Energyefficient mobile tracking in heterogeneous networks using node selection
 Senka Hadzic^{1},
 Du Yang^{1}Email author,
 Manuel Violas^{1, 2} and
 Jonathan Rodriguez^{1}
https://doi.org/10.1186/1687149920142
© Hadzic et al.; licensee Springer. 2014
Received: 23 July 2013
Accepted: 8 December 2013
Published: 4 January 2014
Abstract
Rangebased positioning is capable of achieving better accuracy in heterogeneous networks, where mobile nodes enabled with multiple radio access technologies are allowed to deploy not only the faraway access points but also high spatial density peer nodes as anchor nodes. However, due to peer node energy supply constraint and network capacity constraint, an efficient cooperation strategy is required. In this paper, we propose a cooperation method to track the position of a moving target with high accuracy and reduce the energy consumption and signaling overhead via node selection. It is demonstrated by simulation that in a specific practical scenario, the proposed method is capable of reducing the signaling overhead by about 34% to within 0.5m degradation of accuracy compared to exhaustive cooperation. We also evaluate the achievable performance averaged over randomly located node configurations and compare the proposed scheme with the mostly used nearestnode selection algorithm in terms of accuracy and cost.
Keywords
1 Introduction
Indoor localization via wireless signals is increasingly becoming a prominent feature for intelligent services and applications. Rangebased positioning first estimates the Euclidian distance between the target node and several positionknown anchor nodes via received signal strength (RSS), time of arrival (ToA), or other distancedependent signal metrics and then derives the target node coordinates by exploiting the geometrical relationship between distances and coordinates. In the context of noncooperative homogeneous network, the number of anchor nodes such as WiFi access points are small and far away from each other, which limits the localization accuracy.
In the context of heterogeneous network, a multiRadio Access Technology (RAT) aided mobile device is capable of communicating not only with the access points (APs) but also with peer nodes such as fixed ZigBee/Bluetooth sensors or other mobile nodes if cooperation is supported. The spatial density of peer nodes is typically much higher than APs, so by exploiting these nodes as anchor nodes, we could significantly decrease the distance estimation error and improve the rangebased positioning accuracy. However, peer nodes are energyconstrained. Unlike the APs, they are not supposed to be always in transmission mode broadcasting their coordinates. In order to cooperate with peer nodes, training sequences and extra packets are required for distance estimation and location information exchange, which results in signaling overhead and energy consumption. Hence, an efficient cooperation strategy is required so as to achieve the required positioning accuracy and to minimize the resultant energy consumption and traffic overhead.
In this paper, we investigate a heterogeneous network containing fixed locationknown WiFi APs covering the area of interest and sufficient number of connected multimodal (WiFi and ZigBee) peer nodes. The goal is to estimate the position of a moving node with required accuracy. We propose a cooperation method to reduce the signaling overhead via anchor node selection. The main idea is to select a subset of anchor nodes for location estimation. As the mobile moves, the selected subset remains the same until the required accuracy drops to within a minimum threshold, at which point the reselection process is triggered. Compared to exhaustive cooperation, the proposed method is capable of reducing 34% signaling overhead to within 0.5m degradation of accuracy in a specific practical scenario. We also evaluate the achievable performance averaged over randomly located node configurations and compare the proposed scheme with the mostly used nearestnode selection algorithm [1] in terms of accuracy and cost.
The rest of the paper is organized as follows: in the next section, we present the state of the art solutions in anchor selection. In Section 3, we propose our target scenario. The proposed method is detailed in Section 4. Simulation results and discussion are given in Section 5, and finally, Section 6 concludes the paper.
Notation: we use unhighlighted letters for scalar variables, highlighted lowercase letters for vectors, and highlighted uppercase letters for matrix. ( )^{ T } and ( )^{1} represent matrix transportation and inversing. E ( ) and var ( ) represent the expectation and variance of a random variable. Variables with a hat $\widehat{\left(.\right)}$ represent estimated values directly from estimators or from computations using estimated values. Variables without a hat represent the true value.
2 Related work
The accuracy of positioning algorithm is influenced by both measurement noise and relative node geometry [2, 3]. The Geometric Dilution of Position (GDOP) [4] captures the relative node geometry aspect, while the CramerRao Lower Bound (CRLB) captures both aspects. They are often used as positioning accuracy indicators [5, 6]. Besides positioning accuracy, some works [7–9] apply concepts from coalitional games and utility functions and select anchor nodes according to a cost function jointly considering power consumption and localization performance.
