scholarly journals New Heuristics for Scheduling and Distributing Jobs under Hybrid Dew Computing Environments

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Pablo Sanabria ◽  
Tomás Felipe Tapia ◽  
Andres Neyem ◽  
Jose Ignacio Benedetto ◽  
Matías Hirsch ◽  
...  
Keyword(s):  
Grid Computing ◽  
Power Source ◽  
Energy Aware ◽  
Mobile Grid Computing ◽  
Computing Environments ◽  
Iot Devices ◽  
Dew Computing ◽  
New Algorithms ◽  
Insight Into ◽  
Simulated Hybrid

Mobile grid computing has been a popular topic for researchers due to mobile and IoT devices’ ubiquity and their evergrowing processing potential. While many scheduling algorithms for harnessing these resources exist in the literature for standard grid computing scenarios, surprisingly, there is little insight into this matter in the context of hybrid-powered computing resources, typically found in Dew and Edge computing environments. This paper proposes new algorithms aware of devices’ power source for scheduling tasks in hybrid environments, i.e., where the battery- and non-battery-powered devices cooperate. We simulated hybrid Dew/Edge environments by extending DewSim, a simulator that models battery-driven devices’ battery behavior using battery traces profiled from real mobile devices. We compared the throughput and job completion achieved by algorithms proposed in this paper using as a baseline a previously developed algorithm that considers computing resources but only from battery-dependent devices called Enhanced Simple Energy-Aware Schedule (E-SEAS). The obtained results in the simulation reveal that our proposed algorithms can obtain up to a 90% increment in overall throughput and around 95% of completed jobs in hybrid environments compared to E-SEAS. Finally, we show that incorporating these characteristics gives more awareness of the type of resources present and can enable the algorithms to manage resources more efficiently in more hybrid environments than other algorithms found in the literature.

scholarly journals Energy-Aware Sensing on Battery-Less LoRaWAN Devices with Energy Harvesting

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 904 ◽  
Author(s):  
Adnan Sabovic ◽  
Carmen Delgado ◽  
Dragan Subotic ◽  
Bart Jooris ◽  
Eli De Poorter ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Power Source ◽  
Energy Aware ◽  
Promising Alternative ◽  
Trade Off ◽  
Iot Applications ◽  
Class A ◽  
Current Consumption ◽  
Capacitor Voltage ◽  
Iot Devices

Billions of Internet of Things (IoT) devices rely on batteries as the main power source. These batteries are short-lived, bulky and harmful to the environment. Battery-less devices provide a promising alternative for a sustainable IoT, where energy harvested from the environment is stored in small capacitors. This constrained energy storage and the unpredictable energy harvested result in intermittent on–off behavior of the device. Measuring and understanding the current consumption and execution time of different tasks of IoT applications is crucial to properly operate these battery-less devices. In this paper, we study how to properly schedule sensing and transmission tasks on a battery-less LoRaWAN device. We analyze the trade-off between sleeping and allowing the device to turn off between the execution of application tasks. This study allows us to properly define the device configuration (i.e., capacitor size) based on the application tasks (i.e., sensing and sending) and environmental conditions (i.e., harvesting rate). We define an optimization problem that determines the optimal capacitor voltage at which the device should start performing its tasks. Our results show that a device using LoRaWAN Class A can measure the temperature and transmit its data at least once every 5 s if it can harvest at least 10 mA of current and uses a relatively small capacitor of 10 mF or less. At harvesting rates below 3 mA, it is necessary to turn off the device between application cycles and use a larger supercapacitor of at least 140 mF. In this case, the device can transmit a temperature measurement once every 60–100 s.

scholarly journals Multi-Blockchain-Based IoT Data Processing Techniques to Ensure the Integrity of IoT Data in AIoT Edge Computing Environments

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3515
Author(s):  
Sung-Ho Sim ◽  
Yoon-Su Jeong
Keyword(s):  
Low Cost ◽  
Third Party ◽  
Monitoring And Control ◽  
Constant Size ◽  
Computing Environments ◽  
Iot Devices ◽  
Cloud Servers ◽  
The Cost ◽  
Processing Techniques ◽  
And Control

As the development of IoT technologies has progressed rapidly recently, most IoT data are focused on monitoring and control to process IoT data, but the cost of collecting and linking various IoT data increases, requiring the ability to proactively integrate and analyze collected IoT data so that cloud servers (data centers) can process smartly. In this paper, we propose a blockchain-based IoT big data integrity verification technique to ensure the safety of the Third Party Auditor (TPA), which has a role in auditing the integrity of AIoT data. The proposed technique aims to minimize IoT information loss by multiple blockchain groupings of information and signature keys from IoT devices. The proposed technique allows IoT information to be effectively guaranteed the integrity of AIoT data by linking hash values designated as arbitrary, constant-size blocks with previous blocks in hierarchical chains. The proposed technique performs synchronization using location information between the central server and IoT devices to manage the cost of the integrity of IoT information at low cost. In order to easily control a large number of locations of IoT devices, we perform cross-distributed and blockchain linkage processing under constant rules to improve the load and throughput generated by IoT devices.

scholarly journals Optimized Application Level Checkpoint Based Load Sharing Model for Heterogeneous Mobile Grid Computing

2017 ◽  
Vol 10 (28) ◽  
pp. 1-13
Author(s):  
Imran Rafique ◽  
Hina Gul ◽  
Salman Rafique ◽  
Syed Asad Raza Kazmi ◽  
Awais Qasim ◽  
...  
Keyword(s):  
Grid Computing ◽  
Load Sharing ◽  
Mobile Grid ◽  
Mobile Grid Computing

Job completion prediction using case-based reasoning for Grid computing environments

2007 ◽  
Vol 19 (9) ◽  
pp. 1253-1269 ◽  
Author(s):  
Lilian Noronha Nassif ◽  
José Marcos Nogueira ◽  
Ahmed Karmouch ◽  
Mohamed Ahmed ◽  
Flávio Vinícius de Andrade
Keyword(s):  
Grid Computing ◽  
Case Based Reasoning ◽  
Computing Environments ◽  
Case Based

A new insight into ZIF-67 based triboelectric nanogenerator for self-powered robot object recognition

2021 ◽  
Author(s):  
Sugato Hajra ◽  
Manisha Sahu ◽  
Aneeta Manjari Padhan ◽  
Jaykishon Swain ◽  
Basanta Kumar Panigrahi ◽  
...  
Keyword(s):  
Object Recognition ◽  
Mechanical Energy ◽  
Power Source ◽  
Triboelectric Nanogenerator ◽  
Electrostatic Induction ◽  
Self Powered ◽  
Insight Into

Harvesting mechanical energy from surroundings can be a promising power source for micro/nano-devices. The triboelectric nanogenerator (TENG) works in the principle of triboelectrification and electrostatic induction. So far, the metals...

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