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The concept of Smart City started its development path around two to three decades ago; it has been mainly influenced and driven by radical changes in technological, social and business environments. Big Data, Internet of Things and Networked Cyber-Physical Systems, together with the concepts of Cloud, Fog and Edge Computing, have tremendous impact on the development of Smart City, reforming its frame and tasks and redefining its requirements and challenges. We consider feasible architectures of the IT infrastructure and signal processing, taking into account aspects of Big Data, followed by summary of benefits and main challenges, like security of infrastructure and private data. As a practical example we present a public safety application of multi-sensor imaging system: a smart device with target detection subsystem based on artificial intelligence used for activation of target tracking. The experiments have been performed in the cities of Abu Dhabi and Belgrade, which have very different environment. The experiments have shown the effects of videostreaming compression on thermal imagers and the importance of distributed processing power that optimizes requirements for amount of transmitted data and delay.

In this paper a novel distributed algorithm for blind macro-calibration in sensor networks based on consensus is proposed. The algorithm is formulated as a set of gradient-type recursions for estimating parameters of sensor calibration functions, starting from local criteria defined as weighted sums of mean square differences between the outputs of neighboring sensors. It is proved that the algorithm achieves asymptotic agreement for sensor gains and offsets in the mean square sense and with probability one. In the case of additive measurement noise, additive inter-agent communication noise and communication outages, a modification of the original algorithm is proposed. It is proved using stochastic approximation arguments that the modified algorithm achieves asymptotic consensus for sensor gains and offsets in the mean square sense and with probability one. Special attention is paid to the situation when one sensor is selected as a reference. Illustrative simulation examples are provided.

This paper presents a design of a decentralized fault detection and isolation (FDI) filter by means of an overlapping decentralized estimation algorithm based on a consensus strategy. An efficient solution to the FDI problem can be obtained by an adequate system decomposition into overlapping subsystems and the construction of local FDI estimators aimed at achieving the desired performance. The general aspects and properties of a consensus based estimator are described in the first part of the paper. An applicability of such an estimator to an FDI problem in a large scale system is discussed next. Namely, a case study related to the detection of fire dissymmetry in a thermal power plant boiler is presented, including the process description and identification procedure, comparison between the results obtained by local and decentralized estimators and conclusions concerning their validity.

Existing adaptive control algorithms at best guarantee that the tracking error is a l2 sequence. This paper presents globally stable adaptive control algorithms for linear discrete-time systems providing l1 tracking performance. Two algorithms with different degree of complexity are proposed, one for the case of known control directions, and a separate algorithm for the case of unknown control directions. It is demonstrated that in both cases the tracking error is l1 sequence, while the input and output signals are uniformly bounded. [ABSTRACT FROM AUTHOR]

In this paper, a novel consensus-based adaptive algorithm for distributed target tracking in large scale camera networks is presented, aimed at situations characterized by limited sensing range, high-level clutter, and possibly occulted targets. The concept of Integrated Probabilistic Data Association (IPDA) is introduced in the distributed adaptive tracker design so that the proposed algorithm, named IPDA Adaptive Consensus Filter (IPDA-ACF), incorporates probabilities of acquiring target-originated measurements, conditioned on either target perceivability or target existence. A distributed adaptation scheme represents the core element of the algorithm, allowing fast convergence under a large variety of operating conditions, emphasizing the influence of the nodes with the highest probability of obtaining target-originated measurements. A theoretical analysis of stability and reduction of noise influence allows getting an insight into the relationship between the local trackers and the global consensus scheme. A comparison with analogous existing methods done by extensive simulations shows that the proposed method achieves the best performance, in spite of lower communication and computation requirements. [ABSTRACT FROM AUTHOR]

This paper deals with recently proposed algorithms for real-time distributed blind macro-calibration of sensor networks based on consensus (synchronization). The algorithms are completely decentralized and do not require a fusion center. The goal is to consolidate all of the existing results on the subject, present them in a unified way, and provide additional important analysis of theoretical and practical issues that one can encounter when designing and applying the methodology. We first present the basic algorithm which estimates local calibration parameters by enforcing asymptotic consensus, in the mean-square sense and with probability one (w.p.1), on calibrated sensor gains and calibrated sensor offsets. For the more realistic case in which additive measurement noise, communication dropouts and additive communication noise are present, two algorithm modifications are discussed: one that uses a simple compensation term, and a more robust one based on an instrumental variable. The modified algorithms also achieve asymptotic agreement for calibrated sensor gains and offsets, in the mean-square sense and w.p.1. The convergence rate can be determined in terms of an upper bound on the mean-square error. The case when the communications between nodes is completely asynchronous, which is of substantial importance for real-world applications, is also presented. Suggestions for design of a priori adjustable weights are given. We also present the results for the case in which the underlying sensor network has a subset of (precalibrated) reference sensors with fixed calibration parameters. Wide applicability and efficacy of these algorithms are illustrated on several simulation examples. Finally, important open questions and future research directions are discussed. [ABSTRACT FROM AUTHOR]