HyBloc: Localization in Sensor Networks with Adverse Anchor Placement

To determine the geographical positions of sensors, numerous localization algorithms have been proposed in recent years. The positions of sensors are inferred from the connectivity between sensors and a set of nodes called anchors which know their precise locations. We investigate the effect of adverse placement and density of anchors on the accuracies of different algorithms. We develop an algorithm called HyBrid Localization (HyBloc) to provide reliable localization service with a limited number of clustered anchors. HyBloc is distributed in nature with reasonable message overhead. Through simulations, we demonstrate that HyBloc provides more accurate location estimates than some existing distributed algorithms when there are only a few anchors. HyBloc also performs well when anchors are clustered together

HEA-Loc: A robust localization algorithm for sensor networks of diversified topologies

In recent years, localization in a variety of Wireless Sensor Networks (WSNs) is a compelling but elusive goal. Several algorithms that use different methodologies have been proposed to achieve this goal. The performances of these algorithms depend on several factors, such as the sensor node placement, anchor deployment or network topology. In this paper, we propose a robust localization algorithm called Hybrid Efficient and Accurate Localization (HEA-Loc). HEA-Loc combines two techniques, Extended Kalman Filter (EKF) and Proximity-Distance Map (PDM) to improve localization accuracy. It is distributed in nature and works well in various scenarios as it is less susceptible to anchors deployment and the network topology. Furthermore, HEA-Loc has strong robustness and it can work well even the measurement errors are large. Simulation results show that HEA-Loc outperforms existing algorithms in both computational complexity and communication overhead. ©2010 IEEE.published_or_final_versionThe IEEE Wireless Communications and Networking Conference (WCNC 2010), Sydney, NSW., 18-21 April 2010. In Proceedings of WCNC, 2010, p. 1-

Geometric sensitivity of beacon placement using airborne mobile anchors

Locating fixed sensing devices with a mobile anchor is attractive for covering larger deployment areas. However, the performance sensitivity to the geometric arrangement of anchor beacon positions remains unexplored. Therefore, localization using new RSSI-based localization algorithm, which uses a volumetric probability distribution function is proposed to find the most likely position of a node by information fusion from several mobile beacon radio packets to reduce error over deterministic approaches. This paper presents the guidelines of beacon selection that leads to design the most suitable trajectory, as a trade-off between the energy costs of travelling and transmitting the beacons versus the localization accuracy

Gradient Descent Localization in Wireless Sensor Networks

Meaningful information sharing between the sensors of a wireless sensor network (WSN) necessitates node localization, especially if the information to be shared is the location itself, such as in warehousing and information logistics. Trilateration and multilateration positioning methods can be employed in two-dimensional and three-dimensional space respectively. These methods use distance measurements and analytically estimate the target location; they suffer from decreased accuracy and computational complexity especially in the three-dimensional case. Iterative optimization methods, such as gradient descent (GD), offer an attractive alternative and enable moving target tracking as well. This chapter focuses on positioning in three dimensions using time-of-arrival (TOA) distance measurements between the target and a number of anchor nodes. For centralized localization, a GD-based algorithm is presented for localization of moving sensors in a WSN. Our proposed algorithm is based on systematically replacing anchor nodes to avoid local minima positions which result from the moving target deviating from the convex hull of the anchors. We also propose a GD-based distributed algorithm to localize a fixed target by allowing gossip between anchor nodes. Promising results are obtained in the presence of noise and link failures compared to centralized localization. Convergence factor issues are discussed, and future work is outlined

A Gaussian Mixture Model-Based Continuous Boundary Detection for 3D Sensor Networks

This paper proposes a high precision Gaussian Mixture Model-based novel Boundary Detection 3D (BD3D) scheme with reasonable implementation cost for 3D cases by selecting a minimum number of Boundary sensor Nodes (BNs) in continuous moving objects. It shows apparent advantages in that two classes of boundary and non-boundary sensor nodes can be efficiently classified using the model selection techniques for finite mixture models; furthermore, the set of sensor readings within each sensor node’s spatial neighbors is formulated using a Gaussian Mixture Model; different from DECOMO [1] and COBOM [2], we also formatted a BN Array with an additional own sensor reading to benefit selecting Event BNs (EBNs) and non-EBNs from the observations of BNs. In particular, we propose a Thick Section Model (TSM) to solve the problem of transition between 2D and 3D. It is verified by simulations that the BD3D 2D model outperforms DECOMO and COBOM in terms of average residual energy and the number of BNs selected, while the BD3D 3D model demonstrates sound performance even for sensor networks with low densities especially when the value of the sensor transmission range (r) is larger than the value of Section Thickness (d) in TSM. We have also rigorously proved its correctness for continuous geometric domains and full robustness for sensor networks over 3D terrains

A New Approach towards Solving the Location Discovery Problem in Wireless Sensor Networks

Location discovery in wireless sensor network (WSN) is the process that sensor nodes collaborate to determine the position for unknown sensor nodes. Anchors, sensors that know their locations, are expensive but are required to be deployed into the WSN to solve this problem. Thus it is desirable to minimize the number of anchors for this purpose. In this paper, we propose an anchor deployment scheme and a novel bilateration locationing algorithm to achieve this goal. The basic idea of anchor deployment method is to have three anchors deployed as a group, and locate sensors around them expansively. The novelty of our bilateration algorithm is that it in general requires only two neighbor sensors to determine a node's location. Comparing with the state-of-the-art location discovery approaches, our algorithm gives location estimation with high accuracy, low communication cost and very small anchor percentage. We conduct theoretical analysis about location estimation error and extensive simulation shows that our algorithm can derive sensor location within 4% location error and much less communication cost compared with other algorithms. UMIACS-TR-2003-11