). We simulated the discussed schemes using extensive traces taken from the site of our department. Our findings are very promising for the proposed schemes.

In this paper, we focus on proactive radio resource management schemes that retain the quality of the individual connections by pre-reserving the needed resources in a cellular network. We propose a new scheme, which improves older proactive solutions. Typically, in such solutions an improvement in call dropping probability negatively impacts new call blocking probability. We improve this framework by careful, fine-grained time scheduling of the proactive resource management. We adopt optimal stopping theory for our solution. Our findings are quite promising for the broader framework of proactive resource management and mobile computing.

A “Smart Spaces System”, called MITOS, for improved user connectivity in large wireless LAN installations is proposed. MITOS extends the scope of resource management to the dynamic relocation of nomadic users: the system suggests to a user the best location to move to for obtaining a satisfactory quality of service level, when the controlling access point of its current location becomes congested. The system monitors the traffic and user location across the network, and formulates the appropriate relocation proposal urging specific users to move to better locations at reasonable distances. Two enhancements to the basic MITOS system are introduced for maintaining an almost uniform load level across the considered infrastructure: the first uses microeconomic concepts, while the second borrows game theoretic mechanisms from the Santa Fe Bar problem. Simulation results on the efficiency of the proposed schemes are provided.

Patient telemonitoring systems (PTS) deal with the acquisition, processing, and secure transmission of a patient's physiological and physical parameters to a remote location, where expert medical knowledge is available. In emergency situations, when the patient's life is threatened, the trend in modern PTS is to transmit the current location of the patient. Although research in communications security has led to mechanisms that sufficiently protect medical data, research related to location privacy area is still in its early stages. This paper proposes an architecture that enhances PTS through location privacy and data encryption. We study the most popular PTS technologies in conjunction with location privacy architectures and propose an innovative scheme that exploits a point-to-point protocol called Mist. We describe a prototype implementation, developed for validating the proposed framework along with the corresponding evaluation results.

Context-aware computing relies on sensing environmental parameters—context (e.g., illumination, location), classifying context, and inferring further knowledge about context, i.e., the user's situation. Therefore, the relevant applications cannot handle context as flexibly as their users would expect. To overcome this deficiency, we propose an extension of context representation, classification, and inference. Our model relies on fuzzy-set theory to accommodate the imperfect nature of sensed context. We develop two fuzzy inference engines dealing with context specialization and compatibility relations. We evaluate such engines through a series of experiments involving real users. Our findings indicate the efficiency of the proposed context classification and inference processes.

Present and future Web business models involve the trading of information goods. Information marketplaces can be considered as places where users search and retrieve information goods. Such places appear to be very interesting information retrieval models. Furthermore, software agent technology could help users and providers to work in such open environments providing a variety of advantages. Users as well as information providers could be represented by intelligent agents that work autonomously. The representatives of users assume the role of information buyers while the representatives of information sources could be referred to as sellers. In this paper, we examine a scenario where agents representing entities involved in an information marketplace bargain over the prices of information goods. Bargaining originates in game theory (GT). The rationale is that some entities contest to gain as much profit as possible in an open environment. We study the sellerspsila side. Sellers involved in a number of games with buyers, are trying to achieve as greater prices as possible in order to gain more profit from each game. We present a theoretical model of deadline computation for which sellers are participating in the game. Over this time limit it is useless for sellers to continue the game while buyers reject the proposed prices.

Information marketplaces are places where users search and retrieve information goods. Intelligent agents could represent the participating entities in such places, i.e, assume the role of buyers and sellers of information products. In this paper, we introduce a finite horizon bargaining model between buyers and sellers. We examine the seller's side and define a method for the 'bargaining' deadline calculation based on fuzzy-logic (FL). Such deadline indicates the time for which it is profitable for a seller to participate in the bargaining procedure, i.e., the time threshold for his offers. We represent the seller's knowledge/policy adopting the fuzzy set theory and provide a fuzzy inference engine for reasoning about the bargaining deadline. The result of the reasoning process defines the degree of patience of the seller agent, thus, affecting the time for which that seller participates in the bargaining game.

