Agent-based framework for sensor-to-sensor personalization

Sahli, Nabil; Jabeura, Nafaa; Badra, Mohamad

doi:10.1016/j.jcss.2014.11.005

Cited by 9 publications

(6 citation statements)

References 17 publications

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“…Within the wireless sensor networks (WSN), a common approach for actualising dynamic data driven simulation has been to specify sensors as software agents within the model [84]. Such an architecture allows the sensor data to influence the specification of the agent-based model, while the output from the simulation influences the sensor measurement strategies and network configuration.…”

Section: Dynamic Data-driven Simulation Modelsmentioning

confidence: 99%

Sensor-Driven, Spatially Explicit Agent-Based Models

Oloo¹

2019

Swarm Intelligence - Recent Advances, New Perspectives and Applications

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Conventionally, agent-based models (ABMs) are specified from well-established theory about the systems under investigation. For such models, data is only introduced to ensure the validity of the specified models. In cases where the underlying mechanisms of the system of interest are unknown, rich datasets about the system can reveal patterns and processes of the systems. Sensors have become ubiquitous allowing researchers to capture precise characteristics of entities in both time and space. The combination of data from in situ sensors to geospatial outputs provides a rich resource for characterising geospatial environments and entities on earth. More importantly, the sensor data can capture behaviours and interactions of entities allowing us to visualise emerging patterns from the interactions. However, there is a paucity of standardised methods for the integration of dynamic sensor data streams into ABMs. Further, only few models have attempted to incorporate spatial and temporal data dynamically from sensors for model specification, calibration and validation. This chapter documents the state of the art of methods for bridging the gap between sensor data observations and specification of accurate spatially explicit agent-based models. In addition, this work proposes a conceptual framework for dynamic validation of sensor-driven spatial ABMs to address the risk of model overfitting.

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Section: Dynamic Data-driven Simulation Modelsmentioning

confidence: 99%

Sensor-Driven, Spatially Explicit Agent-Based Models

Oloo¹

2019

Swarm Intelligence - Recent Advances, New Perspectives and Applications

View full text Add to dashboard Cite

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“…A centralized system using the communications model of this framework shows a

reduction in the time to send the map to the agents, compared to the decentralized system. However, the decentralized system using full use of the framework shows a

reduction of data traffic to generate the map, reducing energy use of robotic agents [ 51 ].…”

Section: Decentralized Framework In Marsmentioning

confidence: 99%

A Decentralized Framework for Multi-Agent Robotic Systems

Jiménez

García‐Díaz

Bolaños

2018

Sensors

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Over the past few years, decentralization of multi-agent robotic systems has become an important research area. These systems do not depend on a central control unit, which enables the control and assignment of distributed, asynchronous and robust tasks. However, in some cases, the network communication process between robotic agents is overlooked, and this creates a dependency for each agent to maintain a permanent link with nearby units to be able to fulfill its goals. This article describes a communication framework, where each agent in the system can leave the network or accept new connections, sending its information based on the transfer history of all nodes in the network. To this end, each agent needs to comply with four processes to participate in the system, plus a fifth process for data transfer to the nearest nodes that is based on Received Signal Strength Indicator (RSSI) and data history. To validate this framework, we use differential robotic agents and a monitoring agent to generate a topological map of an environment with the presence of obstacles.

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“…Another mobile agent system is presented recently in [56] where sensor nodes delegate software agents (static or mobile) to collect and exchange data with their neighbor nodes. When the mobile agent migrates data from a big number of nodes, it needs the spatial data from a GIS (Geographic Information System) based host.…”

Section: Agent Based Management Approachesmentioning

confidence: 99%

Managing HeterogeneousWSNs in Smart Cities: Challenges and Requirements

Minh

Bellalta

Oechsner

et al. 2016

EAI Endorsed Trans IoT

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The dramatic advances in wireless communications and electronics have enabled the development of Wireless Sensor Networks (WSNs). WSNs consist of many affordable and portable sensor nodes for collecting data from the environment. In this tutorial article, we address management requirements of WSNs through presenting some key management scenarios in the Smart Cities context, such as intelligent transportation systems, smart grids and smart buildings. The limited resources and heterogeneous characteristics of WSNs pose new challenges in network management, which include the presence of various faults, the difficulty in replacing and repairing a large number of sensor nodes, the existence of an uncertain topology, and the resource allocation. To cope with these challenges, we first discuss advantages and disadvantages of centralized and distributed management approaches and then discuss the benefit of a multilevel management schema. Next, we present in detail the specific features for a management system of WSN in Smart Cities context (WSN-iSC) such as lightweight, self-detection, self-configuration, sharing infrastructure, service monitoring, plug and play, context awareness and interoperability. Finally, we discuss several key enabling technologies for management systems, such as policy based and agent based approaches, as well as we introduce some middleware solutions. This tutorial article aims to be a first reference for any reader interested in WSN-iSC management solutions. It provides an insightful and comprehensible introduction to the scenarios, requirements, open challenges, problems, key technologies and desired features that will shape future developments on this field, as well as it surveys the most relevant and recent works from the literature.

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Agent-based framework for sensor-to-sensor personalization

Cited by 9 publications

References 17 publications

Sensor-Driven, Spatially Explicit Agent-Based Models

Sensor-Driven, Spatially Explicit Agent-Based Models

A Decentralized Framework for Multi-Agent Robotic Systems

Managing HeterogeneousWSNs in Smart Cities: Challenges and Requirements

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