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 v k , the number of its friends/followers, denoted by q k , follows a Zipf's distribution with degree clustering exponent γ; 2) q k anchor points are independently chosen according to a probability distribution with density function proportional to ( E k,X ) −β , where E k,X is the expected number of nodes (population) within the distance |v k − X| to v k , and β is the clustering exponent of friendship formation; 3) finally, q k nodes respectively nearest to those q k anchor points are selected as the friends of v k . 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) ∈ [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 ∈ [0, ∞), 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) ∈ [0, ∞) 3 based on our results over (γ, β) ∈ [0, ∞) 2 .
Urban traffic condition usually serves as basic information for some intelligent urban applications, for example, intelligent transportation system. The traditional acquisition of such information is often costly because of the dependencies on infrastructures, such as cameras and loop detectors. Crowdsensing, as a new economic paradigm, can be utilized together with vehicular networks to efficiently gather vehicle-sensed data for estimating the traffic condition. However, it has the problem of being lack of data uploading efficiency and data usage effectiveness. In this paper, we take into account the topology of the road net to deal with these problems. Specifically, we divide the road net into road sections and junction areas. Based on this division, we introduce a two-phased data collection and processing scheme named road topology-based scheme. It leverages the correlations among adjacent roads. In a junction area, data collected by vehicles are first processed and integrated by a sponsor vehicle to locally calculate traffic condition. Both the selection of the sponsor and the calculation of road condition utilize the road correlation. The sponsor then uploads the local data to a server. By employing the inherent relations among roads, the server processes data and estimates traffic condition for the road sections without vehicular data in a global vision. We conduct experiments based on real vehicle trace data. The results indicate that our design can commendably handle the problems of efficiency and effectiveness in traffic condition evaluation using the vehicular crowdsensing data
In this paper, we study capacity scaling laws of the deterministic dissemination (DD) in random wireless networks under the generalized physical model (GphyM). This is truly not a new topic. Our motivation to readdress this issue is two-fold: Firstly, we aim to propose a more general result to unify the network capacity for general homogeneous random models by investigating the impacts of different parameters of the system on the network capacity. Secondly, we target to close the open gaps between the upper and the lower bounds on the network capacity in the literature. The generality of this work lies in three aspects: (1) We study the homogeneous random network of a general node density λ ∈ [1, n], rather than either random dense network (RDN, λ = n) or random extended network (REN, λ = 1) as in the literature. (2) We address the general deterministic dissemination sessions, i.e., the general multicast sessions, which unify the capacities for unicast and broadcast sessions by setting the number of destinations for each session as a general value n d ∈ [1, n]. (3) We allow the number of sessions to change in the range ns ∈ (1, n], instead of assuming that ns = Θ(n) as in the literature. We derive the general upper bounds on the capacity for the arbitrary case of (λ, n d , ns) by introducing the Poisson Boolean model of continuum percolation, and prove that they are tight according to the existing general lower bounds constructed in the literature.
Urban traffic condition usually serves as a basic in formation for some intelligent urban applications, e.g., intelligent transportation system. But the acquisition of such information is often costly due to the dependency on equipments such as cameras and loop detectors. Crowdsensing can be utilized to gather vehicle-sensed data for traffic condition estimation. This way of data collection is economic. However, it has the problems of data uploading efficiency and data usage effectiveness. To deal with these problems, in this paper, we take into account the topology of the road net. We divide the road net into Road Sections and Junction Areas. Based on this division, we introduce a two-phased data collection and processing scheme named RTS (Road Topology based �cheme). It leverages the correlations among adjacent roads. In a junction area, data collected by vehicles is first processed and integrated by a sponsor vehicle. This sponsor vehicle will calculate the traffic condition locally. Both the selection of the sponsor and the calculation of the traffic condition utilize the road correlation. The sponsor then uploads the local data to a server. By employing the inherent relations among roads, the server processes data and estimates traffic condition for road sections unreached by vehicular data in a global vision. We conduct extensive experiments based on real vehicle trace data. The results indicate that, our design can commendably handle the problems of efficiency and effective ness in the vehicular-crowdsensing-data based traffic condition evaluation.
The low level of knowledge management about research and development is greatly restricting China’s auto companies research and development ability. To solve this problem, the paper analyzes the actual demand of car research activities and constructs the enterprise research and development knowledge system in which knowledge map tool is the core, aiming at improving enterprise knowledge management level and car research and development ability.
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