Self-Configuring Sensors for Uncharted Environments

Salazar, Norman; Rodŕıguez-Aguilar, Juan A.; Arcos, Josep Lluís

doi:10.1109/saso.2010.38

Cited by 6 publications

(7 citation statements)

References 9 publications

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“…Finally, Nebehay et al [2013] study the role of variation not between camera nodes, but as a characteristic of components within the object tracker in a single smart camera. Salazar et al [2010] highlight the importance of dynamic heterogeneous configurations for sensor networks deployed in uncharted environments, i.e. in scenarios about which there is a lack of a priori information.…”

Section: Heterogeneity and Variation In Self-organising Systemsmentioning

confidence: 99%

“…In these cases, adopting different algorithms from each other may enable them to better achieve their own local objectives. It has also been shown that such heterogeneity among nodes can lead to better achievement of system wide objectives [Campbell et al 2011;Anders et al 2012], especially when nodes can adapt independently in response to uncertainty and changes in the environment during the system's lifetime [Salazar et al 2010].…”

Section: Introductionmentioning

confidence: 99%

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Static, Dynamic, and Adaptive Heterogeneity in Distributed Smart Camera Networks

Lewis

Esterle

Chandra

et al. 2015

ACM Trans. Auton. Adapt. Syst.

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We study heterogeneity among nodes in self-organising smart camera networks, which use strategies based on social and economic knowledge to target communication activity efficiently. We compare homogeneous configurations, when cameras use the same strategy, with heterogeneous configurations, when cameras use different strategies. Our first contribution is to establish that static heterogeneity leads to new outcomes which are more efficient than those possible with homogeneity. Next, two forms of dynamic heterogeneity are investigated: non-adaptive mixed strategies, and adaptive strategies which learn online. Our second contribution is to show that mixed strategies offer Pareto efficiency consistently comparable with the most efficient static heterogeneous configurations. Since the particular configuration required for high Pareto efficiency in a scenario will not be known in advance, our third contribution is to show how decentralised online learning can lead to more efficient outcomes than the homogeneous case. In some cases, outcomes from online learning were more efficient than all other evaluated configuration types. Our fourth contribution is to show that online learning typically leads to outcomes more evenly spread over the objective space. Our results provide insight into the relationship between static, dynamic and adaptive heterogeneity, suggesting that all have a key role in achieving efficient self-organisation.

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Section: Heterogeneity and Variation In Self-organising Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Static, Dynamic, and Adaptive Heterogeneity in Distributed Smart Camera Networks

Lewis

Esterle

Chandra

et al. 2015

ACM Trans. Auton. Adapt. Syst.

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“…This result mirrors those from studies of heterogeneity elsewhere in multi-agent and self-organising systems, where specific benefits include adaptation to unknown situations or environments. For example, Anders et al [1] showed that heterogeneity among nodes in a network can lead to better achievement of system wide objectives, and Salazar et al [44] highlight the importance of understanding and harnessing heterogeneity when nodes can adapt independently online, in response to uncertainty and changes in the environment. In dynamic task allocation, Campbell et al [5] show that variation in agent behaviour increases the system's ability to adapt to varying stimuli.…”

Section: Heterogeneitymentioning

confidence: 99%

The Future of Camera Networks

Esterle

Lewis

McBride³

et al. 2017

Proceedings of the 11th International Conference on Distributed Smart Cameras

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Camera networks become smart when they can interpret video data on board, in order to carry out tasks as a collective, such as target tracking and (re-)identification of objects of interest. Unlike today's deployments, which are mainly restricted to lab settings and highly controlled high-value applications, future smart camera networks will be messy and unpredictable. They will operate on a vast scale, drawing on mobile resources connected in networks structured in complex and changing ways. They will comprise heterogeneous and decentralised aggregations of visual sensors, which will come together in temporary alliances, in unforeseen and rapidly unfolding scenarios. The potential to include and harness citizen-contributed mobile streaming, body-worn video, and robotmounted cameras, alongside more traditional fixed or PTZ cameras, and supported by other non-visual sensors, leads to a number of difficult and important challenges. In this position paper, we discuss a variety of potential uses for such complex smart camera networks, and some of the challenges that arise when staying smart in the presence of such complexity. We present a general discussion on the challenges of heterogeneity, coordination, self-reconfigurability, mobility, and collaboration in camera networks. KEYWORDSfuture camera networks, surveillance, mobile smart cameras, body worn cameras, humans in the loop, distributed control ACM Reference format:

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“…Another flavor of sensor networks works by partitioning the world according to its evolution such as the work of Salazar and colleagues [22]. In this system through a diffusion searching algorithm sensors are arranged in certain configurations.…”

Section: Sensor Networkmentioning

confidence: 99%

Self-Calibration: Enabling Self-Management in Autonomous Systems by Preserving Model Fidelity

Javed¹,

Hassan²,

Junejo³

et al. 2012

2012 IEEE 17th International Conference on Engineering of Complex Computer Systems

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Autonomic and autonomous systems exist within a world view of their own. This world view is created from the training data and assumptions that were available at their inception. In most of these systems this world view becomes obsolete over time due to changes in the environment. This brings a level of inaccuracy in the autonomic behavior of the system. When this degradation reaches a certain threshold selfhealing or self-optimizing systems generally recreate the world view using current data and assumptions. However, the selfoptimization process is akin to kill a fly with a hammer for minor tuning of the world view. Instead we propose the idea of self-calibration for self-managing these systems. We define self-calibration as the ability of the system to perceive the need for and the ability to execute minimal tuning to bridge the gap between system's world view and incoming information about the outside world. In this paper we present a case for considering self-calibration as a self-* enabling property of systems specifically for time-critical systems using data-centric AI technologies. We present our case by discussing three case studies from different domains where self-calibration enables a system to become self-healing or self-optimizing. We then place self-calibration in a generic system and explicitly describe the types of systems in which self-calibration can be implemented and the benefits that one can accrue from its inclusion.

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Self-Configuring Sensors for Uncharted Environments

Cited by 6 publications

References 9 publications

Static, Dynamic, and Adaptive Heterogeneity in Distributed Smart Camera Networks

Static, Dynamic, and Adaptive Heterogeneity in Distributed Smart Camera Networks

The Future of Camera Networks

Self-Calibration: Enabling Self-Management in Autonomous Systems by Preserving Model Fidelity

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