Bandwidth Efficient Cooperative TDOA Computation for Multicarrier Signals of Opportunity

Source localization, the problem of determining the physical location of an acoustic or wireless emitter, is commonly encountered in sensor networks which are attempting to locate and track an emitter. Similarly, in navigation systems that do not rely on the global positioning system (GPS), “signals of opportunity” (existing wireless infrastructure) can be used as ad hoc navigation beacons, and the goal is to determine their location relative to a receiver and thus deduce the receiver's position. These two research problems have a very similar mathematical structure. Specifically, in either the source localization or navigation problem, one common approach relies on time difference of arrival (TDOA) measurements to multiple sensors. In this paper, we investigate a bandwidth efficient method of TDOA computation when the signals of opportunity use multicarrier modulation. By exploiting the structure of the multicarrier transmission, much less information needs to be exchanged between sensors compared to the standard cross correlation approach. Analytic and simulation results quantify the performance of the proposed algorithm as a function of the signal-to-noise ratio (SNR) and the bandwidth between the sensors.      

Maximum-likelihood source localization and unknown sensor locationestimation for wideband signals in the near-field

In this paper, we derive the maximum-likelihood (ML) location estimator for wideband sources in the near field of the sensor array. The ML estimator is optimized in a single step, as opposed to other estimators that are optimized separately in relative time-delay and source location estimations. For the multisource case, we propose and demonstrate an efficient alternating projection procedure based on sequential iterative search on single-source parameters. The proposed algorithm is shown to yield superior performance over other suboptimal techniques, including the wideband MUSIC and the two-step least-squares methods, and is efficient with respect to the derived Cramer-Rao bound (CRB). From the CRB analysis, we find that better source location estimates can be obtained for high-frequency signals than low-frequency signals. In addition, large range estimation error results when the source signal is unknown, but such unknown parameter does not have much impact on angle estimation. In some applications, the locations of some sensors may be unknown and must be estimated. The proposed method is extended to estimate the range from a source to an unknown sensor location. After a number of source-location frames, the location of the uncalibrated sensor can be determined based on a least-squares unknown sensor location estimator     

A distributed Newton algorithm for network utility maximization in wireless ad hoc networks

The existing distributed Newton algorithm for network utility maximization cannot be directly applied into the wireless ad hoc networks, as it does not consider the wireless link capacity variation and transmission power consumption. Regarding these, a new joint network utility maximization problem is formulated to optimize the wireless ad hoc network resource utility. The distributed Newton algorithm is implemented to solve for the dual variables and Newton directions by using the local information at each session source and link, respectively. Furthermore, a new iterative method is proposed to improve the convergence rate of the existing matrix‐splitting method. Simulation results validate the efficiency and efficacy of the proposed distributed Newton algorithm for wireless ad hoc network utility maximization.     

目标海拔已知的信号到达幅度比无源定位方法

信号到达幅度比方法因测量简便,可实现对窄带信号的无源定位而广泛应用于射频定位系统、无线传感器网络和声源定位中.当目标海拔已知,将其作为定位方程的约束条件可实现对目标更精确的定位,而现有方法没有考虑这个问题.对此本文建立了海拔约束的信号到达幅度比无源定位模型,推导了定位精度的克拉美劳下界,并提出了一种基于Newton迭代的定位算法.理论推导表明该算法在测量误差服从方差较小的零均值高斯分布时能够达到克拉美劳下界,仿真结果与理论推导一致.无线电栅格化监测试验网的验证结果表明,对系统幅度误差进行校正后,该方法能够实现对辐射源的准确定位.     

Energy-Based Localization in Wireless Sensor Networks Using Second-Order Cone Programming Relaxation

Source localization in wireless sensor networks (WSNs) aims to determine the position of a source in a network, given inaccurate position-bearing measurements. This paper addresses the problem of locating a single source from noisy acoustic energy measurements in WSNs. Under the assumption of Gaussian energy measurement errors, the maximum likelihood (ML) estimator requires the minimization of a nonlinear and nonconvex cost function which may have multiple local optima, thus making the search for the globally optimal solution hard. In this work, an approximate solution to the ML location estimation problem is presented by relaxing the minimization problem to a convex optimization problem, namely second-order cone programming. Simulation results demonstrate the superior performance of the convex relaxation approach. More precisely, the new approach shows an improvement of 20 % in terms of localization accuracy when compared to the existing approaches at moderate to high noise levels. Simulation results further show comparable performance of the new approach and the state-of-art approaches at low noise levels.     