The requirement for ubiquitous service access in wireless environments presents a great challenge in light of well-known problems like high error rate and frequent disconnections. In order to satisfy this requirement, we propose the integration of two modern service technologies: Web Services and Mobile Agents. This integration allows wireless users to access and invoke semantically enriched Web Services without the need for simultaneous, online presence of the service requestor. Moreover, in order to improve the capabilities of Service registries, we exploit the advantages offered by the Semantic Web framework. Specifically, we use enhanced registries enriched with semantic information that provide semantic matching to service queries and published service descriptions. Finally, we discuss the implementation of the proposed framework and present our performance assessment findings.

Context awareness is viewed as one of the most important aspects in the emerging pervasive computing paradigm. We focus our work on situation awareness; a more holistic variant of context awareness where situations are regarded as logically aggregated contexts. One important problem that arises in such systems is the imperfect observations (e.g., sensor readings) that lead to the estimation of the current context of the user. Hence, the knowledge upon which the context / situation aware paradigm is built is rather vague. To deal with this shortcoming, we propose the use of Fuzzy Logic theory with the purpose of determining (inferring) and reasoning about the current situation of the involved user. We elaborate on the architectural model that enables the system to assume actions autonomously according to previous user reactions and current situation. The captured, imperfect contextual information is matched against pre-developed ontologies in order to approximately infer the current situation of the user. Finally, we present a series of experimental results that provide evidence of the flexible, efficient nature of the proposed situation awareness architecture.

Recent studies indicate the presence of a significant amount of idle licensed spectrum, in different time periods and geographic locations. Prompted by the latest regulatory changes and radio technology advances, dynamic spectrum access is becoming increasingly attractive, for alleviating the spectrum scarcity problem in the unlicensed bands. We study the dynamic spectrum access in a CDMA environment. We assume that users selfishly switch from one frequency band to another, so as to maximize their individual benefit, and model the studied problem as a non-cooperative game. We propose a probabilistic, distributed strategy with which users might share the available re sources to satisfy their quality of service requirements, and minimize their transmitted power. The comparison of the proposed strategy against other alternative strategies shows that it combines fast convergence, efficiency and stability, while its simplicity makes it practical for a real network.

The concept of scheduling is relevant to many computer-engineering areas, such as operating systems, computer networks, enterprise platforms and applications. Scheduling at the application level (a.k.a. job scheduling) is a common process in the enterprise domain, but very few IT solutions cover all the required features. Such features include scalability, fault-tolerance and load balancing. In this paper, we present the design and implementation of a modular job scheduling system, developed to work in a distributed clustered environment, which satisfies the aforementioned requirements. Its architecture and implementation is based on the Java 2 Enterprise Edition (J2EE) framework so that it inherently guarantees portability over different platforms through the use of open interfaces. A preliminary performance evaluation of the system provides indications on its behavior under various levels of workload

Context-aware applications sense, combine and reason about contextual information in order to determine and adapt to the current user's context. A very important problem associated with context is the inherent ambiguity and inaccuracy. Contextual information is typically pervaded with imperfect sensing (e.g., noise of sensor readings). A novel context fusion model that represents, determines and reasons about context based on the reliability on sensor readings is proposed. This model adopts dynamic Bayesian networks and fuzzy-set theory in order to deal with the reliability of contextual data at the context inference phase.

Fires are a common, disastrous phenomenon that constitutes a serious threat. Thus, early detection is of great importance as the consequences of a fire are catastrophic. Towards this direction the SCIER project envisages the deployment of wireless sensor networks at the "urban-rural-interface" (URI) and uses sensor fusion techniques to enhance the performance of the early fire detection and fire location estimation processes.

To meet the growing requirements for resource management schemes in the context of mobile and wireless computing, path prediction algorithms are gaining more attention and are being extensively examined. Path prediction allows the network to enhance the user quality of service. A parametric dimensioning of a path prediction algorithm based on learning automata is presented. The algorithmic parameter values are optimised for maximum prediction accuracy. Furthermore, two add-on mechanisms of the path prediction algorithm are introduced, and their performance is evaluated through simulations.

A scheme for the proactive allocation of network resources to mobile users, based on a pricing framework, is proposed. The objective is the reduction of service discontinuation events attributed to handovers in the cellular infrastructure. The future base station, where network resources have to be reserved proactively, is determined by means of a path prediction algorithm. The network receives a fee for providing the advance reservation service to the user. The exact price is determined after a sequential bargaining procedure, modeled as a two-person non-cooperative game between the mobile user and the network The efficiency of the proposed scheme is evaluated through simulations.

