Mohammad Sadegh Yousefpoor

TL;DR: The proposed categorization in this paper helps researchers achieve a comprehensive view of dynamic key management schemes in wireless sensor networks based on the type of keys, key distribution mechanisms, key cryptography methods and network models.

TL;DR: This paper categorizes secure data aggregation methods in wireless sensor networks based on network model, network topology, key cryptography technique, encryption method, application, authentication mechanism, and data recovery ability and believes that this taxonomy can help researchers to design secure and efficient data aggregating methods.

TL;DR: In this paper , a reinforcement learning-based area coverage technique called CoWSN is proposed to intelligently monitor oil and gas pipelines, where the sensing range of each sensor node is converted to a digital matrix to estimate the overlap of this node with other neighboring nodes and a Q-learning-based scheduling mechanism is designed to determine the activity time of sensor nodes based on their overlapping, energy, and distance to the base station.

TL;DR: A smart dynamic key management system for wireless sensor networks which utilizes fuzzy logic for path key generation and for adding new nodes to the network and which is more efficient in terms of communication overload, required memory space, and energy consumption.

TL;DR: In this article, a classification of machine learning-based schemes in healthcare is presented, based on data pre-processing methods (data cleaning methods, data reduction methods), learning methods (unsupervised learning, supervised learning, semi-supervised Learning, and reinforcement learning), evaluation methods (simulation-based evaluation and practical implementationbased evaluation in real environment) and applications (diagnosis, treatment).