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Articles 1 - 10 of 10
Full-Text Articles in Physical Sciences and Mathematics
Mrim: Lightweight Saliency-Based Mixed-Resolution Imaging For Low-Power Pervasive Vision, Jiyan Wu, Vithurson Subasharan, Minh Anh Tuan Tran, Kasun Pramuditha Gamlath, Archan Misra
Mrim: Lightweight Saliency-Based Mixed-Resolution Imaging For Low-Power Pervasive Vision, Jiyan Wu, Vithurson Subasharan, Minh Anh Tuan Tran, Kasun Pramuditha Gamlath, Archan Misra
Research Collection School Of Computing and Information Systems
While many pervasive computing applications increasingly utilize real-time context extracted from a vision sensing infrastructure, the high energy overhead of DNN-based vision sensing pipelines remains a challenge for sustainable in-the-wild deployment. One common approach to reducing such energy overheads is the capture and transmission of lower-resolution images to an edge node (where the DNN inferencing task is executed), but this results in an accuracy-vs-energy tradeoff, as the DNN inference accuracy typically degrades with a drop in resolution. In this work, we introduce MRIM, a simple but effective framework to tackle this tradeoff. Under MRIM, the vision sensor platform first executes …
Private Ethereum Blockchain Implementation And Its Security Features For Smart Home Iot, Hasibul Grande Alam
Private Ethereum Blockchain Implementation And Its Security Features For Smart Home Iot, Hasibul Grande Alam
Theses and Dissertations
The security and privacy of IoT devices have become primary concerns as smart home networks are connected to the internet. Ethereum blockchain can be a solution to mitigate or prevent attacks – sniffing attacks, malware attacks, Eavesdropping, and Distributed Denial of Services (DDoS) attacks. Deploying Ethereum in resource constraint IoT devices is challenging due to resultant energy consumption, computational overhead, and delay. We adopted smart home as a case study to examine our methodology as a model for general IoT applications. This thesis work presents the implementation of private Ethereum blockchain that is optimized and installable on smart home IoT. …
Dual Resource Constrained Flexible Job Shop Energy-Saving Scheduling Considering Delivery Time, Hongliang Zhang, Jingru Xu, Bo Tan, Gongjie Xu
Dual Resource Constrained Flexible Job Shop Energy-Saving Scheduling Considering Delivery Time, Hongliang Zhang, Jingru Xu, Bo Tan, Gongjie Xu
Journal of System Simulation
Abstract: To handle the flexible job shop energy-saving scheduling with machines and workers constraints, on the considering of delivery time, the optimization model of dual resource constrained flexible job shop energy-saving scheduling is established with the goal of minimizing the total earliness and tardiness penalties, and total energy consumption. An improved non-dominated sorting genetic algorithm II(INSGA-II) is proposed. Aiming at the optimized objectives, a three-stage decoding method is designed to gain more feasible solutions. The dynamic adaptive crossover and mutation operators are applied to get more excellent individuals. The crowding distance is improved to obtain a population with better …
Multi-Media Energy Planning Optimization Of Steel Based On Improved Moea/D, Hongcai Ouyang, Dinghui Wu, Junyan Fan, Jing Wang
Multi-Media Energy Planning Optimization Of Steel Based On Improved Moea/D, Hongcai Ouyang, Dinghui Wu, Junyan Fan, Jing Wang
Journal of System Simulation
Abstract: To address the problems of multi-media iron and steel energy planning model with more variables, complex constraints and high difficulty in model solving, an improved MOEA/D (decomposition-based multi-objective evolutionary algorithm) based on adaptive neighborhood is proposed to realize multi-media energy planning optimization. Considering the characteristics of TOU price and the buffer effect of gas holder, the objective function to minimize operation cost and total energy consumption is constructed. And the model constraints are designed such as energy supply and demand balance. The decoding method based on energy production and consumption rules is designed to determine the target value. The …
Elevation-Distributed Multistage Reverse Osmosis Desalination With Seawater Pumped Storage, Hani E. Elsayed-Ali
Elevation-Distributed Multistage Reverse Osmosis Desalination With Seawater Pumped Storage, Hani E. Elsayed-Ali
Electrical & Computer Engineering Faculty Publications
A seawater reverse osmosis (RO) plant layout based on multistage RO with stages located at different elevations above sea level is described. The plant uses the weight of a seawater column from pumped storage as head pressure for RO (gravity-driven multistage RO) or to supplement high-pressure pumps used in RO (gravity-assisted multistage RO). The use of gravitational force reduces the specific energy for RO compared to using high-pressure pumps. By locating the RO stages at different elevations based on demand sites, the total specific energy consumption for RO and permeate transport to different elevations above sea level is reduced from …
