Geographic routing resilient to location errors

Geographic routing is an attractive option for large scale wireless sensor networks (WSNs) because of its low overhead and energy expenditure, but is inefficient in realistic localization conditions. Positioning systems are inevitably imprecise because of inexact range measurements and location errors lead to poor performance of geographic routing in terms of packet delivery ratio (PDR) and energy efficiency. This paper proposes a novel, low-complexity, error-resilient geographic routing method, named conditioned mean square error ratio (CMSER) routing, intended to efficiently make use of existing network information and to successfully route packets when localization is inaccurate. Next hop selection is based on the largest distance to destination (minimizing the number of forwarding hops) and on the smallest estimated error figure associated with the measured neighbor coordinates. It is found that CMSER outperforms other basic greedy forwarding techniques employed by algorithms such as most forward within range (MFR), maximum expectation progress (MEP) and least expected distance (LED). Simulation results show that the throughput for CMSER is higher than for other methods, additionally it also reduces the energy wasted on lost packets by keeping their routing paths short

LER-GR: Location Error Resilient Geographical Routing for Vehicular Ad-hoc Networks

The efficiency and scalability of geographical routing depend on the accuracy of location information of vehicles. Each vehicle determines its location using Global Positioning System (GPS) or other positioning systems. Related literature in geographical routing implicitly assumes accurate location information. However, this assumption is unrealistic considering the accuracy limitation of GPS and obstruction of signals by road side environments. The inaccurate location information results in performance degradation of geographical routing protocols in vehicular environments. In this context, this paper proposes a location error resilient geographical routing (LER-GR) protocol. Rayleigh distribution based error calculation technique is utilized for assessing error in the location of neighbouring vehicles. Kalman filter based location prediction and correction technique is developed to predict the location of the neighbouring vehicles. The next forwarding vehicle (NFV) is selected based on the least error in location information. Simulations are carried out to evaluate the performance of LER-GR in realistic environments, considering junction-based as well as real map-based road networks. The comparative performance evaluation attests the location error resilient capability of LER-GR in a vehicular environment

Energy efficient geographic routing resilient to location errors

The thesis first analyses the importance sensor placement has in a large scale WSN application using geographic routing. A simulation-based topological study is made for a forest fire prevention application using both deterministically and randomly placed nodes. Sensor deployment can be projectile, from the network edge, made through manual scattering or by air release. Results reveal the impact of sensor distribution, density or destination location on the routing component. Furthermore, geographic routing analysis focuses on location information assumptions. Because all methods of localisation are imprecise, it is necessary to consider the use of estimated coordinates instead of the real ones and to first model the location errors as normally distributed. A more realistic evaluation of the routing component requires the use of positioning simulations, considering received signal strength (RSS) and time of arrival (ToA) ranging for localisation (both modelled in this thesis using the linear least square method (LLS) and maximum likelihood (ML) based Levenberg Marquardt (LM) method). Routing behaviour is analysed in terms of throughput, path lengths, energy consumption and failure causes. The energy expenditure of the two ranging methods is also analysed. Efficient routing solutions for large scale WSNs are explored to cope with location error. A novel, low-complexity, error-resilient geographic routing method is proposed, namely the conditioned mean square error ratio (CMSER) algorithm. CMSER is compared to other progress only forwarding methods. A modified version of the algorithm is proposed to further increase energy efficiency and simulation results also confirm this. Furthermore, because CMSER is designed to make use of the Rice distribution (a statistical assumption valid only when the x and y coordinates of a node have the same location error variance) the precision of this approach is investigated. Although the routing behaviour is not severely affected by this simplifying assumption, because the variance of the errors can be very different in reality, a non-Rician version of the algorithm is proposed, which provides similar results under correct assumptions