Christoforos Panayiotou

mPERSONA: personalized portals for the wireless user: An agent approach

The needs of the wireless and mobile user regarding information access and services are quite different than those of the desktop user. This need is not about browsing the Web but about receiving personalized services that are highly sensitive to the immediate environment and requirements of the user. Personalization appears to be the most appropriate solution to this need. It comes into aid by creating personalized portals that are specific for the wireless user, which (a) are focus on the local content and (b) directly tones down factors that break up the functionally of the Internet/wireless services when viewed through wireless devices; factors like the “click count”, user response time (the “choice” factor) and the size of the wireless network traffic. In this paper we present a flexible personalization system for the wireless user that takes into consideration user mobility, the local environment and the user and device profile. The system utilizes the various characteristics of mobile agents to support flexibility, scalability, modularity and user mobility. We present metrics appropriate to the wireless environment, and an initial performance evaluation indicating improvement ranging from 33% up to, for certain metrics, 60%.

Personalized portals for the wireless user based on mobile agents

Wireless users rarely (if ever) benefit from Internet information services thus requiring new type of services and new ways for structuring the needed content. Personalization appears to be the most appropriate solution to this need. It comes into aid by creating personalized portals that directly tones down factors that break up the functionally of the Internet/wireless services when viewed through wireless devices; factors like the click count, user response time and the size of the wireless network traffic. In this paper we present a flexible personalization system tuned for the wireless Internet taking into consideration not only the user profile but the device profile as well. The system utilizes the various characteristics of mobile agents to support flexibility, scalability, modularity and user mobility. We present appropriate, to the wireless environment, metrics and our initial performance evaluation indicates improvement ranging from 33% to, for certain metrics, 60.

Mobile user personalization with dynamic profiles: time and activity

Mobile clients present a new and more demanding breed of users Solutions provided for the desktop users are often found inadequate to support this new breed of users Personalization is such a solution The moving user differs from the desktop user in that his handheld device is truly personal It roams with the user and allows him access to info and services at any given time from anywhere As the moving user is not bound to a fixed place and to a given time period, factors such as time and current experience becomes increasingly important for him His context and preferences are now a function of time and experience and the goal of personalization is to match the local services to this time-depended preferences In this paper we exploit the importance of time and experience in personalization for the moving user and present a system that anticipates and compensates the time-dependant shifting of user interests A prototype system is implemented and our initial evaluation results indicate performance improvements over traditional personalization schemes that range up to 173%.

The PaCMAn metacomputer: parallel computing with Java mobile agents

Abstract The PaCMAn (parallel computing with Java mobile agents) Metacomputer launches multiple Java mobile agents that communicate and cooperate to solve problems in parallel. Each mobile agent can travel anywhere in the Web to perform its tasks. A number of brokers/load forecasters keep track of the available resources and provide load forecast to the clients. The clients select the servers that they will utilize based on the specific resource requirements and the load forecast. The PaCMAn mobile agents are modular; the mobile shell is separated from the specific task code of the target application. To this end, we introduce the concept of TaskHandlers which are Java objects capable of implementing a particular task of the target application. TaskHandlers are dynamically assigned to PaCMAn’s mobile agents. We have developed and tested a prototype system with several applications such as parallel Web querying, a prime number generator, the trapezoidal rule and the RC5 cracking application. Our results demonstrate that PaCMAn provide very good parallel efficiency.

Parallel computing using Java mobile agents

We propose a mobile-agent parallel processing framework that uses multiple Java mobile agents, each one of which utilizes multithreading. A mobile agent can travel at any Web site to perform its tasks. We also introduce the concept of task handlers, which are Java objects capable of implementing a particular task of the target application. TaskHandlers are dynamically assigned to mobile agents. We have developed and tested a prototype application for parallel Web querying as a proof of concept of our proposed framework. Boosted by the inherited benefits of using Java and mobile agents, our proposed methodology brakes the barriers of heterogeneity and provides the means for the parallel deployment of the available on the World Wide Web computational resources.

