TY - GEN
T1 - Distance distributions in finite ad hoc networks
T2 - 8th EAI International Conference on Ad Hoc Networks, ADHOCNETS 2016
AU - Tong, Fei
AU - Pan, Jianping
AU - Zhang, Ruonan
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.
PY - 2017
Y1 - 2017
N2 - Most performance metrics in wireless ad hoc networks, such as interference, Signal-to-Interference-plus-Noise Ratio, path loss, outage probability, link capacity, node degree, hop count, network coverage, and connectivity, are nonlinear functions of the distances among communicating, relaying, and interfering nodes. A probabilistic distance-based model is definitely needed in quantifying these metrics, which eventually involves the Nodal Distance Distribution (NDD) in a finite network intrinsically depending on the network coverage and nodal spatial distribution. In general, there are two types of NDD, i.e., (1) Ref2Ran: the distribution of the distance between a given reference node and a node uniformly distributed at random, and (2) Ran2Ran: the distribution of the distance between two nodes uniformly distributed at random. Traditionally, ad hoc networks were modeled as rectangles or disks. Recently, both types of NDD have been extended to the networks in the shape of one or multiple arbitrary polygons, such as convex, concave, disjoint, or tiered networks. In this paper, we survey the state-of-the-art approaches to the two types of NDD with uniform or nonuniform node distributions and their applications in wireless ad hoc networks, as well as discussing the open issues, challenges, and future research directions.
AB - Most performance metrics in wireless ad hoc networks, such as interference, Signal-to-Interference-plus-Noise Ratio, path loss, outage probability, link capacity, node degree, hop count, network coverage, and connectivity, are nonlinear functions of the distances among communicating, relaying, and interfering nodes. A probabilistic distance-based model is definitely needed in quantifying these metrics, which eventually involves the Nodal Distance Distribution (NDD) in a finite network intrinsically depending on the network coverage and nodal spatial distribution. In general, there are two types of NDD, i.e., (1) Ref2Ran: the distribution of the distance between a given reference node and a node uniformly distributed at random, and (2) Ran2Ran: the distribution of the distance between two nodes uniformly distributed at random. Traditionally, ad hoc networks were modeled as rectangles or disks. Recently, both types of NDD have been extended to the networks in the shape of one or multiple arbitrary polygons, such as convex, concave, disjoint, or tiered networks. In this paper, we survey the state-of-the-art approaches to the two types of NDD with uniform or nonuniform node distributions and their applications in wireless ad hoc networks, as well as discussing the open issues, challenges, and future research directions.
KW - Distance distributions
KW - Performance metrics
KW - Wireless ad hoc networks
UR - http://www.scopus.com/inward/record.url?scp=85009453098&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-51204-4_14
DO - 10.1007/978-3-319-51204-4_14
M3 - 会议稿件
AN - SCOPUS:85009453098
SN - 9783319512037
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 167
EP - 179
BT - Ad Hoc Networks - 8th International Conference, ADHOCNETS 2016, Revised Selected Papers
A2 - Zhou, Yifeng
A2 - Kunz, Thomas
PB - Springer Verlag
Y2 - 26 September 2016 through 27 September 2016
ER -