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.

Photo crowdsourcing with smartphone has attracted considerable attention recently due to the prevalence of smartphones and the rich information provided by photos. In scenarios such as disaster recovery or battlefield, where the cellular network is partly damaged or severely overloaded, Disruption Tolerant Networks (DTNs) become the best way to deliver the crowdsourced photos. Since the bandwidth and storage resources in DTN are very limited and not enough to deliver all the crowdsourced photos, it is important to prioritize more valuable photos to use the limited resources. In this paper, we design a resource-aware photo crowdsourcing framework in DTN, which uses photo metadata including the smartphone's location, orientation, and other built-in camera's parameters, to estimate the value of photos. We propose a photo selection algorithm to maximize the value of photos delivered to the command center considering bandwidth and storage constraints. Both prototype implementation and trace-driven simulations demonstrate the effectiveness of our design.

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, calledSmartPhoto, to quantify the quality (utility) of crowdsourced photos based on the accessible geographical and geometrical information (calledmetadata) 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.

In cognitive radio networks, unlicensed users can use under-utilized licensed spectrum to achieve substantial performance improvement. To avoid interference with licensed users, unlicensed users must vacate the spectrum when it is accessed by licensed (primary) users. Since it takes some time for unlicensed users to switch to other available channels, the ongoing data transmissions may have to be interrupted and the transmission delay can be significantly increased. This makes it hard for cognitive radio networks to meet the delay constraints of many applications. In this paper, we develop caching techniques to address this problem. We formulate the cache placement problem in cognitive radio networks as an optimization problem, where the goal is to minimize the total cost, subject to some delay constraint, i.e., the data access delay can be statistically bounded. To solve this problem, we propose a cost-based approach to minimize the caching cost, and design a delay-based approach to satisfy the delay constraint. Then, we combine them and propose a distributed hybrid approach to minimize the caching cost subject to the delay constraint. Simulation results show that our approaches outperform existing caching solutions in terms of total cost and delay constraint, and the hybrid approach performs the best among the approaches satisfying the delay constraint.

In cognitive radio networks, unlicensed users can use under-utilized licensed spectrum to achieve substantial performance improvement. To avoid interference with licensed users, unlicensed users must vacate the spectrum when it is accessed by licensed (primary) users. Since it takes some time for unlicensed users to switch to other available channels, the ongoing data transmissions may have to be interrupted and the transmission delay can be significantly increased. This makes it hard for cognitive radio networks to meet the delay constraints of many applications. To the best of our knowledge, we are the first to use caching techniques to address this problem. We formulate the cache placement problem in cognitive radio networks as an optimization problem, where the goal is to minimize the total cost, subject to some delay constraint, i.e., the data access delay can be statistically bounded. To solve this problem, we propose three approaches: cost-based, delay-based, and hybrid. Simulation results show that our approaches outperform existing caching solutions in terms of total cost and delay constraint, and the hybrid approach performs the best among the approaches satisfying the delay constraint.

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.

Camera sensors are different from traditional scalar sensors, as cameras at different positions can form very different views of the object. However, traditional coverage model does not consider this intrinsic property of camera sensors. To address this issue, a novel model called full-view coverage is proposed. It uses the angle between the object's facing direction and the camera's viewing direction to measure the quality of coverage. 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. An efficient method is proposed for full-view coverage detection in any given camera sensor networks, and a sufficient condition on the sensor density needed for full-view coverage in a random uniform deployment is derived. In addition, the article shows a necessary and sufficient condition on the sensor density for full-view coverage in a triangular lattice-based deployment. Based on the full-view coverage model, the article further studies the barrier coverage problem. Existing weak and strong barrier coverage models are extended by considering direction issues in camera sensor networks. With these new models, weak/strong barrier coverage verification problems are introduced, and new detection methods are proposed and evaluated.

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.

In directional sensor networks, sensors can steer around to serve multiple target points. Most previous works assume there are always enough deployed sensors so that all target points can be served simultaneously. However, this assumption may not hold when the mission requirement changes or when more target points need to be served. Since it is not always practical to deploy new sensors, we propose to reconfigure the network by letting existing sensors steer and serve the targets periodically. As a result, targets may not be served continuously, and the service delay affects the quality of service. One important problem is how to choose the optimal set of targets to serve by each sensor such that the maximum service delay is minimized. We first show that this problem is NP-complete, and then we propose a centralized protocol whose performance is bounded by a logarithm factor of the optimal solution. We also propose a distributed protocol which achieves the same performance as the centralized protocol. Finally, we extend the optimization model and the protocols by considering the rotation delay, which is critical for some applications but ignored by previous work.

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.

A rate-based switch fabric called smoothed buffer crossbar has been proposed in our recent work. It can provide 100% rate-guaranteed service with only a two-cell buffer at each crosspoint, and can also support best-effort service with an additional bandwidth regulator. However, the widely used max- flow model for bandwidth regulation is neither scalable nor of 100% throughput. In order to evaluate the performance of bandwidth regulator, we first introduce the 100% ideal throughput measurement in this paper. Then we prove that the total arrival average (TAA) algorithm has got this property, which does not happen in the max-flow allocation. Then an O(N) algorithm called proportion scaling allocation (PSA) is presented. Simulations reveal that PSA can deliver nearly 100% throughput even in a frequently changing traffic pattern. As a comparison, both theoretical and experimental evidences are given to show the inefficiency of the max-flow model.