Mobility-enhanced positioning in ad hoc networks (original) (raw)
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A hop-count based positioning algorithm for wireless ad-hoc networks
Wireless Networks, 2014
We propose a range-free localization algorithm for a wireless ad-hoc network utilizing the hopcount metric's ability to indicate proximity to anchors (i.e., nodes with known positions). In traditional sense, hop-count generally means the number of intermediate routers a datagram has to go through between its source and the destination node. We analytically show that hop-count could be used to indicate proximity relative to an anchor node. Our proposed algorithm is computationally feasible for resource constrained wireless ad-hoc nodes, and gives reasonable accuracy. We perform both real experiments and simulations to evaluate the algorithm's performance. Experimental results show that our algorithm outperforms similar proximity based algorithms utilizing Received Signal Strength (RSS) and Expected Transmission Count (ETX). We also analyze the impact of various parameters like the number of anchor nodes, placements of anchor nodes and varying transmission powers of the nodes on the hop-count based localization algorithm's performance through simulation.
A Study of Mobility in Ad Hoc Networks and Its Effects on a Hop Count Based Distance Estimation
2012 5th International Conference on New Technologies, Mobility and Security (NTMS), 2012
Hop Count based distance estimation is an important element for the distributed computation of coordinates in GPS free environments. Deriving a distance estimate from hop counts is prone to error especially when the algorithm is applied to a network of mobile devices. We define and analyze two error models to describe the origin of underestimated and overestimated distances in a mobile ad hoc network. Different movement patterns are examined to get an understanding of their impact on the length of one hop and, thus, the estimated distance. Our experiments and analysis indicate that mobility can have a positive effect on the accuracy of a distance estimate by an emerging effect of asynchronous computation and fluctuating distribution of nodes. In addition, we identify parameters, such as direction and variance in movements,that are responsible for the different influences of the presented mobility models.
Localized positioning in ad hoc networks
Ad Hoc Networks, 2003
Position centric approaches, such as Cartesian routing, geographic routing, and the recently proposed trajectory based forwarding (TBF), address scalability issues in large ad hoc networks by using Euclidean space as a complementary name space. These approaches require that nodes know their position in a common coordinate system. While a GPS receiver in each node would be ideal, in many cases an approximation algorithm is necessary for networks with only a few GPS enabled nodes. These algorithms however require collaboration of large portions of the network, thus imposing an overhead for nodes which do not need positioning, or are mobile. We propose Local Positioning System (LPS), a method that makes use of local node capabilities -angle of arrival, range estimations, compasses and accelerometers, in order to internally position only the groups of nodes involved in particular conversations. Localized positioning enables position centric uses, like discovery, flooding and routing in networks where global positioning is not available.
Radio Communications, 2010
An ad hoc 1 network is defined as a decentralised wireless network that is set up on-the-fly for a specific purpose. These networks were proposed years ago for military use, with the purpose of communicating devices in a highly constrained scenario. Under such a network, devices join and leave the network dynamically; thus, it cannot be expected to have any kind of network infrastructure. This wish for decentralised on-the-fly networks has subsequently expanded to cover several fields besides the military. Today, there are several mobile services requiring the self-organising capabilities that ad hoc networks offer. Examples include packet tracking, online-gaming, and measuring systems, among others. Ad hoc networks have obvious benefits for mobile services, but they also introduce new issues that regular network protocols cannot cope with, including optimum routing, network fragmentation, reduced calculation power, energy-constrained terminals, etc. In ad hoc networks, positioning takes a significant role, mainly due to the on-the-fly condition. In fact, several services require nodes to know the position of the customers in order to perform their duty properly. Wireless sensor networks concentrate most of the services that need positioning to perform their duty. Such networks constitute a subset of ad hoc networks involving dense topologies operating in an ad hoc fashion, and they are composed of small, energy and computation constrained terminals. In ad hoc networks, and especially in wireless sensor networks, nodes are spread over a certain area without a precise