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, we propose a new algorithm for distributed spectrum sensing and channel selection in cognitive radio networks based on consensus. The algorithm operates within a multi-agent reinforcement learning scheme. The proposed consensus strategy, implemented over a directed, typically sparse, time-varying low-bandwidth communication network, enforces collaboration between the agents in a completely decentralized and distributed way. The motivation for the proposed approach comes directly from typical cognitive radio networks’ practical scenarios, where such a decentralized setting and distributed operation is of essential importance. Specifically, the proposed setting provides all the agents, in unknown environmental and application conditions, with viable network-wide information. Hence, a set of participating agents becomes capable of successful calculation of the optimal joint spectrum sensing and channel selection strategy even if the individual agents are not. The proposed algorithm is, by its nature, scalable and robust to node and link failures. The paper presents a detailed discussion and analysis of the algorithm’s characteristics, including the effects of denoising, the possibility of organizing coordinated actions, and the convergence rate improvement induced by the consensus scheme. The results of extensive simulations demonstrate the high effectiveness of the proposed algorithm, and that its behavior is close to the centralized scheme even in the case of sparse neighbor-based inter-node communication.

The Internet of Things (IoT) connects a wide range of sensors and devices in environments that are often dynamic and resource-constrained, where efficient distributed solutions are essential for ensuring robust and scalable operation. This article presents a novel adaptive consensus algorithm, tailored for distributed signal processing in heterogeneous sensor networks, with a focus on distributed estimation and target tracking. The algorithm addresses the challenge posed by networks where intelligent sensors have limited sensing, computation, and communication capabilities, resulting in diverse quality of locally available information, and leading to neighbor-based information exchanges. It employs asynchronous gossip protocols to randomly exchange information between nodes, ensuring robustness to time synchronization and network topology uncertainties, while limiting computational and communication costs. The adaptation mechanism operates in two complementary ways. First, we account for variations in the quality of local processing results, ensuring that asymptotic behavior of the consensus scheme accurately reflects this diversity. Second, we introduce a novel adaptation of the rates at which nodes initiate communication, using the available local information. This enables fast information dissemination and provides a solution that is both effective and efficient. We show that, under appropriate network connectivity assumptions, the results obtained by the algorithm converge to the desired asymptotic values in the mean square sense. Numerical simulations demonstrate the algorithm’s properties and effectiveness, particularly in modeling visual surveillance networks where fixed cameras are augmented by moving drones to extend the coverage area.

Maritime situational awareness at over-the-horizon (OTH) distances in exclusive economic zones can be achieved by deploying networks of high-frequency OTH radars (HF-OTHR) in coastal countries along with exploiting automatic identification system (AIS) data. In some regions the reception of AIS messages can be unreliable and with high latency. This leads to difficulties in properly associating AIS data to OTHR tracks. Long history records about the previous whereabouts of vessels based on both OTHR tracks and AIS data can be maintained in order to increase the chances of fusion. If the quantity of data increases significantly, data cleaning can be done in order to minimize system requirements. This process is performed prior to fusing AIS data and observed OTHR tracks. In this paper, we use fuzzy functional dependencies (FFDs) in the context of data fusion from AIS and OTHR sources. The fuzzy logic approach has been shown to be a promising tool for handling data uncertainty from different sensors. The proposed method is experimentally evaluated for fusing AIS data and the target tracks provided by the OTHR installed in the Gulf of Guinea.

Traffic signal control over urban networks requires coordinating the controllers of multiple signalized intersections toward a shared goal of minimizing network-wide congestion. Multi-agent reinforcement learning (MARL) methods have shown considerable promise in this setting. The epsilon–greedy exploration strategy adopted by many of these methods treats every candidate signal phase as equally worth trying, discarding the rich domain knowledge that traffic theory already provides. This paper proposes fuzzy-guided exploration, in which a multi-criteria fuzzy inference system uses local traffic conditions, with phase pressure as its primary input, to assign each candidate phase a priority. These priorities define a sampling distribution used in place of the uniform draw. We evaluate the method across four MARL algorithms covering independent learning (IQL) and the centralized training with decentralized execution paradigm (VDN, QMIX, and QPLEX) on both a synthetic grid and a real-world network. Fuzzy-guided exploration consistently improves upon the baseline in all combinations, with tangible gains on the synthetic grid and substantially larger improvements on the real-world network. These findings demonstrate that exploration is an effective intervention point for domain-knowledge integration in cooperative MARL, and that pressure-based scoring provides a well-suited signal to serve that role in traffic signal control.