With the surging of smartphone sensing, wireless networking, and mobile social networking techniques, Mobile Crowd Sensing and Computing (MCSC) has become a promising paradigm for cross-space and large-scale sensing. MCSC extends the vision of participatory sensing by leveraging both participatory sensory data from mobile devices (offline) and user-contributed data from mobile social networking services (online). Further, it explores the complementary roles and presents the fusion/collaboration of machine and human intelligence in the crowd sensing and computing processes. This article characterizes the unique features and novel application areas of MCSC and proposes a reference framework for building human-in-the-loop MCSC systems. We further clarify the complementary nature of human and machine intelligence and envision the potential of deep-fused human--machine systems. We conclude by discussing the limitations, open issues, and research opportunities of MCSC.

Mobile Crowd Sensing and Computing: The Review of an Emerging Human-Powered Sensing Paradigm

The wireless sensor network (WSN) technology spawns a surge of unforeseen applications. The diversity of these emerging applications represents the great success of this technology. A fundamental performance benchmark of such applications is topology control, which characterizes how well a sensing field is monitored and how well each pair of sensors is mutually connected in WSNs. This paper provides an overview of topology control techniques. We classify existing topology control techniques into two categories: network coverage and network connectivity. For each category, a surge of existing protocols and techniques are presented with the focus on blanket coverage, barrier coverage, sweep coverage, power management, and power control, five rising aspects that attract significant research attention in recent years. In this survey, we emphasize the basic principles of topology control to understand the state of the arts, while we explore future research directions in the new open areas and propose a series of design guidelines under this topic.

A Survey on Topology Control in Wireless Sensor Networks: Taxonomy, Comparative Study, and Open Issues

Worker selection is a key issue in mobile crowd sensing (MCS). While the previous worker selection approaches mainly focus on selecting a proper subset of workers for a single MCS task, a multitask-oriented worker selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes ActiveCrowd, a worker selection framework for multitask MCS environments. We study the problem of multitask worker selection under two situations: worker selection based on workers’ intentional movement for time-sensitive tasks and unintentional movement for delay-tolerant tasks. For time-sensitive tasks, workers are required to move to the task venue intentionally and the goal is to minimize the total distance moved. For delay-tolerant tasks, we select workers whose route is predicted to pass by the task venues and the goal is to minimize the total number of workers. Two greedy-enhanced genetic algorithms are proposed to solve them. Experiments verify that the proposed algorithms outperform baseline methods under different experiment settings (scale of task sets, available workers, varied task distributions, etc.).

ActiveCrowd: A Framework for Optimized Multitask Allocation in Mobile Crowdsensing Systems

In last two decades, various monitoring systems have been designed and deployed in urban environments, toward the realization of the so called smart cities. Such systems are based on both dedicated sensor nodes, and ubiquitous but not dedicated devices such as smart phones and vehicles’ sensors. When we design sensor network monitoring systems for smart cities, we have two essential problems: node deployment and sensing management. These design problems are challenging, due to large urban areas to monitor, constrained locations for deployments, and heterogeneous type of sensing devices. There is a vast body of literature from different disciplines that have addressed these challenges. However, we do not have yet a comprehensive understanding and sound design guidelines. This paper addresses such a research gap and provides an overview of the theoretical problems we face, and what possible approaches we may use to solve these problems. Specifically, this paper focuses on the problems on both the deployment of the devices (which is the system design/configuration part) and the sensing management of the devices (which is the system running part). We also discuss how to choose the existing algorithms in different type of monitoring applications in smart cities, such as structural health monitoring, water pipeline networks, traffic monitoring. We finally discuss future research opportunities and open challenges for smart city monitoring.

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The Sensable City: A Survey on the Deployment and Management for Smart City Monitoring

The barrier coverage problem in emerging mobile sensor networks has been an interesting research issue due to many related real-life applications. Existing solutions are mainly concerned with deciding one-time movement for individual sensors to construct as many barriers as possible, which may not be suitable when there are no sufficient sensors to form a single barrier. In this paper, we aim to achieve barrier coverage in the sensor scarcity scenario by dynamic sensor patrolling. Specifically, we design a periodic monitoring scheduling (PMS) algorithm in which each point along the barrier line is monitored periodically by mobile sensors. Based on the insight from PMS, we then propose a coordinated sensor patrolling (CSP) algorithm to further improve the barrier coverage, where each sensor's current movement strategy is derived from the information of intruder arrivals in the past. By jointly exploiting sensor mobility and intruder arrival information, CSP is able to significantly enhance barrier coverage. We prove that the total distance that sensors move during each time slot in CSP is the minimum. Considering the decentralized nature of mobile sensor networks, we further introduce two distributed versions of CSP: S-DCSP and G-DCSP. We study the scenario where sensors are moving on two barriers and propose two heuristic algorithms to guide the movement of sensors. Finally, we generalize our results to work for different intruder arrival models. Through extensive simulations, we demonstrate that the proposed algorithms have desired barrier coverage performances.

