Location Awareness of Information Agents

2005 ◽  
pp. 199-208 ◽  
Author(s):  
Merik Meriste ◽  
Jüri Helekivi ◽  
Tõnis Kelder ◽  
Andres Marandi ◽  
Leo Mõtus ◽  
...  
Keyword(s):  
Location Awareness ◽  
Information Agents

Research on Smart Space Oriented Location Awareness Method

2009 ◽  
Vol 20 (3) ◽  
pp. 671-681
Author(s):  
Liang MING ◽  
Gang ZHAO ◽  
Gui-Hai XIE ◽  
Chun-Lei WANG
Keyword(s):  
Location Awareness ◽  
Smart Space

A Plan Based Approach to Information Agents.

1997 ◽  
Author(s):  
Kristian J. Hammond ◽  
Robin D. Burke
Keyword(s):  
Information Agents

Mobile Information Agents

2001 ◽  
Author(s):  
Daniela Rus
Keyword(s):  
Information Agents ◽  
Mobile Information

A Comparative Study of Computational Intelligence Algorithms for Sensor Localization

2019 ◽  
Vol 9 (2) ◽  
pp. 224-236
Author(s):  
Vaishali R. Kulkarni ◽  
Veena Desai ◽  
Raghavendra Kulkarni
Keyword(s):  
Firefly Algorithm ◽  
Heuristic Algorithms ◽  
Computing Time ◽  
Sensor Nodes ◽  
Convergence Time ◽  
Localization Error ◽  
Location Awareness ◽  
Cultural Algorithm ◽  
Localization Accuracy ◽  
Multi Stage

Background & Objective: Location of sensors is an important information in wireless sensor networks for monitoring, tracking and surveillance applications. The accurate and quick estimation of the location of sensor nodes plays an important role. Localization refers to creating location awareness for as many sensor nodes as possible. Multi-stage localization of sensor nodes using bio-inspired, heuristic algorithms is the central theme of this paper. Methodology: Biologically inspired heuristic algorithms offer the advantages of simplicity, resourceefficiency and speed. Four such algorithms have been evaluated in this paper for distributed localization of sensor nodes. Two evolutionary computation-based algorithms, namely cultural algorithm and the genetic algorithm, have been presented to optimize the localization process for minimizing the localization error. The results of these algorithms have been compared with those of swarm intelligence- based optimization algorithms, namely the firefly algorithm and the bee algorithm. Simulation results and analysis of stage-wise localization in terms of number of localized nodes, computing time and accuracy have been presented. The tradeoff between localization accuracy and speed has been investigated. Results: The comparative analysis shows that the firefly algorithm performs the localization in the most accurate manner but takes longest convergence time. Conclusion: Further, the cultural algorithm performs the localization in a very quick time; but, results in high localization error.

scholarly journals Mobility-Assisted on-Demand Routing Algorithm for MANETs in the Presence of Location Errors

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Trung Kien Vu ◽  
Sungoh Kwon
Keyword(s):  
Ad Hoc ◽  
Routing Algorithm ◽  
Mobility Prediction ◽  
Location Awareness ◽  
Mobile Nodes ◽  
Location Errors ◽  
On Demand ◽  
Demand Routing ◽  
Hoc Networks ◽  
The Impact

We propose a mobility-assisted on-demand routing algorithm for mobile ad hoc networks in the presence of location errors. Location awareness enables mobile nodes to predict their mobility and enhances routing performance by estimating link duration and selecting reliable routes. However, measured locations intrinsically include errors in measurement. Such errors degrade mobility prediction and have been ignored in previous work. To mitigate the impact of location errors on routing, we propose an on-demand routing algorithm taking into account location errors. To that end, we adopt the Kalman filter to estimate accurate locations and consider route confidence in discovering routes. Via simulations, we compare our algorithm and previous algorithms in various environments. Our proposed mobility prediction is robust to the location errors.

scholarly journals A Theme Park Tourist Service System with a Personalized Recommendation Strategy

