173991225
Accurate Localization in Wireless Sensor Networks in the Presence of Cross Technology Interference
Localization of mobile nodes in a wireless sensor networks (WSNs) is an active area of research. In this paper, we present a novel RSSI based localization algorithm for 802.15.4 (ZigBee) based WSNs. We propose and implement a novel range based localization algorithm to minimize cross technology interference operating in the same band. The goal is to minimize the mean square error of the localization algorithm. Hardware implementation of the algorithm is in agreement with ideal (no interference) simulation results where an accuracy of less than 0.5m has been achieved.
21368003
Mobile user localization in wireless sensor network using grey prediction method
Knowing the position of mobile user is an important role for location services in the building. The characteristics of wireless sensor network are low power, low cost and low complexity. With these functions, wireless sensor network have great potential to develop indoor position system. However, radio signal propagation is easily affected by diffraction, reflections, and scattering of radio in the building, the received signal strength need good calibration method to improve the accuracy of position estimation system. In this paper we use grey prediction method in wireless sensor network and employ wireless LAN medium (Zigbee/802.15.4). The grey prediction is used to predict the tendency of RSSI (received signal strength indicator), and we also designed dynamic triangular (DTN) location method. We have done some experiments and compare with other classical location finding methods. The mean distant error of RSSI on mobile user can be within 2.3 m at offline stage. As a result, grey predication with DTN provides more accurate predicted position and carries out mean distance error within 1.3 m at run-time stage.
21368003
Mobile user localization in wireless sensor network using grey prediction method
Knowing the position of mobile user is an important role for location services in the building. The characteristics of wireless sensor network are low power, low cost and low complexity. With these functions, wireless sensor network have great potential to develop indoor position system. However, radio signal propagation is easily affected by diffraction, reflections, and scattering of radio in the building, the received signal strength need good calibration method to improve the accuracy of position estimation system. In this paper we use grey prediction method in wireless sensor network and employ wireless LAN medium (Zigbee/802.15.4). The grey prediction is used to predict the tendency of RSSI (received signal strength indicator), and we also designed dynamic triangular (DTN) location method. We have done some experiments and compare with other classical location finding methods. The mean distant error of RSSI on mobile user can be within 2.3 m at offline stage. As a result, grey predication with DTN provides more accurate predicted position and carries out mean distance error within 1.3 m at run-time stage.
10303566
A Hybrid Stochastic Approach for Self-Location of Wireless Sensors in Indoor Environments
Indoor location systems, especially those using wireless sensor networks, are used in many application areas. While the need for these systems is widely proven, there is a clear lack of accuracy. Many of the implemented applications have high errors in their location estimation because of the issues arising in the indoor environment. Two different approaches had been proposed using WLAN location systems: on the one hand, the so-called deductive methods take into account the physical properties of signal propagation. These systems require a propagation model, an environment map, and the position of the radio-stations. On the other hand, the so-called inductive methods require a previous training phase where the system learns the received signal strength (RSS) in each location. This phase can be very time consuming. This paper proposes a new stochastic approach which is based on a combination of deductive and inductive methods whereby wireless sensors could determine their positions using WLAN technology inside a floor of a building. Our goal is to reduce the training phase in an indoor environment, but, without an loss of precision. Finally, we compare the measurements taken using our proposed method in a real environment with the measurements taken by other developed systems. Comparisons between the proposed system and other hybrid methods are also provided.
252999
Localization from mere connectivity
It is often useful to know the geographic positions of nodes in a communications network, but adding GPS receivers or other sophisticated sensors to every node can be expensive. We present an algorithm that uses connectivity information who is within communications range of whom to derive the locations of the nodes in the network. The method can take advantage of additional information, such as estimated distances between neighbors or known positions for certain anchor nodes, if it is available. The algorithm is based on multidimensional scaling, a data analysis technique that takes O(n3) time for a network of n nodes. Through simulation studies, we demonstrate that the algorithm is more robust to measurement error than previous proposals, especially when nodes are positioned relatively uniformly throughout the plane. Furthermore, it can achieve comparable results using many fewer anchor nodes than previous methods, and even yields relative coordinates when no anchor nodes are available.