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Mobility and Intruder Prior Information Improving the Barrier Coverage of Sparse Sensor Networks

Barrier coverage has attracted much attention in the past few years However, most of the previous works focused on traditional scalar sensors We propose to study barrier coverage in camera sensor networks One fundamental difference between camera and scalar sensor is that cameras from different positions can form quite different views of the object As a result, simply combining the sensing range of the cameras across the field does not necessarily form an effective camera barrier since the face image (or the interested aspect) of the object may be missed To address this problem, we use the angle between the object's facing direction and the camera's viewing direction to measure the quality of sensing An object is full-view covered if there is always a camera to cover it no matter which direction it faces and the camera's viewing direction is sufficiently close to the object's facing direction We study the problem of constructing a camera barrier, which is essentially a connected zone across the monitored field such that every point within this zone is full-view covered We propose a novel method to select camera sensors from an arbitrary deployment to form a camera barrier, and present redundancy reduction techniques to effectively reduce the number of cameras used We also present techniques to deploy cameras for barrier coverage in a deterministic environment, and analyze and optimize the number of cameras required for this specific deployment under various parameters

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Barrier coverage in camera sensor networks

Camera sensors are different from traditional scalar sensors as different cameras from different positions can form distinct views of the object. However, traditional disk sensing model does not consider this intrinsic property of camera sensors. To this end, we propose a novel model called full-view coverage. An object is considered to be full-view covered if for any direction from 0 to 2π (object's facing direction), there is always a sensor such that the object is within the sensor's range and more importantly the sensor's viewing direction is sufficiently close to the object's facing direction. With this model, we propose an efficient method for full-view coverage detection in any given camera sensor networks. We also derive a sufficient condition on the sensor density needed for full-view coverage in a random uniform deployment. Finally, we show a necessary and sufficient condition on the sensor density for full-view coverage in a triangular lattice based deployment.

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On full-view coverage in camera sensor networks

Photos obtained via crowdsourcing can be used in many critical applications. Due to the limitations of communication bandwidth, storage and processing capability, it is a challenge to transfer the huge amount of crowdsourced photos. To address this problem, we propose a framework, called SmartPhoto, to quantify the quality (utility) of crowdsourced photos based on the accessible geographical and geometrical information (called metadata) including the smartphone's orientation, position and all related parameters of the built-in camera. From the metadata, we can infer where and how the photo is taken, and then only transmit the most useful photos. Three optimization problems regarding the tradeoffs between photo utility and resource constraints, namely the Max-Utility problem, the online Max-Utility problem and the Min-Selection problem, are studied. Efficient algorithms are proposed and their performance bounds are theoretically proved. We have implemented SmartPhoto in a testbed using Android based smartphones, and proposed techniques to improve the accuracy of the collected metadata by reducing sensor reading errors and solving object occlusion issues. Results based on real implementations and extensive simulations demonstrate the effectiveness of the proposed algorithms.

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SmartPhoto: a resource-aware crowdsourcing approach for image sensing with smartphones

Photos obtained via crowdsourcing can be used in many critical applications. Due to the limitations of communication bandwidth, storage, and processing capability, it is a challenge to transfer the huge amount of crowdsourced photos. To address this problem, we propose a framework, called SmartPhoto , to quantify the quality (utility) of crowdsourced photos based on the accessible geographical and geometrical information (called metadata ) including the smartphone's orientation, position, and all related parameters of the built-in camera. From the metadata, we can infer where and how the photo is taken, and then only transmit the most useful photos. Four optimization problems regarding the tradeoffs between photo utility and resource constraints, namely Max-Utility, online Max-Utility, Min-Selection, and Min-Selection with $k$ -coverage, are studied. Efficient algorithms are proposed and their performance bounds are theoretically proved. We have implemented SmartPhoto in a testbed using Android based smartphones, and proposed techniques to improve the accuracy of the collected metadata by reducing sensor reading errors and solving object occlusion issues. Results based on real implementations and extensive simulations demonstrate the effectiveness of the proposed algorithms.

/pdf/smartphoto-a-resource-aware-crowdsourcing-approach-for-image-4umff1zg6b.pdf

SmartPhoto: A Resource-Aware Crowdsourcing Approach for Image Sensing with Smartphones

Photos crowdsourced from mobile devices can be used in many applications such as disaster recovery to obtain information about a target area. However, such applications often have resource constraints in terms of bandwidth, storage, and processing capability, which limit the number of photos that can be crowdsourced. Thus, it is a challenge to use the limited resources to crowdsource photos that best cover the target area. In this paper, we leverage various geographical and geometrical information about photos, called metadata, to address this challenge. Metadata includes the location, orientation, field of view, and range of a camera. Based on metadata, we define photo utility to measure how well a target area is covered by a set of photos. We propose various techniques to analyze such coverage and calculate photo utility accurately and efficiently. We also study the problem of selecting photos with the largest utility under a resource budget, and propose an efficient algorithm that achieves constant approximation ratio. With our design, the crowdsourcing server can select photos based on metadata instead of real images, and thus use the limited resources to crowdsource the most useful photos. Both simulation and experimental results demonstrate the effectiveness of our design.

Yi Wang

Papers

Barrier coverage in camera sensor networks

On full-view coverage in camera sensor networks

SmartPhoto: a resource-aware crowdsourcing approach for image sensing with smartphones

SmartPhoto: A Resource-Aware Crowdsourcing Approach for Image Sensing with Smartphones

Photo crowdsourcing for area coverage in resource constrained environments