An Architecture for Self-Aware IOT Applications

Future Internet of Things (IoT) applications will face challenges in increased flexibility, uncertainty, dynamics and scalability. Self-aware computing maintains knowledge about the applications state and environment and then uses this knowledge to reason about and adapt behaviours. In this position paper, we introduce self-aware computing as design approach for IoT applications which is centred around a self-aware architecture for IoT nodes. This architecture particularly supports adaptations based on node interactions. We demonstrate our approach with an IoT case study on multi-object coverage with mobile cameras

Smart Cameras with onboard Signcryption for Securing IoT Applications

Cameras are expected to become key sensor devices for various internet of things (IoT) applications. Since cameras often capture highly sensitive information, security is a major concern. Our approach towards data security for smart cameras is rooted on protecting the captured images by signcryption based on elliptic curve cryptography (ECC). Signcryption achieves resource-efficiency by performing data signing and encryption in a single step. By running the signcryption on the sensing unit, we can relax some security assumptions for the camera host unit which typically runs a complex software stack. We introduce our system architecture motivated by a typical case study for camera-based IoT applications, evaluate security properties and present performance results of an ARM-based implementatio

Die unvollendete Sonnenuhr von der Agora der Italiker auf Delos

Only one case of an unfinished ancient sundial can be found in the scientific literature. Found on the Greek island of Delos, it was first reported in 1938, although the sundial was then later considered lost. In our campaign of October 2012, we rediscovered the sundial. Using new and elaborate techniques, we created a 3D model of the sundial, which has enabled us to answer questions concerning its construction principles and the manufacturing processes used. Our first evaluation has revealed that, initially, its creators had intended to construct a cut conical sundial. Its discovery next to a workshop suggests that the sundial was left there in its unfinished state on the destruction of the island’s main town

Self-organising zooms for decentralised redundancy management in visual sensor networks

When visual sensor networks are composed of cameras which can adjust the zoom factor of their own lens, one must determine the optimal zoom levels for the cameras, for a given task. This gives rise to an important trade-off between the overlap of the different cameras’ fields of view, providing redundancy, and image quality. In an object tracking task, having multiple cameras observe the same area allows for quicker recovery, when a camera fails. In contrast having narrow zooms allow for a higher pixel count on regions of interest, leading to increased tracking confidence. In this paper we propose an approach for the self-organisation of redundancy in a distributed visual sensor network, based on decentralised multi-objective online learning using only local information to approximate the global state. We explore the impact of different zoom levels on these trade-offs, when tasking omnidirectional cameras, having perfect 360-degree view, with keeping track of a varying number of moving objects. We further show how employing decentralised reinforcement learning enables zoom configurations to be achieved dynamically at runtime according to an operator’s preference for maximising either the proportion of objects tracked, confidence associated with tracking, or redundancy in expectation of camera failure. We show that explicitly taking account of the level of overlap, even based only on local knowledge, improves resilience when cameras fail. Our results illustrate the trade-off between maintaining high confidence and object coverage, and maintaining redundancy, in anticipation of future failure. Our approach provides a fully tunable decentralised method for the self-organisation of redundancy in a changing environment, according to an operator’s preferences

Autonomous Multicamera Tracking on Embedded Smart Cameras

There is currently a strong trend towards the deployment of advanced computer vision methods on embedded systems. This deployment is very challenging since embedded platforms often provide limited resources such as computing performance, memory, and power. In this paper we present a multicamera tracking method on distributed, embedded smart cameras. Smart cameras combine video sensing, processing, and communication on a single embedded device which is equipped with a multiprocessor computation and communication infrastructure. Our multicamera tracking approach focuses on a fully decentralized handover procedure between adjacent cameras. The basic idea is to initiate a single tracking instance in the multicamera system for each object of interest. The tracker follows the supervised object over the camera network, migrating to the camera which observes the object. Thus, no central coordination is required resulting in an autonomous and scalable tracking approach. We have fully implemented this novel multicamera tracking approach on our embedded smart cameras. Tracking is achieved by the well-known CamShift algorithm; the handover procedure is realized using a mobile agent system available on the smart camera network. Our approach has been successfully evaluated on tracking persons at our campus

Resource-aware configuration in smart camera networks

A recent trend in smart camera networks is that they are able to modify the functionality during runtime to better reflect changes in the observed scenes and in the specified monitoring tasks. In this paper we focus on different configuration methods for such networks. A configuration is given by three components: (i) a description of the camera nodes, (ii) a specification of the area of interest by means of observation points and the associated monitoring activities, and (iii) a description of the analysis tasks. We introduce centralized, distributed and proprioceptive configuration methods and compare their properties and performance