7470618
Scalable and fully distributed localization with mere connectivity
This work proposes a novel connectivity-based localization algorithm, well suitable for large-scale sensor networks with complex shapes and non-uniform nodal distribution. In contrast to current state-of-art connectivity-based localization methods, the proposed algorithm is fully distributed, where each node only needs the information of its neighbors, without cumbersome partitioning and merging process. The algorithm is highly scalable, with limited error propagation and linear computation and communication cost with respect to the size of the network. Moreover, the algorithm is theoretically guaranteed and numerically stable. Extensive simulations and comparison with other methods under various representative network settings are carried out, showing superior performance of the proposed algorithm.
8154710
Experimental Results for and Theoretical Analysis of a Self-Organizing Global Coordinate System for Ad Hoc Sensor Networks
We present an algorithm for achieving robust and reasonably accurate localization in a randomly placed wireless sensor network, without the use of global control, globally-accessible beacon signals, or accurate estimates of inter-sensor distances. We present theoretical analysis, simulation results and recent experimental results. The theoretical analysis shows that there is a critical minimum average neighborhood size of 15 for good accuracy, and simulation results show that position accuracy to within 20% of the local radio range can be achieved, even with upto 10% variation in the radio ranges.
17825532
The Distributed Construction of a Global Coordinate System in a Network of Static Computational Nodes from InterNode Distances ∗
∗This work is sponsored in part by DARPA through the ‘Network Embedded Systems Technology’ program under contract #F30602-01-C-0123, monitored by the Air Force Research Laboratory. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the U.S. Government.
9598925
SHARP: a new approach to relative localization in wireless sensor networks
For wireless sensor networks, localization is crucial due to the dynamic nature of deployment. In relative localization, nodes use the distance measurements to estimate their positions relative to some coordinate system. In absolute localization, a few nodes (called anchors) need to know their absolute positions, and all the other nodes are absolutely localized in the coordinate system of the anchors. Relative and absolute localization methods differ in both the performance and the cost. We present a new approach to relative localization that we refer to as: simple hybrid absolute-relative positioning (SHARP). In SHARP, a relative localization method (M1) is used to relatively localize N/sub r/ reference nodes. Then, an absolute localization method (M2) uses these N/sub r/ nodes as anchors to localize the rest of the nodes. Choosing N/sub r/, M1, and M2 gives a wide range of performance-cost tuning. We have done extensive simulation using the multidimensional scaling (MDS) method as M1 and the ad-hoc positioning system (APS) method as M2. While previous research shows that MDS gives better localization results than APS, our simulation shows that SHARP outperforms MDS if both the localization error and the cost are considered.
2736444
Indirect Radio Interferometric Localization via Pairwise Distances
The Radio Interferometric Positioning System (RIPS), introduced by Maroti et. al. [1], provides a means for very accurate sensor localization with very minimal device hardware requirements. To avoid stopping in a significant number of locally optimal location solutions, RIPS employs a genetic optimization localization algorithm. This paper proposes an indirect localization algorithm which first estimates pairwise distances, and then uses them to estimate coordinates via distributed weighted multidimensional scaling (dwMDS). The pairwise distances are iteratively improved and coordinates re-estimated. While suboptimal, simulations show the proposed method can achieve 50 cm RMS location errors. The algorithm provides a tradeoff between computational complexity and accuracy, and may enable distributed RIM-based localization.
