An Algorithmic Evaluation of Information Search in a Mobile Agent-Based Demand-Oriented Information Service System

The rapid advance in information and communication technology (ICT) has given new impetus to shift the information services paradigm from platform centric to network centric computing. Commercial activity is blooming on the internet. The user or customer has become dynamic and has changing tastes and pattern in the demand of service, and desires the service at any time or place. The service provider therefore has to tailor the service provision format to suit the dynamic nature of users of information services. A business cannot afford to ignore the rapid and evolving nature of its customers. However, the current state of the wide area network services is finding it difficult to respond to constantly changing and heterogeneous demands of modern business, being centralized in nature, with the service provided through a single URL. It is imperative to update the pattern of information service provision and utilization. Faded information field architecture (FIF), reported recently, holds the potential to address these issues, being a demand-oriented architecture. Although research into various aspects of FIF has been reported, we suggest algorithms to characterize the behavior of mobile agents to seek the required information at a given node in the FIF architecture. Simulations were carried out to show the effect of various parameters on the performance of the FIF system

Mechanism design for spatio-temporal request satisfaction in mobile networks

Mobile agents participating in geo-presence-capable crowdsourcing applications should be presumed rational, competitive, and willing to deviate from their routes if given the right incentive. In this paper, we design a mechanism that takes into consideration this rationality for request satisfaction in such applications. We propose the Geo-temporal Request Satisfaction (GRS) problem to be that of finding the optimal assignment of requests with specific spatio-temporal characteristics to competitive mobile agents subject to spatio-temporal constraints. The objective of the GRS problem is to maximize the total profit of the system subject to our rationality assumptions. We define the problem formally, prove that it is NP-Complete, and present a practical solution mechanism, which we prove to be convergent, and which we evaluate experimentally.National Science Foundation (1012798, 0952145, 0820138, 0720604, 0735974

Dagstuhl News January - December 2002

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

Deep Reinforcement Learning for Resource Management in Network Slicing

Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users' activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.Comment: The manuscript has been accepted by IEEE Access in Nov. 201