We discuss surveillance with multiple unmanned aerial vehicles (UAV) that minimize information idleness (the lag between the start of the mission and the moment when the data captured at a sensing location arrives at the base station) and constrain latency (the lag between capturing data at a sensing location and its arrival at the base station). This is important in surveillance scenarios where sensing locations should not only be visited as soon as possible, but the captured data needs to reach the base station in due time, especially if the surveillance area is larger than the communication range. In our approach, multiple UAVs cooperatively transport the data in a store-and-forward fashion along minimum latency paths (MLPs) to guarantee data delivery within a predefined latency bound. Additionally, MLPs specify a lower bound for any latency minimization problem where multiple mobile agents transport data in a store-and-forward fashion. We introduce three variations of a heuristic employing MLPs and compare their performance with an uncooperative approach in a simulation study. The results show that cooperative data transport reduces the information idleness at the base station compared to the uncooperative approach where data is transported individually by the UAVs.

A visual sensor network (VSN) consists of a large amount of camera nodes which are able to process the captured image data locally and to extract the relevant information. The tight resource limitations in these networks of embedded sensors and processors represent a major challenge for the application development. In this paper, we focus on finding optimal VSN configurations which are basically given by: 1) the selection of cameras to sufficiently monitor the area of interest; 2) the setting of the cameras' frame rate and resolution to fulfill the quality of service requirements; and 3) the assignment of processing tasks to cameras to achieve all required monitoring activities. We formally specify this configuration problem and describe an efficient approximation method based on an evolutionary algorithm. We analyze our approximation method on three different scenarios and compare the predicted results with measurements on real implementations on a VSN platform. We finally combine our approximation method with an expectation-maximization algorithm for optimizing the coverage and resource allocation in VSN with pan-tilt-zoom camera nodes.

Mobile robots, especially unmanned aerial vehicles (UAVs), are of increasing interest for surveillance and disaster response scenarios.We consider the problem of multi-robot persistent surveillance with connectivity constraints where robots have to visit sensing locations periodically and maintain a multi-hop connection to a base station. We formally define several problem instances closely related to multi-robot persistent surveillance with connectivity constraints, i.e. connectivity-constrained multi-robot persistent surveillance (CMPS), connectivity-constrained multi-robot reachability (CMR), and connectivityconstrained multi-robot reachability with relay dropping (CMRD), and show that they are all NP-hard on general graphs. We introduce three heuristics with different planning horizons for convex grid graphs and combine these with a tree traversal approach, which can be applied to a partitioning of non-convex grid graphs (CMPS with tree traversal, CMPSTT). In simulation studies we show that a short horizon greedy approach, which requires parameters to be optimized beforehand, can outperform a full horizon approach, which requires a tour through all sensing locations, if the number of robots is larger than the minimum number of robots required to reach all sensing locations. The minimum number required is the number of robots necessary for building a relay chain to the farthest sensing location from the base station. Furthermore, we show that partitioning the area and applying the tree traversal approach can achieve a similar performance to the unpartitioned case up to a certain number of robots but requires less optimization time.

In many missions, multiple robots need to cooperate to complete tasks. The workflow of these multi-robot tasks involves forming coalitions of robots, assigning them to available tasks, and jointly executing the tasks. In this article, we investigate such a workflow in an application-independent yet realistic setting. We abstract the key properties of tasks and robots and propose three distributed coalition formation and task assignment methods. Distributed methods rely on a timely and accurate exchange of state information in multi-robot systems (MRS). Thus we focus on two important communication aspects: (i) how to achieve consistent coalition formation and task assignment in the presence of communication faults, and (ii) how to reduce the communication effort required for the state updates in the MRS. In particular, we investigate the effect of event-triggered, time-triggered, and hybrid communication. We evaluate our distributed approaches in a simulation study using ns-3 and compare them with centralized methods and different network conditions. We demonstrate the sensitivity of complex missions to failure-prone MRS communication and provide robust, effective, and communication-aware methods for coalition formation and task assignment.

In a computational context, self-awareness (SA) is a capability of an autonomous system to describe the acquired experience about itself and its surrounding environment with appropriate models and correlate them incrementally with the currently perceived situation to expand its knowledge continuously. This article introduces a bio-inspired framework for generative and descriptive dynamic models that support SA computationally and efficiently. Generative models facilitate predicting future states, while descriptive models enable the selection of the representation that best fits the current observation. Our framework is founded on the analysis and extension of three bio-inspired theories that have studied SA from different viewpoints, and we demonstrate how probabilistic techniques, such as cognitive dynamic Bayesian networks and generalized filtering paradigms, can learn appropriate models from multidimensional proprioceptive and exteroceptive signals acquired by the autonomous system. We discuss essential capabilities for SA and show how our modeling framework supports these capabilities in theory and through a case study where a mobile robot uses multisensorial data to determine its internal and environmental state as well as distinguishing among normal and abnormal behaviors.

Ubiquitous and networked sensors impose a huge challenge for privacy protection which has become an emerging problem of modern society. Protecting the privacy of visual data is particularly important due to the omnipresence of cameras, and various protection mechanisms for captured images and videos have been proposed. This paper introduces an objective evaluation framework in order to assess such protection methods. Visual privacy protection is typically realised by obfuscating sensitive image regions which often results in some loss of utility. Our evaluation framework assesses the achieved privacy protection and utility by comparing the performance of standard computer vision tasks, such as object recognition, detection and tracking on protected and unprotected visual data. The proposed framework extends the traditional frame-by-frame evaluation approach by introducing two new approaches based on aggregated and fused frames. We demonstrate our framework on eight differently protected video-sets and measure the trade-off between the improved privacy protection due to obfuscating captured image data and the degraded utility of the visual data. Results provided by our objective evaluation method are compared with an available state-of-the-art subjective study of these eight protection techniques.