200115
Improved MDS-based localization
It is often useful to know the geographic positions of nodes in a communications network, but adding GPS receivers or other sophisticated sensors to every node can be expensive. MDS-MAP is a recent localization method based on multidimensional scaling (MDS). It uses connectivity information - who is within communications range of whom - to derive the locations of the nodes in the network, and can take advantage of additional data, such as estimated distances between neighbors or known positions for certain anchor nodes, if they are available. However, MDS-MAP is an inherently centralized algorithm and is therefore of limited utility in many applications. In this paper, we present a new variant of the MDS-MAP method, which we call MDS-MAP(P) standing for MDS-MAP using patches of relative maps, that can be executed in a distributed fashion. Using extensive simulations, we show that the new algorithm not only preserves the good performance of the original method on relatively uniform layouts, but also performs much better than the original on irregularly-shaped networks. The main idea is to build a local map at each node of the immediate vicinity and then merge these maps together to form a global map. This approach works much better for topologies in which the shortest path distance between two nodes does not correspond well to their Euclidean distance. We also discuss an optional refinement step that improves solution quality even further at the expense of additional computation.
14933089
Simulated Annealing based Wireless Sensor Network Localization
In this paper, we describe a novel localization algorithm for ad hoc wireless sensor networks. Accurate selforganization and localization capability is a highly desirable characteristic of wireless sensor networks. Many researchers have approached the localization problem from different perspectives. A major problem in wireless sensor network localization is the flip ambiguity, which introduces large errors in the location estimates. In this paper, we propose a two phase localization method based on the simulated annealing technique to address the issue. Simulated annealingis a technique for combinatorial optimization problems and unlike the gradient search method, it is robust against being trapped into local minima. In this paper we show that our simulated annealing based localization method can be used in ad hoc wireless sensor networks to estimate the locationof nodes accurately. In the first phase of our algorithm, simulated annealing is used to obtain an accurate estimate of location. Then a second phase of optimization is performed only on those nodes that are likely to have flip ambiguity problem. Based on the neighborhood information of nodes, those nodes likely to have been affected by flip ambiguity are identified and moved to the correct position. The proposed scheme is tested using simulation on a sensor network of 200 nodes whose distance measurements are corrupted by Gaussian noise. Simulation results show that the proposed novel scheme gives accurate and consistent location estimates of the nodes, and mitigate errors due to flip ambiguity. The performance of the proposed algorithm is better than the performance of some well-known schemes such as DVhop method and convex optimization based semi-definite programming method.
6536614
DV Based Positioning in Ad Hoc Networks
Many ad hoc network protocols and applications assume the knowledge of geographic location of nodes. The absolute position of each networked node is an assumed fact by most sensor networks which can then present the sensed information on a geographical map. Finding position without the aid of GPS in each node of an ad hoc network is important in cases where GPS is either not accessible, or not practical to use due to power, form factor or line of sight conditions. Position would also enable routing in sufficiently isotropic large networks, without the use of large routing tables. We are proposing APS – a localized, distributed, hop by hop positioning algorithm, that works as an extension of both distance vector routing and GPS positioning in order to provide approximate position for all nodes in a network where only a limited fraction of nodes have self positioning capability.
5981813
Distributed localization in wireless sensor networks: a quantitative compariso
This paper studies the problem of determining the node locations in ad-hoc sensor networks. We compare three distributed localization algorithms (Ad-hoc positioning, Robust positioning, and N-hop multilateration) on a single simulation platform. The algorithms share a common, three-phase structure: (1) determine node-anchor distances, (2) compute node positions, and (3) optionally refine the positions through an iterative procedure. We present a detailed analysis comparing the various alternatives for each phase, as well as a head-to-head comparison of the complete algorithms. The main conclusion is that no single algorithm performs best; which algorithm is to be preferred depends on the conditions (range errors, connectivity, anchor fraction, etc.). In each case, however, there is significant room for improving accuracy and/or increasing coverage.
18232672
Distributed Localization in Wireless Ad Hoc Networks
We present a distributed algorithm for localization of nodes in a discrete model of a random ad hoc communication network. We compute the expected value of the position estimate, AS , and the probability that AS = 1 cell (the perfect estimate). This leads to bounds of the average complexity of the algorithm at each node.
