8,016 research outputs found
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Cost-Bounded Active Classification Using Partially Observable Markov Decision Processes
Active classification, i.e., the sequential decision-making process aimed at
data acquisition for classification purposes, arises naturally in many
applications, including medical diagnosis, intrusion detection, and object
tracking. In this work, we study the problem of actively classifying dynamical
systems with a finite set of Markov decision process (MDP) models. We are
interested in finding strategies that actively interact with the dynamical
system, and observe its reactions so that the true model is determined
efficiently with high confidence. To this end, we present a decision-theoretic
framework based on partially observable Markov decision processes (POMDPs). The
proposed framework relies on assigning a classification belief (a probability
distribution) to each candidate MDP model. Given an initial belief, some
misclassification probabilities, a cost bound, and a finite time horizon, we
design POMDP strategies leading to classification decisions. We present two
different approaches to find such strategies. The first approach computes the
optimal strategy "exactly" using value iteration. To overcome the computational
complexity of finding exact solutions, the second approach is based on adaptive
sampling to approximate the optimal probability of reaching a classification
decision. We illustrate the proposed methodology using two examples from
medical diagnosis and intruder detection
Information Acquisition with Sensing Robots: Algorithms and Error Bounds
Utilizing the capabilities of configurable sensing systems requires
addressing difficult information gathering problems. Near-optimal approaches
exist for sensing systems without internal states. However, when it comes to
optimizing the trajectories of mobile sensors the solutions are often greedy
and rarely provide performance guarantees. Notably, under linear Gaussian
assumptions, the problem becomes deterministic and can be solved off-line.
Approaches based on submodularity have been applied by ignoring the sensor
dynamics and greedily selecting informative locations in the environment. This
paper presents a non-greedy algorithm with suboptimality guarantees, which does
not rely on submodularity and takes the sensor dynamics into account. Our
method performs provably better than the widely used greedy one. Coupled with
linearization and model predictive control, it can be used to generate adaptive
policies for mobile sensors with non-linear sensing models. Applications in gas
concentration mapping and target tracking are presented.Comment: 9 pages (two-column); 2 figures; Manuscript submitted to the 2014
IEEE International Conference on Robotics and Automatio
Attentive monitoring of multiple video streams driven by a Bayesian foraging strategy
In this paper we shall consider the problem of deploying attention to subsets
of the video streams for collating the most relevant data and information of
interest related to a given task. We formalize this monitoring problem as a
foraging problem. We propose a probabilistic framework to model observer's
attentive behavior as the behavior of a forager. The forager, moment to moment,
focuses its attention on the most informative stream/camera, detects
interesting objects or activities, or switches to a more profitable stream. The
approach proposed here is suitable to be exploited for multi-stream video
summarization. Meanwhile, it can serve as a preliminary step for more
sophisticated video surveillance, e.g. activity and behavior analysis.
Experimental results achieved on the UCR Videoweb Activities Dataset, a
publicly available dataset, are presented to illustrate the utility of the
proposed technique.Comment: Accepted to IEEE Transactions on Image Processin
Informative Path Planning for Active Field Mapping under Localization Uncertainty
Information gathering algorithms play a key role in unlocking the potential
of robots for efficient data collection in a wide range of applications.
However, most existing strategies neglect the fundamental problem of the robot
pose uncertainty, which is an implicit requirement for creating robust,
high-quality maps. To address this issue, we introduce an informative planning
framework for active mapping that explicitly accounts for the pose uncertainty
in both the mapping and planning tasks. Our strategy exploits a Gaussian
Process (GP) model to capture a target environmental field given the
uncertainty on its inputs. For planning, we formulate a new utility function
that couples the localization and field mapping objectives in GP-based mapping
scenarios in a principled way, without relying on any manually tuned
parameters. Extensive simulations show that our approach outperforms existing
strategies, with reductions in mean pose uncertainty and map error. We also
present a proof of concept in an indoor temperature mapping scenario.Comment: 8 pages, 7 figures, submission (revised) to Robotics & Automation
Letters (and IEEE International Conference on Robotics and Automation
Information inference for cyber-physical systems with application to aviation safety and space situational awareness
Due to the rapid advancement of technologies on sensors and processors, engineering systems have become more complex and highly automated to meet ever stringent performance and safety requirements. These systems are usually composed of physical plants (e.g., aircraft, spacecraft, ground vehicles, etc.) and cyber components (e.g., sensing, communication, and computing units), and thus called as Cyber-Physical Systems (CPSs). For safe, efficient, and sustainable operation of a CPS, the states and physical characteristics of the system need to be effectively estimated or inferred from sensing data by proper information inference algorithms. However, due to the complex nature of the interacting multiple-heterogeneous elements of the CPS, the information inference of the CPS is a challenging task, where exiting methods designed for a single-element dynamic system (or for even dynamic systems with multiple-homogenous elements) could not be applicable. Moreover, the increasing number of sensor resources in CPSs makes the task even more challenging as meaningful information needs to be accurately and effectively inferred from huge amount of data, which is usually noise corrupted. Many aerospace systems such as air traffic control systems, pilot-automation integrated systems, networked unmanned aircraft systems, and space surveillance systems are good examples of CPSs and thus have the aforementioned challenging problems.
The goals of this research are to 1) overcome the challenges in complex CPSs by developing new information inference methodologies based on control, estimation, hybrid systems and information theories, and 2) successfully apply them to various complex and safety-critical aerospace systems such as air transportation systems, space surveillance systems, and integrated human-machine systems, to promote their efficiency and safety
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