1,429 research outputs found
Coordination and Self-Adaptive Communication Primitives for Low-Power Wireless Networks
The Internet of Things (IoT) is a recent trend where objects are augmented with computing and communication capabilities, often via low-power wireless radios. The Internet of Things is an enabler for a connected and more sustainable modern society: smart grids are deployed to improve energy production and consumption, wireless monitoring systems allow smart factories to detect faults early and reduce waste, while connected vehicles coordinate on the road to ensure our safety and save fuel. Many recent IoT applications have stringent requirements for their wireless communication substrate: devices must cooperate and coordinate, must perform efficiently under varying and sometimes extreme environments, while strict deadlines must be met. Current distributed coordination algorithms have high overheads and are unfit to meet the requirements of today\u27s wireless applications, while current wireless protocols are often best-effort and lack the guarantees provided by well-studied coordination solutions. Further, many communication primitives available today lack the ability to adapt to dynamic environments, and are often tuned during their design phase to reach a target performance, rather than be continuously updated at runtime to adapt to reality.In this thesis, we study the problem of efficient and low-latency consensus in the context of low-power wireless networks, where communication is unreliable and nodes can fail, and we investigate the design of a self-adaptive wireless stack, where the communication substrate is able to adapt to changes to its environment. We propose three new communication primitives: Wireless Paxos brings fault-tolerant consensus to low-power wireless networking, STARC is a middleware for safe vehicular coordination at intersections, while Dimmer builds on reinforcement learning to provide adaptivity to low-power wireless networks. We evaluate in-depth each primitive on testbed deployments and we provide an open-source implementation to enable their use and improvement by the community
Structurally dynamic spin market networks
The agent-based model of stock price dynamics on a directed evolving complex
network is suggested and studied by direct simulation. The stationary regime is
maintained as a result of the balance between the extremal dynamics, adaptivity
of strategic variables and reconnection rules. The inherent structure of node
agent "brain" is modeled by a recursive neural network with local and global
inputs and feedback connections. For specific parametric combination the
complex network displays small-world phenomenon combined with scale-free
behavior. The identification of a local leader (network hub, agent whose
strategies are frequently adapted by its neighbors) is carried out by repeated
random walk process through network. The simulations show empirically relevant
dynamics of price returns and volatility clustering. The additional emerging
aspects of stylized market statistics are Zipfian distributions of fitness.Comment: 13 pages, 5 figures, accepted in IJMPC, references added, minor
changes in model, new results and modified figure
Multiagent Reinforcement Learning for Autonomous Routing and Pickup Problem with Adaptation to Variable Demand
We derive a learning framework to generate routing/pickup policies for a
fleet of vehicles tasked with servicing stochastically appearing requests on a
city map. We focus on policies that 1) give rise to coordination amongst the
vehicles, thereby reducing wait times for servicing requests, 2) are
non-myopic, considering a-priori unknown potential future requests, and 3) can
adapt to changes in the underlying demand distribution. Specifically, we are
interested in adapting to fluctuations of actual demand conditions in urban
environments, such as on-peak vs. off-peak hours. We achieve this through a
combination of (i) online play, a lookahead optimization method that improves
the performance of rollout methods via an approximate policy iteration step,
and (ii) an offline approximation scheme that allows for adapting to changes in
the underlying demand model. In particular, we achieve adaptivity of our
learned policy to different demand distributions by quantifying a region of
validity using the q-valid radius of a Wasserstein Ambiguity Set. We propose a
mechanism for switching the originally trained offline approximation when the
current demand is outside the original validity region. In this case, we
propose to use an offline architecture, trained on a historical demand model
that is closer to the current demand in terms of Wasserstein distance. We learn
routing and pickup policies over real taxicab requests in downtown San
Francisco with high variability between on-peak and off-peak hours,
demonstrating the ability of our method to adapt to real fluctuation in demand
distributions. Our numerical results demonstrate that our method outperforms
rollout-based reinforcement learning, as well as several benchmarks based on
classical methods from the field of operations research.Comment: 7 pages, 6 figures, 3 tables, submitted to ICR
Collaborative, Intelligent, and Adaptive Systems for the Low-Power Internet of Things
With the emergence of the Internet of Things (IoT), more and more devices are getting equipped with communication capabilities, often via wireless radios. Their deployments pave the way for new and mission-critical applications: cars will communicate with nearby vehicles to coordinate at intersections; industrial wireless closed-loop systems will improve operational safety in factories; while swarms of drones will coordinate to plan collision-free trajectories. To achieve these goals, IoT devices will need to communicate, coordinate, and collaborate over the wireless medium. However, these envisioned applications necessitate new characteristics that current solutions and protocols cannot fulfill: IoT devices require consistency guarantees from their communication and demand for adaptive behavior in complex and dynamic environments.In this thesis, we design, implement, and evaluate systems and mechanisms to enable safe coordination and adaptivity for the smallest IoT devices. To ensure consistent coordination, we bring fault-tolerant consensus to low-power wireless communication and introduce Wireless Paxos, a flavor of the Paxos algorithm specifically tailored to low-power IoT. We then present STARC, a wireless coordination mechanism for intersection management combining commit semantics with synchronous transmissions. To enable adaptivity in the wireless networking stack, we introduce Dimmer and eAFH. Dimmer combines Reinforcement Learning and Multi-Armed Bandits to adapt its communication parameters and counteract the adverse effects of wireless interference at runtime while optimizing energy consumption in normal conditions. eAFH provides dynamic channel management in Bluetooth Low Energy by excluding and dynamically re-including channels in scenarios with mobility. Finally, we demonstrate with BlueSeer that a device can classify its environment, i.e., recognize whether it is located in a home, office, street, or transport, solely from received Bluetooth Low Energy signals fed into an embedded machine learning model. BlueSeer therefore increases the intelligence of the smallest IoT devices, allowing them to adapt their behaviors to their current surroundings
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
Regulating Highly Automated Robot Ecologies: Insights from Three User Studies
Highly automated robot ecologies (HARE), or societies of independent
autonomous robots or agents, are rapidly becoming an important part of much of
the world's critical infrastructure. As with human societies, regulation,
wherein a governing body designs rules and processes for the society, plays an
important role in ensuring that HARE meet societal objectives. However, to
date, a careful study of interactions between a regulator and HARE is lacking.
In this paper, we report on three user studies which give insights into how to
design systems that allow people, acting as the regulatory authority, to
effectively interact with HARE. As in the study of political systems in which
governments regulate human societies, our studies analyze how interactions
between HARE and regulators are impacted by regulatory power and individual
(robot or agent) autonomy. Our results show that regulator power, decision
support, and adaptive autonomy can each diminish the social welfare of HARE,
and hint at how these seemingly desirable mechanisms can be designed so that
they become part of successful HARE.Comment: 10 pages, 7 figures, to appear in the 5th International Conference on
Human Agent Interaction (HAI-2017), Bielefeld, German
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
- …