While Internet-of-Things (IoT) significantly facilitates the convenience of people's daily life, the lack of security practice raises the risk of privacy-sensitive user data leakage. Securing data transmission among IoT devices is therefore a critical capability of IoT environments such as Intelligent Connected Vehicles, Smart Home, Intelligent City and so forth. However, cryptographic communication scheme is challenged by the limited resource of low-cost IoT devices, even negligible extra CPU usage of batterypowered sensors would result in dramatical decrease of the battery life. In this paper, to minimize the resource consumption, we propose a communication protocol involving only the symmetric key-based scheme, which provides ultra-lightweight yet effective encryptions to protect the data transmissions. Symmetric keys generated in this protocol are delegated based on a chaotic system, i.e., Logistic Map, to resist against the key reset and device capture attacks. We semantically model such protocol and analyze the security properties. Moreover, the resource consumption is also evaluated to guarantee runtime efficacy.
The fusion of multi-source sensor data is an effective method for improving the accuracy of vehicle navigation. The generalization abilities of neural-network-based inertial devices and GPS integrated navigation systems weaken as the nonlinearity in the system increases, resulting in decreased positioning accuracy. Therefore, a KF-GDBT-PSO (Kalman Filter-Gradient Boosting Decision Tree-Particle Swarm Optimization, KGP) data fusion method was proposed in this work. This method establishes an Inertial Navigation System (INS) error compensation model by integrating Kalman Filter (KF) and Gradient Boosting Decision Tree (GBDT). To improve the prediction accuracy of the GBDT, we optimized the learning algorithm and the fitness parameter using Particle Swarm Optimization (PSO). When the GPS signal was stable, the KGP method was used to solve the nonlinearity issue between the vehicle feature and positioning data. When the GPS signal was unstable, the training model was used to correct the positioning error for the INS, thereby improving the positioning accuracy and continuity. The experimental results show that our method increased the positioning accuracy by 28.20–59.89% compared with the multi-layer perceptual neural network and random forest regression.
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