We consider the uplink power control problem in a single cell, multi-user, CDMA wireless data system and formulate it as a cooperative game. We use the Nash bargaining solution concept, in order to determine the socially optimum solution, which is both Pareto efficient and fair. In our formulation, the BS plays the role of the arbitrator, Le., solves the power control problem, and broadcasts the relevant information to all users in order to enforce convergence to the optimal operating point. The comparison of the cooperative scheme to the non-cooperative scheme shows significant reduction in the transmission power of the mobile terminals.

In this paper we discuss a system that exploits observations derived from sensors, in order to estimate a key factor for pervasive computing and context aware applications: the location of a user. The term "sensors" includes Wi-Fi adapters, IR receivers, etc. The core of the system is the fusion engine which is based on dynamic Bayesian networks (DBNs), a powerful mathematical tool for integrating heterogeneous sensor observations. In closing, is provided an evaluation of the system as it comes out from the experimental results

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 capable of adapting to these changes to ensure the best possible level of service to the user. Therefore, such adaptive applications may have pre-evaluated the appropriate knowledge of their environment to act efficiently. Such knowledge is not known a priori, so information prediction and proactivity should enhance and extend the functionality of such applications in order to be adaptable to the future changes of their underlying computational environment. In this paper, we discuss and evaluate such a context prediction algorithm.

The PoLoS platform, implemented in the context of the European Union Information Society Technologies (IST) program, focuses on the development, deployment and provision of services and consolidates several technologies (and operation schemes) of the Location Based Services world under a single framework. The demonstration aims to provide an operational overview of the PoLoS platform and highlight the benefits the platform brings in a large-scale, competitive and fast-changing environment.

The scheduling paradigm offers an alternative execution model for services and applications, which is of particular interest for the enterprise domain. Therefore, task scheduling is considered as an integral part of many contemporary information systems and service platforms. In this paper we present, the architectural aspects of an integrated scheduling framework, targeted to the mobile applications market along with an implementation prototype, which was also developed with the purpose of demonstrating its capabilities and identifying its limitations

Over the last years, a number of mechanisms have been proposed for scheduling different types of traffic over base stations-oriented wireless and mobile systems. The majority of these mechanisms focus on access control in the base station-to-mobile units part of the wireless and mobile system. Recent proposals for the unlicensed spectrum in the 5GHz band have redefined the problem, since base stations, operated by different operators in overlapping geographical areas, need access resolution mechanisms to allocate wireless resources. This issue is addressed here, and a novel mechanism for dynamic channel allocation in unlicensed wireless LANs (WLANs) or public area wireless networks (PAWNs) environments is presented. The proposed method exploits the learning automata technique for the efficient allocation of wireless resources in a distributed manner. Nearby base stations that compete to access and reserve time on separate frequencies are driven by the output of a learning automaton, which determines the available carrier illustrating minimal competition. The paper discusses contention resolution disciplines while the learning automaton algorithm as well as its knowledge base structure are also discussed and evaluated.

Over the last years, a number of mechanisms have been proposed for scheduling different types of traffic over base stations-oriented wireless and mobile systems. The majority of these mechanisms focus on access control in the base station-to-mobile units segment of the wireless and mobile system. Recent proposals for the unlicensed spectrum in the 5 GHz band have redefined the problem, since base stations, operated by different operators in overlapping geographical areas, need access resolution mechanisms to allocate wireless resources. This issue is addressed here, and a novel mechanism for dynamic channel allocation in unlicensed wireless LANs (wLANs) or public area wireless networks (PAWNs) environments is presented. The proposed method exploits the learning automata technique for the efficient allocation of wireless resources in a distributed manner. Nearby base stations that compete to access and reserve time on separate frequencies are driven by the output of a learning automaton, which determines the available carrier that demonstrates minimal competition. The paper discusses contention resolution disciplines while the learning automaton algorithm as well as its knowledge base structure are also discussed and evaluated.

The operation of TCP in wireless-mobile environments is discussed. After briefly presenting previous efforts to ameliorate TCP performance in the considered environments, we propose a new mechanism for tackling the slowdown problems caused by handovers. Our mechanism is based on stochastic datagram relocation. Traffic destined to the mobile terminal is tunneled to and cached into adjacent cells according to the output of a path prediction algorithm. To reduce the associated overhead, only percentages of inbound traffic are copied to the cell's neighborhood on the basis of estimated probabilities. The time scheduling for datagram relocation is also taken into account. Simulations of the proposed architecture show substantial performance improvements for TCP traffic.

