Distributed Constraint-Based Location Discovery In Ad Hoc Networks (original) (raw)
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Location Discovery using Ad Hoc Networks
2006
This paper presents a generic service which allows a device to discover the location of other devices in an ad hoc network. The service has advantages in a variety of scenarios, since it does not rely on location infrastructures such as GPS satellites or GSM cellular base stations. An outline of the technology that will be needed to realise the service is given, along with a look at the fundamental security issues which surround the use of this location discovery service.
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.
Position Certainty Propagation: A Localization Service for Ad-Hoc Networks
Computers, 2019
Location services for ad-hoc networks are of indispensable value for a wide range of applications, such as the Internet of Things (IoT) and vehicular ad-hoc networks (VANETs). Each context requires a solution that addresses the specific needs of the application. For instance, IoT sensor nodes have resource constraints (i.e., computational capabilities), and so a localization service should be highly efficient to conserve the lifespan of these nodes. We propose an optimized energy-aware and low computational solution, requiring 3-GPS equipped nodes (anchor nodes) in the network. Moreover, the computations are lightweight and can be implemented distributively among nodes. Knowing the maximum range of communication for all nodes and distances between 1-hop neighbors, each node localizes itself and shares its location with the network in an efficient manner. We simulate our proposed algorithm in a NS-3 simulator, and compare our solution with state-of-the-art methods. Our method is capa...
Position Certainty Propagation: A Location Service for MANETs
Lecture Notes in Computer Science, 2019
Localization in Mobile Ad-hoc Networks (MANETs) and Wireless Sensor Networks (WSNs) is an issue of great interest, especially in applications such as the IoT and VANETs. We propose a solution that overcomes two limiting characteristics of these types of networks. The first is the high cost of nodes with a location sensor (such as GPS) which we will refer to as anchor nodes. The second is the low computational capability of nodes in the network. The proposed algorithm addresses two issues; self-localization where each non-anchor node should discover its own position, and global localization where a node establishes knowledge of the position of all the nodes in the network. We address the problem as a graph where vertices are nodes in the network and edges indicate connectivity between nodes. The weights of edges represent the Euclidean distance between the nodes. Given a graph with at least three anchor nodes and knowing the maximum communication range for each node, we are able to localize nodes using fairly simple computations in a moderately dense graph.
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.
A scalable location service for geographic ad hoc routing
2000
GLS is a new distributed location service which tracks mobile node locations. GLS combined with geographic forwarding allows the construction of ad hoc mobile networks that scale to a larger number of nodes than possible with previous work. GLS is decentralized and runs on the mobile nodes themselves, requiring no fixed infrastructure. Each mobile node periodically updates a small set of other nodes (its location servers) with its current location. A node sends its position updates to its location servers without knowing their actual identities, assisted by a predefined ordering of node identifiers and a predefined geographic hierarchy. Queries for a mobile node's location also use the predefined identifier ordering and spatial hierarchy to find a location server for that node.
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.
Discovery and Verification of Neighbor Positions in Mobile Ad Hoc Networks
IEEE Transactions on Mobile Computing, 2013
A growing number of ad hoc networking protocols and location-aware services require that mobile nodes learn the position of their neighbors. However, such a process can be easily abused or disrupted by adversarial nodes. In absence of a-priori trusted nodes, the discovery and verification of neighbor positions presents challenges that have been scarcely investigated in the literature. In this paper, we address this open issue by proposing a fully-distributed cooperative solution that is robust against independent and colluding adversaries, and can be impaired only by an overwhelming presence of adversaries. Results show that our protocol can thwart more than 99% of the attacks under the best possible conditions for the adversaries, with minimal false positive rates.
Lecture Notes in Computer Science, 2005
We present here a novel approach where we identify a region within which a node is guaranteed to be found, in contrast to the existing approaches where no such confining region for a node can be guaranteed, but only the location could be estimated either with no definitive error bound or only with some probabilistic error. The location identification algorithm presented here minimizes the size of this region, using computational geometric methods. The proposed technique iteratively improves the region of residence of all the nodes in the network through the exchange of region information among neighbors in O(nD) time, where n and D are the number of nodes and diameter of the network respectively. Simulation results also show encouraging results with this approach.