Robust Localization Algorithm Based on Best Length Optimization for Wireless Sensor Networks

In this paper, a robust range-free localization algorithm by realizing best hop length optimization is proposed for node localization problem in wireless sensor networks (WSNs). This algorithm is derived from classic DV-Hop method but the critical hop length between any relay nodes is accurately computed and refined in space WSNs with arbitrary network connectivity. In case of network parameters hop length between nodes can be derived without complicated computation and further optimized using Kalman filtering in which guarantees robustness even in complicated environment with random node communication range. Especially sensor fusion techniques used has well gained robustness, accuracy, scalability, and power efficiency even without accurate distance or angle measurement which is more suitable in nonlinear conditions and power limited WSNs environment. Simulation results indicate it gained high accuracy compared with DV-Hop and Centroid methods in random communication range conditions which proves it gives characteristic of high robustness. Also it needs relatively little computation time which possesses high efficiency. It can well solve localization problem with many unknown nosed in the network and results prove the theoretical analysis

A survey of localization in wireless sensor network

Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network

A Robust Zero-Calibration RF-based Localization System for Realistic Environments

Due to the noisy indoor radio propagation channel, Radio Frequency (RF)-based location determination systems usually require a tedious calibration phase to construct an RF fingerprint of the area of interest. This fingerprint varies with the used mobile device, changes of the transmit power of smart access points (APs), and dynamic changes in the environment; requiring re-calibration of the area of interest; which reduces the technology ease of use. In this paper, we present IncVoronoi: a novel system that can provide zero-calibration accurate RF-based indoor localization that works in realistic environments. The basic idea is that the relative relation between the received signal strength from two APs at a certain location reflects the relative distance from this location to the respective APs. Building on this, IncVoronoi incrementally reduces the user ambiguity region based on refining the Voronoi tessellation of the area of interest. IncVoronoi also includes a number of modules to efficiently run in realtime as well as to handle practical deployment issues including the noisy wireless environment, obstacles in the environment, heterogeneous devices hardware, and smart APs. We have deployed IncVoronoi on different Android phones using the iBeacons technology in a university campus. Evaluation of IncVoronoi with a side-by-side comparison with traditional fingerprinting techniques shows that it can achieve a consistent median accuracy of 2.8m under different scenarios with a low beacon density of one beacon every 44m2. Compared to fingerprinting techniques, whose accuracy degrades by at least 156%, this accuracy comes with no training overhead and is robust to the different user devices, different transmit powers, and over temporal changes in the environment. This highlights the promise of IncVoronoi as a next generation indoor localization system.Comment: 9 pages, 13 figures, published in SECON 201

Power Optimization for Network Localization

Reliable and accurate localization of mobile objects is essential for many applications in wireless networks. In range-based localization, the position of the object can be inferred using the distance measurements from wireless signals exchanged with active objects or reflected by passive ones. Power allocation for ranging signals is important since it affects not only network lifetime and throughput but also localization accuracy. In this paper, we establish a unifying optimization framework for power allocation in both active and passive localization networks. In particular, we first determine the functional properties of the localization accuracy metric, which enable us to transform the power allocation problems into second-order cone programs (SOCPs). We then propose the robust counterparts of the problems in the presence of parameter uncertainty and develop asymptotically optimal and efficient near-optimal SOCP-based algorithms. Our simulation results validate the efficiency and robustness of the proposed algorithms.Comment: 15 pages, 7 figure