Most of the previous works related to anchor node selection are in the context of homogeneous network, especially sensor network [3, 8–11]. Anchor node selection in heterogeneous network is less addressed [5, 6]. The authors in [7] considered iterative cooperative localization among static nodes having imperfect position information. The algorithm in [5] includes both transmit and receive censoring. Transmit censoring prevents broadcast of unreliable position estimates, while receive censoring discards inadequate links for position estimation. All censoring decisions are distributed and based on a modified CRLB. In [6], unreliable links are consecutively discarded based on CRLB analysis. A comparison of different selection criteria, namely CRLB and GDOP, and analysis of their correlation with localization error in both cooperative and noncooperative scenarios have been given in [12]. Here, the mobile scenario has been studied, so the selection criteria are used for predicting the best set of anchor nodes.
An important aspect in localization is energy saving. The use of coalitional games has been proposed in [8] with the purpose of determining which nodes can stay in sleep mode while only a subset participates in the positioning algorithm. In [1], experiments were performed to increase the energy efficiency of a localization system in wireless sensor networks. The idea is to use the closest anchor nodes, and the remaining ones stay in semiactive state. Besides radio localization, there are also works that consider multimedia (camera) sensors for energy aware target tracking [13].
 1.
The proposed method exploits the knowledge of indoor layout to improve the RSSbased distance estimation accuracy.
 2.
We propose a new positioning accuracy indicator for linear weighted least square (LWLS) estimator and demonstrate that it outperforms the estimated CRLB positioning accuracy indicator.
3 Target scenario
The notation ${\mathbf{C}}_{\widehat{\mathbf{X}}}$ represents the covariance matrix of estimated vector $\widehat{\mathbf{x}}$. The required accuracy is denoted as RMSE_{req} (in meters). Our goal is to select a subset of nodes N_{S}(N_{S} ⊆ N_{A}) having a fixed cardinality N_{S} such that (1) the required accuracy can be achieved or approached as close as possible and (2) the remaining unselected anchors could remain silent so as to save energy consumption and reduce traffic overhead.
4 Proposed method
4.1 General description
As the target node moves on, it periodically transmits training sequences and seeks assistance from those selected peer nodes. Upon receiving the measurements, the connected AP will estimate the achievable RMSE using the selected set of anchors. If the required accuracy is satisfied, the estimation result $\widehat{\mathbf{x}}$ using this chosen set N_{S} is transmitted to the target node. Otherwise, a reselection process is triggered, and a new set of N_{S} anchors providing the best accuracy will be chosen.
In addition, the indoor layout map is assumed to be available at the AP, which will be used to improve the RSSbased distance estimation by exploiting the knowledge of locationdependent channel parameters such as path loss, shadowing, and lineofsight (LoS)/nonlineofsight (NLoS) conditions. The coordinates of all anchor nodes are also recorded and updated at the AP to avoid the overhead traffic caused by exchanging location information between peer nodes and target nodes.
4.2 Positioning accuracy indicator
4.2.1 Estimated CRLB ($\widehat{\mathit{CRLB}}$)
The Fisher information matrix F(x) is a function of the second derivation of the likelihood function. If F(x) contains any unknown parameters, they are replaced by their estimated values, and the resultant bound is called estimated CRLB, which is denoted as $\widehat{\mathrm{CRLB}}$. For example, the CRLB of RSSbased ranging in a twodimensional space derived in [12, 14] is a function of the true distances d_{ n }, which are in practice unknown. Hence, it is only feasible to calculate the estimated $\widehat{\mathrm{CRLB}}$ using $\widehat{{d}_{n}}$.
4.2.2 RMSE for linear weighted least square estimator
5 Simulation results and analysis
where d_{ n,m } is the distance between the n th and m th anchors, and the term $\widehat{{d}_{t\perp n,m}}$ is the estimated shortest distance from the target to the segment connecting the n th and m th anchors. The term b accounts for channel conditions and is calculated as $b=\frac{10\alpha}{\sigma ln10}$.
5.1 A specific scenario
We consider a practical scenario illustrated in Figure 3, which consists of one WiFi AP and seven peer nodes. The target moves from the corridor to a room. Along the movement trajectory, propagation conditions between the target and the other nodes change (LoS or NLoS) as modeled by the WINNER II channel [16].
The target node moves at the speed of 1 m/s. We trace the location of the target node every 1 s (T_{ s } = 1 s), which results in 38 footprints. The WINNER model [16] for indoor scenario at a carrier frequency of 2.4 GHz is used to simulate the channel between AP/peer nodes and target node. The path loss parameter α is set to α_{LoS} = 1.85 and α_{NLoS} = 3.68. The variance of zeromean lognormal shadowing σ^{2} is set to ${\sigma}_{\mathrm{LoS}}^{2}=2\phantom{\rule{0.25em}{0ex}}\mathit{dB}$ and ${\sigma}_{\mathrm{NLoS}}^{2}=5\phantom{\rule{0.25em}{0ex}}\mathit{dB}$, respectively. The true location RMSE is averaged over 1,000 independent shadowing samples. Setting N_{s} and RMSE_{req} in different values, we simulate the following four schemes:

