Weijie Wu

Time Dependent Pricing in Wireless Data Networks: Flat-Rate vs. Usage-Based Schemes

With the advances of bandwidth-intensive mobile devices, we see severe congestion problems in wireless data networks. Recently, research emerges to solve this problem from a pricing point of view. Time dependent pricing has been introduced, and initial investigations have shown its advantages over the conventional time independent pricing. Nevertheless, much is unknown in how a practical and effective time dependent pricing scheme can be designed. In this paper, we explore the design space of time dependent pricing. In particular, we focus on a number of schemes, e.g., the usage-based scheme, the flat-rate scheme, and a mixture of them which we called a cap scheme. Our findings include: 1) the ISP obtains a higher profit with usage-based (or flat-rate) scheme if the capacity is insufficient (or sufficient); 2) the usage-based scheme usually achieves a higher consumer surplus and more efficient traffic utilization than the flat-rate scheme; and 3) the cap scheme is strongly preferred by the ISP to further increase its revenue. We believe our findings provide important insights for ISPs to design effective pricing schemes.

On the performance of successive interference cancellation in D2D-enabled cellular networks

Device-to-device (D2D) communication underlaying cellular networks is a promising technology to improve network resource utilization. In D2D-enabled cellular networks, the interference among spectrum-sharing links is more severer than that in traditional cellular networks, which motivates the adoption of interference cancellation techniques such as successive interference cancellation (SIC) at the receivers. However, to date, how SIC can affect the performance of D2D-enabled cellular networks is still unknown. In this paper, we present an analytical framework for studying the performance of SIC in large-scale D2D-enabled cellular networks using the tools from stochastic geometry. To facilitate the interference analysis, we propose the approach of stochastic equivalence of the interference, which converts the two-tier interference (interference from both the cellular tier and D2D tier) to an equivalent single-tier interference. Based on the proposed stochastic equivalence models, we derive the general expressions for the successful transmission probabilities of cellular uplinks and D2D links with infinite and finite SIC capabilities respectively. We demonstrate how SIC affects the performance of large-scale D2D-enabled cellular networks by both analytical and numerical results.

Exploring Bundling Sale Strategy in Online Service Markets with Network Effects

In recent years, we have witnessed a growing trend for online service companies to offer “bundling sales” to increase revenue. Bundling sale means that a company groups a set of its products/services and charges this bundle at a fixed price, which is usually less than the total price of individual items. In this paper, our goal is to understand the underlying dynamics of bundling, in particular, what is the optimal bundling sale strategy and under what situations it will be more attractive than the separate sales. We focus on online service markets that exhibit network effect. We provide mathematical models to capture the interactions between buyers and sellers, analyze the market equilibrium and its stability, and formulate an optimization framework to determine the optimal sale strategy for the service provider. We analyze the impact of the key factors, including the network effects and operating costs, on the profitability of bundling. We show that bundling is more profitable than separate sale in most cases; however, the heterogeneity of services and the asymmetry of operating costs reduce the advantage of bundling. These findings provide important insights in designing proper sale strategies for online services.

The collocation of measurement points in large open indoor environment

With the pervasion of mobile devices, crowdsourcing based received signal strength (RSS) fingerprint collection method has drawn much attention to facilitate the indoor localization since it is effective and requires no pre-deployment. However, in large open indoor environment like museums and exhibition centres, RSS measurement points cannot be collocated densely, which degrades localization accuracy. This paper focuses on measurement point collocation in different cases and their effects on localization accuracy. We first study two simple preliminary cases under assumption that users are uniformly distributed: when measurement points are collocated regularly, we propose a collocation pattern which is most beneficial to localization accuracy; when measurement points are collocated randomly, we prove that localization accuracy is limited by a tight bound. Under the general case that users are distributed asymmetrically, we show the best allocation scheme of measurement points: measurement point density ρ is proportional to (cμ)2/3 in every part of the region, where μ is user density and c is a constant determined by the collocation pattern. We also give some guidelines on collocation choice and perform extensive simulations to validate our assumptions and results.

Markov approximation for Multi-RAT selection

Multiple Radio Access Technologies (Multi-RAT) make it possible to exploit the advantages of Heterogeneous networks (HetNets) resulting from a joint consideration of the networks as a whole. Users in HetNets can be served with a proper RAT to maximize the system-level utility. Especially, when user dynamics are considered, they can stay in a RAT or handover to another RAT with a transition probability depending on system configuration. By formulating these dynamics as a Markov chain model, the system-level utility is defined as a combinatorial object function. However, the combinatorial optimization is NP-hard, thus we can only use exhaustive search to obtain the optimum solution, which comes up with high computational complexity and is not practical. To this end, we use Markov approximation to obtain the approximate utility and transition probability. In addition, we propose a Count Down and Select (CDS) algorithm to implement the RAT selection. Numerical results validate the convergence of Markov approximation and the effectiveness of the CDS algorithm.

