Topological hole detection in wireless sensor networks and its applications
DIALM-POMC '05 Proceedings of the 2005 joint workshop on Foundations of mobile computing
Deterministic boundary recognition and topology extraction for large sensor networks
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Hole detection or: "how much geometry hides in connectivity?"
Proceedings of the twenty-second annual symposium on Computational geometry
Coverage and hole-detection in sensor networks via homology
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Boundary recognition in sensor networks by topological methods
Proceedings of the 12th annual international conference on Mobile computing and networking
Topological Hole Detection in Sensor Networks with Cooperative Neighbors
ICSNC '06 Proceedings of the International Conference on Systems and Networks Communication
Coordinate-free Coverage in Sensor Networks with Controlled Boundaries via Homology
International Journal of Robotics Research
Fine-grained boundary recognition in wireless ad hoc and sensor networks by topological methods
Proceedings of the tenth ACM international symposium on Mobile ad hoc networking and computing
On boundary recognition without location information in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
An algorithm for boundary discovery in wireless sensor networks
HiPC'05 Proceedings of the 12th international conference on High Performance Computing
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We propose two novel algorithms for distributed and locationfree boundary recognition in wireless sensor networks. Both approaches enable a node to decide autonomously whether it is a boundary node, based solely on connectivity information of a small neighborhood. This makes our algorithms highly applicable for dynamic networks where nodes can move or become inoperative. We compare our algorithms qualitatively and quantitatively with several previous approaches. In extensive simulations, we consider various models and scenarios. Although our algorithms use less information than most other approaches, they produce significantly better results. They are very robust against variations in node degree and do not rely on simplified assumptions of the communication model. Moreover, they are much easier to implement on real sensor nodes than most existing approaches.