Smart Cities sensing is an emerging paradigm to facilitate the transition into smart city services. The advent of the Internet of Things (IoT) and the widespread use of mobile devices with computing and sensing capabilities has motivated applications that require data acquisition at a societal scale. These valuable data can be leveraged to train advanced Artificial Intelligence (AI) models that serve various smart services that benefit society in all aspects. Despite their effectiveness, legacy data acquisition models backed with centralized Machine Learning models entail security and privacy concerns, and lead to less participation in large-scale sensing and data provision for smart city services. To overcome these challenges, Federated Learning is a novel concept that can serve as a solution to the privacy and security issues encountered within the process of data collection. This survey article presents an overview of smart city sensing and its current challenges followed by the potential of Federated Learning in addressing those challenges. A comprehensive discussion of the state-of-the-art methods for Federated Learning is provided along with an in-depth discussion on the applicability of Federated Learning in smart city sensing; clear insights on open issues, challenges, and opportunities in this field are provided as guidance for the researchers studying this subject matter.

Synchronization is fundamental for mirroring real-world entities in real-time and supporting effective operations of Digital Twins (DTs). Such synchronization is enabled by the communication between the physical and virtual realms, and it is mostly assumed to occur in real-time. However, this is not the case, as real-life scenarios witness performance degradation that may lead to synchronization problems. Hence, as such a problem has yet to be thoroughly analyzed in the literature, this work attempts to uncover potential challenges by emulating and analyzing the DT traffic flows in networks of different scales, for different communication protocols, and with various flow configurations. We propose a Twin Alignment Ratio metric to evaluate the synchronization performance to achieve this goal. Consequently, the findings reveal the interplay of network infrastructure, protocol selection, and twinning rate on synchronization and performance.

Ubiquity, heterogeneity and dense deployment of sensors have yielded the Internet of Things (IoT) concept, which is an integral component of various smart spaces including smart cities. Applications and services in a smart city ecosystem aim at minimizing the cost and maximizing the quality of living. Among these services, waste management is a unique service that covers both aspects. To this end, in this paper, we propose a WSN-driven system for smart waste management in urban areas. In our proposed framework, the waste bins are equipped with sensors that continuously monitor the waste level and trigger alarms that are wirelessly communicated to a cloud platform to actuate the municipal agents, i.e., waste collection trucks. We formulate an Integer Linear Programming (ILP) model to find the best set of trajectory-truck with the objectives of minimum cost or minimum delay. In order for the trajectory assistance to work in real time, we propose three heuristics, one of which is a greedy one. Through simulations, we show that the ILP formulation can provide a baseline reference to the heuristics, whereas the non-greedy heuristics can significantly outperform the greedy approach regarding cost and delay under moderate waste accumulation scenarios.

In this paper, we propose novel deployment strategies for Wireless Sensor Networks and study 3D coverage under these heterogeneous deployment strategies. Our aim is to find answers to questions like how many sensors are needed to cover a specified 3D terrain at a specified coverage percentage. Furthermore, the effect of sleeping on coverage is also studied. Simulation results demonstrate that depending on application requirements an appropriate deployment strategy can be selected to obtain required 3D coverage.

Localization play a key role in location based services such as parking assistance, location dependent advertising, people tracking, security management, etc. Usually, mobile users are the driving force of those applications and the third dimension plays an important role on the functionality of the system. Accurate localization in three dimensional space requires fast and robust algorithms. The widely recognized lateration technique uses anchor locations and distance measurements to locate a user. In real life, distance measurements are distorted due to environmental factors and it is well known that distance measurement errors affect the accuracy of the estimated location. Moreover, the complexity of the localization technique is a significant issue. Mobile users of emerging technologies such as wearable computers demand "light-weight" techniques. In this paper, we propose 3D-AML (Three Dimensional Adapted Multi-Lateration) to provide light-weight and accurate localization. 3DAML uses the common concept of intersecting circles in two dimensional environments in form of intersecting spheres in 3D. 3D-AML also uses geometric properties to estimate the location of a sensor node. Hence, its time complexity is reasonably low by using arithmetic operations. We compare 3D-AML to the conventional multi-lateration technique of GPS. We show that the 3D-AML method has lower localization error than the multi-lateration technique for noisy measurements that are modeled with Gaussian distribution with varying standard deviations.