What is he best known for?
The fields of study he is best known for:
- Computer network
- Computer security
- Artificial intelligence
His primary scientific interests are in Mobile edge computing, Distributed computing, Server, Computer network and Demand response.
In his research on the topic of Distributed computing, Service provider is strongly related with Computation offloading.
In his research, Smart contract is intimately related to Edge computing, which falls under the overarching field of Server.
His Computer network study combines topics in areas such as Electricity generation, Electronic engineering, Cognitive radio and Power transmission.
His Demand response research overlaps with other disciplines such as Stackelberg competition, Distributed algorithm, Computer security, Environmental economics and Security analysis.
His work on Honeypot as part of general Computer security research is frequently linked to Software deployment, Denial-of-service attack and Electricity market, thereby connecting diverse disciplines of science.
His most cited work include:
- Dependable Demand Response Management in the Smart Grid: A Stackelberg Game Approach (488 citations)
- Enabling Localized Peer-to-Peer Electricity Trading Among Plug-in Hybrid Electric Vehicles Using Consortium Blockchains (453 citations)
- Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks (413 citations)
What are the main themes of his work throughout his whole career to date?
Sabita Maharjan spends much of his time researching Distributed computing, Computer network, Server, Reinforcement learning and Enhanced Data Rates for GSM Evolution.
Sabita Maharjan has researched Distributed computing in several fields, including Computation offloading, Edge computing, Resource allocation and Mobile edge computing.
Sabita Maharjan combines subjects such as Deep learning, Cellular network, Unsupervised learning and Communications system with his study of Mobile edge computing.
His research in Computer network intersects with topics in Cognitive radio and Blockchain.
His research on Reinforcement learning also deals with topics like
- Base station most often made with reference to Wireless network,
- Benchmark that connect with fields like Operations research and Quality of service.
His biological study spans a wide range of topics, including Systems design, Multimedia, Quality of experience and Service improvement.
He most often published in these fields:
- Distributed computing (36.54%)
- Computer network (30.77%)
- Server (21.15%)
What were the highlights of his more recent work (between 2019-2021)?
- Reinforcement learning (19.23%)
- Distributed computing (36.54%)
- Computer network (30.77%)
In recent papers he was focusing on the following fields of study:
Sabita Maharjan mostly deals with Reinforcement learning, Distributed computing, Computer network, Edge computing and Server.
Sabita Maharjan interconnects Enhanced Data Rates for GSM Evolution, Computation offloading, Asynchronous communication, Base station and Information privacy in the investigation of issues within Distributed computing.
His Computer network research is multidisciplinary, relying on both Blockchain and Benchmark.
In his study, Reliability engineering is inextricably linked to Optimization problem, which falls within the broad field of Edge computing.
His Server study integrates concerns from other disciplines, such as Wireless network and Shared resource.
His study looks at the relationship between Shared resource and topics such as Differential privacy, which overlap with Mobile edge computing.
Between 2019 and 2021, his most popular works were:
- Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT (123 citations)
- Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles (54 citations)
- Vehicular Edge Computing and Networking: A Survey (53 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Computer security
- Artificial intelligence
Sabita Maharjan mainly focuses on Reinforcement learning, Distributed computing, Information privacy, Computer network and Mobile edge computing.
His work carried out in the field of Reinforcement learning brings together such families of science as Optimization problem, Scheduling, Edge computing and Integer programming.
His Edge computing research is multidisciplinary, incorporating perspectives in Markov decision process, Bandwidth allocation, Base station and Cache.
Many of his Information privacy research pursuits overlap with Data sharing and Resource management.
The concepts of his Computer network study are interwoven with issues in Enhanced Data Rates for GSM Evolution and Key.
His study in Mobile edge computing is interdisciplinary in nature, drawing from both Energy and Stochastic optimization.
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