You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
We propose two novel algorithms for distributed and location-free 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.

Cited Abstract(s):
[1]: We describe a new approach for dealing with the following central problem in the self-organization of a geometric sensor network: Given a polygonal region R, and a large, dense set of sensor nodes that are scattered uniformly at random in R. There is no central control unit, and nodes can only communicate locally by wireless radio to all other nodes that are within communication radius r, without knowing their coordinates or distances to other nodes. The objective is to develop a simple distributed protocol that allows nodes to identify themselves as being located near the boundary of R and form connected pieces of the boundary. We give a comparison of several centrality measures commonly used in the analysis of social networks and show that restricted stress centrality is particularly suited for geometric networks; we provide mathematical as well as experimental evidence for the quality of this measure.

[2]: We consider a crucial aspect of self-organization of a sensor network consisting of a large set of simple sensor nodes with no location hardware and only very limited communication range. After having been distributed randomly in a given two-dimensional region, the nodes are required to develop a sense for the environment, based on a limited amount of local communication. We describe algorithmic approaches for determining the structure of boundary nodes of the region, and the topology of the region. We also develop methods for determining the outside boundary, the distance to the closest boundary for each point, the Voronoi diagram of the different boundaries, and the geometric thickness of the network. Our methods rely on a number of natural assumptions that are present in densely distributed sets of nodes, and make use of a combination of stochastics, topology, and geometry. Evaluation requires only a limited number of simple local computations.

[3]: In a wireless sensor network, the regions in which a large percentage of sensor nodes are not available may form holes in the network. In holes, sensor nodes may be depleted or not dense enough to communicate with others. So those holes may degrade some applications? performances in sensor networks. In this paper, we study the problem of detecting topological holes in sensor networks with no localization information in any node. We present a distributed cooperative scheme which is only based on the communication topology graph. To identify holes, each node only needs to exchange information with its 1-hop and 2-hop neighbors. With the cooperation of its 2-hop neighbors, each node could independently decide whether it is on the boundary of a hole. Experimental results demonstrate that our algorithm could indeed capture the characteristics of those boundary nodes and describe those holes in sensor networks of moderate node densities.
try to recognize boundary nodes by low node degree or similar statistical properties. As long as nodes are smoothly distributed, this works quite well as boundary nodes usually have less neighbors than interior nodes. However, as soon as node degrees fluctuate noticeably, most statistical approaches produce many misclassifications. Besides, these algorithms often require unrealistic high average node degrees. Prominent statistical approaches are Fekete [1] [2] , and Bi [2]7 .