Distributed Geometric Distance Estimation in Ad Hoc Networks

Merkel, Sabrina; Mostaghim, Sanaz; Schmeck, Hartmut

doi:10.1007/978-3-642-31638-8_3

Cited by 9 publications

(8 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most localization solutions use ranging techniques based on measurements provided by the hardware: the angle of arrival (AoA), the time of flight (ToF), the RSSI [6], or the phase difference between transmitter and receiver [7]. A second class of localization approaches uses network topology [8] or neighborhood correlation [9] information to estimate the distance between nodes.…”

Section: Related Workmentioning

confidence: 99%

Node ranking in wireless sensor networks with linear topology

et al. 2017

View full text Add to dashboard Cite

In wireless sensor networks with linear topology, knowing the physical order in which nodes are deployed is useful not only for the target application, but also to some network services, like routing or data aggregation. Considering the limited resources of sensor nodes, the design of autonomous protocols to find this order is a challenging topic. In this paper, we propose a distributed and iterative centroid-based algorithm to address this problem. At each iteration, the algorithm selects two virtual anchors and finds the order of a subset of nodes, placed between these two anchors. The proposed algorithm requires local node connectivity knowledge and the identifier of the first sensor node of the network, which is the only manually configured parameter. This solution, scalable and lightweight from the deployment and maintenance point of view, is shown to be robust to connectivity degradation, correctly ordering more than 95% of the nodes, even under very low connectivity conditions

show abstract

Section: Related Workmentioning

confidence: 99%

Node ranking in wireless sensor networks with linear topology

et al. 2017

View full text Add to dashboard Cite

show abstract

“…In [27], the hop-count based weighted centroid localization algorithm is improved by adding the node degree on the paths to the referenced anchors into the weights. Merkel et al uses the whole neighbor set information of a particular node to approximate its distance to each neighbor geometrically [6].…”

Section: Related Workmentioning

confidence: 99%

“…However, it is not a cost-effective approach to equip each ad-hoc node with GPS receivers [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A hop-count based positioning algorithm for wireless ad-hoc networks

2014

View full text Add to dashboard Cite

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.

show abstract

“…An empirical method for deriving the distance from the ratio between the number of common neighbors between two nodes and the total number of nodes in their neighborhoods has been proposed by Villafuerte et al [9], while an analytical evaluation for this mapping has been obtained by means of a first order Taylor series expansion [10]. Finally, Merkel et al proposed an alternative derivation of the distance from the above mentioned ratio using regression [11]. In general, these methods restrict distance estimation to couples of neighboring nodes.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Merkel et al proposed a distributed algorithm for extending the computation to non-neighboring (i.e. out of radio-range) nodes [11]. The algorithm essentially works by firstly computing estimates among communicating nodes and then by propagating this information along shortest paths.…”

Section: Introductionmentioning

confidence: 99%

A statistical geometry approach to distance estimation in wireless sensor networks

Freschi

Lattanzi

Bogliolo

2013

2013 IEEE International Conference on Communications Workshops (ICC)

View full text Add to dashboard Cite

Algorithmic approaches to the estimation of pairwise distances between the nodes of a wireless sensor network are highly attractive to provide information for routing and localization without requiring specific hardware to be added to cost/resource-constrained nodes. This paper exploits statistical geometry to derive robust estimators of the pairwise Euclidean distances from topological information typically available in any network. Extensive Monte Carlo experiments conducted on synthetic benchmarks demonstrate the improved quality of the proposed estimators with respect to the state of the art.

show abstract

Distributed Geometric Distance Estimation in Ad Hoc Networks

Cited by 9 publications

References 27 publications

Node ranking in wireless sensor networks with linear topology

Node ranking in wireless sensor networks with linear topology

A hop-count based positioning algorithm for wireless ad-hoc networks

A statistical geometry approach to distance estimation in wireless sensor networks

Contact Info

Product

Resources

About