Bowu Zhang

Sparse target counting and localization in sensor networks based on compressive sensing

In this paper, we propose a novel compressive sensing (CS) based approach for sparse target counting and positioning in wireless sensor networks. While this is not the first work on applying CS to count and localize targets, it is the first to rigorously justify the validity of the problem formulation. Moreover, we propose a novel greedy matching pursuit algorithm (GMP) that complements the well-known signal recovery algorithms in CS theory and prove that GMP can accurately recover a sparse signal with a high probability. We also propose a framework for counting and positioning targets from multiple categories, a novel problem that has never been addressed before. Finally, we perform a comprehensive set of simulations whose results demonstrate the superiority of our approach over the existing CS and non-CS based techniques.

Traffic clustering and online traffic prediction in vehicle networks: A social influence perspective

In this paper we investigate the dynamic traffic relationship characterized by a similarity value from one road point to another in vehicle networks. Due to the regularity of human mobility, traffic exhibits strong correlations in both temporal domain and spatial domain. By exploiting the similarity values, we derive application-specific message update rules for affinity propagation, based on which we propose an instant traffic clustering algorithm to partition the road points into time variant clusters, where the traffics within the same cluster are strongly spatially correlated. Online traffic clustering is also considered by clustering combination via evidence accumulation for further influence study. We also present a neural network based traffic prediction algorithm to predict the traffic conditions cluster by cluster for a future time based on the current and historical traffic data. Simulation study on real traffic data demonstrates that our proposed algorithms are able to identify the true influences among road points and provide accurate traffic predictions.

Robust Compressive Data Gathering in Wireless Sensor Networks

Compressive data gathering, which is based on the recent breakthroughs in compressive sensing theory, has been proposed as a viable approach for sensor network data collection at low communication overhead. Nevertheless, it suffers from a low data recovery accuracy when outlying sensor readings and broken links exist. In this paper, we investigate the impact of outlying sensor readings and broken links on high-fidelity data gathering, and propose approaches based on the compressive sensing theory to identify outlying sensor readings and derive the corresponding accurate values, and to infer broken links. Our design is validated by a comparison based extensive simulation study, and the results indicate that compressive data gathering is superior over traditional in-network data compression techniques for practical sensor network settings.

Three connected dominating set algorithms for wireless sensor networks

Wireless Sensor Networks WSNs are gaining more interest in a variety of applications. Of their different characteristics and challenges, network management and lifetime elongation are the most considered issues in WSN based systems. Connected Dominating Set CDS is known to be an efficient strategy to control network topology, reduce overhead, and extend network lifetime. Designing a CDS algorithm for WSNs is very challenging. This paper provides a review on connected dominating set construction techniques for wireless sensor networks.

A community based vaccination strategy over mobile phone records

Every year, millions of people die from epidemic diseases around the world. The current disease control is mainly conducted by mass vaccination strategies to immunize as many people as possible. However, due to side effects and high costs, it is difficult to be implemented within a large population. In this paper, we address this challenging problem by designing a target vaccination strategy which efficiently prevents diseases from spreading through the population by vaccinating a small fraction of the population. We take advantage of social relationship details extracted from mobile phone records to partition the whole population into communities. A two-level vaccination strategy is then proposed to prevent local and inter-community infections. We evaluate our approach by simulations over real world data and compare its performance with those of other methods. The experimental results show that our approach has the best performance in that it can reduce the number of infections significantly.

Two Connected Dominating Set Algorithms for Wireless Sensor Networks

In this work, we present the design of two novel algorithms for constructing connected dominating set (CDS) in wireless sensor networks (WSNs). Both algorithms are intended to minimize CDS size. The first algorithm has a performance factor of 5 from the optimal solution, which outperforms the best-published results (5.8+ ln 4) in [1]. The second algorithm is an improved version of the first one. We included the theoretical analysis and simulation results showing the effectiveness of both algorithms.

Target Counting in Wireless Sensor Networks

Target counting in wireless sensor networks has attracted a lot of attention in recent years from both academia and industry. In this chapter, we review various problem formulations and technical approaches proposed in recent literature for target counting. Major existing works are classified into the following four categories: binary counting, numeric counting, energy counting, and compressive counting, based on the sensing capabilities of the network and the underlying theoretical foundations of the technical approaches. Within each category, we summarize the representative works according to their objectives, technical methods, performances, and advantages and disadvantages. Comparative evaluations are provided to illustrate the influence of different sensor network settings on the target counting accuracy. The applicable environments of these algorithms are also discussed at the end of the chapter.

Social communications assisted epidemic disease influence minimization

This work explores the use of social communications for epidemic disease control. Since the most infectious diseases spread through human contacts, we focus on modeling the diffusion of diseases by analyzing the social relationship among individuals. In other words, we try to capture the interaction pattern among human beings using the social contact information, and investigate its impact on the spread of diseases. Particularly, we investigate the problem of minimizing the expected number of infected persons by treating a small fraction of the population with vaccines. We prove that this problem is NP-hard, and propose an approximate algorithm representing a preventive disease control strategy based on the social patterns. Simulation results confirm the superiority of our strategy over existing ones.

Mobile phone based social relationship identification for target vaccination in mobile healthcare

In general, vaccination is believed to be the best method to protect humans against diseases. Many works suggest that outbreaks can be controlled by target vaccination strategies without resorting to the traditional mass vaccination. In this paper, we explore the use of mobile phone records for extracting user social relationship information, which is then employed in target vaccination. In particular, we investigate the influence of vaccination through the social network on disease control. To the best of our knowledge, this is the first work to consider the diffusion of vaccination influence. We evaluate our approach over real world data and simulation results indicate that our approach outperforms well employed methods by reducing not only the number of infections but also the cost of vaccination.

Queuing modeling for delay analysis in mission oriented sensor networks under the protocol interference model

The success and increasing deployment of mission-oriented sensor networks has required sensors to collaboratively accomplish many complex real time tasks. In this paper, we focus on many-to-one mission-oriented sensor networks, where data are collected from multiple resources to one data sink. A critical component in realizing real-time services over such a network is the estimation of end-to-end delay. This problem has been widely investigated for wireless sensor networks under various assumptions such as Poisson packet arrivals or infinite queue length. In this work, we consider a more practical network setting in which the packets need to be forwarded to a data sink along multi-hop communications, the packet arrival rate and service rate are both generally distributed, and the queue length is finite. Our analytical expressions of the G/G/1/K queuing model under the popular protocol interference model when CSMA/CA is adopted for MAC control are carefully derived. An extensive simulation study is carried out and the results indicate that the proposed G/G/1/K queueing model outperforms M/M/1/K and G/G/1 under a high network load while it provides competitive results when the network is lightly loaded.