Stationary Agent Research Articles

Distributed Localization: A Linear Theory

Fifth-generation (5G) networks providing much higher bandwidth and faster data rates will allow connecting vast number of stationary and mobile devices, sensors, agents, users, machines, and vehicles, supporting Internet-of-Things (IoT), real-time dynamic networks of mobile things . Positioning and location awareness will become increasingly important, enabling deployment of new services and contributing to significantly improving the overall performance of the 5G system. Many of the currently talked about solutions to positioning in 5G are centralized, mostly requiring direct communication to the access nodes (or anchors, i.e., nodes with known locations), which in turn requires a high density of anchors. But such centralized positioning solutions may become unwieldy as the number of users and devices continues to grow without limit in sight. As an alternative to the centralized solutions, this paper discusses distributed localization in a 5G-enabled IoT environment where many low power devices, users, or agents are to locate themselves without a direct access to anchors. Even though positioning is essentially a nonlinear problem (solving circle equations by trilateration or triangulation), we discuss a cooperative linear distributed iterative solution with only local measurements, local communication, and local computation needed at each agent. Linearity is obtained by reparametrization of the agent location through barycentric coordinate representations based on local neighborhood geometry that may be computed in terms of certain Cayley–Menger determinants involving relative local inter-agent distance measurements. After a brief introduction to the localization problem, and other available distributed solutions primarily based on directly addressing the nonlinear formulation, we present the distributed linear solution for stationary agent networks and study its convergence, its robustness to noise, and extensions to mobile scenarios, in which agents, users, and (possibly) anchors are dynamic.

Equation-free Model Reduction in Agent-based Computations: Coarse-grained Bifurcation and Variable-free Rare Event Analysis

We study the coarse-grained, reduced dynamics of an agent-based market model due to Omurtag and Sirovich []. We first describe the large agent number, deterministic limit of the system dynamics by performing numerical bifurcation calculations on a continuum approximation of their model. By exploring a broad parameter space, we observe several interesting phenomena including turning points leading to unstable stationary agent density distributions as well as a type of “termination point.” Close to these deterministic turning points we expect the stochastic underlying model to exhibit rare event transitions. We then proceed to discuss a coarse-grained approach to the quantitative study of these rare events. The basic assumption is that the dynamics of the system can be decomposed into fast (noise) and slow (single reaction coordinate) dynamics, so that the system can be described by an effective, coarse-grained Fokker-Planck(FP) equation. An explicit form of this effective FP equation is not available; in our computations we bypass the lack of a closed form equation by numerically estimating its components - the drift and diffusion coefficients - from ensembles of short bursts of microscopic simulations with judiciously chosen initial conditions. The reaction coordinate is first constructed based on our understanding of the continuum model close to the turning points, and it gives results reasonably close to those from brute-force direct simulations. When no guidelines for the selection of a good reaction coordinate are available, data-mining tools, in particular Diffusion Maps, can be used to determine a suitable reaction coordinate. In the third part of this work we demonstrate this “variable-free” approach by constructing a reaction coordinate simply based on the data from the simulation itself. This Diffusion Map based, empirical coordinate gives results consistent with the direct simulation.

GIS: agent-based modeling and evaluation of an earthquake-stricken area with a case study in Tehran, Iran