knowledge about the topology. In fact, this topology is variable. Accordingly, there are several unknowns (e.g., node density and coverage, network's energy map, the presence of shadowed zones, nodes' placement in the network coverage area) that are likely to constrain the performance of ad hoc services. Knowledge of the terminals' locations can substantially improve the service performance. Positioning is not only important for the service provisioning; it is also crucial in the ad hoc protocol stack development. Due to the changes in the topology and the lack of communication infrastructure, ad hoc protocols have to address several issues not present in regular cellular networks. Routing is one of the best examples of the dependence of ad hoc networks on positioning. Studies such as (Stojmenovic, 2002) demonstrate that only position-based routing protocols are scalable, i.e., able to cope with a higher density of 1 "Ad hoc" is actually a Latin phrase that means "to this (thing, purpose, end, etc.)" 31 www.intechopen.com Radio Communications 620 nodes in the network. The same seems to apply to other management and operation tasks in ad hoc networks. 1.1 The Location problem in ad hoc networks Nodes in an ad hoc network can be grouped into three categories according to the positioning capabilities: beacon nodes, settled nodes, and unknown nodes. Beacon nodes, also known as anchors or landmarks, are those able to compute their position on their own, i.e., without using an ad hoc location algorithm. Accordingly, they implement at least one location technique (e.g., GPS, map matching), which can be used as standalone. Beacon nodes usually constitute the reference frame necessary to set up a location algorithm. Unknown nodes are those nodes that do not know their position yet. When an ad hoc network is set up, all nodes except the beacon nodes are unknown. Settled nodes are unknown nodes that are able to compute their position from the information that they exchange with beacon nodes and/or other settled nodes. The purpose of the ad hoc location system is thus to position as many nodes as possible, turning them from unknown to settled nodes (Bourkerche et al., 2007). Location systems in ad hoc networks function in two steps: local positioning and positioning algorithm. The former is responsible for computing the position of an unknown node from the metrics gathered. The second step consists of the positioning algorithm, which indicates how the position information is managed in order to maximise the number of nodes being settled. 1.2 Measuring the performance of location solutions Performance of location solutions in ad hoc networks can be computed according to several parameters. The main ones are presented below. 1.2.1 Accuracy Ad hoc positioning requires good accuracy since most of the networks in ad hoc mode are deployed in constrained scenarios, often indoors. In such environments, accuracy is especially relevant, since a few meters of error in the position may cause the node to be identified in another room, floor, or even building. Furthermore, nodes are expected to be very close (e.g., in medical applications), and inaccurate positions could hinder operation and maintenance tasks or even prevent location-based applications from performing their duty. Thus, location algorithms for ad hoc networks must produce positions for settled nodes of the highest possible accuracy.
Hop count based distance estimation in mobile ad hoc networks – Challenges and consequences
Ad Hoc Networks, 2014
Hop Count based distance estimation is an important element for localization of devices in mobile ad hoc networks. Deriving distance estimates from hop counts is prone to error, especially in networks with low density. This paper shows that mobility can affect the accuracy of hop count based distance estimation. Two types of error are defined to describe and analyze the source of underestimation and overestimation of distances in a mobile ad hoc network. Different movement patterns are examined to get an insight of their impact on the hop counts and the estimated distances accordingly. Our experiments and analysis indicate that mobility can have a positive effect on the accuracy of distance estimates which results from a combination of asynchronous computation of hop counts and mobility of the nodes. At the same time, this positive effect can turn into a negative one with increasing mobility. Therefore, we determine characteristics, such as direction, speed, and similarity in movements of neighbors which are responsible for the disparity in the influences of the investigated mobility patterns. A study of these properties is presented and their individual effect is explained in detail. The difference between mobility and density induced error is discussed and their individual adverse effect is weighted against each other. In addition, we introduce a modified algorithm to determine hop counts which is designed to mitigate the effect of mobility. Two indicators are presented to identify and characterize the mobility of devices in a decentralized way.
