Xiaohua Tian

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.

Quality-Driven Auction-Based Incentive Mechanism for Mobile Crowd Sensing

The recent paradigm of mobile crowd sensing (MCS) enables a broad range of mobile applications. A critical challenge for the paradigm is to incentivize phone users to be workers providing sensing services. While some theoretical incentive mechanisms for general-purpose crowdsourcing have been proposed, it is still an open issue as to how to incorporate the theoretical framework into the practical MCS system. In this paper, we propose an incentive mechanism based on a quality-driven auction (QDA). The mechanism is specifically for the MCS system, where the worker is paid off based on the quality of sensed data instead of working time, as adopted in the literature. We theoretically prove that the mechanism is truthful, individual rational, platform profitable, and social-welfare optimal. Moreover, we incorporate our incentive mechanism into a Wi-Fi fingerprint-based indoor localization system to incentivize the MCS-based fingerprint collection. We present a probabilistic model to evaluate the reliability of the submitted data, which resolves the issue that the ground truth for the data reliability is unavailable. We realize and deploy an indoor localization system to evaluate our proposed incentive mechanism and present extensive experimental results.

Interference Exploitation in D2D-Enabled Cellular Networks: A Secrecy Perspective

Device-to-device (D2D) communication underlaying cellular networks is a promising technology to improve network resource utilization. In D2D-enabled cellular networks, interference generated by D2D communications is usually viewed as an obstacle to cellular communications. However, in this paper, we present a new perspective on the role of D2D interference by taking security issues into consideration. We consider a large-scale D2D-enabled cellular network with eavesdroppers overhearing cellular communications. Using stochastic geometry, we model such a network and analyze the signal-to-interference-plus-noise ratio (SINR) distributions, connection probabilities and secrecy probabilities of both the cellular and D2D links. We propose two criteria for guaranteeing performances of secure cellular communications, namely the strong and weak performance guarantee criteria. Based on the obtained analytical results of link characteristics, we design optimal D2D link scheduling schemes under these two criteria respectively. Both analytical and numerical results show that the interference from D2D communications can enhance physical layer security of cellular communications and at the same time create extra transmission opportunities for D2D users.

On content-centric wireless delivery networks

The flux of social media and the convenience of mobile connectivity have created a mobile data phenomenon that is expected to overwhelm mobile cellular networks in the foreseeable future. Despite the advent of 4G/LTE, the growth rate of wireless data has far exceeded the capacity increase of mobile networks. A fundamentally new design paradigm is required to tackle the ever growing wireless data challenge. In this article, we investigate the problem of massive content delivery over wireless networks, and present a systematic view of content-centric network design and its underlying challenges. Toward this end, we first review some of the recent advancements in information-centric networking, which provide the basis of how media contents can be labeled, distributed, and placed across the networks. We then formulate the content delivery task into a content rate maximization problem over a shared wireless channel, which, in contrast to the conventional wisdom that attempts to increase the bit rate of a unicast system, maximizes the content delivery capability with a fixed amount of wireless resources. This conceptually simple change enables us to exploit the content diversity and network diversity by leveraging the abundant computation sources (through application-layer encoding, pushing and caching, etc.) within the existing wireless networks. A network architecture that enables wireless network crowdsourcing for content delivery is then described, followed by an exemplary campus wireless network that encompasses the above concepts.

Fundamental limits of RSS fingerprinting based indoor localization

Indoor localization has been an active research field for decades, where the received signal strength (RSS) fingerprinting based methodology is widely adopted and induces many important localization techniques such as the recently proposed one building the fingerprint database with crowd-sourcing. While efforts have been dedicated to improve the accuracy and efficiency of localization, the fundamental limits of RSS fingerprinting based methodology itself is still unknown in a theoretical perspective. In this paper, we present a general probabilistic model to shed light on a fundamental question: how good the RSS fingerprinting based indoor localization can achieve? Concretely, we present the probability that a user can be localized in a region with certain size, given the RSS fingerprints submitted to the system. We reveal the interaction among the localization accuracy, the reliability of location estimation and the number of measurements in the RSS fingerprinting based location determination. Moreover, we present the optimal fingerprints reporting strategy that can achieve the best accuracy for given reliability and the number of measurements, which provides a design guideline for the RSS fingerprinting based indoor localization facilitated by crowdsourcing paradigm.

Capacity and Delay Analysis for Data Gathering with Compressive Sensing in Wireless Sensor Networks

Compressive sensing (CS) provides a new paradigm for efficient data gathering in wireless sensor networks (WSNs). In this paper, with the assumption that sensor data is sparse we apply the theory of CS to data gathering for a WSN where n nodes are randomly deployed. We investigate the fundamental limitation of data gathering with CS for both single-sink and multi-sink random networks under protocol interference model, in terms of capacity and delay. For the single-sink case, we present a simple scheme for data gathering with CS and derive the bounds of the data gathering capacity. We show that the proposed scheme can achieve the capacity Θ(\frac{nW}{M}) and the delay Θ(M\sqrtfrac{nlog n}), where W is the data rate on each link and M is the number of random projections required for reconstructing a snapshot. The results show that the proposed scheme can achieve a capacity gain of Θ (\frac{n}{M}) over the baseline transmission scheme and the delay can also be reduced by a factor of Θ(\fracsqrt{n\log n}{M}). For the multi-sink case, we consider the scenario where n_d sinks are present in the network and each sink collects one random projection from n_s randomly selected source nodes. We construct a simple architecture for multi-session data gathering with CS. We show that the per-session capacity of data gathering with CS is Θ(\frac{n\sqrt{n}W}{M n_d \sqrt{n_s \log n}}) and the per-session delay is Θ(M\sqrtfrac{{n}{log n}}). Finally, we validate our theoretical results for the scaling laws of the capacity in both single-sink and multi-sink networks through simulations.

