scholarly journals Internet of Things in Space: A Review of Opportunities and Challenges from Satellite-Aided Computing to Digitally-Enhanced Space Living

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8117
Author(s):  
Jonathan Kua ◽  
Seng W. Loke ◽  
Chetan Arora ◽  
Niroshinie Fernando ◽  
Chathurika Ranaweera
Keyword(s):  
Internet Of Things ◽  
Large Scale ◽  
Space Exploration ◽  
Big Data Analytics ◽  
Data Communication ◽  
Survey Paper ◽  
Space Industry ◽  
Look Ahead ◽  
Challenges And Opportunities ◽  
Terrestrial Environments

Recent scientific and technological advancements driven by the Internet of Things (IoT), Machine Learning (ML) and Artificial Intelligence (AI), distributed computing and data communication technologies have opened up a vast range of opportunities in many scientific fields—spanning from fast, reliable and efficient data communication to large-scale cloud/edge computing and intelligent big data analytics. Technological innovations and developments in these areas have also enabled many opportunities in the space industry. The successful Mars landing of NASA’s Perseverance rover on 18 February 2021 represents another giant leap for humankind in space exploration. Emerging research and developments of connectivity and computing technologies in IoT for space/non-terrestrial environments is expected to yield significant benefits in the near future. This survey paper presents a broad overview of the area and provides a look-ahead of the opportunities made possible by IoT and space-based technologies. We first survey the current developments of IoT and space industry, and identify key challenges and opportunities in these areas. We then review the state-of-the-art and discuss future opportunities for IoT developments, deployment and integration to support future endeavors in space exploration.

Future Directions to the Application of Distributed Fog Computing in Smart Grid Systems

2018 ◽  
pp. 162-195 ◽  
Author(s):  
Arash Anzalchi ◽  
Aditya Sundararajan ◽  
Longfei Wei ◽  
Amir Moghadasi ◽  
Arif Sarwat
Keyword(s):  
Smart Grid ◽  
Large Scale ◽  
New Technologies ◽  
Fog Computing ◽  
Data Communication ◽  
Grid Systems ◽  
Challenges And Opportunities ◽  
Future Challenges ◽  
Large Scale Integration ◽  
Integration Of Renewables

The rapid growth of new technologies in power systems requires real-time monitoring and control of bidirectional data communication and electric power flow. Cloud computing has centralized architecture and is not scalable towards the emerging internet of things (IoT) landscape of the grid. Further, under large-scale integration of renewables, this framework could be bogged down by congestion, latency, and subsequently poor quality of service (QoS). This calls for a distributed architecture called fog computing, which imbibes both clouds as well as the end-devices to collect, process, and act upon the data locally at the edge for low latency applications prior to forwarding them to the cloud for more complex operations. Fog computing offers high performance and interoperability, better scalability and visibility, and greater availability in comparison to a grid relying only on the cloud. In this chapter, a prospective research roadmap, future challenges, and opportunities to apply fog computing on smart grid systems is presented.

Future Directions to the Application of Distributed Fog Computing in Smart Grid Systems

2019 ◽  
pp. 2186-2212 ◽  
Author(s):  
Arash Anzalchi ◽  
Aditya Sundararajan ◽  
Longfei Wei ◽  
Amir Moghadasi ◽  
Arif Sarwat
Keyword(s):  
Smart Grid ◽  
Large Scale ◽  
New Technologies ◽  
Fog Computing ◽  
Data Communication ◽  
Grid Systems ◽  
Challenges And Opportunities ◽  
Future Challenges ◽  
Large Scale Integration ◽  
Integration Of Renewables

The rapid growth of new technologies in power systems requires real-time monitoring and control of bidirectional data communication and electric power flow. Cloud computing has centralized architecture and is not scalable towards the emerging internet of things (IoT) landscape of the grid. Further, under large-scale integration of renewables, this framework could be bogged down by congestion, latency, and subsequently poor quality of service (QoS). This calls for a distributed architecture called fog computing, which imbibes both clouds as well as the end-devices to collect, process, and act upon the data locally at the edge for low latency applications prior to forwarding them to the cloud for more complex operations. Fog computing offers high performance and interoperability, better scalability and visibility, and greater availability in comparison to a grid relying only on the cloud. In this chapter, a prospective research roadmap, future challenges, and opportunities to apply fog computing on smart grid systems is presented.