Mobility weakens the distinction between multicast and unicast

Comparing with the unicast technology, multiple flows from the same source in multicast scenario can be aggregated even if their destinations are different. This paper evaluates such distinction by the multicast gain on per-node capacity and delay, which are defined as the per-node capacity and delay ratios between multi-unicast and multicast ( m destinations for each multicast session). Particularly, the restricted mobility model is proposed, which is a representative mobility model characterizing a class of mobility models with different average moving speeds. The theoretical analysis of this model indicates that the mobility significantly decreases the multicast gain on per-node capacity and delay, though the per-node capacity of both unicast and multicast can be enhanced by mobility. This finding suggests that mobility weakens the distinction between multicast and unicast. Finally, a general framework of multicast study is constituted by analyzing the upper-bound ( Θ(m)), the lower-bound ( Θ(1)) and the main determinants of the multicast gain on both per-node capacity and delay regardless of mobility model.

A Distributed Algorithm to Construct Multicast Trees in WSNs: An Approximate Steiner Tree Approach

Multicast tree is a key structure for data dissemination from one source to multiple receivers in wireless networks. Minimum length multicast tree can be modeled as the Steiner Tree Problem, and is proven to be NP-hard. In this paper, we explore how to efficiently generate minimum length multicast trees in wireless sensor networks (WSNs), where only limited knowledge of network topology is available at each node. We design and analyze a simple and distributed algorithm, which we call Toward Source Tree (TST), to build multicast trees in WSNs. We show three metrics of TST algorithm, i.e., running time, tree length and energy efficiency. We prove that its running time is O(√nlog n), the best among all existing solutions to our best knowledge. We prove that TST tree length is in the same order as Steiner tree, give a theoretical upper bound and use simulations to show the ratio between them is only 1.114 when nodes are uniformly distributed. We evaluate energy efficiency in terms of the number of forwarding nodes in multicast trees, and prove that it is order-optimal. We give an efficient way to construct multicast tree in support of transmission of voluminous data.

Seeking powerful information initial spreaders in online social networks: a dense group perspective

The rapid growth of online social networks (OSNs) has ultimately facilitated information spreading and changed the economics of mobile networks. It is important to understand how to spread information as widely as possible. In this paper, we aim to seek powerful information initial spreaders with an efficient manner. We use the mean-field theory to characterize the process of information spreading based on the Susceptible Infected (SI) model and validate that the prevalence of information depends on the network density. Inspired by this result, we seek the initial spreaders from closely integrated groups of nodes, i.e., dense groups (DGs). In OSNs, DGs distribute dispersedly over the network, so our approach can be fulfilled in a distributed way by seeking the spreaders in each DG. We first design a DG Generating Algorithm to detect DGs, where nodes within the DG have more internal connections than external ones. Second, based on the detected DGs, we design a criterion to seek powerful initial spreaders from each DG. We conduct experiments as well as statistical analysis on real OSNs. The results show that our approach provides a satisfactory performance as well as computational efficiency.

Near-Optimal Scheme for Cognitive Radio Networks With Heterogeneous Mobile Secondary Users

In this paper, we study the throughput and delay scaling laws for cognitive radio network with static primary users and heterogeneous mobile secondary users coexist in the unit planar region for both without base-station case and with base-station case. The primary network consists of n randomly and uniformly distributed static primary users with higher priority to access the spectrum. The secondary network consists of m = (h + 1)n1+ϵ heterogeneous mobile secondary users with h = O(log n)1 and ϵ > 0, which should access the spectrum opportunistically. Each secondary user moves within a circular region, centered at its home-point with a restricted speed. The moving region of each mobile SU is n-α, where α is a random variable following the discrete uniform distribution with h + 1 different values, ranging from 0 to α0(α0 > 0). We propose a scheme which consists of routing and scheduling schemes and show that the primary network and secondary network can achieve near-optimal throughput and delay scalings if we increase the heterogeneity of secondary users. In this near-optimal condition, both the primary network and part of the secondary network can achieve constant throughput and delay scalings except for poly-logarithmic factor. Furthermore, we study the case that there are nβ uniformly distributed static base-stations (BSs) where β > 0 and propose the corresponding scheme for PUs, SUs and BSs. Finally, the theoretical results indicate that if β is large enough, BSs can improve the throughput and delay performance by relaying packets through cable backhaul.

Cooperation Improves Delay in Cognitive Networks With Hybrid Random Walk

In this paper, we study the capacity and delay scaling laws of cognitive radio networks (CRN) with static primary nodes (PNs) and mobile secondary nodes (SNs). The primary network consists of randomly distributed primary nodes of density n, which have a higher priority to access the spectrum. The secondary network consists of randomly distributed secondary nodes of density m = nβ, where β represents the density relationship in CRN. Secondary nodes move according to hybrid random walk models with parameter α (0 ≤ α <; 1/2), and the step size of the mobility model is Θ(m-2α). Motivated by observation that the performance of CRN can benefit from the cooperation among primary nodes and secondary nodes, we propose a novel cooperative scheduling mechanism to fully utilize the mobility and geographic information of secondary nodes to enhance the performance of the primary network. For both networks, the delay performance varies with α. We show that the delay performance of primary network can be significantly improved from O(n/log n) [16] to Θ(nβ/3 log n) when β <; 3 for an optimal value of α, while a near-optimal throughput of Θ(1/log n) is obtained. Furthermore, the secondary network can still achieve the same throughput and delay scaling laws as a stand-alone network simultaneously.