arXiv subjects

Branislav Kusy

Publications and source records attributed to Branislav Kusy.

8 recordsLinked to original sources

arXiv2017

Multi-mode Tracking of a Group of Mobile Agents

We consider the problem of tracking a group of mobile nodes with limited available computational and energy resources given noisy RSSI measurements and position estimates from group members. The multilateration solutions are known for energy efficiency. However, these solutions are not directly applicable to dynamic grouping scenarios where neighbourhoods and resource availability may frequently change. Existing algorithms such as cluster-based GPS duty-cycling, individual-based tracking, and multilateration-based tracking can only partially deal with the challenges of dynamic grouping scenarios. To cope with these challenges in an effective manner, we propose a new group-based multi-mode tracking algorithm. The proposed algorithm takes the topological structure of the group as well as the availability of the resources into consideration and decides the best solution at any particular time instance. We consider a clustering approach where a cluster head coordinates the usage of resources among the cluster members. We evaluate the energy-accuracy trade-off of the proposed algorithm for various fixed sampling intervals. The evaluation is based on the 2D position tracks of 40 nodes generated using Reynolds' flocking model. For a given energy budget, the proposed algorithm reduces the mean tracking error by up to $20\%$ in comparison to the existing energy-efficient cooperative algorithms. Moreover, the proposed algorithm is as accurate as the individual-based tracking while using almost half the energy.

cs.IT↗

arXiv2017

RSSI-Based Self-Localization with Perturbed Anchor Positions

We consider the problem of self-localization by a resource-constrained mobile node given perturbed anchor position information and distance estimates from the anchor nodes. We consider normally-distributed noise in anchor position information. The distance estimates are based on the log-normal shadowing path-loss model for the RSSI measurements. The available solutions to this problem are based on complex and iterative optimization techniques such as semidefinite programming or second-order cone programming, which are not suitable for resource-constrained environments. In this paper, we propose a closed-form weighted least-squares solution. We calculate the weights by taking into account the statistical properties of the perturbations in both RSSI and anchor position information. We also estimate the bias of the proposed solution and subtract it from the proposed solution. We evaluate the performance of the proposed algorithm considering a set of arbitrary network topologies in comparison to an existing algorithm that is based on a similar approach but only accounts for perturbations in the RSSI measurements. We also compare the results with the corresponding Cramer-Rao lower bound. Our experimental evaluation shows that the proposed algorithm can substantially improve the localization performance in terms of both root mean square error and bias.

cs.IT↗

arXiv2017

Cluster-based Position Tracking of Mobile Sensors

Tracking movement of mobile nodes has received significant scientific and commercial interest, but long term tracking of resource-constrained mobile nodes remains challenging due to the high energy consumption of satellite receivers. Cooperative position tracking has been proposed for energy efficiency, however, all the cooperative schemes use opportunistic cooperation and optimize for either energy or accuracy. Considering the existence of a reasonably stable group of mobile nodes like animals, birds, and mobile assets, we propose a cluster-based cooperative tracking algorithm, where cluster head centrally coordinates resource usage among cluster members. Variants of this strategy include the use of a cooperative Kalman filter with and without inertial sensor inputs to estimate nodes positions. We use the Boid flocking algorithm to generate group position movements in 3D and perform various experiments to compare the energy and position accuracy tradeoff of our proposed scheme with individual-based tracking and existing cooperative schemes. We perform the experiments for fixed periodic GPS sampling and dynamic GPS sampling triggered by node position error uncertainty tolerance limit. Experiments results show that in periodic sampling scheme cooperative tracking yields more than one-quarter reduction in energy consumption and more than one-third improvement in position accuracy over individual-based tracking, however, results for dynamic sampling scheme are comparable with existing cooperative scheme.

