Topological Hole Detection in Sensor Networks with Cooperative Neighbors

Bi, Ke; Tu, Kun; Gu, Naijie; Dong, Wan Lin; Liu, Xiaohu

doi:10.1109/icsnc.2006.71

Cited by 32 publications

(16 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to ensure accuracy, both of these techniques require a uniform deployment and an average node degree greater than 100. Bi et al [1] present another statistical method that identifies boundary nodes because boundary nodes typically have smaller degrees than their 2-hop neighbors do.…”

Section: Related Workmentioning

confidence: 99%

Robust Edge Detection in Wireless Sensor Networks

Mallery

Medidi

2008

IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference

View full text Add to dashboard Cite

The ability to geometrically represent sensed phenomena within a wireless sensor network can provide a more concise view than enumeration of all nodes identifying a phenomenon. A more concise view of sensed data can reduce the communication and energy costs of data analysis and extend the lifetime of the network. We propose a distributed edge detection technique that identifies connected perimeters for sensed phenomena within wireless sensor networks. The technique operates in arbitrarily deployed wireless sensor networks, such as those containing connectivity holes, and is capable of correctly identifying the perimeters of irregularly shaped phenomena. We implemented our technique and conducted extensive experiments; results show that our technique provides accurate perimeters for all sensed phenomena within a wireless sensor network even in the presence of irregularly shaped phenomena.

show abstract

Section: Related Workmentioning

confidence: 99%

Robust Edge Detection in Wireless Sensor Networks

Mallery

Medidi

2008

IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference

View full text Add to dashboard Cite

show abstract

“…The holes are merged into a composite hole. The network is again flooded from the hole boundary nodes to find the network boundary (a) synchronising nodes before constructing the tree the network connectivity degree must be 11 or higher (b) flooding the network twice for holes and then network boundaries detection (c) merging holes Kroller [14] searches for flowers and then augments all the cycles until boundaries are reached (a) searching for flower structures (a) may not find a flower for network connectivity degree less than 10 (b) finding the 8-hop neighbours of every node (b) in random topology, the flower structure appears only for average network degree between 20-30 Saukh [15] searches for patterns nodes inside the pattern are considered as inner nodes (a) searching for patterns that may not always appear can work for minimum degree of 4 and 6, but requires 6-hop neighbourhood information to be accurate Bi [18] boundary nodes have smaller degrees than their 2-hop neighbours do (a) high exchange of messages between every node and its 2-hop neighbours during their cooperation to find the position of that node no information is available of the network connectivity degree required but the approach produces bad results for randomly deployed dense networks our proposed hop-based approach every node determines its x-hop neighbours. The connectivity of these neighbours is reviewed to create a communication path.…”

Section: Fundamentalsmentioning

confidence: 99%

“…Topological approaches [4,5,7,14,15,18] use the information on the connectivity of sensor nodes to detect holes and WSN boundaries. In this context, Funke and Klein [4] have presented an algorithm based on the idea of iso-levels each of which represents a set of nodes having the same hop distance from a specific root node [19].…”

Section: Related Workmentioning

confidence: 99%

Hop-based approach for holes and boundary detection in wireless sensor networks

Khan

Jabeur

Zeadally

2012

IET Wirel. Sens. Syst.

View full text Add to dashboard Cite

In wireless sensor networks (WSNs), sensors are frequently prone to failure because of their limited batteries lifetime and external spatio-temporal events. Consequently, coverage and communication holes may appear preventing reliable and durable routing pathways. To efficiently deal with the effects of holes and prevent their expansions, it is unavoidable for any solution to detect their boundaries. This issue had been addressed by several works, many of which are based on topological approaches where sensor nodes' connectivity is used. The authors propose a topological approach for the detection of the boundaries of holes. The approach also allows for the detection of the boundaries of the sensor network which is an important concern when sensors are deployed randomly in harsh, remote areas. The approach is based on the identification and the checking of the connectivity of the x-hop neighbours surrounding every node. The results of our simulations conducted on randomly and uniformly deployed WSNs demonstrate high accuracy and reduced requirements in terms of network connectivity degree and communication overhead compared to other existing approaches.

show abstract

“…Another issue in [9] is that the decision by one-hop information has higher variance, so Bi. et al [10] improved the threshold by averaging the number of one-hop neighboring nodes in the two-hop region. The algorithm proposed by Ghrist et al [11] detects the hole via homology.…”

Section: Introductionmentioning

confidence: 99%

Optimal self boundary recognition with two-hop information for ad hoc networks

Chen

Chung

et al. 2012

2012 IEEE Wireless Communications and Networking Conference (WCNC)

View full text Add to dashboard Cite

Abstract-The ad hoc network is composed of multiple sensor nodes to serve various applications, such as data collection or environmental monitoring. In many applications, the sensor nodes near the boundary of the deployment region provide biased or low-quality information because they have limited number of neighboring nodes and only partial information is available. Hence, the boundary recognition is an important issue in the ad hoc networks. By the statistical approach in high node density networks, Fekete's pioneer work identified the boundary node by number of neighboring nodes and using a specific threshold. By exploiting the number of nodes in the two-hop region, our proposed algorithm has significant improvement of boundary recognition contrasted with Fekete's algorithm in the low-density network. Given the information topology and the cost function, the analyses provide a framework to obtain the optimal threshold for boundary recognition. Besides, the simulation results reveal the proposed algorithm has greater than 90% detection rate and lower than 10% false alarm rate.

show abstract

Topological Hole Detection in Sensor Networks with Cooperative Neighbors

Cited by 32 publications

References 5 publications

Robust Edge Detection in Wireless Sensor Networks

Robust Edge Detection in Wireless Sensor Networks

Hop-based approach for holes and boundary detection in wireless sensor networks

Optimal self boundary recognition with two-hop information for ad hoc networks

Contact Info

Product

Resources

About