Privacy Preserving Data Mining Approach for IoT based WSN in Smart City

Abstract

Wireless Sensor Network (WSN) is one of the most fundamental technologies of Internet of Things (IoT). Various IoT devices are connected to the internet by making use of WSN composed of different sensor nodes and actuators, where these sensor nodes collaborate and accomplish their tasks dynamically. The main objective of deploying WSN-based applications is to make high precision real-time observations, and it is extremely challenging because of the limited computing power of the sensors operating under constrained environments, resource constraints like energy, computation speed, bandwidth and memory, huge volume of high speed, heterogeneous and fast-changing WSN data. These challenges encouraged the researchers to concentrate deeper on exploring data mining techniques to extract the required information from the fast-changing sensor data in WSN and thereby efficiently handle the massive data generated by the WSNs. The increasing need of data mining techniques for WSN has inspired us to propose a distributed data mining technique that effectively handles the data generated by the nodes in the WSN and prolongs the lifespan of the network. Our work provides a novel cluster based scheme to mine the sensors data without moving it to cluster head (CH) or base station (BS) to achieve maximum performance in a WSN environment. The basic idea of the proposed work is that local computations are performed by utilizing the computing power at each sensor node and then the minimum higher level statistical summaries are exchanged, which decreases the energy dissipation in communication as the amount of the sensor data transferred is considerably reduced, and thereby the sensor network lifetime is maximized and also preserve the privacy of the sensor data.

Internet of things based wireless sensor network: a review

“…Several devices of IoT are linked to the internet via WSN, which is made up of various sensor nodes and actuators that cooperate and perform tasks in a dynamic manner. The primary goal of deploying IoT applications is to produce high observations in real-time, which is too difficult due to the resource constraints, the limited computing power of sensors, such as speed, energy, bandwidth, computation, ands memory, and a large volume of heterogeneous, high-speed WSN data [18]. Imran et al [87] have outlined a few key challenges in IoT and sensing technologies, as well as key issues such as self-organization, energy efficiency, security and scalability that must be considered when utilizing these newer technologies for the benefit of the society.…”

Privacy Preserving Data Mining Framework for Negative Association Rules: An Application to Healthcare Informatics

“…From this point of view, the work in [50] may represent the basis on which the proposed model is built, in which the fuzzy K-medoid machine learning algorithm is used for cluster formation and suggested optimization-based data sanitization is employed for data privacy. The latest references [51][52][53][54][55][56] provide more information on how machine learning techniques were used for privacy preserving data mining in IoT-based healthcare applications.…”

Advanced Technologies and Their Use in Smart City Management

“…The integration of Cloud computing, the Internet of Things and Big Data analysis is mediated through SDN (Software-Defined Network). The main goal of the Software-Defined Network is to create a vision of a Smart City based on a unified communication infrastructure of stakeholders [25]. Centralized system control creates a secure user environment in real time.…”

“…IoT-WSN (Internet of Things-Wireless Sensor Network) is composed of a huge number of sensors and nodes that are located on moving objects in a large area of a Smart City. Technology is used primarily in the process of monitoring, management and decision making [25].…”