Although many researchers have attempted to prevent or mitigate damages, estimate vulnerabilities and control natural catastrophes, an important phase of disaster management that has received less attention is the challenge of managing people and resources directly after an incident. Because earthquakes can be one of the most disastrous events, especially in Iran, the purpose of this article is to develop an easy-to-use multi-agent simulation and modeling environment for the assessment of different disaster management scenarios as measured in terms of saving additional lives. The earthquake-induced damages to buildings, streets and citizens are the inputs to the proposed model. Five mobile agent types: citizen, paramedic, street-opener, police and robber; one stationary agent type: gas valve; and five inactive agent types: building, street, shelter, hospital and fire station form the context of the model. Disaster managers are provided with customizable settings for the number and attributes of the agents in the user interface and can assess various statistical and visual results to determine the optimal number and characteristics of the agents as well as evaluate the effectiveness of the location of shelters. To simulate the extent of a calamity, this model is tested on a small region in Tehran, Iran. The agents are created and input to the model automatically via vector GIS data layers and the entire model is vector-based. A comparison between the results of two different scenarios highlights that increasing the number of street-openers and paramedics would not create the desired improvement as long as these agents are focused on fire stations and hospitals. Additionally, the impact of street blockages is significant after an earthquake, and therefore, the potential capacity of the street-openers demonstrated in the simulations points to the necessity of retrofitting buildings and widening streets before an earthquake occurs.

Pair-wise Agreement Using Set-Valued Sensors

In this paper, we study the effect that set-valued sensors have on coordination algorithms. In particular, we investigate the two-agent rendezvous problem with severely limited sensing. We characterize conditions under which a agent can locate a stationary target when the sensing modality is probabilistic and set-valued. We generalize this to the case where a mobile agent is locating the target and show that the location of the stationary agent can be approximated up to a set of measure zero.

DBS-IC: An adaptive Data Bundling System for Intermittent Connectivity

As mobile and wireless technologies become more pervasive in our society, people begin to depend on network connectivity, regardless of their location. Their mobility, however, implies a dynamic topology where routes to a destination cannot always be guaranteed. The intermittent connectivity that results from this lack of end-to-end connection is a dominant problem that leads to user frustration. Existing research to provide the mobile user with a facade of constant connectivity generally presents mechanisms to handle disconnections when they occur. In contrast, the system we propose in this paper provides ways to handle disconnections before they occur. We present a Data Bundling System for Intermittent Connections (DBS-IC) comprised of a Stationary Agent (SA) and a Mobile Agent (MA). The SA pro-actively gathers data the user has previously specified, and opportunistically sends this data to the MA. The SA groups the user-requested data into one or more data bundles, which are then incrementally delivered to the MA during short periods of connectivity. We fully implement DBS-IC and evaluate its performance via live tests under varying network conditions. Results show that our system decreases data retrieval time by a factor of two in the average case and by a factor of 20 in the best case.

Data warehouse access using multi-agent system

The new approach that we will propose, in this paper deals with the dynamic data distribution of the data warehouse (DWH) on a set of servers. This distribution is different from the "classical" one which depends on how data is used. It consists in distributing data when the machine reaches its storage limit capacity. The proposed approach insures the scalability and exploits the storage and processing resources available in the organization using the DWH. It is worth noting that our approach is based on a multi-agent model mixed with the scalability distribution proposed by the Scalable Distributed Data Structures. Our multi-agent model is made up of stationary agent classes: Client, Dispatcher, Domain and Server, and a mobile agent class: Messenger. These agents collaborate and achieve automatically the storage, splitting, redirection and access operations on the distributed DWH. In this paper, we focus on the global dynamic for the data access operation and we present the inherent experimental results.

AGENT BASED DATA STORAGE AND DISTRIBUTION IN DATA WAREHOUSES

The data warehouse (DWH) is usually presented as a centralized database. In this paper, we propose a new approach to manage data storage and distribution in a data warehouse environment. This approach deals with the dynamic data distribution of the DWH on a set of servers. The data distribution that we consider is different from the "classical" one which depends on the data use. The distribution in our approach consists in distributing data when the server reaches its storage capacity limit. This distribution assures the scalability and exploits the storage and processing resources available in the organization using the data warehouse. It is worth noting that our approach is based on a multi-agent model mixed with the scalability distribution proposed by the Scalable Distributed Data Structures. The proposed multi-agent model is composed of stationary agent classes: Client, Dispatcher, Domain and Server, and a mobile agent class called Messenger. These agents collaborate and interact to achieve automatically the storage, the splitting (distribution), the redirection and the access operations on the distributed data warehouse. In this paper, we demonstrate the improvements obtained when we have used the multi-agent system and the Messenger agents in the data storage operation.

