
Venkata Prashant Modekurthy- Doctor of Philosophy
- Professor (Assistant) at University of Nevada, Las Vegas
Venkata Prashant Modekurthy
- Doctor of Philosophy
- Professor (Assistant) at University of Nevada, Las Vegas
About
28
Publications
3,667
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Introduction
Dr. Venkata Prashant Modekurthy is an assistant professor of computer science at University of Nevada, Las Vegas. He received his Ph. D. from Wayne State University under the supervision of Dr. Abusayeed Saifullah. His research interests include Cyber Physical Systems (CPS), Real-Time and Embedded Systems , Parallel and Distributed Computing, Internet-of-Things
Current institution
Additional affiliations
January 2021 - present
Education
January 2017 - August 2020
January 2014 - December 2015
Publications
Publications (28)
Optimizing task-to-core allocation can substantially reduce power consumption in multi-core platforms without degrading user experience. However, many existing approaches overlook critical factors such as parallelism, compute intensity, and heterogeneous core types. In this paper, we introduce a statistical learning approach for feature selection t...
Industrial and agricultural Internet of Things (IoT) are emerging in very large-scale and wide-area applications (e.g., oil-field management, smart farming) that may spread over hundreds of square miles (e.g., 45mi×12mi East Texas Oil-field). Although a single Low-Power Wide-Area Network (LPWAN) covers several miles, it faces coverage challenge in...
The industrial Internet of Things (IIoT) is prominently emerging in applications of large-scale and wide-area applications, such as oilfield management, smart grid management, real-time equipment monitoring, and integration of traffic management systems for smart cities. Relying on short-range wireless technologies (e.g., WirelessHART and ISA100.11...
The concept of Industry 4.0 introduces the unification of industrial Internet-of-Things (IoT), cyber physical systems, and data-driven business modeling to improve production efficiency of the factories. To ensure high production efficiency, Industry 4.0 requires industrial IoT to be adaptable, scalable, real-time, and reliable. Recent successful i...
Today, industrial and agricultural Internet of Things (IoT) are emerging in very large-scale and wide-area applications (e.g., oilfield management, smart farming) that may spread over hundreds of square miles (e.g., 45mi×12mi East Texas Oilfield). Although a single Low-Power Wide-Area Network (LPWAN) covers several miles, it faces coverage challeng...
Low-Power Wide-Area Network (LPWAN) is an enabling Internet-of-Things (IoT) technology that supports long-range, low-power, and low-cost connectivity to numerous devices. To avoid the crowd in the limited ISM band (where most LPWANs operate) and cost of licensed band, the recently proposed SNOW (Sensor Network over White Spaces) is a promising LPWA...
Low-Power Wide-Area Network (LPWAN) is an enabling Internet-of-Things (IoT) technology that supports long-range, low-power, and low-cost connectivity to numerous devices. To avoid the crowd in the limited ISM band (where most LPWANs operate) and cost of licensed band, the recently proposed SNOW (Sensor Network over White Spaces) is a promising LPWA...
Industrial internet of Things (IIoT) are gaining popularity for use in large-scale applications such as oil-field management (e.g., $74\times 8$km$^2$ East Texas Oil-field), smart farming, smart manufacturing, smart grid, and data center power management. These applications require the wireless stack to provide a scalable, reliable, low-power and l...
Prolonging the network lifetime is a major consideration in many Internet of Things applications. In this paper, we study maximizing the network lifetime of an energy-harvesting LoRa network. Such a network is characterized by heterogeneous recharging capabilities across the nodes that is not taken into account in existing work. We propose a link-l...
Industrial internet of Things (IIoT) are gaining popularity for use in large-scale applications such as oil-field management (e.g., $74\times 8$km$^2$ East Texas Oil-field), smart farming, smart manufacturing, smart grid, and data center power management. These applications require the wireless stack to provide a scalable, reliable, low-power and l...
Multiprocessor scheduling of hard real-time tasks modeled by directed acyclic graphs (DAGs) exploits the inherent parallelism presented by the model. For DAG tasks, a node represents a request to execute an object on one of the available processors. In one DAG task, there may be multiple execution requests for one object, each represented by a dist...
Recent advancements in Industrial Internet-of-Things and cyber-physical systems through the development of wireless standards like WirelessHART and ISA100 for real-time and reliable communication paved the way for a new industrial trend called Industry 4.0. Industry 4.0 proposes to improve production efficiency by employing smart factories. One app...
Low-Power Wide-Area Network (LPWAN) is an enabling Internet-of-Things (IoT) technology that supports long-range, low-power, and low-cost connectivity to numerous devices. To avoid the crowd in the limited ISM band (where most LPWANs operate) and the cost of licensed band, the recently proposed SNOW (Sensor Network over White Spaces) is a promising...
Communication reliability in a Wireless Sensor and Actuator Network (WSAN) has a high impact on stability of industrial process monitoring and control. To make reliable and real-time communication in highly unreliable environments, industrial WSANs such as those based on WirelessHART adopt graph routing. In graph routing, each packet is scheduled o...
Wireless Sensor Networks (WSNs) are popular for their usage in various application like environmental monitoring because of their small size and ease of deployment. However, there is a considerable cost of owning and maintaining involved which might impede small to medium scale industries from availing their services. Even for large-scale industrie...
With increasing number of cloud additive manufacturing (AM) service providers, cloud AM services are becoming decentralized and it is difficult for consumers to discover cloud AM services according to their personal preferences and tradeoffs. Existing frameworks of cloud manufacturing either do not have brokers between cloud manufacturing service p...