Jinwei Liu

A Survey of Mobile Crowdsensing Techniques: A Critical Component for the Internet of Things

Mobile crowdsensing serves as a critical building block for the emerging Internet of Things (IoT) applications. However, the sensing devices continuously generate a large amount of data, which consumes much resources (e.g., bandwidth, energy and storage), and may sacrifice the quality-of-service (QoS) of applications. Prior work has demonstrated that there is significant redundancy in the content of the sensed data. By judiciously reducing the redundant data, the data size and the load can be significantly reduced, thereby reducing resource cost, facilitating the timely delivery of unique, probably critical information and enhancing QoS. This paper presents a survey of existing works for the mobile crowdsensing strategies with emphasis on reducing the resource cost and achieving high QoS. We start by introducing the motivation for this survey, and present the necessary background of crowdsensing and IoT. We then present various mobile crowdsensing strategies and discuss their strengths and limitations. Finally, we discuss the future research directions for mobile crowdsensing. The survey addresses a broad range of techniques, methods, models, systems and applications related to mobile crowdsensing and IoT. Our goal is not only to analyze and compare the strategies proposed in the prior works but also to discuss their applicability towards the IoT, and provide the guidance on the future research direction of mobile crowdsensing.

A Low-Cost Multi-failure Resilient Replication Scheme for High Data Availability in Cloud Storage

Replication is a common approach to enhance data availability in cloud storage systems. Previously proposed replication schemes cannot effectively handle both correlated and non-correlated machine failures while increasing the data availability with the limited resource. The schemes for correlated machine failures must create a constant number of replicas for each data object, which neglects diverse data popularities and cannot utilize the resource to maximize the expected data availability. Also, the previous schemes neglect the consistency maintenance cost and the storage cost caused by replication. It is critical for cloud providers to maximize data availability (hence minimize SLA violations) while minimizing cost caused by replication in order to maximize the revenue. In this paper, we build a nonlinear integer programming model to maximize data availability in both types of failures and minimize the cost caused by replication. Based on the models solution for the replication degree of each data object, we propose a low-cost multi-failure resilient replication scheme (MRR). MRR can effectively handle both correlated and non-correlated machine failures, considers data popularities to enhance data availability, and also tries to minimize consistency maintenance cost and storage cost. Extensive numerical results from trace parameters and experiments from real-world Amazon S3 show that MRR achieves high data availability, low data loss probability and low consistency maintenance cost and storage cost compared to previous replication schemes.

Question Quality Analysis and Prediction in Community Question Answering Services with Coupled Mutual Reinforcement

Community question answering services (CQAS) (e.g., Yahoo! Answers) provides a platform where people post questions and answer questions posed by others. Previous works analyzed the answer quality (AQ) based on answer-related features, but neglect the question-related features on AQ. Previous work analyzed how asker- and question-related features affect the question quality (QQ) regarding the amount of attention from users, the number of answers and the question solving latency, but neglect the correlation between QQ and AQ (measured by the rating of the best answer), which is critical to quality of service (QoS). We handle this problem from two aspects. First, we additionally use QQ in measuring AQ, and analyze the correlation between a comprehensive list of features (including answer-related features) and QQ. Second, we propose the first method that estimates the probability for a given question to obtain high AQ. Our analysis on the Yahoo! Answers trace confirmed that the list of our identified features exert influence on AQ, which determines QQ. For the correlation analysis, the previous classification algorithms cannot consider the mutual interactions between multiple (

Characterizing data deliverability of greedy routing in wireless sensor networks

>

Knowledge Sharing in the Online Social Network of Yahoo! Answers and Its Implications

2) classes of features. We then propose a novel Coupled Semi-Supervised Mutual Reinforcement-based Label Propagation (CSMRLP) algorithm for this purpose. Our extensive experiments show that CSMRLP outperforms the Mutual Reinforcement-based Label Propagation (MRLP) and five other traditional classification algorithms in the accuracy of AQ classification, and the effectiveness of our proposed method in AQ prediction. Finally, we provide suggestions on how to create a question that will receive high AQ, which can be exploited to improve the QoS of CQAS.

Load-aware and congestion-free state management in network function virtualization

As a popular routing protocol in wireless sensor networks (WSNs), greedy routing has received great attention. The previous works characterize its data deliverability in WSNs by the probability of all nodes successfully sending their data to the base station. Their analysis, however, neither provides the information of the quantitative relation between successful data delivery ratio and transmission power of sensor nodes nor considers the impact of the network congestion or link collision on the data deliverability. To address these problems, in this paper, we characterize the data deliverability of greedy routing by the ratio of successful data transmissions from sensors to the base station. We introduce η-guaranteed delivery which means that the ratio of successful data deliveries is not less than η, and study the relationship between the transmission power of sensors and the probability of achieving η-guaranteed delivery. Furthermore, with considering the effect of network congestion and link collision, we provide a more precise and full characterization for the deliverability of greedy routing. Extensive simulation and real-world experimental results show the correctness and tightness of the upper bound of the smallest transmission power for achieving η-guaranteed delivery.