A platform for the simulation of mobility functions (e.g., protocols, and application architectures) is presented. This platform, named RMPG, is based on the assumption that real humans exhibit considerable spatial and temporal regularity in their moves. We base our simulation environment on the random way-point algorithm incorporated in the ns tool. Using the Java-based RMPG we performed a series of simulations by considering a path prediction algorithm as a mobility function. We present our results.

The operation of TCP in wireless/mobile environments is considered. After briefly presenting previous efforts to ameliorate TCP performance in the considered environments, we propose a new mechanism for tackling the problems caused by handovers. Our mechanism is based on stochastic datagram relocation. Traffic destined to the mobile terminal is tunneled to adjacent cells according to the output of a path prediction algorithm. To reduce the associated overhead, only percentages of inbound traffic are copied to the cell's neighborhood on the basis of estimated probabilities. Through simulations, we have measured the effects that stochastic datagram relocation has on TCP dynamics.

Article Share on Using proxy cache relocation to accelerate Web browsing in wireless/mobile communications Authors: Stathes Hadjiefthymiades University of Athens, Dept. of Informatics and Telecommunications, Panepistimioupolis, Ilisia, Athens, 15784, Greece University of Athens, Dept. of Informatics and Telecommunications, Panepistimioupolis, Ilisia, Athens, 15784, GreeceView Profile , Lazaros Merakos University of Athens, Dept. of Informatics and Telecommunications, Panepistimioupolis, Ilisia, Athens, 15784, Greece University of Athens, Dept. of Informatics and Telecommunications, Panepistimioupolis, Ilisia, Athens, 15784, GreeceView Profile Authors Info & Claims WWW '01: Proceedings of the 10th international conference on World Wide WebMay 2001Pages 26–35https://doi.org/10.1145/371920.371927Published:01 April 2001Publication History 34citation983DownloadsMetricsTotal Citations34Total Downloads983Last 12 Months4Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Publisher SiteGet Access

Nowadays, the WWW is believed to be the ideal technological platform for the introduction of online telematic applications as well as information systems in enterprise intranets due to its wide acceptance and standardisation (W3C). In the majority of cases, the databases supporting such novel applications are hosted by RDBMSs. The paper touches upon the incompatible nature of WWW compliant database applications and classical database applications (developed with Embedded SQL/3GLs, 4GLs, etc.). The WWW is often blamed for the stateless character of one of its constituents: the HyperText Transfer protocol (HTTP). The so-called "user session" concept is not applicable in its client/server architecture. Communication between clients and servers is realised by means of fairly simple request-response interactions ("hits") which are treated independently (without memory state) by servers. We propose an architecture for the deployment of stateful database applications in the context of conventional WWW servers. In this architecture, state information preserved in the WWW client as well as in specialised agents that operate behind the WWW server, provides the required basis for a "session-aware" database application. The main benefits of this architecture are that it doesn't modify existing server software and requires minimum programming effort for the porting of existing "session-aware" applications to the WWW environment.

The introduction of ATM in wireless CPN environments (WATM) necessitates the design of mobility related protocols, since the existing versions of B-ISDN signalling (ITU-T Q.2931, ATMF UNI 3.1) do not provide the means for terminal mobility. Such protocols can be deployed either as extensions to the standard signalling capabilities or as individual solutions that have little or no impact on existing infrastructures (switches, signalling software, etc.). In this paper, after presenting a WATM architecture, we study the problem of the switching/rerouting of the signalling connections that need to be performed whenever a mobile terminal crosses cell boundaries (handover, location update). Registration and forward hard handover algorithms are proposed.

The introduction of ATM in wireless CPN environments (WATM) necessitates the design of mobility signaling protocols, since the existing versions of B-ISDN signaling do not support terminal mobility. These protocols can be deployed as overlay solutions that have little or no impact on existing infrastructures (switches, signaling software, etc.). We present a WATM architecture and discuss the design of a mobility management and control protocol (MMC). After presenting what procedures the protocol specifies for the forward handover scenario we provide simulation results related to the performance of the system.

The aim of this paper is to introduce a semantic modeling architecture for hypermedia as well as to focus on the possibilities that emanate from it, namely dynamic linking, hypergraph clustering and querying. Aspects of a prototype system, "Platon" that incorporates the above architecture, are also discussed.