Agencies for Perception in Environmental Monitoring

Amigoni, Francesco; Brandolini, A.; Caglioti, Vincenzo; Lecce, Vincenzo Di; Guerriero, Andrea; Lazzaroni, M.; Lombardi, Franco; Ottoboni, R.; Pasero, Eros; Piuri, Vincenzo; Scotti, O.; Somenzi, Damiano Marino

doi:10.1109/tim.2006.877747

Cited by 16 publications

(6 citation statements)

References 30 publications

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“…On the other hand, environment monitoring systems have been combined with multi-agent systems [9] to support realistic application of robots' interaction with their surrounding environment. Feature extraction and object class recognition on target objects that robotic agents encounter, while exploring a workspace, play a critical role for a reliable estimation of the specialized agents' qualification to intervene on the detected targets.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Allocation of Specialized Robots on Targets Detected Using Deep Learning Networks

Al-Buraiki¹,

Wu²,

Payeur³

2020

Robotics

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Task allocation for specialized unmanned robotic agents is addressed in this paper. Based on the assumptions that each individual robotic agent possesses specialized capabilities and that targets representing the tasks to be performed in the surrounding environment impose specific requirements, the proposed approach computes task-agent fitting probabilities to efficiently match the available robotic agents with the detected targets. The framework is supported by a deep learning method with an object instance segmentation capability, Mask R-CNN, that is adapted to provide target object recognition and localization estimates from vision sensors mounted on the robotic agents. Experimental validation, for indoor search-and-rescue (SAR) scenarios, is conducted and results demonstrate the reliability and efficiency of the proposed approach.

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Section: Related Workmentioning

confidence: 99%

Probabilistic Allocation of Specialized Robots on Targets Detected Using Deep Learning Networks

Al-Buraiki¹,

Wu²,

Payeur³

2020

Robotics

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“…According to the overall architecture of the system used for data acquisition and measurement processing, environmental monitoring systems can be classified as: centralized, distributed, and remote sensing systems [7]. Centralized systems are composed of a single processor or controller, a limited number of sensors and a simple output presentation interface.…”

Section: A System Architecturesmentioning

confidence: 99%

Privacy and security in environmental monitoring systems

Vimercati

Livraga

Piuri

et al. 2012

2012 IEEE First AESS European Conference on Satellite Telecommunications (ESTEL)

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Abstract-There is today an increasing interest in environmental monitoring for a variety of specific applications, with great impact especially on natural resource management and preservation, economy, and people's life and health. Typical uses encompass, for example, Earth observation, meteorology, natural resource monitoring, agricultural and forest monitoring, pollution control, natural disaster observation and prediction, and critical infrastructure monitoring. While on one hand these systems play an important role in our society, on the other hand their adoption can raise a number of security and privacy concerns, representing a possible obstacle for the development of future environmental applications. In this paper, we analyze the security and privacy issues characterizing both the environmental monitoring infrastructures and the data collected and processed by them. We also provide an overview of possible countermeasures for diminishing the effects of these issues.

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“…This concept has more recently led to the development of a new generation of environment monitoring systems combining Sensor Networks and Multi-Agent Systems (Chong and Kumar 2003;Lesser et al 2003;Amigoni et al 2005;Liscano et al 2005;Abielmona et al 2006;Takahashi et al 2009;Suzuki et al 2009). …”

Section: Introductionmentioning

confidence: 99%

Distributed intelligent sensor agent system for environment mapping

Abielmona

Petriu

Whalen

2010

J Ambient Intell Human Comput

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This paper discusses a multi-agent system consisting of a limited set of mobile intelligent sensor agents that are exploring an environment with the goal of minimizing the environment mapping uncertainty, i.e. the entropy. A novel tree in-motion mapping method combining simplicity and speed of computation with low storage and communications requirements is proposed for the management of a network of robotic agents, each possessing limited sensing, processing and communicating operational entities. Simulation and experimental results demonstrate the efficiency of the proposed system architecture and mapping method.

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Agencies for Perception in Environmental Monitoring

Cited by 16 publications

References 30 publications

Probabilistic Allocation of Specialized Robots on Targets Detected Using Deep Learning Networks

Probabilistic Allocation of Specialized Robots on Targets Detected Using Deep Learning Networks

Privacy and security in environmental monitoring systems

Distributed intelligent sensor agent system for environment mapping

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