Geo-Distributed Multi-Tier Workload Migration Over Multi-Timescale Electricity Markets, Sourav Kanti Addya, Anurag Satpathy, Bishakh Chandra Ghosh, Sandip Chakraborty, Soumya K. Ghosh, Sajal K. Das
Geo-Distributed Multi-Tier Workload Migration Over Multi-Timescale Electricity Markets, Sourav Kanti Addya, Anurag Satpathy, Bishakh Chandra Ghosh, Sandip Chakraborty, Soumya K. Ghosh, Sajal K. Das
Computer Science Faculty Research & Creative Works
Virtual machine (VM) migration enables cloud service providers (CSPs) to balance workload, perform zero-downtime maintenance, and reduce applications' power consumption and response time. Migrating a VM consumes energy at the source, destination, and backbone networks, i.e., intermediate routers and switches, especially in a Geo-distributed setting. In this context, we propose a VM migration model called Low Energy Application Workload Migration (LEAWM) aimed at reducing the per-bit migration cost in migrating VMs over Geo-distributed clouds. With a Geo-distributed cloud connected through multiple Internet Service Providers (ISPs), we develop an approach to find out the migration path across ISPs leading to the …
Deep Meta Q-Learning Based Multi-Task Offloading In Edge-Cloud Systems, Nelson Sharma, Aswini Ghosh, Rajiv Misra, Sajal K. Das
Deep Meta Q-Learning Based Multi-Task Offloading In Edge-Cloud Systems, Nelson Sharma, Aswini Ghosh, Rajiv Misra, Sajal K. Das
Computer Science Faculty Research & Creative Works
Resource-Constrained Edge Devices Can Not Efficiently Handle the Explosive Growth of Mobile Data and the Increasing Computational Demand of Modern-Day User Applications. Task Offloading Allows the Migration of Complex Tasks from User Devices to the Remote Edge-Cloud Servers Thereby Reducing their Computational Burden and Energy Consumption While Also Improving the Efficiency of Task Processing. However, Obtaining the Optimal Offloading Strategy in a Multi-Task Offloading Decision-Making Process is an NP-Hard Problem. Existing Deep Learning Techniques with Slow Learning Rates and Weak Adaptability Are Not Suitable for Dynamic Multi-User Scenarios. in This Article, We Propose a Novel Deep Meta-Reinforcement Learning-Based Approach to …
Machine-Learning-Based Vulnerability Detection And Classification In Internet Of Things Device Security, Sarah Bin Hulayyil, Shancang Li, Li Da Xu
Machine-Learning-Based Vulnerability Detection And Classification In Internet Of Things Device Security, Sarah Bin Hulayyil, Shancang Li, Li Da Xu
Information Technology & Decision Sciences Faculty Publications
Detecting cyber security vulnerabilities in the Internet of Things (IoT) devices before they are exploited is increasingly challenging and is one of the key technologies to protect IoT devices from cyber attacks. This work conducts a comprehensive survey to investigate the methods and tools used in vulnerability detection in IoT environments utilizing machine learning techniques on various datasets, i.e., IoT23. During this study, the common potential vulnerabilities of IoT architectures are analyzed on each layer and the machine learning workflow is described for detecting IoT vulnerabilities. A vulnerability detection and mitigation framework was proposed for machine learning-based vulnerability detection in …
Energy-Efficient Multi-Rate Opportunistic Routing In Wireless Mesh Networks, Mohammad Ali Mansouri Khah, Neda Moghim, Nasrin Gholami, Sachin Shetty
Energy-Efficient Multi-Rate Opportunistic Routing In Wireless Mesh Networks, Mohammad Ali Mansouri Khah, Neda Moghim, Nasrin Gholami, Sachin Shetty
VMASC Publications
Opportunistic or anypath routing protocols are focused on improving the performance of traditional routing in wireless mesh networks. They do so by leveraging the broadcast nature of the wireless medium and the spatial diversity of the network. Using a set of neighboring nodes, instead of a single specific node, as the next hop forwarder is a crucial aspect of opportunistic routing protocols, and the selection of the forwarder set plays a vital role in their performance. However, most opportunistic routing protocols consider a single transmission rate and power for the nodes, which limits their potential. To address this limitation, this …
Thermotrophy Exploratory Study, James Weifu Lee
Thermotrophy Exploratory Study, James Weifu Lee
Chemistry & Biochemistry Faculty Publications
The question of whether environmental heat energy could be utilized as a source of energy for biological metabolism is the center of this exploratory research. In 1979, this author postulated a hypothesis for the existence of thermotrophs that could isothermally utilize environmental heat energy as a source of their energy on Earth. According to this hypothesis, the thermotrophs could be the first primitive forms of life in the early Earth environment. The chemotrophs and phototrophs that we currently are all well familiar with might have been evolved somehow from the primitive thermotrophs. Furthermore, all the organisms currently regarded as the …