A Systematic Review of Localization in WSN: Machine Learning and Optimization-Based approaches

In recent years, wireless sensor networks (WSNs) have been widely used in various applications. The localization problem has been identified as one of the biggest problems faced by WSNs. The traditional localization techniques may not be able to handle the issues during the scenario to estimate the location of sensor nodes due to anchor mobility, mobile WSNs, latency, energy harvesting, unfavorable environmental states, and many more issues. However, these issues open the door for the amalgamation of machine learning (ML) and optimization techniques with localization techniques. Motivated by the earlier discussion, we explored various ML and optimization techniques to estimate the location coordinates in a sensor network in this paper. Finally, a comparison of existing ML algorithms concerning optimization techniques has been presented, highlighting their improved outcomes. This research offers a detailed survey by exploring the various parameters for location estimation through tabular forms by incorporating ML and optimized localization techniques. A survey of surveys is also presented to identify the key limitations of existing surveys and to introduce the novelty in the comprehensive study done in this paper. A year-wise evaluation of ML Techniques with localization (2011–2022) is also discussed and presented over various performance parameters, including energy-efficiency, accuracy, error, and complexity. This discussion concluded that Hybrid Techniques are least explored for using optimized localization machine learning. Further, a summarized discussion of the various comparison tables paves the path for future research in the area of localization in WSN.     

改进的无线传感器节点定位算法

由于无线传感器网络具有其它网络不可比拟的各种优势,使得它在很多领域都有广泛的应用。对于无线传感器网络中的未知节点本身的定位工作是网络的各项应用的基础。本文主要分析无线传感器网络的节点定位技术,研究已有的定位算法,并根据现有算法提出一种改进的分布式的节点定位算法。该算法使用RSSI方法测距,无需增加新的硬件设备,通过分布式的算法来提高效率降低能耗,利用多次定位的平均值提高定位精度,降低了网络中的能量消耗,延长网络寿命。     

Source localization in a waveguide using polynomial rooting

Source localization in acoustic waveguides involves a multidimensional search procedure. We propose a new algorithm in which the search in the depth direction is replaced by polynomial rooting. Using the proposed algorithm, range and depth estimation by a vertical array requires a 1-D search procedure. For a 3-D localization problem (i.e., range, depth, and direction-of-arrival (DOA) estimation), the algorithm involves a 2-D search procedure. Consequently, the proposed algorithm requires significantly less computation than other methods that are based on a brute-force search procedure over the source location parameters. In order to evaluate the performance of the algorithm, an error analysis is carried out, and Monte-Carlo simulations are performed. The results are compared with the Cramer-Rao bound (CRB) and to the maximum likelihood (ML) simulation performance. The algorithm is shown to be efficient, while being computationally simpler than the ML or the Bartlett processors. The disadvantage of the algorithm is that its SNR threshold occurs in lower SNR than in the ML algorithm     

Optimal rate allocation for energy-efficient multipath routing in wireless ad hoc networks

In this paper, we address the problem of energy efficiency in wireless ad hoc networks. We consider an ad hoc network comprising a set of sources, communicating with their destinations using multiple routes. Each source is associated with a utility function which increases with the total traffic flowing over the available source-destination routes. The network lifetime is defined as the time until the first node in the network runs out of energy. We formulate the problem as one of maximizing the sum of the source utilities subject to a required constraint on the network lifetime. We present a primal formulation of the problem, which uses penalty functions to take into account the system constraints, and we introduce a new methodology for solving the problem. The proposed approach leads to a flow control algorithm, which provides the optimal source rates and can be easily implemented in a distributed manner. When compared with the minimum transmission energy routing scheme, the proposed algorithm gives significantly higher source rates for the same network lifetime guarantee.     

