Wireless sensor networks (WSNs) are the backbone of the Internet of Things in smart cities, delivering the real-time insights that keep urban services adaptive and resilient. However, positioning those sensors within a dynamic urban environment is a holistic, multi-objective challenge that must consider spatial coverage, urban infrastructure and reliability, without sacrificing energy efficiency, sensing coverage, and connectivity. In order to address this issue and enhance sensors coverage in different smart city scenarios, a quality-aware optimization framework driven by the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is introduced. The method is aimed to optimize coverage, sensing and network-connectivity quality for heterogeneous WSNs that mix scalar and visual sensor nodes. NSGA-II hyper-parameters are tuned through grid search, and the resulting layouts are benchmarked in both ideal and randomly distributed deployments. The proposed methodology consistently yields high-quality, cost-effective topologies that may strengthen smart city monitoring in diverse environments.

The persistence of wildfires in natural landscapes calls for innovative early detection methods that leverage cutting-edge technologies. Traditional approaches, which rely solely on visual sensors or isolated devices, while valuable, often fall short in terms of accuracy, cost, scalability, and contextual adaptability. In response to these challenges, this paper introduces a novel fire detection system that integrates a sensor-based model with a dynamically triggered visual analysis module at edge devices. Central to our approach is a multi-sensor monitoring architecture that employs a TinyML classifier to continuously monitor environmental conditions under strict energy constraints. Upon detecting potential fire indicators, the system promptly activates a visual sensor that uses a camera platform to adjust its orientation based on the target position, capturing and analyzing images through a lightweight Convolutional Neural Network (CNN). This proposed system achieves an accuracy of up to 92%, while the quantized CNN models deliver an 83% reduction in inference time and a 74% and 70% decrease in peak RAM and Flash usage, respectively. Simulations also demonstrated that the system reduced the false-positive rate with minimal power increase.

This study addresses the challenges of dependability in Wireless Sensor Networks by proposing a Machine Learning-based approach using Convolutional Neural Networks for network planning for smart cities. Simulated scenarios were used to train the model, which predicts sensor placement and communication configurations to optimize coverage and availability. Results show significant improvements, including an average of 10.7% increase in dependability index and a rise in area coverage from 59% to 73% in 7-node networks, while reducing path failure rates by 27.6%. The method proves effective for enhancing WSN performance and adaptability in safety-critical applications.

The maturation of Wireless Visual Sensor Networks (WVSN) in the last years, with new communication technologies and continuous releasing of embedded hardware development platforms, has significantly enlarged the number and relevance of visual monitoring applications, better supporting smart city and Internet of Things initiatives. However, there are still several challenges to be addressed, especially when visual sensors are used for critical monitoring. When availability issues are addressed in WVSN, different metrics and algorithms can be employed, but such approaches are usually focused on a single coverage goal. Actually, some monitoring applications may want to simultaneously optimize coverage over targets and areas alike, which requires an appropriate perception of the level of availability of the application at any given time. In this context, this article proposes a new availability metric for WVSN, considering that all targets and areas should be optimally and simultaneously covered by the cameras. In addition, optimization algorithms are proposed and compared, aiming at improving the availability of applications when rotatable visual sensors are employed.

Wireless visual sensor networks (WVSN) bring a more comprehensive perception of monitored environments, leading to an increase adoption of such networks as a promising solution for a wide range of applications. Among many examples, highlight industrial applications related to the industry 4.0 paradigm, which increasingly require more data from manufacturing systems. Those sensor-based applications are in many cases safety-critical, requiring dependability guarantees mainly related with reliability and availability, that should be maintained during the whole network operation. Although several approaches have provided network deployment with dependability guarantees, sometimes the monitored environment or the application configurations can change during the network operation, which can violate the dependability requirements and demand network redeployment in order to keep those guarantees. In this paper we propose a novel algorithm to redeploy WVSN guided by the optimization of the application dependability, considering changes on cameras' orientations. A methodology is defined to support dependability analysis. We compare the results of the proposed algorithm with previous algorithms found in literature. The achieved results show that the proposed algorithm is useful and efficient to provide network redeployment, keeping or improving the application dependability.

Area coverage is an inherent and important topic when dealing with wireless visual sensor networks, since it may be desired when addressing availability and fault tolerance in critical applications. This problem arises because more than one visual sensor may cover the same area, generating overlapped regions that can be exploited for different kinds of optimization and quality enhancement approaches. Actually, some methods to compute the resulted covered area by a set of sensors have been proposed, and they are initial steps to compute availability metrics that are necessary for many monitoring scenarios. Particularly, approximate approaches are promising when computing area coverage, potentially achieving good results, although such methods lack proper evaluation and analysis about complexity, performance and precision. In this context, we perform an evaluation of a recent algorithm based on approximation for area coverage computing, comparing it with a precise algorithm developed in this work for this purpose. Doing so, it is desired to assess performance and accuracy of both algorithms, indicating the most appropriate approach when addressing availability in visual sensor networks.