In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this study, a bibliographical review on distributed Kalman filtering (DKF) is provided. The study contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area.

Internet of Things (IoT) is a developing technology that provides the simplicity and benefits of exchanging data with other devices using the cloud or wireless networks. However, the changes and developments in the IoT environment are making IoT systems susceptible to cyber attacks which could possibly lead to malicious intrusions. The impacts of these intrusions could lead to physical and economical damages. This article primarily focuses on the IoT system/framework, the IoT, learning-based methods, and the difficulties faced by the IoT devices or systems after the occurrence of an attack. Learning-based methods are reviewed using different types of cyber attacks, such as denial-of-service (DoS), distributed denial-of-service (DDoS), probing, user-to-root (U2R), remote-to-local (R2L), botnet attack, spoofing, and man-in-the-middle (MITM) attacks. For learning-based methods, both machine and deep learning methods are presented and analyzed in relation to the detection of cyber attacks in IoT systems. A comprehensive list of publications to date in the literature is integrated to present a complete picture of various developments in this area. Finally, future research directions are also provided in the paper.

Monitoring critical infrastructures is highly dependent on the accuracy of the installed sensors and the robustness of estimation algorithms. Data-injection attacks can degrade the operational reliability and security of any cyber-physical infrastructures. An attacker can compromise the integrity of the monitoring algorithms by hijacking a subset of sensor measurements and sending manipulated readings. Such approach can result in wide-area blackouts in power grids. This paper considers several cases of severe data-injections with high probabilities of information loss. To achieve an accurate supervision, a Bayesian-based approximated filter (BAF) has been proposed at each monitoring node using a distributed architecture. To maintain a reduced communication overhead and time complexity, upper and lower bound methods have been developed. The performance of the proposed technique has been demonstrated in a mature synchrophasor application known as the oscillation detection. Two test cases have been generated to examine the immunity of the proposed estimation scheme in New Zealand and Oman power grids. The tests were conducted in the presence of harsh data-injection attacks and multiple system disturbances. Results show the proposed BAF method can accurately extract the oscillatory parameters from the contaminated measurements.

Monitoring and control of electrical power grids are highly reliant on the accuracy of the digital measurements. These digital measurements reflect the precision of the installed sensors, which are vulnerable to the injection of unknown parameters in the form of device malfunction and cyberattacks. This may question the operational security and reliability of many cyberphysical infrastructure such as smart grid. To resolve this issue, a multisensor temporal prediction based wide-area control scheme is proposed in this paper. The feasibility of the designed scheme is verified in an advanced synchrophasor measurements based wide-area monitoring and control system (WAMCS). This WAMCS adopts a flexible ac transmission system device (the primary controller) for controlling the smart grid’s voltage profile. The algorithm is validated in a real-time environment with an innovative software-in-the-loop testing setup. The performance of the proposed technique in the presence of false-data-injection attacks shows promising results.