cs.NI↗

arXiv2017

Fast and robust pushbroom hyperspectral imaging via DMD-based scanning

We describe a new pushbroom hyperspectral imaging device that has no macro moving part. The main components of the proposed hyperspectral imager are a digital micromirror device (DMD), a CMOS image sensor with no filter as the spectral sensor, a CMOS color (RGB) image sensor as the auxiliary image sensor, and a diffraction grating. Using the image sensor pair, the device can simultaneously capture hyperspectral data as well as RGB images of the scene. The RGB images captured by the auxiliary image sensor can facilitate geometric co-registration of the hyperspectral image slices captured by the spectral sensor. In addition, the information discernible from the RGB images can lead to capturing the spectral data of only the regions of interest within the scene. The proposed hyperspectral imaging architecture is cost-effective, fast, and robust. It also enables a trade-off between resolution and speed. We have built an initial prototype based on the proposed design. The prototype can capture a hyperspectral image datacube with a spatial resolution of 192x192 pixels and a spectral resolution of 500 bands in less than thirty seconds.

cs.CV↗

arXiv2016

Fair Scheduling for Data Collection in Mobile Sensor Networks with Energy Harvesting

We consider the problem of data collection from a continental-scale network of energy harvesting sensors, applied to tracking mobile assets in rural environments. Our application constraints favour a highly asymmetric solution, with heavily duty-cycled sensor nodes communicating with powered base stations. We study a novel scheduling optimisation problem for energy harvesting mobile sensor network, that maximises the amount of collected data under the constraints of radio link quality and energy harvesting efficiency, while ensuring a fair data reception. We show that the problem is NP-complete and propose a heuristic algorithm to approximate the optimal scheduling solution in polynomial time. Moreover, our algorithm is flexible in handling progressive energy harvesting events, such as with solar panels, or opportunistic and bursty events, such as with Wireless Power Transfer. We use empirical link quality data, solar energy, and WPT efficiency to evaluate the proposed algorithm in extensive simulations and compare its performance to state-of-the-art. We show that our algorithm achieves high data reception rates, under different fairness and node lifetime constraints.

cs.NI↗

arXiv2015

Delay-Tolerant Networking for Long-Term Animal Tracking

Enabling Internet connectivity for mobile objects that do not have a permanent home or regular movements is a challenge due to their varying energy budget, intermittent wireless connectivity, and inaccessibility. We present a hardware and software framework that offers robust data collection, adaptive execution of sensing tasks, and flexible remote reconfiguration of devices deployed on nomadic mobile objects such as animals. The framework addresses the overall complexity through a multi-tier architecture with low tier devices operating on a tight energy harvesting budget and high tier cloud services offering seamless delay-tolerant presentation of data to end users. Based on our multi-year experience of applying this framework to animal tracking and monitoring applications, we present the main challenges that we have encountered, the design of software building blocks that address these challenges, and examples of the data we collected on flying foxes.

cs.NI↗

arXiv2015

Autonomous surveillance for biosecurity

The global movement of people and goods has increased the risk of biosecurity threats and their potential to incur large economic, social, and environmental costs. Conventional manual biosecurity surveillance methods are limited by their scalability in space and time. This article focuses on autonomous surveillance systems, comprising sensor networks, robots, and intelligent algorithms, and their applicability to biosecurity threats. We discuss the spatial and temporal attributes of autonomous surveillance technologies and map them to three broad categories of biosecurity threat: (i) vector-borne diseases; (ii) plant pests; and (iii) aquatic pests. Our discussion reveals a broad range of opportunities to serve biosecurity needs through autonomous surveillance.

cs.RO↗

arXiv2014

Optimal Lévy-flight foraging in a finite landscape

We present a simple model to study Lévy-flight foraging in a finite landscape with countable targets. In our approach, foraging is a step-based exploratory random search process with a power-law step-size distribution $P(l) \propto l^{-μ}$. We find that, when the termination is regulated by a finite number of steps $N$, the optimum value of $μ$ that maximises the foraging efficiency can vary substantially in the interval $μ\in (1,3)$, depending on the landscape features (landscape size and number of targets). We further demonstrate that subjective returning can be another significant factor that affects the foraging efficiency in such context. Our results suggest that Lévy-flight foraging may arise through an interaction between the environmental context and the termination of exploitation, and particularly that the number of steps can play an important role in this scenario which is overlooked by most previous work. Our study not only provides a new perspective on Lévy-flight foraging, but also opens new avenues for investigating the interaction between foraging dynamics and environment as well as offers a realistic framework for analysing animal movement patterns from empirical data.

cond-mat.stat-mech↗