Dynamics of money and income distributions

We study the model of interacting agents proposed by Chakraborti and Chakrabarti [Eur. Phys. J. B 17 (2000) 167] that allows agents to both save and exchange wealth. Closed equations for the wealth distribution are developed using a mean field approximation. We show that when all agents have the same fixed savings propensity, subject to certain well-defined approximations defined in the text, these equations yield the conjecture proposed by Chakraborti and Chakrabarti [Eur. Phys. J. B 17 (2000) 167] for the form of the stationary agent wealth distribution. If the savings propensity for the equations is chosen according to some random distribution, we show further that the wealth distribution for large values of wealth displays a Pareto-like power-law tail, i.e., P ( w ) ∼ w 1 + a . However, the value of a for the model is exactly 1. Exact numerical simulations for the model illustrate how, as the savings distribution function narrows to zero, the wealth distribution changes from a Pareto form to an exponential function. Intermediate regions of wealth may be approximately described by a power law with a > 1 . However, the value never reaches values of ∼ 1.6 –1.7 that characterise empirical wealth data. This conclusion is not changed if three-body agent exchange processes are allowed. We conclude that other mechanisms are required if the model is to agree with empirical wealth data.

Introduction to Special Issue on Mobile Agents

Over the last years, mobility has been one of the most thoroughly researched topics of the agent field. As the technology has evolved and matured, research on mobility is now focusing on subtopics crucial to the long-term success of mobile agents. For this special two-part issue, we have selected seven papers which, collectively, provide in-depth coverage of essential research issues, such as composition, security, resource management, and communication. Five of these papers have appeared, in shorter versions, at the First Joint Symposium of Agent Applications and Systems, and Mobile Agents ’99 [1]. The remaining two papers are published here for the first time. In the context of agent technology, what does the term mobile mean? A mobile agent is not bound to the system where it begins execution. It has the unique ability to transport itself from one system in a network to another system. Created in one execution environment, it can transport its state and code to another execution environment in the network, where it resumes execution. By the term state, we typically mean the attribute values of the agent that help it determine what to do when it resumes execution at its destination. The term code, in an object-oriented context, means the class code necessary for the agent to execute. The ability to travel allows a mobile agent to move to a system that contains an object with which the agent wants to interact, and take advantage of being in the same host or network as the object. Mobility is an orthogonal property of agents—that is, not all agents are mobile. These that do not or cannot move are called stationary agents. A stationary agent executes only on the system where it begins execution. If it needs information that is not on that system, or needs to interact with an agent on a different system, it uses conventional means of communication such as various forms of remote procedure calling and messaging. In (Lange and Oshima 1999) [2] we listed seven reasons for choosing mobile agents in software application development:

A framework for linking distributed simulations using software agents

The paper presents the basic ideas behind the use of software agent technology for distributed simulation and data assimilation. A software agent is an autonomous computer program that operates on behalf of someone or something. A mobile agent has the ability to migrate during execution from machine to machine in a heterogeneous network, while a stationary agent executes only on the system on which it began execution. To understand the role of agents in distributed simulation, note that simulations often operate on static datasets and data sources. Many simulations would produce more accurate results if they could access dynamically changing data from other sources, such as sensors or even other simulations. From the perspective of one simulation, other simulations are data resources, producing information possibly relevant to the past, present, or future of the system being modeled. Software agents allow dynamic linking between distributed simulations and efficient monitoring of and access to remote data resources. Specifically, they conserve bandwidth, provide custom operations without precompiling or preloading, and adapt to support disconnected operations. The paper describes the development of a software agent based framework for dynamically linking distributed simulations and other remote data resources. The framework called ABELS (Agent-Based Environment for Linking Simulations) allows independently designed simulations to communicate seamlessly with no a priori knowledge of the details of other simulations and data sources. We discuss our architecture and current implementation developed using the D'Agents mobile agent system.