Optimal resource allocation in wireless ad hoc networks: a price-based approach

The shared-medium multihop nature of wireless ad hoc networks poses fundamental challenges to the design of effective resource allocation algorithms that are optimal with respect to resource utilization and fair across different network flows. None of the existing resource allocation algorithms in wireless ad hoc networks have realistically considered end-to-end flows spanning multiple hops. Moreover, strategies proposed in wireline networks are not applicable in the context of wireless ad hoc networks, due to their unique characteristics of location-dependent contention. In this paper, we propose a new price-based resource allocation framework in wireless ad hoc networks to achieve optimal resource utilization and fairness among competing end-to-end flows. We build our pricing framework on the notion of maximal cliques in wireless ad hoc networks, as compared to individual links in traditional wide-area wireline networks. Based on such a price-based theoretical framework, we present a two-tier iterative algorithm. Distributed across wireless nodes, the algorithm converges to a global network optimum with respect to resource allocations. We further improve the algorithm toward asynchronous network settings and prove its convergence. Extensive simulations under a variety of network environments have been conducted to validate our theoretical claims.     

CRWSNP: cooperative range-free wireless sensor network positioning algorithm

Sensing events occur in an area without knowing the events locations, is meaningless. Since there is no priorly knowledge about the locations of most of the sensors which scattered randomly in an area, wireless sensor network localization methods try to find out where sensors are located. A new cooperative and distributed range-free localization algorithm, based on only connectivity information is proposed in this paper. The method first uses convex optimization techniques to find primitive target nodes locations estimation, then nodes cooperate with each other in several iterations to improve the whole network location estimation. CRWSNP converges after a finite number of iterations because of applying two novel heuristic location correction techniques. As well as, results of the algorithm have been compared with six range-free based methods like CPE, DV-hop, APIT; and CRWSNP algorithm provides more accurate results over 50 random topologies for the network, in mean error and maximum error metrics.     

Inner-Circle Consistency for Wireless Ad Hoc Networks

This paper proposes and evaluates strategies to build reliable and secure wireless ad hoc networks. Our contribution is based on the notion of inner-circle consistency, where local node interaction is used to neutralize errors/attacks at the source, both preventing errors/attacks from propagating in the network and improving the fidelity of the propagated information. We achieve this goal by combining statistical (a proposed fault-tolerant duster algorithm) and security (threshold cryptography) techniques with application-aware checks to exploit the data/computation that is partially and naturally replicated in wireless applications. We have prototyped an inner-circle framework and used it to demonstrate the idea of inner-circle consistency in two significant wireless scenarios: 1) the neutralization of black hole attacks in AODV networks and 2) the neutralization of sensor errors in a target detection/ localization application executed over a wireless sensor network     

Fuzzy-logic based routing for dense wireless sensor networks

The task of routing data from a source to the sink is a critical issue in ad hoc and wireless sensor networks. In this paper, the use of fuzzy logic to perform role assignment during route establishment and maintenance is proposed. An incremental approach is presented and compared with similar existing routing protocols. Efficient routing approaches provide network load balance to extend network lifetime, efficiency improvements, and data loss avoidance. Experiments show promising results for our proposals and its suitability for operating with dense networks, obtaining quick route creation as well as energy efficiency.     

信号强度和运动向量结合的无线传感器网络移动节点定位

无线传感器网络的定位是近年来无线传感器网络研究的重要课题.本文首先介绍了无线传感器网络的来源、重要性以及无线传感器网络定位的分类.然后提出了一种全新定位算法,信号强度和运动向量结合的无线传感器网络移动节点定位,简称SSMV算法,在外围布置四个锚节点,得用信号强度和未知节点在运动中向量的变化,对锚节点在内的未知节点进行定位,并对该算法进行了仿真和总结.通过与凸规划法进行比较,仿真结果表明,该算法有更高的定位精度.     

TOA-based distributed localisation with unknown internal delays and clock frequency offsets in wireless sensor networks

Locating sensor nodes in an ad hoc wireless sensor network (WSN) is a challenging task. In general, the network nodes are not synchronised and the internal delays within the nodes are unknown. Here, time-of-arrival (TOA)-based localisation is investigated when practical parameters such as clock time offset, clock frequency offset and system internal delay are all involved. The TOA measurements are made between each pair of nodes that are within radio range. First, an efficient frequency offset (FO) estimation algorithm is derived. Then, a two-stage localisation scheme is proposed. In the first stage, localisation starts from the nodes with the largest numbers of neighbouring anchors and priority is always given to nodes with more neighbouring anchors and/or localised nodes. In the second stage, the locations of all neighbouring nodes are exploited to improve location accuracy. Two iterative algorithms are developed: the Taylor series-based least squares (TS-LS) method and the sequential quadratic programming (SQP) optimisation method. During the localisation process, a number of measures are taken to ensure the reliability of each location estimate to avoid abnormal errors and reduce error propagation. The Cramer?Rao lower bound is also derived to benchmark the location accuracy.     

