15,322 research outputs found
Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning
With the advent of the Internet of Things (IoT), an increasing number of
energy harvesting methods are being used to supplement or supplant battery
based sensors. Energy harvesting sensors need to be configured according to the
application, hardware, and environmental conditions to maximize their
usefulness. As of today, the configuration of sensors is either manual or
heuristics based, requiring valuable domain expertise. Reinforcement learning
(RL) is a promising approach to automate configuration and efficiently scale
IoT deployments, but it is not yet adopted in practice. We propose solutions to
bridge this gap: reduce the training phase of RL so that nodes are operational
within a short time after deployment and reduce the computational requirements
to scale to large deployments. We focus on configuration of the sampling rate
of indoor solar panel based energy harvesting sensors. We created a simulator
based on 3 months of data collected from 5 sensor nodes subject to different
lighting conditions. Our simulation results show that RL can effectively learn
energy availability patterns and configure the sampling rate of the sensor
nodes to maximize the sensing data while ensuring that energy storage is not
depleted. The nodes can be operational within the first day by using our
methods. We show that it is possible to reduce the number of RL policies by
using a single policy for nodes that share similar lighting conditions.Comment: 7 pages, 5 figure
Land cover maps for environmental modeling at multiple scales
As described in the ECOCHANGE proposal, Task01.02.02 “Map production and aggregation”, two major products are generated within this WP. Firstly, land cover maps at high spatial resolutions will be produced for the European Union and for the reference years of 1960, 1990 and 2000. Secondly, thematic and spatial aggregated products will be derived at coarser spatial resolutions in order to synthesize the fragmentation and variability within coarser cells for biodiversity assessment and modelling. The name of the official deliverable is D01.02.01 “Land cover maps for environmental modelling at multiple scales” and includes this report, the digital land cover products and an interactive website to view the data at all thematic and spatial scales
Energy Harvesting and Management for Wireless Autonomous Sensors
Wireless autonomous sensors that harvest ambient energy are attractive solutions, due to their convenience and economic benefits. A number of wireless autonomous sensor platforms which consume less than 100?W under duty-cycled operation are available. Energy harvesting technology (including photovoltaics, vibration harvesters, and thermoelectrics) can be used to power autonomous sensors. A developed system is presented that uses a photovoltaic module to efficiently charge a supercapacitor, which in turn provides energy to a microcontroller-based autonomous sensing platform. The embedded software on the node is structured around a framework in which equal precedent is given to each aspect of the sensor node through the inclusion of distinct software stacks for energy management and sensor processing. This promotes structured and modular design, allowing for efficient code reuse and encourages the standardisation of interchangeable protocols
Pible: Battery-Free Mote for Perpetual Indoor BLE Applications
Smart building applications require a large-scale deployment of sensors
distributed across the environment. Recent innovations in smart environments
are driven by wireless networked sensors as they are easy to deploy. However,
replacing these batteries at scale is a non-trivial, labor-intensive task.
Energy harvesting has emerged as a potential solution to avoid battery
replacement but requires compromises such as application specific design,
simplified communication protocol or reduced quality of service. We explore the
design space of battery-free sensor nodes using commercial off the shelf
components, and present Pible: a Perpetual Indoor BLE sensor node that
leverages ambient light and can support numerous smart building applications.
