Ph.D. Dissertation:

A Light-Weight Cluster-Based Secure Sensing Model for Body Area Networks in IoT Applications

In recent years, advances in electronics and wireless communications have made it possible to design and manufacture small, low-cost sensors in a variety of applications. These sensors, which are capable of sending, receiving, and processing information, have led to the development of ideas such as clinical health care with the help of body area networks. Here, the two challenges of security and energy consumption have led to the use of clinical health care not being pervasive with the help of body area networks. Therefore, in order to solve two challenges, in this study, a light-weight cluster-based secure sensing model for body area networks in IoT applications is proposed. In this model, patients’ data is labeled according to the proposed prioritizing mechanism. This provides a prioritized and delay-less service in the server side for the patients with critical conditions. In the proposed model, the sensed data is monitored in a real time way to calculate its sparsity level. Then, the ,calculated sparsity level is used to determine the number of required measurements for data sampling. This allows to sample the data with the number of measurements proportional to the sparsity level and information content of the data. Moreover, the particular design of the measurement matrix causes the aggregated data to be encrypted and its security be guaranteed. Simulation results show that compared to its counterpart schemes, the proposed sensing model not only provides security but also reduces the average energy consumption of the sensor nodes and the average packet delivery delay. This improvement originates from the reduction of the number of required bits for transferring the sensed data and is due to the consideration of the information content and sparsity level variation in the sensed data.

Keywords: Internet of Things, Body Area Networks, Light-Weight Secure Sensing, Energy, Delay