Most modern mobile embedded devices have the ability to increase their computational power typically at the cost of increased heat dissipation. This may result in temperatures above the design limit, especially if active cooling is inapplicable. Thus, it is necessary to consider processor temperature while scheduling tasks. This means estimating the change in temperature due to changed workload is crucial for high performance mobile embedded devices. To address this challenge, we first introduce a model to estimate the temperature and classify the system dependent model parameters. Then, to determine these parameters, we develop a new method, which can be applied on any mobile embedded device. The only requirement for our new method is learning the device characteristics by processing a certain task while recording the temperature with built-in sensors. Our results show that our method can achieve high accuracy within a short testing period. (( 25 th INTERNATIONAL WORKSHOP on Thermal Investigations of ICs and Systems )) September 2019, Lecco / IT www.therminic2019.eu ISBN 978-1-7281-2078-2 (( 25 th INTERNATIONAL WORKSHOP Thermal Investigations of ICs and Systems )) 2019 (( 25 th INTERNATIONAL WORKSHOP on Thermal Investigations of ICs and Systems ))
Predicting the device temperature is crucial for high performance mobile devices since a high temperature reduces the device reliability and lifetime, and increases the power dissipation per processing activity. For these reasons, thermal models are used to predict the temperature and schedule the workloads according to these predictions. This means that more accurate predictions can improve the reliability, lifetime and energy-efficiency of devices. We introduce two different generic methods to extend a thermal model to improve the prediction accuracy. The first method is to extend a thermal model with a Kalman filter. This approach enables a device to adapt to environmental changes more easily and to reduce the effect of noise by combining sensor data and dynamic behavior of the system. However, it assumes every random variable to be normally distributed. The second method is to extend a thermal model with a particle filter. In addition to the ability of adapting better to environmental changes, this approach enables a device to approximate any arbitrary distribution to reduce the effect of noise. Both methods are applicable to any dynamic thermal model to improve its prediction accuracy. Our experimental results show that the new methods indeed improve the prediction accuracy.
Mobile ad-hoc networks (MANETs) have been widely employed in many fields including critical information delivery in open terrains as in tactical area, vehicular or disaster area network scenarios. To provide effective network maintenance for those MANETs, it is essential to adopt proper control communication methods, which provide reliable delivery of network information. However, it is difficult to provide control communication that meets the quality of service requirements due to the broadcasting of control messages and dynamic nature of MANETs. Interference is one of the major problems that degrades this service quality, especially for the networks deployed in open terrain environments. Moreover, large and dense MANET scenarios further complicate this problem. Even though many communication methods have been proposed in the literature, a satisfactory one for periodic and broadcast delivery of control packets considering the interference awareness and at the same time yielding high reliability, goodput, energy efficiency still does not exist for MANETs. To that end, we present a new control communication protocol, which is referred to as the Interference-Aware Periodic Broadcast Messaging (IPBM) protocol, for those MANETs deployed in interference-rich open terrains. We show that the proposed interference awareness mechanism of IPBM, in which the transmission power of the units is increased at certain periods, significantly reduces the packet loss caused by the interference problem. Moreover, IPBM achieves an important amount of increase in the link reliability as well as in goodput with the least power consumption among the evaluated solutions. The mobility support feature of IPBM makes the protocol applicable not only to large and dense but also to highly dynamic MANETs. Besides, IPBM can properly work alongside the deployed main data communication protocol, since it provides valuable network information to be manipulated. Simulation results under both static and dynamic topologies confirm that adopting the IPBM protocol in the intended MANET scenarios significantly increases the reliability and goodput as well as the energy efficiency.
Emerging use of the Internet of Things concept in various fields has led to the introduction of new technologies to meet the new application requirements in recent years. IPv6 over IEEE 802.15.4e TSCH (6TiSCH) standard is one of the innovations that integrates IPv6-based upper protocol layers with IEEE 802.15.4e time-slotted channel hopping (TSCH)-based medium access control layer. TSCH has received much attention in the industrial field due to its features, like high reliability and low power consumption. End devices, e.g., sensors and actuators, of these networks usually generate sporadic heterogeneous traffic in which quality of service (QoS) criteria are difficult to meet with existing scheduling strategies. To that end, we introduce a shared cell schedule optimization method and an algorithm, referred to as DIVVY, for those networks. The proposed method grounds on a heuristic network model and aims to find the most energy-efficient network configuration while considering the QoS constraints.
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