Efficient Large-scale Medical Data (eHealth Big Data) Analytics in Internet of Things

2017 ◽  
Author(s):  
Andreas P. Plageras ◽  
Christos Stergiou ◽  
George Kokkonis ◽  
Kostas E. Psannis ◽  
Yutaka Ishibashi ◽  
...  
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Medical Data

scholarly journals Revealing the Challenges of Smart Rainwater Harvesting for Integrated and Digital Resilience of Urban Water Infrastructure

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1902
Author(s):  
Martin Oberascher ◽  
Aun Dastgir ◽  
Jiada Li ◽  
Sina Hesarkazzazi ◽  
Mohsen Hajibabaei ◽  
...  
Keyword(s):  
Large Scale ◽  
Rainwater Harvesting ◽  
Data Communication ◽  
Integrated System ◽  
Coupled Systems ◽  
Urban Resilience ◽  
System Failures ◽  
Household Level ◽  
Performance Improvements ◽  
Smart Water

Smart rainwater harvesting (RWH) systems can automatically release stormwater prior to rainfall events to increase detention capacity on a household level. However, impacts and benefits of a widespread implementation of these systems are often unknown. This works aims to investigate the effect of a large-scale implementation of smart RWH systems on urban resilience by hypothetically retrofitting an Alpine municipality with smart rain barrels. Smart RWH systems represent dynamic systems, and therefore, the interaction between the coupled systems RWH units, an urban drainage network (UDN) and digital infrastructure is critical for evaluating resilience against system failures. In particular, digital parameters (e.g., accuracy of weather forecasts, or reliability of data communication) can differ from an ideal performance. Therefore, different digital parameters are varied to determine the range of uncertainties associated with smart RWH systems. As the results demonstrate, smart RWH systems can further increase integrated system resilience but require a coordinated integration into the overall system. Additionally, sufficient consideration of digital uncertainties is of great importance for smart water systems, as uncertainties can reduce/eliminate gained performance improvements. Moreover, a long-term simulation should be applied to investigate resilience with digital applications to reduce dependence on boundary conditions and rainfall patterns.

scholarly journals Remote Sensing Methods for the Biophysical Characterization of Protected Areas Globally: Challenges and Opportunities

2021 ◽  
Vol 10 (6) ◽  
pp. 384
Author(s):  
Javier Martínez-López ◽  
Bastian Bertzky ◽  
Simon Willcock ◽  
Marine Robuchon ◽  
María Almagro ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Large Scale ◽  
Anthropogenic Activities ◽  
Global Scale ◽  
Area Network ◽  
Biophysical Characterization ◽  
Challenges And Opportunities ◽  
Scale Characterization

Protected areas (PAs) are a key strategy to reverse global biodiversity declines, but they are under increasing pressure from anthropogenic activities and concomitant effects. Thus, the heterogeneous landscapes within PAs, containing a number of different habitats and ecosystem types, are in various degrees of disturbance. Characterizing habitats and ecosystems within the global protected area network requires large-scale monitoring over long time scales. This study reviews methods for the biophysical characterization of terrestrial PAs at a global scale by means of remote sensing (RS) and provides further recommendations. To this end, we first discuss the importance of taking into account the structural and functional attributes, as well as integrating a broad spectrum of variables, to account for the different ecosystem and habitat types within PAs, considering examples at local and regional scales. We then discuss potential variables, challenges and limitations of existing global environmental stratifications, as well as the biophysical characterization of PAs, and finally offer some recommendations. Computational and interoperability issues are also discussed, as well as the potential of cloud-based platforms linked to earth observations to support large-scale characterization of PAs. Using RS to characterize PAs globally is a crucial approach to help ensure sustainable development, but it requires further work before such studies are able to inform large-scale conservation actions. This study proposes 14 recommendations in order to improve existing initiatives to biophysically characterize PAs at a global scale.

Taking a Deep Dive Into The Batteryless Internet of Things With Shepherd

2021 ◽  
Vol 24 (3) ◽  
pp. 5-8
Author(s):  
Kai Geissdoerfer ◽  
Mikołaj Chwalisz ◽  
Marco Zimmerling
Keyword(s):  
Energy Storage ◽  
Internet Of Things ◽  
Sensor Nodes ◽  
Energy Availability ◽  
Deep Dive ◽  
Temporal Fluctuations ◽  
Temporal Characteristics ◽  
Challenges And Opportunities ◽  
Spatio Temporal

Collaboration of batteryless devices is essential to their success in replacing traditional battery-based systems. Without significant energy storage, spatio-temporal fluctuations of ambient energy availability become critical for the correct functioning of these systems. We present Shepherd, a testbed for the batteryless Internet of Things (IoT) that can record and reproduce spatio-temporal characteristics of real energy environments to obtain insights into the challenges and opportunities of operating groups of batteryless sensor nodes.

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