Trust assistance in Sensor-Cloud

Abstract

Incorporating 1) the ubiquitous data gathering ability of wireless sensor networks (WSNs) and 2) the powerful data storage and data processing capabilities of cloud computing (CC), Sensor-Cloud (SC) is attracting increasing attention from both academia and industry. In this paper, focusing on improving the quality of service (QoS) of SC for users to obtain sensory data from the cloud, we propose Trust-Assisted SC (TASC). In TASC, trusted sensors (i.e., sensors with trust values exceeding a threshold) collect and transmit sensory data to the cloud. Then the cloud selects the trusted data centers (i.e., data centers with trust values exceeding a threshold) to store, process the sensory data and further transmit the processed sensory data to users on demand. With extensive simulation results, we show that the TASC can substantially improve the throughput and response time for users to obtain sensory data from the cloud, compared with SC without trust assistance (SCWTA).

WITHDRAWN: Towards sensor-cloud integration: A survey of enabling technologies and architectures

“…Sensors are tiny devices that effectively monitor real-time environments using dynamic, reconfigurable, and self-manageable heterogeneous network topology. By design, sensors are resource constrained devices that lead to limited network life if not properly managed (Zhu et al, 2015). The state-of-the-art SC power management protocols schedule sensor power states subject to communication and computation activities (Ojha, Bera, Misra, & Raghuwanshi, 2014).…”

Data Collection in Smart Communities Using Sensor Cloud: Recent Advances, Taxonomy, and Future Research Directions

“…A trust-assisted sensor cloud model has also been proposed in [10] to improve the user experience. In the model, sensory data are collected from sensor nodes on the basis of the trust value assigned to the sensor node.…”

An Indoor Ultrasonic Positioning System Based on TOA for Internet of Things

“…Taking into account the possibility of loss of the ultrasonic positioning signal, based on Inertial Units measuring (IMUs), the literature [25] proposed a combined moving target location method, which utilizes extended Kalman filter (EKF) and Least Squares Support Vector Machine (LS-SVM). In the research of indoor positioning system, the energy strategy [26][27][28] is also important. This is mainly because the nodes used in the system can only be supplied by battery in most of the cases.…”