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

Robust Power Allocation for Energy-Efficient Location-Aware Networks

In wireless location-aware networks, mobile nodes (agents) typically obtain their positions using the range measurements to the nodes with known positions. Transmit power allocation not only affects network lifetime and throughput, but also determines localization accuracy. In this paper, we present an optimization framework for robust power allocation in network localization with imperfect knowledge of network parameters. In particular, we formulate power allocation problems to minimize localization errors for a given power budget and show that such formulations can be solved via conic programming. Moreover, we design a distributed power allocation algorithm that allows parallel computation among agents. The simulation results show that the proposed schemes significantly outperform uniform power allocation, and the robust schemes outperform their non-robust counterparts when the network parameters are subject to uncertainty.National Natural Science Foundation (China) (Project 61201261)National Basic Research Program of China (973 Program) (61101131)University Grants Committee (Hong Kong, China) (GRF Grant Project 419509)National Science Foundation (U.S.) (Grant ECCS-0901034)United States. Office of Naval Research (Grant N00014-11-1-0397)Massachusetts Institute of Technology. Institute for Soldier Nanotechnologie

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

Joint 3D Deployment and Resource Allocation for UAV-assisted Integrated Communication and Localization

In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted integrated communication and localization network in emergency scenarios where a single UAV is deployed as both an airborne base station (BS) and anchor node to assist ground BSs in communication and localization services. We formulate an optimization problem to maximize the sum communication rate of all users under localization accuracy constraints by jointly optimizing the 3D position of the UAV, and communication bandwidth and power allocation of the UAV and ground BSs. To address the intractable localization accuracy constraints, we introduce a new performance metric and geometrically characterize the UAV feasible deployment region in which the localization accuracy constraints are satisfied. Accordingly, we combine Gibbs sampling (GS) and block coordinate descent (BCD) techniques to tackle the non-convex joint optimization problem. Numerical results show that the proposed method attains almost identical rate performance as the meta-heuristic benchmark method while reducing the CPU time by 89.3%.Comment: The paper has been accepted for publication by IEEE Wireless Communications Letter

Ultra-low-power Range Error Mitigation for Ultra-wideband Precise Localization

Precise and accurate localization in outdoor and indoor environments is a challenging problem that currently constitutes a significant limitation for several practical applications. Ultra-wideband (UWB) localization technology represents a valuable low-cost solution to the problem. However, non-line-of-sight (NLOS) conditions and complexity of the specific radio environment can easily introduce a positive bias in the ranging measurement, resulting in highly inaccurate and unsatisfactory position estimation. In the light of this, we leverage the latest advancement in deep neural network optimization techniques and their implementation on ultra-low-power microcontrollers to introduce an effective range error mitigation solution that provides corrections in either NLOS or LOS conditions with a few mW of power. Our extensive experimentation endorses the advantages and improvements of our low-cost and power-efficient methodology