Multicast Capacity for VANETs with Directional Antenna and Delay Constraint

Vehicular Ad Hoc Networks (VANETs) with base stations are called hybrid VANET, where base stations are deployed to improve the throughput capacity. In this paper, we study the multicast throughput capacity for hybrid wireless VANET with a directional antenna on each vehicle and the end-to-end delay is constrained. In the hybrid VANET, there are n mobile vehicles (or nodes) distributed in a unit area with m strategically deployed base stations connected using high-bandwidth wire links. There are n_s multicast sessions and each multicast session has one source which transmits identical data to its associated p destinations. We investigate the multicast throughput capacity for two mobility models with two mobility scales, respectively, while each vehicular node is equipped with a directional antenna and with a tolerant delay D. That is, a source node transmits to its p destinations only with the help of normal nodes within D consecutive time slots. Otherwise, the transmission will be performed with in the infrastructure mode, i.e., with the help of base stations. We demonstrate that the one dimensional i.i.d. slow mobility pattern catch the main feature of VANETs. And we find that the multicast throughput capacity of the hybrid wireless VANET greatly depends on the delay constraint D, the number of base stations m, and the beamwidth of directional antenna θ. In the order of magnitude, we obtain the closed form of the multicast throughput capacity of the hybrid directional VANET, where the impact of D, m and θ on the multicast throughput capacity is analyzed. Moreover, we derive the lower bound of the muticast throughput using a similar raptor coding approach.

Energy and latency analysis for in-network computation with compressive sensing in wireless sensor networks

In this paper, we study data gathering with compressive sensing from the perspective of in-network computation in random networks, in which n nodes are uniformly and independently deployed in a unit square area. We formulate the problem of data gathering to compute multiround random linear function. We study the performance of in-network computation with compressive sensing in terms of energy consumption and latency in centralized and distributed fashions. For the centralized approach, we propose a tree-based protocol for computing multiround random linear function. The complexity of computation shows that the proposed protocol can save energy and reduce latency by a factor of Θ(√(n/log n)) for data gathering comparing with the traditional approach, respectively. For the distributed approach, we propose a gossip-based approach and study the performance of energy and latency through theoretical analysis. We show that our approach needs fewer transmissions than the scheme using randomized gossip.

Design of a Scalable Multicast Scheme With an Application-Network Cross-Layer Approach

This paper develops an efficient and scalable multicast scheme for high-quality multimedia distribution. The traditional IP multicast, a pure network-layer solution, is bandwidth efficient in data delivery but not scalable in managing the multicast tree. The more recent overlay multicast establishes the data-dissemination structure at the application layer; however, it induces redundant traffic at the network layer. We propose an application-oriented multicast (AOM) protocol, which exploits the application-network cross-layer design. With AOM, each packet carries explicit destinations information, instead of an implicit group address, to facilitate the multicast data delivery; each router leverages the unicast IP routing table to determine necessary multicast copies and next-hop interfaces. In our design, all the multicast membership and addressing information traversing the network is encoded with bloom filters for low storage and bandwidth overhead. We theoretically prove that the AOM service model is loop-free and incurs no redundant traffic. The false positive performance of the bloom filter implementation is also analyzed. Moreover, we show that the AOM protocol is a generic design, applicable for both intra-domain and inter-domain scenarios with either symmetric or asymmetric routing.

Coalitional Double Auction for Spatial Spectrum Allocation in Cognitive Radio Networks

Recently, many dynamic spectrum allocation schemes based on economics are proposed to improve spectrum utilization in cognitive radio networks (CRNs). However, existing mechanisms do not take into account the economic efficiency and the spatial reusability simultaneously, which leaves room to further enhance the spectrum efficiency. In this paper, we introduce the coalition double auction for efficient spectrum allocation in CRNs, where secondary users (SUs) are partitioned into several coalitions and the spectrum reusability can be executed within each coalition. The partition formation process is not only related to the interference condition between SUs, but also the expected economic goals. Therefore, we propose a fully-economic spatial spectrum allocation mechanism by incorporating the coalition formation approach with auction theory. With the proposed scheme, the primary operator acts as an auctioneer, who performs multiple virtual auctions to form a stable partition of SUs and conducts a final auction to decide the winning SUs. Moreover, we propose a possible operation rules for the primary operator to iteratively change the partition, and prove that the virtual auctions could converge in finite time. Comprehensive theoretical analysis and simulation results are presented to show that our scheme can satisfy the crucial economic robustness properties of double auction, and outperform existing mechanisms.