Mobile Beacon Research Articles

Monte Carlo localization of wireless sensor networks with a single mobile beacon

One of the most important tasks in sensor networks is to determine the physical location of sensory nodes as they may not all be equipped with GPS receivers. In this paper we propose a localization method for wireless sensor networks (WSNs) using a single mobile beacon. The sensor locations are maintained as probability distributions that are sequentially updated using Monte Carlo sampling as the mobile beacon moves over the deployment area. Our method relieves much of the localization tasks from the less powerful sensor nodes themselves and relies on the more powerful beacon to perform the calculation. We discuss the Monte Carlo sampling steps in the context of the localization using a single beacon for various types of observations such as ranging, Angle of Arrival (AoA), connectivity and combinations of those. We also discuss the communication protocol that relays the observation data to the beacon and the localization result back to the sensors. We consider security issues in the localization process and the necessary steps to guard against the scenario in which a small number of sensors are compromised. Our simulation shows that our method is able to achieve less than 50% localization error and over 80% coverage with a very sparse network of degree less than 4 while achieving significantly better results if network connectivity increases.

Distributed localization using mobile beacons in wireless sensor networks

A new distributed node localization algorithm named mobile beacons–improved particle filter (MB-IPF) was proposed. In the algorithm, the mobile nodes equipped with globe position system (GPS) move around in the wireless sensor network (WSN) field based on the Gauss-Markov mobility model, and periodically broadcast the beacon messages. Each unknown node estimates its location in a fully distributed mode based on the received mobile beacons. The localization algorithm is based on the IPF and several refinements, including the proposed weighted centroid algorithm, the residual resampling algorithm, and the markov chain monte carlo (MCMC) method etc., which were also introduced for performance improvement. The simulation results show that our proposed algorithm is efficient for most applications.

Designing reduced beacon trajectory for sensor localization

Localization is one of the substantial issues in wireless sensor networks. The key problem for the mobile beacon localization is how to choose the appropriate beacon trajectory. However, little research has been done on it. In this paper, firstly, we deduce the number of positions for a beacon to send a packet according to the acreage of ROI (region of interest); and next we present a novel method based on virtual force to arrange the positions in arbitrary ROI; then we apply TSP (travelling salesman problem) algorithm to the positions sequence to obtain the optimal touring path, i.e. the reduced beacon trajectory. When a mobile beacon moves along the touring path, sending RF signals at every position, the sensors in ROI can work out their position with trilateration. Experimental results demonstrate that the localization method, based on the beacon reduced path, is efficient and has flexible accuracy.

Localization and coverage for high density sensor networks

This paper tackles the problems of localization and coverage in randomly deployed high density sensor networks. In particular, it presents a novel and integrated approach that performs at once localization and coverage. We introduce here an approach based on a single mobile beacon aware of its position. Sensor nodes receiving beacon packets will be able to locate themselves. On the other hand, We exploit the localization phase to construct sets of active nodes that ensure as much as possible the zone coverage. In our approach the mobile beacon follows a Hilbert curve. The results of experiments conducted using the discrete event simulator Omnet++, are discussed, they allow us to justify our approach and to compare it to existing ones.

Mobility aided localization of wireless sensor networks with inaccurate range measurements

This paper studies the relationship between mobility, navigation and localization in the context of wireless sensor networks with mobile beacons. It is observed that mobility can aid in network node localization and that once localized, the network nodes can localize and track a mobile object and guide its navigation. A distributed kernel-based algorithm is proposed that enables the nodes to establish confident position estimates in the presence of ranging inaccuracies. The proposed approach features robustness with respect to range measurement inaccuracies, low complexity and distributed implementation, using only local information. Simulation validates our approach viable.

Comparative Accuracy of Aerial and Ground Telemetry Locations of Foraging Raptors

Widely ranging raptors are difficult to radio-track from fixed locations on the ground; therefore, we investigated the feasibility of tracking Prairie Falcons (Falco mexicanus) from a Cessna 182 airplane outfitted with a belly-mounted, rotary, H-antenna. Locations were estimated by flying directly over the signal's source, and recorded with an on-board global positioning system. Location estimates of stationary and mobile beacons derived from aerial tracking were more accurate than locations derived from triangulation by 4-6 ground-based trackers (x̄ 95% confidence ellipses: aerial = 112 ha, ground = 875 ha). Aerial accuracy was not influenced by mobility of a beacon and was similar for two observers. However, because falcons spend a majority of their time in proximity of their aerie, most aerial fixes were close to the nest site. This resulted in significant underestimates of falcon foraging ranges, especially for breeding males.