Kranthi Alluri, S. Gopikrishnan^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 943-961, 2024, DOI:10.32604/cmc.2024.048621

Abstract Secure authentication and accurate localization among Internet of Things (IoT) sensors are pivotal for the functionality and integrity of IoT networks. IoT authentication and localization are intricate and symbiotic, impacting both the security and operational functionality of IoT systems. Hence, accurate localization and lightweight authentication on resource-constrained IoT devices pose several challenges. To overcome these challenges, recent approaches have used encryption techniques with well-known key infrastructures. However, these methods are inefficient due to the increasing number of data breaches in their localization approaches. This proposed research efficiently integrates authentication and localization processes in such a way that they complement each… More >

Tao Liu¹, Kejia Zhang^1,*, Jingsong Yin¹, Yan Zhang¹, Zihao Mu¹, Chunsheng Li¹, Yanan Hu²

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2563-2582, 2023, DOI:10.32604/csse.2023.041228

Abstract Spatio-temporal heterogeneous data is the database for decision-making in many fields, and checking its accuracy can provide data support for making decisions. Due to the randomness, complexity, global and local correlation of spatiotemporal heterogeneous data in the temporal and spatial dimensions, traditional detection methods can not guarantee both detection speed and accuracy. Therefore, this article proposes a method for detecting the accuracy of spatiotemporal heterogeneous data by fusing graph convolution and temporal convolution networks. Firstly, the geographic weighting function is introduced and improved to quantify the degree of association between nodes and calculate the weighted adjacency value to simplify the… More >

S. Karthik¹, Robin Singh Bhadoria², Jeong Gon Lee^3,*, Arun Kumar Sivaraman⁴, Sovan Samanta⁵, A. Balasundaram⁶, Brijesh Kumar Chaurasia⁷, S. Ashokkumar⁸

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 243-259, 2022, DOI:10.32604/cmc.2022.023864

Abstract Data is always a crucial issue of concern especially during its prediction and computation in digital revolution. This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication. It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data. The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means. The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters… More >

Ahmad A. A. Alkhatib^*, Qusai Abed-Al

Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1071-1087, 2022, DOI:10.32604/iasc.2022.020461

Abstract Wireless sensor networks have been a very important means in forest monitoring applications. A clustered sensor network comprises a set of cluster members and one cluster head. The cluster members are normally located close to each other, with overlaps among their sensing coverage within the cluster. The cluster members concurrently detect the same event to send to the Cluster Head node. This is where data aggregation is deployed to remove redundant data at the cost of data accuracy, where some data generated by the sensing process might be an outlier. Thus, it is important to conserve the aggregated data’s accuracy… More >