Feng Yang

Data Gathering with Compressive Sensing in Wireless Sensor Networks: A Random Walk Based Approach

In this paper, we study the problem of data gathering with compressive sensing (CS) in wireless sensor networks (WSNs). Unlike the conventional approaches, which require uniform sampling in the traditional CS theory, we propose a random walk algorithm for data gathering in WSNs. However, such an approach will conform to path constraints in networks and result in the non-uniform selection of measurements. It is still unknown whether such a non-uniform method can be used for CS to recover sparse signals in WSNs. In this paper, from the perspectives of CS theory and graph theory, we provide mathematical foundations to allow random measurements to be collected in a random walk based manner. We find that the random matrix constructed from our random walk algorithm can satisfy the expansion property of expander graphs. The theoretical analysis shows that a k-sparse signal can be recovered using `1 minimization decoding algorithm when it takes m = O(k log(n=k)) independent random walks with the length of each walk t = O(n=k) in a random geometric network with n nodes. We also carry out simulations to demonstrate the effectiveness of the proposed scheme. Simulation results show that our proposed scheme can significantly reduce communication cost compared to the conventional schemes using dense random projections and sparse random projections, indicating that our scheme can be a more practical alternative for data gathering applications in WSNs.

Cooperative Spectrum Sharing in Cognitive Radio Networks: A Distributed Matching Approach

We study the relay-based communication schemes for cooperative spectrum sharing among multiple primary users (PUs) and multiple secondary users (SUs) with incomplete information. Inspired by the matching theory, we model the network as a matching market. In this market, each PU proposes a certain proposal representing a combination of relay power and spectrum access time to attract the SUs, while each SU maximizes its utility by selecting the most suitable PU. We derive the sufficient and necessary conditions for a stable matching in which none of the PUs or SUs would like to change its decision. We further establish a distributed matching algorithm (DMA) and a DMA with utility increasing (DMA-UI) to achieve the equilibria in partially incomplete and incomplete information scenarios, respectively. Moreover, we provide detailed discussions on the implementation of the distributed algorithms in practical networks. Simulation results show that the losses of PUs total utilities caused by incomplete information are diminished when the number of SUs increases. Specifically, the effects of the incomplete information are reduced as the competition among SUs (PUs) is more intensive than that among PUs (SUs).

Two Dimension Spectrum Allocation for Cognitive Radio Networks

In this paper, we develop a truthful and efficient combinatorial auction scheme under a novel spectrum allocation model that can achieve a worst-case approximation ratio sqrt{m} in social welfare. We propose to tackle the dynamic spectrum access problem in cognitive radio (CR) networks with time-frequency flexibility requirements. We model the spectrum opportunity in a time-frequency division manner and the spectrum allocation as a combinatorial auction. Then we design an auction mechanism to reach the upper bound in polynomial time and propose a combined approach to improve the bound in the cost of increasing computational complexity. A truthful payment that gives incentive to the SUs for revealing the truthful valuation of the desirable bundle of slots is presented. In order to reduce the complexity, we simplify the general model to a modified model that only allows frequency flexibility, and then present a truthful, optimal and computationally efficient auction mechanism. Extensive simulation results of the social welfare and spectrum ratio show that the performance of the combined approximation algorithm is better than the sorting based greedy algorithm.

Joint Estimation of Clock Skew and Offset in Pairwise Broadcast Synchronization Mechanism

The problem of jointly estimating clock skew and offset for wireless sensor networks (WSNs) in a pairwise broadcast synchronization (PBS) protocol is considered. The random part of the delay is supposed to be an exponential random variable. We consider two estimators, i.e., joint maximum-likelihood estimator (JMLE) and generalized ML-like estimator (GMLLE) proposed by Leng and Wu . For both estimators, the corresponding algorithms are explicitly derived and presented. For the GMLLE, the corresponding performance bound based on the reduced set of observations is derived and the optimal value of a user-defined parameter is identified accordingly. At last, analytical results are corroborated by numerical experiments. We observe that: (i) JMLE usually outperforms GMLLE at the cost of larger computational complexity; (ii) JMLE, while achieving the same estimation accuracy as that of the LP method presented in , enjoys significantly lower computational complexity than that of the latter.

A Distributed-Centralized Scheme for Short- and Long-Term Spectrum Sharing with a Random Leader in Cognitive Radio Networks

In Cognitive Radio Networks (CRNs), Primary Users (PUs) can share their idle spectra with Secondary Users (SUs) under certain mechanisms. In this paper, we propose a distributed-centralized and incentive-aware spectrum sharing scheme for the multiple-PU scenario, which introduces a Random Leader who is elected randomly from SUs or PUs. The distributed aspect of our scheme lies in that it requires no central control entities, which can be independently implemented within a distributed spectrum market. The centralized aspect is that the leader draws up and assigns the socially optimal contracts for all PUs and SUs in a centralized manner, which maximizes the throughput of the whole network and attains the economic robustness (including Incentive Compatibility and Individual Rationality). Analysis shows that the proposed scheme takes in the advantages of both centralized and distributed schemes but overcomes their weaknesses. We use the proposed scheme to study two sharing scenarios: the short-term and the long-term spectrum sharing. The Short-Term Sharing (STS) focuses on distributing PUs idle spectra within one time slot while the Long-Term Sharing (LTS) considers multiple slots, where the spectrum mobility must be investigated. As an integrated design of both STS and LTS, our scheme not only fulfils SUs heterogeneous spectrum requirements but also obtains the socially optimal throughput while accounting for both SUs and PUs incentives.