ExTraCT: Expediting Offloading Transfers Through Intervehicle Communication Transmissions

Vehicular connectivity is considered as one of the most highly anticipated emerging technologies since it promises to transform the automotive sector and have a significant impact on all related markets. Data gathered (and information generated) within and around vehicles will be used to improve road safety, travelling efficiency, and passenger comfort and convenience. However, delivering such data to the infrastructure (to process information and generate intelligence) is a challenging task, mainly due to the very large volume of data traffic produced. A promising approach to support these communication needs is to deliver data traffic opportunistically through the available WiFi APs. Evidently, the intermittent connectivity of these hotspots and the inherent mobility of the vehicles severely limit the volume of traffic sent at any one instance in time. The latter limitation is studied in this paper, where decision policies are derived for vehicle-to-vehicle-assisted offloading to maximize the transmission opportunities and thus expedite data traffic delivery. As illustrated in this paper, these policies are easy to implement in practice and offer significant improvement in vehicular data traffic offloading as compared with opportunistic offloading and basic relaying practices.

Time based personalization for the moving user

Unlike desktop users, mobile users are a new and more demanding breed. Technology provided for the first group is often found lacking for the later. Personalization is such an example. The moving user differs from the desktop user in that his handheld device is truly personal. It roams with the user and allows him access to info and services at any given time from anywhere. As the moving user is not limited to a fix place and to a given time period, factors such as time and current experience becomes increasingly important for him. His context is now a function of time and experience and the goal of personalization is to match the local services to this context. In this paper we exploit the importance of time and experience in personalization for the moving user and present a system that anticipates and compensates the time-dependant shifting of user interests.

Using time and activity in personalization for the mobile user

Mobile clients present a new and more demanding breed of users. Solutions provided for the desktop users are often found inadequate to support this new breed of users. Personalization is such a solution. The moving user differs from the desktop user in that his handheld device is truly personal. It roams with the user and allows him access to info and services at any given time from anywhere. As the moving user is not bound to a fixed place and to a given time period, factors such as time and current experience becomes increasingly important for him. His context and preferences are now a function of time and experience and the goal of personalization is to match the local services to this time-depended preferences. In this paper we exploit the importance of time and experience in personalization for the moving user and present a system that anticipates and compensates the time-dependant shifting of user interests. A prototype system is implemented and our initial evaluation results indicate performance improvements over traditional personalization schemes that range up to 173%.

Data-Driven Event Triggering for IoT Applications

Event-triggering (ET) is an up-and-coming technological paradigm for monitoring, optimization, and control in the Internet of Things (IoT) that achieves improved levels of operational efficiency. This paper first defines the envisioned ET architecture for the IoT domain. It then classifies and reviews the various different ET approaches obtained from the available literature for the three phases of ET, namely behavior modeling, event detection, and event handling. Thereafter, a novel data-driven technique is developed to address all three phases of ET in an efficient and reliable manner. Finally, the applicability of the proposed data-driven technique is showcased in a real-world public transport scenario, demonstrating a substantial improvement in energy and spectrum efficiency compared to existing periodic techniques.

ftTRACK: Fault-Tolerant Target Tracking in Binary Sensor Networks

The provision of accurate and reliable localization and tracking information for a target moving inside a binary Wireless Sensor Network (WSN) is quite challenging, especially when sensor failures due to hardware and/or software malfunctions or adversary attacks are considered. Most tracking algorithms assume fault-free scenarios and exploit all binary sensor observations, thus their accuracy may degrade when faults are present in the field. Spatiotemporal information available while the target is traversing the sensor field can be used not only for tracking the target, but also for detecting certain types of faults that appear highly correlated both in time and space. Our main contribution is ftTRACK, a target tracking architecture that is resilient to sensor faults and consists of three main components, namely the sensor health-state estimator, a fault-tolerant localization algorithm, and a location smoothing component. The key idea in the ftTRACK architecture lies in the sensor health-state estimator that leverages spatiotemporal information from previous estimation steps to intelligently choose which sensors to employ in the localization and tracking tasks. Simulation results indicate that ftTRACK maintains a high level of tracking accuracy, even when a large number of sensors fail.