2018 ◽  
Vol 8 (10) ◽  
pp. 1745 ◽  
Author(s):  
Feng-Chi Yu ◽  
Pei-Chun Lee ◽  
Pei-Hsuan Ku ◽  
Sheng-Shih Wang
Keyword(s):  
Waiting Time ◽  
Service System ◽  
Field Testing ◽  
Mobile App ◽  
Personalized Recommendation ◽  
Theme Park ◽  
Location Awareness ◽  
Session Time ◽  
Recommendation Strategy ◽  
Tourist Service

In general, there exists numerous attractions installed in a theme park, and tourists in a theme park dynamically change their locations during a tour. Thus, a tourist may cope with the issues of selecting the attractions to visit while planning the tour route. This paper, based on the concept of location awareness, proposes a novel waiting time, called the personalized waiting time, to introduce a location-aware recommendation strategy. In addition, this paper presents an architecture of tourist service system using the proposed recommendation strategy to relieve the pressure on tourists and create the pleasant experience in their tours. The proposed location-based system consists of mobile app, ticket-reader, detecting/counting, and central subsystems, and the whole system was implemented in this study. We conducted numerous experiments and field testing results validated that the entire proposed system can correctly provide information, such as attraction introduction, recommended session time, estimated moving and waiting time, tour map, and the number of reservations. The system functions, including dynamical scheduling, attraction reservation, ticket verification, visitor detection, and visitor counting, also worked well.

COORDINATION OF MULTIPLE INTELLIGENT SOFTWARE AGENTS

1996 ◽  
Vol 05 (02n03) ◽  
pp. 181-211 ◽  
Author(s):  
KATIA SYCARA ◽  
DAJUN ZENG
Keyword(s):  
System Architecture ◽  
Information Needs ◽  
Software Agents ◽  
User Preferences ◽  
Organizational Decision Making ◽  
Interface Agents ◽  
Intelligent Software ◽  
Information Agents ◽  
Agent Collaboration ◽  
Guide System

We are investigating techniques for developing distributed and adaptive collections of information agents that coordinate to retrieve, filter and fuse information relevant to the user, task and situation, as well as anticipate user's information needs. In our system of agents, information gathering is seamlessly integrated with decision support. The task for which particular information is requested of the agents does not remain in the user's head but it is explicitly represented and supported through agent collaboration. In this paper we present the distributed system architecture, agent collaboration interactions, and a reusable set of software components for structuring agents. The system architecture has three types of agents: Interface agents interact with the user receiving user specifications and delivering results. They acquire, model, and utilize user preferences to guide system coordination in support of the user's tasks. Task agents help users perform tasks by formulating problem solving plans and carrying out these plans through querying and exchanging information with other software agents. Information agents provide intelligent access to a heterogeneous collection of information sources. We have implemented this system framework and are developing collaborating agents in diverse complex real world tasks, such as organizational decision making, investment counseling, health care and electronic commerce.

Agents in bioinformatics

2005 ◽  
Vol 20 (2) ◽  
pp. 117-125 ◽  
Author(s):  
MICHAEL LUCK ◽  
EMANUELA MERELLI
Keyword(s):  
Data Analysis ◽  
Simulation Models ◽  
Biological Data ◽  
System Modelling ◽  
Agent Based Simulation ◽  
Huge Amount ◽  
Agent Based ◽  
Information Agents ◽  
New Knowledge ◽  
Definition Of

The scope of the Technical Forum Group (TFG) on Agents in Bioinformatics (BIOAGENTS) was to inspire collaboration between the agent and bioinformatics communities with the aim of creating an opportunity to propose a different (agent-based) approach to the development of computational frameworks both for data analysis in bioinformatics and for system modelling in computational biology. During the day, the participants examined the future of research on agents in bioinformatics primarily through 12 invited talks selected to cover the most relevant topics. From the discussions, it became clear that there are many perspectives to the field, ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages for use by information agents, and to the use of Grid agents, each of which requires further exploration. The interactions between participants encouraged the development of applications that describe a way of creating agent-based simulation models of biological systems, starting from an hypothesis and inferring new knowledge (or relations) by mining and analysing the huge amount of public biological data. In this report we summarize and reflect on the presentations and discussions.

Sign in / Sign up

Export Citation Format

Share Document