8,396 research outputs found
Caching-Aided Collaborative D2D Operation for Predictive Data Dissemination in Industrial IoT
Industrial automation deployments constitute challenging environments where
moving IoT machines may produce high-definition video and other heavy sensor
data during surveying and inspection operations. Transporting massive contents
to the edge network infrastructure and then eventually to the remote human
operator requires reliable and high-rate radio links supported by intelligent
data caching and delivery mechanisms. In this work, we address the challenges
of contents dissemination in characteristic factory automation scenarios by
proposing to engage moving industrial machines as device-to-device (D2D)
caching helpers. With the goal to improve reliability of high-rate
millimeter-wave (mmWave) data connections, we introduce the alternative
contents dissemination modes and then construct a novel mobility-aware
methodology that helps develop predictive mode selection strategies based on
the anticipated radio link conditions. We also conduct a thorough system-level
evaluation of representative data dissemination strategies to confirm the
benefits of predictive solutions that employ D2D-enabled collaborative caching
at the wireless edge to lower contents delivery latency and improve data
acquisition reliability
Adaptive data synchronization algorithm for IoT-oriented low-power wide-area networks
The Internet of Things (IoT) is by now very close to be realized, leading the world towards a new technological era where people’s lives and habits will be definitively revolutionized. Furthermore, the incoming 5G technology promises significant enhancements concerning the Quality of Service (QoS) in mobile communications. Having billions of devices simultaneously connected has opened new challenges about network management and data exchange rules that need to be tailored to the characteristics of the considered scenario. A large part of the IoT market is pointing to Low-Power Wide-Area Networks (LPWANs) representing the infrastructure for several applications having energy saving as a mandatory goal besides other aspects of QoS. In this context, we propose a low-power IoT-oriented file synchronization protocol that, by dynamically optimizing the amount of data to be transferred, limits the device level of interaction within the network, therefore extending the battery life. This protocol can be adopted with different Layer 2 technologies and provides energy savings at the IoT device level that can be exploited by different applications
Movers and Shakers: Kinetic Energy Harvesting for the Internet of Things
Numerous energy harvesting wireless devices that will serve as building
blocks for the Internet of Things (IoT) are currently under development.
However, there is still only limited understanding of the properties of various
energy sources and their impact on energy harvesting adaptive algorithms.
Hence, we focus on characterizing the kinetic (motion) energy that can be
harvested by a wireless node with an IoT form factor and on developing energy
allocation algorithms for such nodes. In this paper, we describe methods for
estimating harvested energy from acceleration traces. To characterize the
energy availability associated with specific human activities (e.g., relaxing,
walking, cycling), we analyze a motion dataset with over 40 participants. Based
on acceleration measurements that we collected for over 200 hours, we study
energy generation processes associated with day-long human routines. We also
briefly summarize our experiments with moving objects. We develop energy
allocation algorithms that take into account practical IoT node design
considerations, and evaluate the algorithms using the collected measurements.
Our observations provide insights into the design of motion energy harvesters,
IoT nodes, and energy harvesting adaptive algorithms.Comment: 15 pages, 11 figure
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