17,357 research outputs found
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks
Information fusion is an essential part of numerous engineering systems and
biological functions, e.g., human cognition. Fusion occurs at many levels,
ranging from the low-level combination of signals to the high-level aggregation
of heterogeneous decision-making processes. While the last decade has witnessed
an explosion of research in deep learning, fusion in neural networks has not
observed the same revolution. Specifically, most neural fusion approaches are
ad hoc, are not understood, are distributed versus localized, and/or
explainability is low (if present at all). Herein, we prove that the fuzzy
Choquet integral (ChI), a powerful nonlinear aggregation function, can be
represented as a multi-layer network, referred to hereafter as ChIMP. We also
put forth an improved ChIMP (iChIMP) that leads to a stochastic gradient
descent-based optimization in light of the exponential number of ChI inequality
constraints. An additional benefit of ChIMP/iChIMP is that it enables
eXplainable AI (XAI). Synthetic validation experiments are provided and iChIMP
is applied to the fusion of a set of heterogeneous architecture deep models in
remote sensing. We show an improvement in model accuracy and our previously
established XAI indices shed light on the quality of our data, model, and its
decisions.Comment: IEEE Transactions on Fuzzy System
Energy Sharing for Multiple Sensor Nodes with Finite Buffers
We consider the problem of finding optimal energy sharing policies that
maximize the network performance of a system comprising of multiple sensor
nodes and a single energy harvesting (EH) source. Sensor nodes periodically
sense the random field and generate data, which is stored in the corresponding
data queues. The EH source harnesses energy from ambient energy sources and the
generated energy is stored in an energy buffer. Sensor nodes receive energy for
data transmission from the EH source. The EH source has to efficiently share
the stored energy among the nodes in order to minimize the long-run average
delay in data transmission. We formulate the problem of energy sharing between
the nodes in the framework of average cost infinite-horizon Markov decision
processes (MDPs). We develop efficient energy sharing algorithms, namely
Q-learning algorithm with exploration mechanisms based on the -greedy
method as well as upper confidence bound (UCB). We extend these algorithms by
incorporating state and action space aggregation to tackle state-action space
explosion in the MDP. We also develop a cross entropy based method that
incorporates policy parameterization in order to find near optimal energy
sharing policies. Through simulations, we show that our algorithms yield energy
sharing policies that outperform the heuristic greedy method.Comment: 38 pages, 10 figure
Virtual spring damper method for nonholonomic robotic swarm self-organization and leader following
In this paper, we demonstrate a method for self-organization and leader following of nonholonomic robotic swarm based on spring damper mesh. By self-organization of swarm robots we mean the emergence of order in a swarm as the result of interactions among the single robots. In other words the self-organization of swarm robots mimics some natural behavior of social animals like ants among others. The dynamics of two-wheel robot is derived, and a relation between virtual forces and robot control inputs is defined in order to establish stable swarm formation. Two cases of swarm control are analyzed. In the first case the swarm cohesion is achieved by virtual spring damper mesh connecting nearest neighboring robots without designated leader. In the second case we introduce a swarm leader interacting with nearest and second neighbors allowing the swarm to follow the leader. The paper ends with numeric simulation for performance evaluation of the proposed control method
SpaceSemantics: an architecture for modeling environments
The notion of modeling location is fundamental to location awareness in ubiquitous computing environments. The investigation of models and the integration with the myriad of location sensing technologies makes for a challenging discipline. Despite notable development of location models, we believe that many challenges remain unresolved. Complexity and scalability, diverse environments coupled with various sensors and managing the privacy and security of sensitive information are open issues. In this paper we discuss our previous experience combining location sensing with mobile agents and how the lessons learnt have lead to the conception of SpaceSemantics, an open architecture for modeling environments
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