In modern distributed cyber-physical systems (CPS), information fusion often plays a key role in automate and self-adaptive decision making process. However, given the heterogeneous and distributed nature of modern CPSs, it is a great challenge to operate CPSs with the compromised data integrity and unreliable communication links. In this paper, we study the distributed state estimation problem under the false data injection attack (FDIA) with probabilistic communication networks. We propose an integrated ”detection + fusion” solution, which is based on the Kullback-Leibler divergences (KLD) between local posteriors and therefore does not require the exchange of raw sensor data. For the FDIA detection step, the KLDs are used to cluster nodes in the probability space and to partition the space into secure and insecure subspaces. By approximating the distribution of the KLDs with a general$\chi ^2$distribution and calculating its tail probability, we provide an analysis of the detection error rate. For the information fusion step, we discuss the potential risk of double counting the shared prior information in the KLD-based consensus formulation method. We show that if the local posteriors are updated from the shared prior, the increased number of neighbouring nodes will lead to the diminished information gain. To overcome this problem, we propose a near-optimal distributed information fusion solution with properly weighted prior and data likelihood. Finally, we present simulation results for the integrated solution. We discuss the impact of network connectivity on the empirical detection error rate and the accuracy of state estimation.

Predictors are learned using past training data which may contain features that are unavailable at the time of prediction. We develop an approach that is robust against outlying missing features, based on the optimality properties of an oracle predictor which observes them. The robustness properties of the approach are demonstrated on both real and synthetic data.

Real-time accurate air quality data is very important for pollution exposure monitoring and urban planning. However, there are limited high-quality air quality monitoring stations (AQMS) in cities due to their high equipment costs. To provide real-time and accurate data covering large area, this paper proposes a novel scheme that jointly considers online multi-hop calibration and spatio-temporal coverage in route selection for mobile sensors. A novel sensor carrier selection problem (SCSP) is formulated, which aims to maximize the spatio-temporal coverage ratio and guarantee the accuracy of measurements through sensor calibration. An online Bayesian based collaborative calibration (OBCC) scheme is proposed to relax the multi-hop calibration constraint in the SCSP. Based on the OBCC, a multi-hop calibration judgment algorithm (MCJA) is proposed to decide whether the data accuracy of a given set of routes can be guaranteed through collaborative calibration. Furthermore, a heuristic sensor route selection algorithm (SRSA) is then developed to solve the SCSP.

Wireless sensor networks (WSN) based on unmanned aerial vehicles (UAV) are ideal platforms for monitoring dynamics over larger service area. On the other hand, aerial sensor networks (ASNs) are often required to be connected with a command center for sending data and receiving control messages in real time. In this paper, we study the problem of path planning for ASNs with connectivity constraints. The primary goal of path planning is driving UAVs to locations where the most informative measurements can be collected. Meanwhile, the worst link's capacity is assured to be greater than a pre-defined requirement. We proposed a solution for the path planning problem and it consists of two modules: network coordinator (NC) and motion controller (MC). The NC manages the topology of relay-assisted wireless communication networks. For the design of MC, we compare two motion strategies: maximum entropy and maximum mutual information. The simulation results show that our proposed solution achieves accurate signal reconstruction while maintaining the connectivity. We conclude that it's important to enable UAV-to-UAV communications for future ASN-based applications.

In this paper, the multidimensional output Gaussian process (GP) is applied to model urban environmental data collected by sensor networks. Measurements from sensors at different locations are correlated. Moreover, we observe that the pollution level in urban area is highly coupled with human activities and shows periodic patterns accordingly. Based on these observations, we discuss the design of mean and kernel functions with two approaches: (1) composed kernel and maximum likelihood estimation of hyper-parameters, (2) Wiener-Khinchin theorem based approximation of sample covariances. To validate the models, the accuracy of interpolations given by different approaches are compared. The experimental results show that, for the application of interpolation, the dependent GP with the approximated sample covariances as kernels can provide better performance than the independent GP model with composed kernels.