P. Saravanakumar¹, T. V. P. Sundararajan², Rajesh Kumar Dhanaraj³, Kashif Nisar^4,*, Fida Hussain Memon^5,6, Ag. Asri Bin Ag. Ibrahim⁴

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1835-1847, 2022, DOI:10.32604/iasc.2022.018953

Abstract Confidentiality and data integrity are essential paradigms in data aggregation owing to the various cyberattacks in wireless sensor networks (WSNs). This study proposes a novel technique named Lamport certificateless signcryption-based shift-invariant connectionist artificial deep neural networks (LCS-SICADNN) by using artificial deep neural networks to develop the data aggregation security model. This model utilises the input layer with several sensor nodes, four hidden layers to overcome different attacks (data injection, compromised node, Sybil and black hole attacks) and the output layer to analyse the given input. The Lamport one-time certificateless signcryption technique involving three different processes (key generation, signcryption and unsigncryption)… More >

Weidong Fang¹, Wuxiong Zhang^1,2,*, QianQian Zhao^1,2, Xiaohong Ji³, Wei Chen⁴, Biruk Assefa⁵

CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 583-599, 2019, DOI:10.32604/cmc.2019.05237

Abstract As an Industrial Wireless Sensor Network (IWSN) is usually deployed in a harsh or unattended environment, the privacy security of data aggregation is facing more and more challenges. Currently, the data aggregation protocols mainly focus on improving the efficiency of data transmitting and aggregating, alternately, the aim at enhancing the security of data. The performances of the secure data aggregation protocols are the trade-off of several metrics, which involves the transmission/fusion, the energy efficiency and the security in Wireless Sensor Network (WSN). Unfortunately, there is no paper in systematic analysis about the performance of the secure data aggregation protocols whether… More >