We analyze node-lifetime, quality of service and light availability trade-offs
and present a predictive algorithm that adapts to changing lighting conditions
to maximize node lifetime and application quality of service. Using a 20 node,
15-day deployment in a real building under varying lighting conditions, we show
feasible applications that can be implemented using Pible and the boundary
conditions under which they can fail.Comment: 4 pages, 4 figures, BuildSys '18: Conference on Systems for Built
Environments, November 7--8, 2018, Shenzen, Chin
A Structured Hardware/Software Architecture for Embedded Sensor Nodes
Owing to the limited requirement for sensor processing in early networked sensor nodes, embedded software was generally built around the communication stack. Modern sensor nodes have evolved to contain significant on-board functionality in addition to communications, including sensor processing, energy management, actuation and locationing. The embedded software for this functionality, however, is often implemented in the application layer of the communications stack, resulting in an unstructured, top-heavy and complex stack. In this paper, we propose an embedded system architecture to formally specify multiple interfaces on a sensor node. This architecture differs from existing solutions by providing a sensor node with multiple stacks (each stack implements a separate node function), all linked by a shared application layer. This establishes a structured platform for the formal design, specification and implementation of modern sensor and wireless sensor nodes. We describe a practical prototype of an intelligent sensing, energy-aware, sensor node that has been developed using this architecture, implementing stacks for communications, sensing and energy management. The structure and operation of the intelligent sensing and energy management stacks are described in detail. The proposed architecture promotes structured and modular design, allowing for efficient code reuse and being suitable for future generations of sensor nodes featuring interchangeable components
SecuCode: Intrinsic PUF Entangled Secure Wireless Code Dissemination for Computational RFID Devices
The simplicity of deployment and perpetual operation of energy harvesting
devices provides a compelling proposition for a new class of edge devices for
the Internet of Things. In particular, Computational Radio Frequency
Identification (CRFID) devices are an emerging class of battery-free,
computational, sensing enhanced devices that harvest all of their energy for
operation. Despite wireless connectivity and powering, secure wireless firmware
updates remains an open challenge for CRFID devices due to: intermittent
powering, limited computational capabilities, and the absence of a supervisory
operating system. We present, for the first time, a secure wireless code
dissemination (SecuCode) mechanism for CRFIDs by entangling a device intrinsic
hardware security primitive Static Random Access Memory Physical Unclonable
Function (SRAM PUF) to a firmware update protocol. The design of SecuCode: i)
overcomes the resource-constrained and intermittently powered nature of the
CRFID devices; ii) is fully compatible with existing communication protocols
employed by CRFID devices in particular, ISO-18000-6C protocol; and ii) is
built upon a standard and industry compliant firmware compilation and update
method realized by extending a recent framework for firmware updates provided
by Texas Instruments. We build an end-to-end SecuCode implementation and
conduct extensive experiments to demonstrate standards compliance, evaluate
performance and security.Comment: Accepted to the IEEE Transactions on Dependable and Secure Computin
Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data
Informal settlements are home to the most socially and economically
vulnerable people on the planet. In order to deliver effective economic and
social aid, non-government organizations (NGOs), such as the United Nations
Children's Fund (UNICEF), require detailed maps of the locations of informal
settlements. However, data regarding informal and formal settlements is
primarily unavailable and if available is often incomplete. This is due, in
part, to the cost and complexity of gathering data on a large scale. To address
these challenges, we, in this work, provide three contributions. 1) A brand new
machine learning data-set, purposely developed for informal settlement
detection. 2) We show that it is possible to detect informal settlements using
freely available low-resolution (LR) data, in contrast to previous studies that
use very-high resolution (VHR) satellite and aerial imagery, something that is
cost-prohibitive for NGOs. 3) We demonstrate two effective classification
schemes on our curated data set, one that is cost-efficient for NGOs and
another that is cost-prohibitive for NGOs, but has additional utility. We
integrate these schemes into a semi-automated pipeline that converts either a
LR or VHR satellite image into a binary map that encodes the locations of
informal settlements.Comment: Published at the AAAI/ACM Conference on AI, ethics and society.
Extended results from our previous workshop: arXiv:1812.0081
Design and comparative analysis of single-path and epidemic approaches to information and energy management in wireless sensor networks
Intelligent energy management is a key challenge in Wireless Sensor Networks. The choice of an appropriate routing algorithm constitutes a critical factor, especially in unstructured networks where, due to their dynamic nature, a reactive routing protocol is necessary. Such networks often favour packet flooding to fulfil this need. One such algorithm is IDEALS, a technique proposed in the literature, which balances energy consumed with information delivered. This paper evaluates the use of a single-path solution with IDEALS to increase efficiency. Simulation results comparing the two approaches show that the single-path algorithm outperforms flooding in terms of energy consumption for any network size. Furthermore the benefit of IDEALS is preserved as its combination with the single-path algorithm maximises information throughput
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