20,050 research outputs found
Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges
Participatory sensing is a powerful paradigm which takes advantage of
smartphones to collect and analyze data beyond the scale of what was previously
possible. Given that participatory sensing systems rely completely on the
users' willingness to submit up-to-date and accurate information, it is
paramount to effectively incentivize users' active and reliable participation.
In this paper, we survey existing literature on incentive mechanisms for
participatory sensing systems. In particular, we present a taxonomy of existing
incentive mechanisms for participatory sensing systems, which are subsequently
discussed in depth by comparing and contrasting different approaches. Finally,
we discuss an agenda of open research challenges in incentivizing users in
participatory sensing.Comment: Updated version, 4/25/201
Control Aware Radio Resource Allocation in Low Latency Wireless Control Systems
We consider the problem of allocating radio resources over wireless
communication links to control a series of independent wireless control
systems. Low-latency transmissions are necessary in enabling time-sensitive
control systems to operate over wireless links with high reliability. Achieving
fast data rates over wireless links thus comes at the cost of reliability in
the form of high packet error rates compared to wired links due to channel
noise and interference. However, the effect of the communication link errors on
the control system performance depends dynamically on the control system state.
We propose a novel control-communication co-design approach to the low-latency
resource allocation problem. We incorporate control and channel state
information to make scheduling decisions over time on frequency, bandwidth and
data rates across the next-generation Wi-Fi based wireless communication links
that close the control loops. Control systems that are closer to instability or
further from a desired range in a given control cycle are given higher packet
delivery rate targets to meet. Rather than a simple priority ranking, we derive
precise packet error rate targets for each system needed to satisfy stability
targets and make scheduling decisions to meet such targets while reducing total
transmission time. The resulting Control-Aware Low Latency Scheduling (CALLS)
method is tested in numerous simulation experiments that demonstrate its
effectiveness in meeting control-based goals under tight latency constraints
relative to control-agnostic scheduling
Resource Management in Heterogeneous Wireless Sensor Networks
We propose a first approach in the direction of a general framework for resource management in wireless sensor networks (WSN). The basic components of the approach are a model for WSNs and a task model. Based on these models, a first version of an algorithm for assigning tasks to a WSN is presented. The models and the algorithm are designed in such a way that an extension to more complex models is possible. Furthermore, the developed approach to solve the RM problem allows an easy adaptation, to fit more complex models. In this way, a flexible approach is achieved, which may form the base for many RM approaches.\ud
The possibilities and limitations of the presented approach are tested on randomly generated instances. The aim of these tests is to show that the chosen models and algorithm form a proper starting point to design RM tools
Jointly Optimizing Placement and Inference for Beacon-based Localization
The ability of robots to estimate their location is crucial for a wide
variety of autonomous operations. In settings where GPS is unavailable,
measurements of transmissions from fixed beacons provide an effective means of
estimating a robot's location as it navigates. The accuracy of such a
beacon-based localization system depends both on how beacons are distributed in
the environment, and how the robot's location is inferred based on noisy and
potentially ambiguous measurements. We propose an approach for making these
design decisions automatically and without expert supervision, by explicitly
searching for the placement and inference strategies that, together, are
optimal for a given environment. Since this search is computationally
expensive, our approach encodes beacon placement as a differential neural layer
that interfaces with a neural network for inference. This formulation allows us
to employ standard techniques for training neural networks to carry out the
joint optimization. We evaluate this approach on a variety of environments and
settings, and find that it is able to discover designs that enable high
localization accuracy.Comment: Appeared at 2017 International Conference on Intelligent Robots and
Systems (IROS
Exploiting the user interaction context for automatic task detection
Detecting the task a user is performing on her computer desktop is important for providing her with contextualized and personalized support. Some recent approaches propose to perform automatic user task detection by means of classifiers using captured user context data. In this paper we improve on that by using an ontology-based user interaction context model that can be automatically populated by (i) capturing simple user interaction events on the computer desktop and (ii) applying rule-based and information extraction mechanisms. We present evaluation results from a large user study we have carried out in a knowledge-intensive business environment, showing that our ontology-based approach provides new contextual features yielding good task detection performance. We also argue that good results can be achieved by training task classifiers `online' on user context data gathered in laboratory settings. Finally, we isolate a combination of contextual features that present a significantly better discriminative power than classical ones
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