3,441 research outputs found
Self-Synchronization in Duty-cycled Internet of Things (IoT) Applications
In recent years, the networks of low-power devices have gained popularity.
Typically these devices are wireless and interact to form large networks such
as the Machine to Machine (M2M) networks, Internet of Things (IoT), Wearable
Computing, and Wireless Sensor Networks. The collaboration among these devices
is a key to achieving the full potential of these networks. A major problem in
this field is to guarantee robust communication between elements while keeping
the whole network energy efficient. In this paper, we introduce an extended and
improved emergent broadcast slot (EBS) scheme, which facilitates collaboration
for robust communication and is energy efficient. In the EBS, nodes
communication unit remains in sleeping mode and are awake just to communicate.
The EBS scheme is fully decentralized, that is, nodes coordinate their wake-up
window in partially overlapped manner within each duty-cycle to avoid message
collisions. We show the theoretical convergence behavior of the scheme, which
is confirmed through real test-bed experimentation.Comment: 12 Pages, 11 Figures, Journa
A Chemistry-Inspired Framework for Achieving Consensus in Wireless Sensor Networks
The aim of this paper is to show how simple interaction mechanisms, inspired
by chemical systems, can provide the basic tools to design and analyze a
mathematical model for achieving consensus in wireless sensor networks,
characterized by balanced directed graphs. The convergence and stability of the
model are first proven by using new mathematical tools, which are borrowed
directly from chemical theory, and then validated by means of simulation
results, for different network topologies and number of sensors. The underlying
chemical theory is also used to derive simple interaction rules that may
account for practical issues, such as the estimation of the number of neighbors
and the robustness against perturbations. Finally, the proposed chemical
solution is validated under real-world conditions by means of a four-node
hardware implementation where the exchange of information among nodes takes
place in a distributed manner (with no need for any admission control and
synchronism procedure), simply relying on the transmission of a pulse whose
rate is proportional to the state of each sensor.Comment: 12 pages, 10 figures, submitted to IEEE Sensors Journa
Robotic ubiquitous cognitive ecology for smart homes
Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work
Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout
Heart-rate estimation is a fundamental feature of modern wearable devices. In
this paper we propose a machine intelligent approach for heart-rate estimation
from electrocardiogram (ECG) data collected using wearable devices. The novelty
of our approach lies in (1) encoding spatio-temporal properties of ECG signals
directly into spike train and using this to excite recurrently connected
spiking neurons in a Liquid State Machine computation model; (2) a novel
learning algorithm; and (3) an intelligently designed unsupervised readout
based on Fuzzy c-Means clustering of spike responses from a subset of neurons
(Liquid states), selected using particle swarm optimization. Our approach
differs from existing works by learning directly from ECG signals (allowing
personalization), without requiring costly data annotations. Additionally, our
approach can be easily implemented on state-of-the-art spiking-based
neuromorphic systems, offering high accuracy, yet significantly low energy
footprint, leading to an extended battery life of wearable devices. We
validated our approach with CARLsim, a GPU accelerated spiking neural network
simulator modeling Izhikevich spiking neurons with Spike Timing Dependent
Plasticity (STDP) and homeostatic scaling. A range of subjects are considered
from in-house clinical trials and public ECG databases. Results show high
accuracy and low energy footprint in heart-rate estimation across subjects with
and without cardiac irregularities, signifying the strong potential of this
approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at
Elsevier Neural Network
Fault-tolerant wireless sensor networks using evolutionary games
This dissertation proposes an approach to creating robust communication systems in wireless sensor networks, inspired by biological and ecological systems, particularly by evolutionary game theory. In this approach, a virtual community of agents live inside the network nodes and carry out network functions. The agents use different strategies to execute their functions, and these strategies are tested and selected by playing evolutionary games. Over time, agents with the best strategies survive, while others die. The strategies and the game rules provide the network with an adaptive behavior that allows it to react to changes in environmental conditions by adapting and improving network behavior. To evaluate the viability of this approach, this dissertation also describes a micro-component framework for implementing agent-based wireless sensor network services, an evolutionary data collection protocol built using this framework, ECP, and experiments evaluating the performance of this protocol in a faulty environment. The framework addresses many of the programming challenges in writing network software for wireless sensor networks, while the protocol built using the framework provides a means of evaluating the general viability of the agent-based approach. The results of this evaluation show that an evolutionary approach to designing wireless sensor networks can improve the performance of wireless sensor network protocols in the presence of node failures. In particular, we compared the performance of ECP with a non-evolutionary rule-based variant of ECP. While the purely-evolutionary version of ECP has more routing timeouts than the rule-based approach in failure-free networks, it sends significantly fewer beacon packets and incurs statistically fewer routing timeouts in both simple fault and periodic fault scenarios
Living IoT: A Flying Wireless Platform on Live Insects
Sensor networks with devices capable of moving could enable applications
ranging from precision irrigation to environmental sensing. Using mechanical
drones to move sensors, however, severely limits operation time since flight
time is limited by the energy density of current battery technology. We explore
an alternative, biology-based solution: integrate sensing, computing and
communication functionalities onto live flying insects to create a mobile IoT
platform.
Such an approach takes advantage of these tiny, highly efficient biological
insects which are ubiquitous in many outdoor ecosystems, to essentially provide
mobility for free. Doing so however requires addressing key technical
challenges of power, size, weight and self-localization in order for the
insects to perform location-dependent sensing operations as they carry our IoT
payload through the environment. We develop and deploy our platform on
bumblebees which includes backscatter communication, low-power
self-localization hardware, sensors, and a power source. We show that our
platform is capable of sensing, backscattering data at 1 kbps when the insects
are back at the hive, and localizing itself up to distances of 80 m from the
access points, all within a total weight budget of 102 mg.Comment: Co-primary authors: Vikram Iyer, Rajalakshmi Nandakumar, Anran Wang,
In Proceedings of Mobicom. ACM, New York, NY, USA, 15 pages, 201
A firefly-inspired scheme for energy-efficient transmission scheduling using a self-organizing method in a wireless sensor network
Various types of natural phenomena are regarded as primary sources of information for artificial occurrences that involve spontaneous synchronization. Among the artificial occurrences that mimic natural phenomena are Wireless Sensor Networks (WSNs) and the Pulse Coupled Oscillator (PCO), which utilizes firefly synchronization for attracting mating partners. However, the PCO model was not appropriate for wireless sensor networks because sensor nodes are typically not capable to collect sensor data packets during transmission (because of packet collision and deafness). To avert these limitations, this study proposed a self-organizing time synchronization algorithm that was adapted from the traditional PCO model of fireflies flashing synchronization. Energy consumption and transmission delay will be reduced by using this method. Using the proposed model, a simulation exercise was performed and a significant improvement in energy efficiency was observed, as reflected by an improved transmission scheduling and a coordinated duty cycling and data gathering ratio. Therefore, the energy-efficient data gathering is enhanced in the proposed model than in the original PCO-based wave-traveling model. The battery lifetime of the Sensor Nodes (SNs) was also extended by using the proposed model
Algorithms in nature: the convergence of systems biology and computational thinking
Biologists rely on computational methods to analyze and integrate large data sets, while several computational methods were inspired by the high-level design principles of biological systems. This Perspectives discusses the recent convergence of these two ways of thinking
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