1170382
Localization in Sensor Networks with Limited Number of Anchors and Clustered Placement
Many localization algorithms have been proposed in recent years. Although different algorithms based on different methodologies, the use of anchors is common to most algorithms. The placement and the density of anchors affect the accuracy of different algorithms to different extent. Location estimates are usually more accurate with a higher density of anchors. When there are only a few anchors, efficient algorithms tend to perform poorly. However, having more anchors will increase the cost of a sensor network. In this paper, we present an algorithm which uses two different localization techniques, multidimensional scaling (MDS) and proximity-distance map (PDM), in a phased approach. MDS has a high complexity but can give good results when there are only very few anchors. PDM, on the other hand, is a distributed algorithm but performs poorly when anchors are scarce. The phased approach has comparable complexity to PDM but less than MDS. With extensive simulations, we demonstrate that the proposed algorithm gives accurate solution with very few anchors or clustered anchors which is intrinsically a difficult challenge to most existing algorithms.
590062
Radio interferometric geolocation
We present a novel radio interference based sensor localization method for wireless sensor networks. The technique relies on a pair of nodes emitting radio waves simultaneously at slightly different frequencies. The carrier frequency of the composite signal is between the two frequencies, but has a very low frequency envelope. Neighboring nodes can measure the energy of the envelope signal as the signal strength. The relative phase offset of this signal measured at two receivers is a function of the distances between the four nodes involved and the carrier frequency. By making multiple measurements in an at least 8-node network, it is possible to reconstruct the relative location of the nodes in 3D. Our prototype implementation on the MICA2 platform yields an average localization error as small as 3 cm and a range of up to 160 meters. In addition to this high precision and long range, the other main advantage of the Radio Interferometric Positioning System (RIPS) is the fact that it does not require any sensors other than the radio used for wireless communication.
14371703
Complexity and Error Propagation of Localization Using Interferometric Ranging
An interferometric ranging technique has been recently proposed as a possible way to localize ad hoc and sensor networks. Compared to the more common techniques such as received signal strength, time of arrival, and angle of arrival ranging, interferometric ranging has the advantage that the measurement could be highly precise. However, localization using interferometric ranging is difficult as it requires a large number of measurement readings. In this paper, we provide a formal proof of this difficulty in terms of algorithmic complexity. Furthermore, we propose an iterative algorithm that calculates node locations from a set of seeding anchors, gradually building a more global localization solution. Compared to previous localization algorithms, which treat localization as a global optimization problem, the iterative algorithm is a distributed algorithm that is simple to implement in larger networks. More importantly, the iterative algorithm allows us to study the error propagation behavior of localization using interferometric ranging. Using simulations, we validate the performance of the iterative algorithm in terms of localization error and coverage.
1513863
Range-free localization schemes for large scale sensor networks
Wireless Sensor Networks have been proposed for a multitude of location-dependent applications. For such systems, the cost and limitations of the hardware on sensing nodes prevent the use of range-based localization schemes that depend on absolute point-to-point distance estimates. Because coarse accuracy is sufficient for most sensor network applications, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. In this paper, we present APIT, a novel localization algorithm that is range-free. We show that our APIT scheme performs best when an irregular radio pattern and random node placement are considered, and low communication overhead is desired. We compare our work via extensive simulation, with three state-of-the-art range-free localization schemes to identify the preferable system configurations of each. In addition, we study the effect of location error on routing and tracking performance. We show that routing performance and tracking accuracy are not significantly affected by localization error when the error is less than 0.4 times the communication radio radius.
