Sunday, June 9, 2019

Self-awareness and autonomy in pervasive computing systems

Associate editor: Danilo Pianini (@DanySK86)

The number of computing systems in our environment is constantly increasing, and it’s not just the obvious individual devices we have all added to our lives. This trend is accelerating even further due to unseen advances in the areas of pervasive computing, cyber-physical systems, the Internet-of-Things, Industry 4.0, as they manifest in smart cities, smart homes, smart offices, and smart transport. The numbers alone make centralized control problematic from an engineering point of view, even without considering the speed of dissemination and adoption. The vast and unmeasured diversity of interfaces and interactional requirements are imposing an as-yet-unmeasured increase in cognitive and physiological demands on all of us. One way to lessen the impacts of these human and technological demands is by offloading some control to some of the individual devices. This not only relieves demands on miniaturization, control systems, and server infrastructures, but also relieves cognitive and physiological demands on the users, and allows the devices to react more quickly to new situations, and even to known or anticipated situations that unfold more rapidly than current hierarchical control systems can accommodate.
One approach to imbuing individual devices with more autonomy is to design them to be self-aware. This would enable devices to learnabout themselves and their environment, to develop and refine thesemodels during runtime, and to reason about them in order to makeprofound decisions. Different levels of self-awareness have been proposed, addressing the various degrees to which a computational system can be aware. It has been demonstrated that this can improvesystem performance, even when collaborating with others.
We offer an outline of three important factors that have the potential to challenge the success of collaborating self-aware systems.


Systems distributed in a real-world environment will perceive that environment differently, even when their abilities to perceive it are equal and they are in close proximity to one another. The following figure depicts a network of 3 smart-cameras, able to perceive their environment and process this information locally.

This network illustrates two problems with respect to situatedness of individual devices. Camera A and B are physically very close, mounted on a common pole. However, due to their constrained perception of the world, they cannot perceive the same objects at the same time. On the other hand, cameras C is mounted on a house and observes the same area as camera B but from a different perspective, which means that their individual perceptions of a simultaneously viewed object can be different. Figure 1 shows us that, while camera B sees a smooth round object that is mostly green, camera C observes an object of non-uniform shape, that is mostly red. Even if they share their information, they would need to also share an understanding of their differing perspectives in order to combine their perceptions and recognize that they are seeing the same object.


When operating alongside or in collaboration with others, a system might not be able to simply make assumptions about the abilities and behavior of another system. As an example, please consider two digital cameras that both perceive their environment. Even though these two cameras may observe the same object in the same way, their perceptual tools may differ, and this could conceivably result in completely different perceptions of the same object. One might imagine a black-and-white sensor and a standard color sensor in the two cameras. Here the cameras cannot simply exchange color information about objects as this would not result in a common understanding. In a similar case, different zooms can lead to different resolutions permitting a camera to perceive details another camera might not be able to see.


Systems are often designed to perform very specific tasks. If they are intended to collaborate with others, this collaboration is usually clearly defined at the time of their design. If we want future systems to be able to establish collaboration autonomously, without a priori knowledge of their potential collaborators, we will have to build them with the ability to model the potential collaborators that they encounter. In addition, they have to be able to model the behavior of those new collaborators and adapt their own behavior according to larger collaborative models that were developed on the fly.


Current work on self-aware systems focusses on the individual computing systems, rather than on defining, designing, and developing features that would enable and improve heterogenous collaboration during runtime. In order to facilitate collaboration among systems, we have proposed additional levels of networked self-awareness [1]. Implementing these additional levels of networked self-awareness will enable systems to develop adaptable models of their environment, of other systems, and of themselves, as well as the ways in which those models interact and impact one another. Such models should be able to meet the challenges outlined above, and collaborate with other systems in achieving their shared and unshared goals.


  1. L. Esterle and J. N. Brown, "I Think Therefore You Are: Models for Interaction in Collectives of Self-Aware Cyber-physical Systems," Transactions on Cyber-physical Systems, under review, p. 24, 2019.

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