分布式粒子滤波算法在面向跟踪的无线传感器网络中的应用

无线传感器网络在目标跟踪应用中的优势主要体现在跟踪更精细可靠且及时隐蔽,但是由于传感器网络中没有中心控制机制,无线通信的带宽有限,这就要求采用合理的跟踪策略,高效的分布式信息处理算法。将“ad hoc跟踪群”的概念移植到面向跟踪的无线传感器网络中作为传感器的组织策略,并和适用范围较大的粒子滤波相结合实现跟踪任务。仿真实验证明,在不影响跟踪精度要求的情况下,此方案能够降低通信能量开销。     

Minimum power multicast algorithms for wireless networks with a Lagrangian relaxation approach

Energy efficiency is crucial to the implementation of broadcast and multicast services in wireless ad hoc and sensor networks. This paper proposes a path-based power-efficient approach that minimizes energy consumption in the network by optimizing the transmission ranges of all nodes. Unlike existing link-based and node-based solutions, the proposed approach, which is applicable to both static and mobile wireless networks, constructs minimum-power broadcast trees. This study uses Lagrangian Relaxation (LR) to decompose the tree-construction problem, which is NP-complete, into multiple independently solvable sub-problems. The resulting Lagrange dual solution ensures a lower bound on the objective function value. The proposed approximation heuristic solves the primal problem by obtaining the upper bound of the objective value based on the information from the set of Lagrange multipliers. This study develops a decentralized, near-optimal algorithm to solve the problem. Simulation results show that in randomly generated networks, the proposed method outperforms the existing Minimum Shortest Path Tree (MSPT) algorithm, Prim’s Minimum Spanning Tree (PMST) algorithm, and the Broadcast Incremental Power (BIP) algorithm by 30, 10, and 5%, respectively.     

基于压缩感知的室内多目标无线定位算法

针对现存无线传感器网络定位算法中需要采集、存储和处理大量数据导致运算量较大与能耗过高的问题,提出了一种改进的基于贝叶斯压缩感知的多目标定位算法.该算法利用锚节点对监控区域的划分,结合贝叶斯压缩感知理论将多目标定位问题转换为稀疏信号重构的问题.针对传统观测矩阵难以实现的缺陷,该算法中改进观测矩阵的设计可实现且与稀疏变换基相关性较低,进而使得算法的重构性能较高,从而降低了定位的误差.仿真结果表明,与现有的一些方法相比,所提算法在保证较低的计算复杂度的情况下更加充分地利用了网络节点,有效提高了定位精度,同时具有较强的鲁棒性.     

Incorporating Data from Multiple Sensors for Localizing Nodes in Mobile Ad Hoc Networks

The ad hoc network localization problem deals with estimating the geographical location of all nodes in an ad hoc network, focusing on those nodes that do not have a direct way (for example, GPS) to determine their own location. Proposed solutions to the ad hoc localization problem (AHLP) assume that nodes are capable of measuring received signal strength indication (RSSI) and/or are able to do coarse (sectoring) or fine signal angle-of-arrival (AoA) measurements. Existing algorithms exploit different aspects of such sensory data to provide either better localization accuracy or higher localization coverage. However, there is a need for a framework that could benefit from the interactions of nodes with mixed types of sensors. In this paper, we study the behavior of RSSI and AoA sensory data in the context of AHLP by using both geometric analysis and computer simulations. We show which type of sensor is better suited for which type of network scenario. We study how nodes using either, both, or none of these sensors could coexist in the same localization framework. We then provide a general particle-filtering framework, the first of its kind, that allows heterogeneity in the types of sensory data to solve the localization problem. We show that, when compared to localization scenarios where only one type of sensor is used, our framework provides significantly better localization results. Furthermore, our framework provides not only a location estimate for each nonanchor, but also an implicit confidence measure as to how accurate this estimate is. This confidence measure enables nodes to further improve on their location estimates using a local, iterative one-hop simple message exchange without having to rely on synchronized multiphase operations like in traditional multilateration methods.