A popularity-aware cost-effective replication scheme for high data durability in cloud storage

Question and Answer (Q&A) websites such as Yahoo! Answers provide a platform where users can post questions and receive answers. These systems take advantage of the collective intelligence of users to find information. In this paper, we analyze the online social network (OSN) in Yahoo! Answers. Based on a large amount of our collected data, we studied the OSNs structural properties, which reveals strikingly distinct properties such as low link symmetry and weak correlation between indegree and outdegree. After studying the knowledge base and behaviors of the users, we find that a small number of top contributors answer most of the questions in the system. Also, each top contributor focuses only on a few knowledge categories. In addition, the knowledge categories of the users are highly clustered. We also study the knowledge base in a users social network, which reveals that the members in a users social network share only a few knowledge categories. Based on the findings, we provide guidance in the design of spammer detection algorithms and distributed Q&A systems. We also propose a friendship-knowledge oriented Q&A framework that synergistically combines current OSN-based Q&A and web Q&A. We believe that the results presented in this paper are crucial in understanding the collective intelligence in the web Q&A OSNs and lay a cornerstone for the evolution of next-generation Q&A systems.

A Survey of Mobile Crowdsensing Techniques: A Critical Component for The Internet of Things

Software-defined networks are constantly evolving due to the updates such as network function (NF) state updates, VM migrations. Network functions virtualization (NFV) with software-defined networking (SDN) has the capability of accurately monitoring and manipulating network traffic, and reducing operating cost. However, it cannot effectively handle the congestion and satisfy service level agreements (SLAs) on NF performance (e.g., throughput) while minimizing the operating cost in the scenarios of requirements for packet processing to be redistributed across a collection of NF instances simultaneously. Although OpenNF, a control plane architecture can allow quick, safe, and fine-grained reallocation of flows across NF instances, it neglects the congestion existing in practical scenarios for scheduling the updates, which can result in SLA violations. Also, it does not consider the load of links to schedule the updates for minimizing the operating cost. To address this problem, we adequately consider the congestion caused by the competition for limited resource (e.g., bandwidth) and utilize the load information to propose a load-aware and congestion-free state management (LCSM) strategy. LCSM can provide congestion-free scheduling of updates and minimize the operating cost. Extensive simulation results show the advantages of our proposed LCSM.

SCPS: A Social-Aware Distributed Cyber-Physical Human-Centric Search Engine

Cloud storage system usually experiences data loss, hindering data durability. Three-way random replication is commonly used to prevent data loss in cloud storage systems. However, it cannot effectively handle correlated machine failures. Although Copyset Replication and Tiered Replication can reduce data loss in correlated and independent failures and enhance data durability, they fail to leverage different data popularities to substantially reduce the storage cost and bandwidth cost caused by replication. To address these issues, we present a popularity-aware multi-failure resilient and cost-effective replication (PM-CR) scheme for high data durability in cloud storage. PMCR splits the cloud storage system into primary tier and backup tier, and classifies data into hot data, warm data and cold data based on data popularities. To handle both correlated and independent failures, PMCR stores the three replicas of the same data into one Copyset formed by two servers in the primary tier and one server in the backup tier. For the third replicas of warm data and cold data in the backup tier, PMCR uses the Similar Compression method for read-intensive data and uses the Delta Compression method for write-intensive data to reduce storage cost and bandwidth cost. As a result, these costs are reduced and data durability and availability are enhanced without compromising data request delay greatly. Extensive experiment results based on trace parameters show that PMCR achieves high data durability, low probability of data loss, and low storage cost and bandwidth cost compared to previous replication schemes.

Dependency-Aware and Resource-Efficient Scheduling for Heterogeneous Jobs in Clouds

Mobile crowdsensing serves as a critical building block for the emerging Internet of Things (IoT) applications. However, the sensing devices continuously generate a large amount of data, which consumes much resources (e.g., bandwidth, energy and storage), and may sacrifice the quality-of-service (QoS) of applications. Prior work has demonstrated that there is significant redundancy in the content of the sensed data. By judiciously reducing the redundant data, the data size and the load can be significantly reduced, thereby reducing resource cost, facilitating the timely delivery of unique, probably critical information and enhancing QoS. This paper presents a survey of existing works for the mobile crowdsensing strategies with emphasis on reducing the resource cost and achieving high QoS. We start by introducing the motivation for this survey, and present the necessary background of crowdsensing and IoT. We then present various mobile crowdsensing strategies and discuss their strengths and limitations. Finally, we discuss the future research directions for mobile crowdsensing. The survey addresses a broad range of techniques, methods, models, systems and applications related to mobile crowdsensing and IoT. Our goal is not only to analyze and compare the strategies proposed in the prior works but also to discuss their applicability towards the IoT, and provide the guidance on the future research direction of mobile crowdsensing.