160 research outputs found
Efficient Machine-type Communication using Multi-metric Context-awareness for Cars used as Mobile Sensors in Upcoming 5G Networks
Upcoming 5G-based communication networks will be confronted with huge
increases in the amount of transmitted sensor data related to massive
deployments of static and mobile Internet of Things (IoT) systems. Cars acting
as mobile sensors will become important data sources for cloud-based
applications like predictive maintenance and dynamic traffic forecast. Due to
the limitation of available communication resources, it is expected that the
grows in Machine-Type Communication (MTC) will cause severe interference with
Human-to-human (H2H) communication. Consequently, more efficient transmission
methods are highly required. In this paper, we present a probabilistic scheme
for efficient transmission of vehicular sensor data which leverages favorable
channel conditions and avoids transmissions when they are expected to be highly
resource-consuming. Multiple variants of the proposed scheme are evaluated in
comprehensive realworld experiments. Through machine learning based combination
of multiple context metrics, the proposed scheme is able to achieve up to 164%
higher average data rate values for sensor applications with soft deadline
requirements compared to regular periodic transmission.Comment: Best Student Paper Awar
Conditioning a current source using OCCII-GIC for EIT systems
Multi-Frequency EIT has been developed to evaluate the permittivity spectrum of a medium using an improved Howland source. Stray capacitance reduces the output impedance, bandwidth of the data collection leading to erroneous measurements. To address this, we have designed a current source, which uses a novel OCCII - GIC to cancel stray capacitance. The system achieves high output impedance over a 5 MHz frequency range
PhyNetLab: An IoT-Based Warehouse Testbed
Future warehouses will be made of modular embedded entities with
communication ability and energy aware operation attached to the traditional
materials handling and warehousing objects. This advancement is mainly to
fulfill the flexibility and scalability needs of the emerging warehouses.
However, it leads to a new layer of complexity during development and
evaluation of such systems due to the multidisciplinarity in logistics,
embedded systems, and wireless communications. Although each discipline
provides theoretical approaches and simulations for these tasks, many issues
are often discovered in a real deployment of the full system. In this paper we
introduce PhyNetLab as a real scale warehouse testbed made of cyber physical
objects (PhyNodes) developed for this type of application. The presented
platform provides a possibility to check the industrial requirement of an
IoT-based warehouse in addition to the typical wireless sensor networks tests.
We describe the hardware and software components of the nodes in addition to
the overall structure of the testbed. Finally, we will demonstrate the
advantages of the testbed by evaluating the performance of the ETSI compliant
radio channel access procedure for an IoT warehouse
Machine learning based context-predictive car-to-cloud communication using multi-layer connectivity maps for upcoming 5G networks
While cars were only considered as means of personal transportation for a
long time, they are currently transcending to mobile sensor nodes that gather
highly up-to-date information for crowdsensing-enabled big data services in a
smart city context. Consequently, upcoming 5G communication networks will be
confronted with massive increases in Machine-type Communication (MTC) and
require resource-efficient transmission methods in order to optimize the
overall system performance and provide interference-free coexistence with human
data traffic that is using the same public cellular network. In this paper, we
bring together mobility prediction and machine learning based channel quality
estimation in order to improve the resource-efficiency of car-to-cloud data
transfer by scheduling the transmission time of the sensor data with respect to
the anticipated behavior of the communication context. In a comprehensive field
evaluation campaign, we evaluate the proposed context-predictive approach in a
public cellular network scenario where it is able to increase the average data
rate by up to 194% while simultaneously reducing the mean uplink power
consumption by up to 54%
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