Cooperative Localization In Wireless Networks Research Articles

Incentive Mechanism for Cooperative Localization in Wireless Networks

Cooperative localization has emerged as an appealing technology since it can improve the localization performance without any infrastructure change compared with non-cooperative localization. However, some well-localized agents may not be willing to sacrifice additional power to improve the others' localization accuracy. This paper proposes an incentive mechanism from an economic perspective for cooperative localization, whereby a pricing scheme is designed to lead each agent to the optimal state. A game-theoretic algorithm is proposed where each player (agent) can obtain the optimal budget strategy to minimize its individual utility. To make profits in the game, the relationship between the agent's network condition and its budget strategy is derived. Furthermore, a fairness-aware price allocation rule (PAR) is developed to distribute the budget among the reference agents proportional to each node's contribution. Analytical and numerical results show that agents with better network conditions are more likely to join the cooperation under the proposed incentive mechanism, leading to an improved localization performance.

Position-Constrained Stochastic Inference for Cooperative Indoor Localization

We address the problem of distributed cooperative localization in wireless networks, i.e., nodes without prior position knowledge (agents) wish to determine their own positions. In non-cooperative approaches, positioning is only based on information from reference nodes with known positions (anchors). However, in cooperative positioning, information from other agents is considered as well. Cooperative positioning requires encoding of the uncertainties of agents' positions. To cope with that demand, we consider stochastic inference for localization, which inherently takes the position uncertainties of agents into consideration. Generally, stochastic inference comes at the expense of high costs in terms of computation and information exchange. To relax the requirements of inference algorithms, we propose the framework of position-constrained stochastic inference, in which we first confine the positions of nodes to constrained regions. These regions assist inference algorithms to concentrate on the important areas of the sample space rather than the entire sample space. In contrast to many state-of-the-art approaches, our approach does not require prior knowledge on the positions of agents. We show through simulations that increased localization accuracy, reduced computational complexity, and quicker convergence can be achieved when compared to state-of-the-art non-constrained inference algorithms.

On the Spatial Error Propagation Characteristics of Cooperative Localization in Wireless Networks

Cooperative localization is an important technique in wireless networks. However, there are always errors in network node localization, which will spatially propagate among network nodes when performing network localization. In this paper, we study the spatial error propagation (EP) characteristics of network localization in terms of Fisher information. First, the spatial propagation function is proposed to reveal the spatial cooperation principle of network localization. Second, the convergence property of spatial localization information propagation (SLIP) is analyzed to shed light on the performance limits of network localization through spatial information propagation. It is shown that 1) the network localization error propagates in the way of Ohm's law in electric circuit theory, where the measurement accuracy, node location accuracy, and geometric-resolution information behave like the resistances connected in parallel or series; 2) the network location error gradually diminishes with spatial localization cooperation procedures, due to the cooperative localization information propagation; and 3) the essence of spatial localization cooperation among network nodes is the spatial propagation of localization information.

Sequential Uniformly Reweighted Sum-Product Algorithm for Cooperative Localization in Wireless Networks

Graphical models have been widely applied in solving distributed inference problems in wireless networks. In this paper, we formulate the cooperative localization problem in a mobile network as an inference problem on a factor graph. Using a sequential schedule of message updates, a sequential uniformly reweighted sum-product algorithm (SURW-SPA) is developed for mobile localization problems. The proposed algorithm combines the distributed nature of belief propagation (BP) with the improved performance of sequential tree-reweighted message passing (TRW-S) algorithm. We apply the SURW-SPA to cooperative localization in both static and mobile networks, and evaluate its performance in terms of localization accuracy and convergence speed.

Nonparametric generalized belief propagation based on pseudo-junction tree for cooperative localization in wireless networks

Non-parametric belief propagation (NBP) is a well-known message passing method for cooperative localization in wireless networks. However, due to the over-counting problem in the networks with loops, NBP’s convergence is not guaranteed, and its estimates are typically less accurate. One solution for this problem is non-parametric generalized belief propagation based on junction tree. However, this method is intractable in large-scale networks due to the high-complexity of the junction tree formation, and the high-dimensionality of the particles. Therefore, in this article, we propose the non-parametric generalized belief propagation based on pseudo-junction tree (NGBP-PJT). The main difference comparing with the standard method is the formation of pseudo-junction tree, which represents the approximated junction tree based on thin graph. In addition, in order to decrease the number of high-dimensional particles, we use more informative importance density function, and reduce the dimensionality of the messages. As by-product, we also propose NBP based on thin graph (NBP-TG), a cheaper variant of NBP, which runs on the same graph as NGBP-PJT. According to our simulation and experimental results, NGBP-PJT method outperforms NBP and NBP-TG in terms of accuracy, computational, and communication cost in reasonably sized networks.

Cooperative Localization in Wireless Networks

Location-aware technologies will revolutionize many aspects of commercial, public service, and military sectors, and are expected to spawn numerous unforeseen applications. A new era of highly accurate ubiquitous location-awareness is on the horizon, enabled by a paradigm of cooperation between nodes. In this paper, we give an overview of cooperative localization approaches and apply them to ultrawide bandwidth (UWB) wireless networks. UWB transmission technology is particularly attractive for short- to medium-range localization, especially in GPS-denied environments: wide transmission bandwidths enable robust communication in dense multipath scenarios, and the ability to resolve subnanosecond delays results in centimeter-level distance resolution. We will describe several cooperative localization algorithms and quantify their performance, based on realistic UWB ranging models developed through an extensive measurement campaign using FCC-compliant UWB radios. We will also present a powerful localization algorithm by mapping a graphical model for statistical inference onto the network topology, which results in a net-factor graph, and by developing a suitable net-message passing schedule. The resulting algorithm (SPAWN) is fully distributed, can cope with a wide variety of scenarios, and requires little communication overhead to achieve accurate and robust localization.