Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns. Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies, and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas.

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A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

Recent years have witnessed the emergence of mobile crowd sensing (MCS) systems, which leverage the public crowd equipped with various mobile devices for large scale sensing tasks. In this paper, we study a critical problem in MCS systems, namely, incentivizing user participation. Different from existing work, we incorporate a crucial metric, called users' quality of information (QoI), into our incentive mechanisms for MCS systems. Due to various factors (e.g., sensor quality, noise, etc.) the quality of the sensory data contributed by individual users varies significantly. Obtaining high quality data with little expense is always the ideal of MCS platforms. Technically, we design incentive mechanisms based on reverse combinatorial auctions. We investigate both the single-minded and multi-minded combinatorial auction models. For the former, we design a truthful, individual rational and computationally efficient mechanism that approximately maximizes the social welfare with a guaranteed approximation ratio. For the latter, we design an iterative descending mechanism that achieves close-to-optimal social welfare while satisfying individual rationality and computational efficiency. Through extensive simulations, we validate our theoretical analysis about the close-to-optimal social welfare and fast running time of our mechanisms.

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Quality of Information Aware Incentive Mechanisms for Mobile Crowd Sensing Systems

The Internet of Things (IoT) contains a large number of different devices and heterogeneous networks, which make it difficult to satisfy different quality of service (QoS) requirements and achieve rapid services composition and deployment In addition, some services in service-oriented IoT are required to be reconfigurable and composable for QoS-aware services This paper proposed a three-layer QoS scheduling model for service-oriented IoT At application layer, the QoS schedule scheme explores optimal QoS-aware services composition by using the knowledge of each component service At network layer, the model aims at dealing with scheduling of heterogeneous networks environment; at sensing layer, it deals with the information acquisition and resource allocation scheduling for different services The proposed QoS-aware scheduling for service-oriented IoT architecture is able to optimize the scheduling performance of IoT network and minimize the resource costs

QoS-Aware Scheduling of Services-Oriented Internet of Things

Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions

Storm has emerged as a promising computation platform for stream data processing. In this paper, we first show inefficiencies of the current practice of Storm scheduling and challenges associated with applying traffic-aware online scheduling in Storm via experimental results and analysis. Motivated by our observations, we design and implement a new stream data processing system based on Storm, namely, T-Storm. Compared to Storm, T-Storm has the following desirable features: 1) based on runtime states, it accelerates data processing by leveraging effective traffic-aware scheduling for assigning/re-assigning tasks dynamically, which minimizes inter-node and inter-process traffic while ensuring no worker nodes are overloaded, 2) it enables fine-grained control over worker node consolidation such that T-Storm can achieve better performance with even fewer worker nodes, 3) it allows hot-swapping of scheduling algorithms and adjustment of scheduling parameters on the fly, and 4) it is transparent to Storm users (i.e., Storm applications can be ported to run on T-Storm without any changes). We conducted real experiments in a cluster using well-known data processing applications for performance evaluation. Extensive experimental results show that compared to Storm (with the default scheduler), T-Storm can achieve over 84% and 27% speedup on lightly and heavily loaded topologies respectively (in terms of average processing time) with 30% less number of worker nodes.

T-Storm: Traffic-Aware Online Scheduling in Storm

Sensors on (or attached to) mobile phones can enable attractive sensing applications in different domains, such as environmental monitoring, social networking, healthcare, transportation, etc. We introduce a new concept, sensing as a service (S2aaS), i.e., providing sensing services using mobile phones via a cloud computing system. An S2aaS cloud needs to meet the following requirements: 1) it must be able to support various mobile phone sensing applications on different smartphone platforms; 2) it must be energy-efficient; and 3) it must have effective incentive mechanisms that can be used to attract mobile users to participate in sensing activities. In this vision paper, we identify unique challenges of designing and implementing an S2aaS cloud, review existing systems and methods, present viable solutions, and point out future research directions.

Sensing as a Service: Challenges, Solutions and Future Directions

Sensors on (or attached to) mobile phones can enable attractive sensing applications in different domains such as environmental monitoring, social networking, healthcare, etc. We introduce a new concept, Sensing-as-a-Service (S2aaS), i.e., providing sensing services using mobile phones via a cloud computing system. An S2aaS cloud should meet the following requirements: 1) It must be able to support various mobile phone sensing applications on different smartphone platforms. 2) It must be energy-efficient. 3) It must have effective incentive mechanisms that can be used to attract mobile users to participate in sensing activities. In this paper, we identify unique challenges of designing and implementing an S2aaS cloud, review existing systems and methods, present viable solutions, and point out future research directions.

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Sensing as a service: A cloud computing system for mobile phone sensing

In this paper, we consider leveraging GPS-less energy-efficient sensing scheduling for mobile crowd sensing. We present a probabilistic model for sensing coverage without accurate location information (provided by GPS), based on which we formally define the Energy-constrained Maximum Coverage Sensing Scheduling (E-MCSS) problem for maximum coverage and the Fair Maximum Coverage Sensing Scheduling (F-MCSS) problem for fairness. Assuming that moving trajectories of mobile users are known beforehand, we present a (1 - 1/e)-approximation algorithm and a 1/2-approximation algorithm to solve the E-MCSS and F-MCSS problems in polynomial time, respectively, which can serve as benchmarks for performance evaluation. Under realistic assumptions, we present a GPS-less energy-efficient protocol for sensing scheduling based on the proposed algorithms. We developed an Android-based mobile crowd sensing system, on which we implemented the proposed protocol. Simulation results and experimental results (from a field test) are presented to validate and justify effectiveness of the proposed algorithms and protocol.

Leveraging GPS-Less Sensing Scheduling for Green Mobile Crowd Sensing

In this paper, we present a novel cross-job framework for MapReduce scheduling, which aims to minimize the total processing time of a sequence of related jobs by combining reduce and map phases of two consecutive jobs and streaming data between them. The proposed framework has the following desirable properties: (1) It can accelerate the execution of a sequence of related MapReduce jobs by achieving a good tradeoff between data locality and parallelism. (2) It can support all the existing MapReduce applications with no changes to their source code. (3) It is a general framework, which can work with different scheduling algorithms. We built a new MapReduce runtime system called cross-job Hadoop by integrating the proposed cross-job framework into Hadoop. We conducted extensive experiments to evaluate its performance using PageRank and an Apache Pig application. Our experimental results show that the cross-job Hadoop can significantly reduce both the total processing time of a job sequence and the size of data transferred over the network.

Xuejie Xiao

Papers

Sensing as a Service: Challenges, Solutions and Future Directions

Sensing as a service: A cloud computing system for mobile phone sensing

Leveraging GPS-Less Sensing Scheduling for Green Mobile Crowd Sensing

A cross-job framework for MapReduce scheduling