Direction of Encounter (DoE): A Mobility-Based Location Method for Wireless Networks
IEEE Transactions on Mobile Computing, 2014
Traditional location methods require specialized network infrastructure or add-on location hardware in order to estimate node positions. As an opposite approach, DoE (direction of encounter) uses standard wireless networking equipment and takes advantage of node mobility to establish static node locations. In DoE, as a mobile node enters and leaves a static node's coverage area, it is able to discover the static node's location with respect to its own trajectory. Mobile nodes are able to determine the position of a set of static nodes by collaborating in this discovery process. In this work, this set is called a constellation. This collaboration consists of exchanging constellation data in order to establish and improve the accuracy of the position estimates. Not only does DoE establish static node positions, but it also allows mobile users to be aware of the direction where static nodes can be found. DoE needs minimal user intervention, although fully automatic operation can be achieved if inertial sensors are available. This method can be used to develop both location-based applications and guiding procedures. By means of simulations and experiments, we carried out a performance evaluation of DoE under diverse conditions. The results show that the DoE algorithm indeed is able to estimate the static node positions without requiring additional functionality from static nodes. We believe this is an important requirement for a successful deployment of a location method.
Location information services in mobile ad hoc networks
2002
In recent years, many location based routing protocols have been developed for ad hoc networks. Some of these protocols assume a location service exists which provides location information on all the mobile nodes in the network. In this paper, we evaluate three location service alternatives. One is a reactive protocol; the other two are proactive protocols. Of the proactive protocols, one sends location tables to neighbors and the other sends location information to all nodes. In our evaluation, one proactive protocol proved to have the best performance overall. Thus, we also evaluate the main input parameter associated with this protocol for optimal performance.
Location service in ad-hoc networks: Modeling and analysis
2004
Location-based routing significantly reduces the control overhead in mobile ad hoc networks (MANETs) by utilizing position information of mobile nodes in forwarding decisions. However a location service is needed before any forwarding scheme can be applied. Therefore the scalability of the location services directly affects the overall scalability of location-based routing. Recently, several location service schemes have been proposed, most of which are evaluated based on only one or two performance metrics, and under only the uniform traffic pattern. We believe that a comprehensive comparative study is needed to gain a deeper understanding of the design trade-offs in developing scalable location services. In this paper, we first present a taxonomy of existing schemes and explore the design space and tradeoffs involved. We then develop a common theoretical framework to analyze five existing and representative schemes in terms of three important cost metrics-location maintenance cost, location query cost, and storage cost-and under different traffic patterns. Our analysis shows that the design of location services involves tradeoffs among all three cost metrics, and overlooking any of them may lead to biased conclusions. We also show that some of the schemes are more effective in exploiting localized traffic patterns, thereby more suitable for large scale MANETs, where traffic patterns are more likely to be highly localized.
DV Based Positioning in Ad Hoc Networks
Telecommunication Systems, 2003
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 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.
In ad-hoc wireless networks, devices that normally cannot directly communicate, route their messages through intermediate nodes. The number of those nodes is called hop count, a useful metric in estimating the distance between two nodes. Current methods usually depend on special nodes, called anchors, that need accurate localization information, in order to calculate an estimate for the average distance traversed per hop. The drawback of this approach is that anchor nodes increase the overall cost and complexity of the system. To address this problem, this letter proposes a novel, anchor-node-free, algorithm that can achieve a useful estimate for actual distance between two nodes, by analytically finding an estimate for the average maximum distance traveled per hop and multiplying with the hop count. The only requirement is the a priori knowledge of the networks' node density and the node range. The performance of our method is compared to a recent anchor-node-based method and is shown to yield similar location estimation accuracy, despite the fact that it does not employ anchor nodes Index Terms-distance estimation, hop count, adhoc, wireless, sensor networks, ad-hoc, multi-hop.