DocumentCode
71070
Title
Capacity Scaling of Wireless Social Networks
Author
Cheng Wang ; Lu Shao ; Zhong Li ; Lei Yang ; Xiang-Yang Li ; Changjun Jiang
Author_Institution
Dept. of Comput. Sci. & Technol., Tongji Univ., Shanghai, China
Volume
26
Issue
7
fYear
2015
fDate
July 1 2015
Firstpage
1839
Lastpage
1850
Abstract
In this paper, we investigate capacity scaling laws of wireless social networks under the social-based session formation. We model a wireless social network as a three-layered structure, consisting of the physical layer, social layer, and session layer; and we introduce a cross-layer distance & density-aware model, called the population-based formation model, under which: 1) for each node vk, the number of its friends/followers, denoted by qk, follows a Zipf´s distribution with degree clustering exponent g; 2) qk anchor points are independently chosen according to a probability distribution with density function proportional to (Ek,X)-β, where Ek;X is the expected number of nodes (population) within the distance |vk-X| to vk, and β is the clustering exponent of friendship formation; 3) finally, qk nodes respectively nearest to those qk anchor points are selected as the friends of vk. We present the general density function of social relationship distribution, with general distribution of physical layer, serving as the basis for studying general capacity of wireless social networks. As the first step of addressing this issue, for the homogeneous physical layer, we derive the social-broadcast capacity under both generalized physical and protocol interference models, taking into account general clustering exponents of both friendship degree and friendship formation in a 2-dimensional parameter space, i.e., (γ,β) ϵ[0,∞)2. Importantly, we notice that the adopted model with homogenous physical layer does not sufficiently reflect the advantages of the population-based formation model in terms of realistic validity and practicability. Accordingly, we introduce a random network model, called the center-clustering random model (CCRM) with node distribution exponent δ ϵ [0, &#- 221E;), highlighting the clustering and inhomogeneity property in real-life networks, and discuss how to further derive more general network capacity over 3-dimensional parameter space (δ,γ,β) ϵ [0, ∞)3 based on our results over (γ,β) ϵ [0, ∞)2.
Keywords
protocols; radiofrequency interference; social networking (online); statistical distributions; 2D parameter space; 3D parameter space; CCRM; Zipf distribution; capacity scaling laws; center-clustering random model; cross-layer distance; degree clustering exponent; density function; density-aware model; friendship degree; friendship formation; homogeneous physical layer; inhomogeneity property; network capacity; network model; node distribution exponent; population-based formation model; probability distribution; protocol interference models; session layer; social layer; social relationship distribution; social-based session formation; social-broadcast capacity; three-layered structure; wireless social networks; Analytical models; Density functional theory; Nonhomogeneous media; Physical layer; Social network services; Wireless communication; Wireless sensor networks; Scaling laws; network capacity; social networks; wireless networks;
fLanguage
English
Journal_Title
Parallel and Distributed Systems, IEEE Transactions on
Publisher
ieee
ISSN
1045-9219
Type
jour
DOI
10.1109/TPDS.2014.2333524
Filename
6844853
Link To Document