18269730
A RSSI-based and calibrated centralized localization technique for wireless sensor networks
This paper presents a multi-hop localization technique for WSNs exploiting acquired received signal strength indications. The proposed system aims at providing an effective solution for the self localization of nodes in static/semi-static wireless sensor networks without requiring previous deployment information
1796185
The bits and flops of the n-hop multilateration primitive for node localization problems
The recent advances in MEMS, embedded systems and wireless communication technologies are making the realization and deployment of networked wireless microsensors a tangible task. Vital to the success of wireless microsensor networks is the ability of microsensors to ``collectively perform sensing and computation''. In this paper, we study one of the fundamental challenges in sensor networks, node localization. The collaborative multilateration presented here, enables ad-hoc deployed sensor nodes to accurately estimate their locations by using known beacon locations that are several hops away and distance measurements to neighboring nodes. To prevent error accumulation in the network, node locations are computed by setting up and solving a global non-linear optimization problem. The solution is presented in two computation models, centralized and a fully distributed approximation of the centralized model. Our simulation results show that using the fully distributed model, resource constrained sensor nodes can collectively solve a large non-linear optimization problem that none of the nodes can solve individually. This approach results in significant savings in computation and communication, that allows fine-grained localization to run on a low cost sensor node we have developed.
2583188
Quantifying the channel quality for interference-aware wireless sensor networks
Reliability of communications is key to expand application domains for sensor networks. Since Wireless Sensor Networks (WSN) operate in the license-free Industrial Scientific and Medical (ISM) bands and hence share the spectrum with other wireless technologies, addressing interference is an important challenge. In order to minimize its effect, nodes can dynamically adapt radio resources provided information about current spectrum usage is available.
 We present a new channel quality metric, based on availability of the channel over time, which meaningfully quantifies spectrum usage. We discuss the optimum scanning time for capturing the channel condition while maintaining energy-efficiency. Using data collected from a number of Wi-Fi networks operating in a library building, we show that our metric has strong correlation with the Packet Reception Rate (PRR). This suggests that quantifying interference in the channel can help in adapting resources for better reliability. We present a discussion of the usage of our metric for various resource allocation and adaptation strategies.
18504939
ZigBee networks performance under WLAN 802.11b/g interference
ZigBee is a standard protocol suite for low rate wireless personal area networks (LR-WPANs) and has been widely utilized in various fields. Since both ZigBee and WLAN (802.11b/g) operate in the 2.4GHz unlicensed industrial scientific medical (ISM) frequency band, mutual interference is likely to exist and cause signal degradation. In this paper, an interference analysis model is suggested to obtain an estimate of Packet Error Rate (PER) of a ZigBee network. A ZigBee evaluation kit, AVR RZ200, was implemented for measurement of Packet Loss Ratio (PLR). The measured PLR matches the estimated PER, with varied distances and different center frequency offsets of different channels.
12994795
Minimising the effect of WiFi interference in 802.15.4 wireless sensor networks
Interference from colocated networks operating over the same frequency range, becomes an increasingly severe problem as the number of networks overlapping geographically increases. Our experiments show that such interference is indeed a major problem, causing up to 58% packet loss to a multihop 802.15.4 sensor network competing for radio spectrum with a WiFi network. We present interference estimators that can be efficiently implemented on resource constrained motes using off-the-shelf radios and outline distributed algorithms that use these estimators to dynamically switch frequencies as interference is detected. Lastly, we evaluate the proposed algorithms in the context of a real-life application that downloads large amounts of data over multihop network paths. Our results show that the proposed approach successfully detects interference from competing WiFi channels and selects non-overlapping 802.15.4 channels. As a result, the proposed solution reduces end-to-end loss rate from 22% 58% to < 1%.
5920558
A RSSI-based algorithm for indoor localization using ZigBee in wireless sensor network
For the various applications in home automation, the service system requires to precisely estimate user’s locations by certain sensors. It is considered as a challenge to automatically serve a mobile user in the house. However, indoor localization cannot be carried out effectively by the well-know Global Positioning System (GPS). In recent years, Wireless Sensor Networks (WSNs) are thus popularly used to locate a mobile object in an indoor environment. Some physical features are widely discussed to solve indoor localization in WSN. In this paper, we inquired about the RSSI solutions on indoor localization, and proposed a Closer Tracking Algorithm (CTA) to locate a mobile user in the house. The proposed CTA was implemented by using ZigBee CC2431 modules. The experimental results show that the proposed CTA can accurately determine the position with error distance less than 1 meter. At the same time, the proposed CTA has at least 85% precision when the distance is less than one meter.