Scheme 1. N_{S} = N_{AP} + N_{P}, RMSE_{req} = 0. This is equivalent to exhaustive cooperation, where all reachable APs and peer nodes are used for location estimation at every sampling time.

Scheme 2. N_{S} = 3, RMSE_{req} = 0, using ${\widehat{\mathrm{CRLB}}}_{\mathrm{RSS}}$ in Equation 8 as indicator and LWLS/ML location estimator.

Scheme 3. N_{S} = 3, RMSE_{req} = 0, using ${\widehat{\mathrm{RMSE}}}_{\mathrm{LWLS}}$ in Equations 6 and 9 as indicator and LWLS location estimator.

Scheme 4. N_{S} = 3, RMSE_{req} = 1 and 2, using ${\widehat{\mathrm{RMSE}}}_{\mathrm{LWLS}}$ in Equations 6 and 9 as indicator and LWLS location estimator.
5.1.1 Location accuracy indicator comparison
Based on these two figures, we could conclude that ${\widehat{\mathrm{RMSE}}}_{\mathrm{LWLS}}$ is a better RMSE indicator than $\widehat{\mathrm{CRLB}}$, which can avoid choosing near collinear anchors, and provides a more accurate estimation of the achievable RMSE LWLS estimator deployed. Hence, we will use the ${\widehat{\mathrm{RMSE}}}_{\mathrm{LWLS}}$ indicator and LWLS estimator to evaluate the proposed method.
5.1.2 Exhaustive cooperation versus the proposed method
5.2 Generalized scenario
In order to extend the validity of the results presented for the specific scenario from Figure 3, we evaluated the proposed method in more generalized scenarios. We consider a mobile moving across a 25m × 10m room. The number of anchors in the room is 20, where one of them is the access point (N_{AP} = 1) and the remaining ones are peer nodes (N_{P} = 19). We generated 10 setups having anchor nodes randomly distributed over the room, while the target node follows the same trajectory from the bottom left to the upper right. The averaged performance that sat 30 sampled locations along the trajectory is evaluated.
Again, the WINNER model for indoor scenario at a carrier frequency of 2.4 GHz are used with the path loss parameter α set to α_{LoS} = 1.85 and α_{NLoS} = 3.68. The variance of zeromean lognormal shadowing σ^{2} is set to ${\sigma}_{\mathrm{LoS}}^{2}=2\phantom{\rule{0.25em}{0ex}}\mathit{dB}$ and ${\sigma}_{\mathrm{NLoS}}^{2}=5\phantom{\rule{0.25em}{0ex}}\mathit{dB}$, respectively. The true location RMSE is averaged over 1,000 independent shadowing samples. We simulate the following two schemes:

Proposed algorithm: using ${\widehat{\mathrm{RMSE}}}_{\mathrm{LWLS}}$ in Equations 6 and 9 as indicator and LWLS location estimator, RMSE_{req} = 0.6 m, and various combinations of N_{S} and N_{A} (N_{S}, N_{A}) = {(3,7), (3,14), (3,20), (5,7), (7,7), (7,14), (20,20)}.

Nearestthreenode selection algorithm used in [1].
5.2.1 Comparison of different combination of N_{ S } and N_{A}
Energy consumption for overhead messages
Case  Proposed algorithm with different combination of N_{S} and N_{A}  Nearestthree nodes  

20 of 20  3 of 20  3 of 7  5 of 7  
Energy (mJ)  19.2  16.315  5.903  3.661  1.92 
5.2.2 Nearestnodes algorithm versus the proposed method
Analysis of performance tradeoffs
Performance metric  Mean RMSE (m)  Communication overhead  Size of search space  Computational complexity  

(packet number)  (size of A in Equation 4)  
The nearestthree nodes  2.109  60  1  3 × 3  
Proposed algorithm  20 of 20  0.2975  600  1  20 × 20 
3 of 20  0.5424  465  1,140  3 × 3  
3 of 7  0.5716  136  21  3 × 3  
5 of 7  0.4501  128  35  5 × 5 
6 Conclusions
In this paper, we proposed a cooperation method for rangebased positioning in a heterogeneous network via node selection in order to reduce communication and energy cost. Inactive nodes do not waste energy while collecting, processing, and communicating measurements. We analyzed a specific scenario and generalized one that corresponds to realistic indoor environments. We presented an extensive study of different setups in order to determine the best tradeoff between desired accuracy and cost. In our future work, we aim at obtaining experimental results of the proposed method. Another extension will be to consider more practical scenarios and to investigate moving peer nodes and imperfect prior knowledge of anchor locations. These virtual anchors are the result of error propagation in the localization procedure.
Declarations
Acknowledgements
The research leading to these results was partly funded from the European Community's Seventh Framework Programme [FP7/20072013] under grant agreement n° 264759 [GREENET], n° 248894 [WHERE2], and funding from FEDER through Programa Operacional Factores de Competitividade – COMPETE and from National funds from FCT (Portugal) – Fundação para a Ciência e a Tecnologia under the project PTDC/EEATEL/119228/2010 – SMARTVISION. Senka Hadzic would like to acknowledge the support of the FCT  Portugal through the scholarship SFRH/BD/61023/2009.
Authors’ Affiliations
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This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.