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Mingwei Gong is an Associate Professor at the Department of Mathematics and Computing at Mount Royal University. He received his B.Eng. degree in computer engineering from Tianjin University, Tianjin, China, in 2001, and his M.Sc. and Ph.D. degrees in Computer Science from the University of Calgary, in 2003 and 2009, respectively. His research interests are in computer networking, resource allocation, and network security.
Technological advancements have led to increased confidence in the design of large-scale wireless networks that comprise small energy constraint devices. Despite the boost in technological advancements, energy dissipation and fault tolerance are amongst the key deciding factors while designing and deploying wireless sensor networks. This paper proposes a Fault-tolerant Energy-efficient Hierarchical Clustering Algorithm (FEHCA) for wireless sensor networks (WSNs), which demonstrates energy-efficient clustering and fault-tolerant operation of cluster heads (CHs). It treats CHs as no special node but equally prone to faults as normal sensing nodes of the cluster. The proposed scheme addresses some of the limitations of prominent hierarchical clustering algorithms, such as the randomized election of the cluster heads after each round, which results in significant energy dissipation; non-consideration of the residual energy of the sensing nodes while selecting cluster heads, etc. It utilizes the capability of vector quantization to partition the deployed sensors into an optimal number of clusters and ensures that almost the entire area to be monitored is alive for most of the network’s lifetime. This supports better decision-making compared to decisions made on the basis of limited area sensing data after a few rounds of communication. The scheme is implemented for both friendly as well as hostile deployments. The simulation results are encouraging and validate the proposed algorithm.
Ankur Choudhary; Santosh Kumar; Sharad Gupta; Mingwei Gong; Aniket Mahanti. FEHCA: A Fault-Tolerant Energy-Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks. Energies 2021, 14, 3935 .
AMA StyleAnkur Choudhary, Santosh Kumar, Sharad Gupta, Mingwei Gong, Aniket Mahanti. FEHCA: A Fault-Tolerant Energy-Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks. Energies. 2021; 14 (13):3935.
Chicago/Turabian StyleAnkur Choudhary; Santosh Kumar; Sharad Gupta; Mingwei Gong; Aniket Mahanti. 2021. "FEHCA: A Fault-Tolerant Energy-Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks." Energies 14, no. 13: 3935.
Fog computing is an emerging computing paradigm that has come into consideration for the deployment of Internet of Things (IoT) applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern that should come into consideration. Therefore, to provide the necessary security for Fog devices, there is a need to understand what the security concerns are with regards to Fog. All aspects of Fog security, which have not been covered by other literature works, need to be identified and aggregated. On the other hand, privacy preservation for user’s data in Fog devices and application data processed in Fog devices is another concern. To provide the appropriate level of trust and privacy, there is a need to focus on authentication, threats and access control mechanisms as well as privacy protection techniques in Fog computing. In this paper, a survey along with a taxonomy is proposed, which presents an overview of existing security concerns in the context of the Fog computing paradigm. Moreover, the Blockchain-based solutions towards a secure Fog computing environment is presented and various research challenges and directions for future research are discussed.
Abdullah Patwary; Ranesh Naha; Saurabh Garg; Sudheer Battula; Anwarul Kaium Patwary; Erfan Aghasian; Muhammad Amin; Aniket Mahanti; Mingwei Gong. Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control. Electronics 2021, 10, 1171 .
AMA StyleAbdullah Patwary, Ranesh Naha, Saurabh Garg, Sudheer Battula, Anwarul Kaium Patwary, Erfan Aghasian, Muhammad Amin, Aniket Mahanti, Mingwei Gong. Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control. Electronics. 2021; 10 (10):1171.
Chicago/Turabian StyleAbdullah Patwary; Ranesh Naha; Saurabh Garg; Sudheer Battula; Anwarul Kaium Patwary; Erfan Aghasian; Muhammad Amin; Aniket Mahanti; Mingwei Gong. 2021. "Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control." Electronics 10, no. 10: 1171.
This paper presents two methods for detecting abnormal electricity consumption by utilizing usage patterns in the vicinity. The methods use contextual and factual information including, energy consumption patterns, nature of supply and category of day to logically group meters and find abnormalities. Using heuristics proposed in the paper, data collected from fifty smart meters deployed inside hostels of IIIT-Delhi were investigated for abnormal electricity consumption. Multiple abnormalities were found and their causes were verified after discussion with campus administrators. Our results show that the proposed heuristics successfully found abnormal energy consumption behavior. Therefore, these methods could be used for real-time abnormality detection. This will result in reducing operating costs by automatically detecting and reporting abnormalities without human intervention.
Ankur Sial; Amarjeet Singh; Aniket Mahanti; Mingwei Gong. Heuristics-Based Detection of Abnormal Energy Consumption. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018, 21 -31.
AMA StyleAnkur Sial, Amarjeet Singh, Aniket Mahanti, Mingwei Gong. Heuristics-Based Detection of Abnormal Energy Consumption. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2018; ():21-31.
Chicago/Turabian StyleAnkur Sial; Amarjeet Singh; Aniket Mahanti; Mingwei Gong. 2018. "Heuristics-Based Detection of Abnormal Energy Consumption." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 21-31.