7398942
Dual and Mixture Monte Carlo Localization Algorithms for Mobile Wireless Sensor Networks
In this paper, we consider a mobile wireless sensor network where both sensor nodes and the seeds are moving. We propose and analyze two variations of the Monte Carlo localization (MCL) algorithms, namely: dual MCL and mixture MCL, for mobile sensor networks. We conduct simulation experiments to evaluate the performance of these two algorithms by varying the number of seeds, number of nodes, number of samples, velocity of nodes, and radio pattern degree of irregularity. Results show that both dual MCL and mixture MCL are more accurate than the original MCL algorithm. In terms of the trade off between the computational time and estimated location accuracy, the mixture MCL has a better performance than both dual MCL and the original MCL algorithms.
209399974
Anchor-Free Distributed Localization in Sensor Networks
Many sensor network applications require that each node’s sensor stream be annotated with its physical location in some common coordinate system. Manual measurement and configuration methods for obtaining location don’t scale and are error-prone, and equipping sensors with GPS is often expensive and does not work in indoor and urban deployments. Sensor networks can therefore benefit from a self-configuring method where nodes cooperate with each other, estimate local distances to their neighbors, and converge to a consistent coordinate assignment. This paper describes a fully decentralized algorithm called AFL (Anchor-Free Localization) where nodes start from a random initial coordinate assignment and converge to a consistent solution using only local node interactions. The key idea in AFL is fold-freedom, where nodes first configure into a topology that resembles a scaled and unfolded version of the true configuration, and then run a force-based relaxation procedure. We show using extensive simulations under a variety of network sizes, node densities, and distance estimation errors that our algorithm is superior to previously proposed methods that incrementally compute the coordinates of nodes in the network, in terms of its ability to compute correct coordinates under a wider variety of conditions and its robustness to measurement errors.
2269436
Robust distributed network localization with noisy range measurements
This paper describes a distributed, linear-time algorithm for localizing sensor network nodes in the presence of range measurement noise and demonstrates the algorithm on a physical network. We introduce the probabilistic notion of robust quadrilaterals as a way to avoid flip ambiguities that otherwise corrupt localization computations. We formulate the localization problem as a two-dimensional graph realization problem: given a planar graph with approximately known edge lengths, recover the Euclidean position of each vertex up to a global rotation and translation. This formulation is applicable to the localization of sensor networks in which each node can estimate the distance to each of its neighbors, but no absolute position reference such as GPS or fixed anchor nodes is available.
 We implemented the algorithm on a physical sensor network and empirically assessed its accuracy and performance. Also, in simulation, we demonstrate that the algorithm scales to large networks and handles real-world deployment geometries. Finally, we show how the algorithm supports localization of mobile nodes.
23963391
An Error Propagation Aware Algorithm for Precise Cooperative Indoor Localization
Recently, node localization for sensor networks has attracted considerable attention for military application. Despite the recent proposals, the relationship between the channel behavior and the performance analysis of cooperative algorithms has not been addressed. The assumptions about the statistics of the ranging error used in the literature are either too general or overly optimistic. Additionally when sensors collaborate to localize each other, there is no attempt to characterize the error in the position of the nodes. This lack of error propagation-awareness can degrade the performance of an algorithm and create divergence problems. In this paper we first introduce detailed modeling of channel propagation in indoor environments in the form of novel empirical path loss (PL) and distance measurement error (DME) models developed from the results of UWB channel measurements. Then we integrate these models in developing error propagation aware (EPA) precise cooperative localization algorithm that tracks the extent of the position error in each sensor node and its overall effect on subsequent multi-laterations. Finally we compare the algorithm against the Cramer-Rao lower bound (CRLB)