Capacity of Wireless Networks With Social Characteristics

This paper studies the throughput capacity of wireless networks with social characteristics. We propose a simple model to reflect both the social relations between nodes and power-law node degree distribution, and then examine their impact on capacity. We show the fact that two features above lead to traffic locality and improve capacity. Moreover, multicasting may be employed to further enhance performance when information is desired to be published from the source to all its contacts, of which the number follows power-law distribution. In addition, we propose the corresponding capacity-achieving communication schemes, which optimally exploit the underlying structure. Our study is an attempt to understand how social relations may impact on network capacity from a theoretical perspective, and provides fundamental insight on the design and analysis of real wireless networks.

Topology Analysis of Wireless Sensor Networks Based on Nodes' Spatial Distribution

In this paper, we explore methods to generate optimal network topologies for wireless sensor networks (WSNs) with and without obstacles. Specifically, we investigate a dense network with n sensor nodes and m=nb (0<;b<;1) helping nodes, and assess the impact of topology on its throughput capacity. For networks without obstacles, we find that uniformly distributed sensor nodes and regularly distributed helping nodes have some advantages in improving the throughput capacity. We also explore properties of networks composed of some isomorphic sub-networks. For networks with obstacles, we assume there are M= Θ (nv) (0 <; v ≤ 1) arbitrarily or randomly distributed obstacles, which block cells they are located in, i.e., sensor nodes cannot be placed in these cells and nodes communication cannot cross them directly. We find that the overall throughput capacity is bounded by the transmission burden in areas around these blocked cells and introduce a novel algorithm of complexity O(M) to generate optimal sensor nodes topologies for any given obstacles distributions. We further analyze its performance for regularly distributed obstacles, which can be taken to estimate the lower bound of the algorithms performance.

Optimal Capacity–Delay Tradeoff in MANETs With Correlation of Node Mobility

In this paper, we analyze the capacity and delay in mobile ad hoc networks (MANETs), considering the correlation of node mobility (correlated mobility). Previous studies on correlated mobility investigated the maximum capacity with the corresponding delay in several subcases; the problem of optimal capacity under various delay constraints (the optimal capacity–delay tradeoff) still remains open. To this end, we deeply explore the characteristics of correlated mobility and figure out the fundamental relationships between the network performance and the scheduling parameters. Based on that, we establish the overall upper bound of the capacity–delay tradeoff in all the subcases of correlated mobility. Then, we try to obtain the achievable lower bound by identifying the optimal scheduling parameters on certain constraints. Results demonstrate the whole picture of how the correlation of node mobility impacts the capacity, the delay, and the corresponding tradeoff between them.

Subjective QoE based HEVC encoder adaptation scheme for multi-user video streaming

Video streaming over networks has grown rapidly in recent years. Increasing focus has gradually turned from Quality of Service (QoS) awareness to user Quality of Experience (QoE) awareness. In this paper, we propose a subjective QoE model based HEVC encoder adaptation scheme for multi-user video streaming. Firstly, the impact of HEVC encoder on video streams is investigated with different configurations to generate an encoder parameter model. Secondly, considering loss-prone channels, the effect of network impairment factor together with HEVC encoder is taken into account to derive a subjective QoE prediction model. Thirdly, an encoder parameter adaptation scheme is modeled as an optimization problem based on the subjective QoE model and encoder parameter model, aiming at maximizing user satisfaction with constraint bandwidth. To validate the performance of our proposed scheme, a set of simulations are carried out. Compared with fixed encoder parameter method, our proposal achieves maximal end users MOS and supports the weighted encoder adaptation according to the user priority.

On Multicast Capacity and Delay in Cognitive Radio Mobile Ad Hoc Networks

In this paper, we focus on the capacity and delay tradeoff for multicast traffic pattern in cognitive radio mobile ad hoc networks (MANETs). In our system model, the primary network consisting of n primary nodes overlaps with the secondary network consisting of m secondary nodes in a unit square. Assume that all nodes move according to an independent and identically distributed mobility model, and each primary node serves as a source that multicasts its packets to kp primary destination nodes, whereas each secondary source node multicasts its packets to ks secondary destination nodes. Under the cell partitioned network model, we study the capacity and delay for the primary networks under two communication schemes, i.e., noncooperative scheme and cooperative scheme. The communication pattern considered for the secondary network is cooperative scheme. Given that m = n^β (β > 1), we show that per-node capacities O(1/k_p) and O(1/k_s) are achievable for the primary network and the secondary network, with average delays Θ(n log k_p) and Θ(m log k_s), respectively. Moreover, to reduce the average delay in the secondary network, we employ a redundancy scheme and prove that a per-node capacity O(1/k_s √m log k_s) is achievable with average delay Θ(√m log k_s). We find that the fundamental delay-capacity tradeoff in the secondary network is delay/capacity ≥ O(mk_s log k_s) under both cooperative and redundancy schemes.