Technologies for Smart Cities

The Global Positioning System (GPS) is currently the most widely spread and most used Global Navigation Satellite System (GNSS). Numerous industries and people rely daily on the system’s ability to determine their position as well as to synchronize time with the atomic clock carried onboard the satellites. It is now more important than ever for GNSS to operate securely and reliably, to ensure the safety of its users. However, in this chapter, it is highlighted that this might not always be the case, as attackers constantly find new ways to exploit the GNSS features and pose an imminent threat to the system’s reliability. GNSS receivers are especially vulnerable to three types of malicious attacks, in particular blocking, jamming and spoofing. In this chapter, a thorough research was conducted for the operation of both GNSS receivers and spoofing devices. A literature review based on the current available studies and research for detection and mitigation measures, was made. Then a new spoofing detection method is proposed and the principles and the research that was based on are analysed. Experimental data and results were gathered along with data extracted from simulated spoofed signals. The results from both the experiment and the simulation are reviewed, compared and discussed. Finally, based on those results conclusions are drawn.

This paper proposes a way to implement potential attraction/repulsion fields. We consider the implementation of a multi-agent system that moves toward a common target in unbounded space, using swarming algorithms based on Reynolds rules. Three swarm modeling approaches are presented, jointly implementing a finite algorithm that ensures collision avoidance with all available barrier types. The algorithms are described in more detail in our previous paper [1]. We propose the joint application of the investigated swarming algorithm and a predator avoidance model based on reinforcement learning to provide collision avoidance with dynamically occurring barriers. Thus, it is possible to temporarily evade the target to ensure the safety of the agents' movement. The algorithm is based on Q-learning, the result of which is an action function. We consider the behavior of a multi-agent system modeled using the proposed approaches in a bounded space—a polygon or polygon. In this case, in addition to the described interactions, the movement of a group of agents is influenced by repulsive forces from walls. There is a problem of compensation of repulsive and attractive potentials, accompanied by braking of agent or ignoring of walls when moving to the target. This problem is proposed to be solved using function-voxel models. The principle of movement of agents according to the local geometric characteristics stored in the represented graphical M-images of the simulated polygon is described. In the paper, the solution of problems for hazard avoidance using the approach of potential fields, which are expressed by voxel surfaces, is obtained. The advantages of using these models and the need for an algorithm for predator avoidance are highlighted.

In the motion tasks of an autonomous vehicle it is necessary to consider the position of individual obstacles and various unsafe zones. We consider a multi-agent system whose motion is carried out towards a common goal according to algorithms of swarm behavior based on Reynolds rules: speed matching, collision avoidance with neighbors and attraction to neighbors. Three approaches to modeling swarming behavior based on articles (Olfati-Saber, Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans Autom Control, 51(3), 2016; Olfati-Saber, Murray, Flocking with obstacle avoidance: cooperation with limited communication in mobile networks. In: 42nd IEEE international conference on decision and control, vol 2, 2022–2028) are presented. The peculiarities of this work are that the approaches considered together help to realize such a model of motion, which ensures the avoidance of collision with all available types of obstacles. The method is intended for implementation in those spaces, where there will be many autonomous vehicles. The main problem is that when a dynamic obstacle is encountered, congestion can occur—the agents closest to the obstacle react quickly, while distant agents do so with a lag and create crush. The goals of this paper are to propose a method of indirectly transmitting danger information between swarm members without using communication channels. This means that those robots that do not see the danger can get information about it from other agents by observing their behavior. For this purpose, a method of escaping from a pack from a “predator” based on Q-learning is implemented.

Mobility-as-a-Service (MaaS) is a digitalized platform that connects all forms of mobility (including public transportation) within a single app environment. The ability to find (discover), book (reserve) and pay within a single app, along with constructing your entire journey door to door in a seamless manner is a path that many providers and developers are currently undertaking. Successful MaaS environments are characterized by a fragmented, multiple player Mobility Service provider (MSP) ecosystem and strong public transit, as the lynchpin. There are four main MaaS environments, to be explored in this paper: Private MaaS (walled garden), Hybrid MaaS (middle path), Public MaaS (Business to Government B2G), and Open MaaS (Iomob). A global survey of unique MaaS approaches has been undertaken in this paper across the following cities: Denver, Colorado, USA—Private MaaS, Berlin, Germany—Public MaaS, Lisbon, Portugal—Hybrid MaaS, Singapore—Hybrid MaaS, and Skane Region (Malmo), Sweden—Open MaaS. Based on Iomob’s MaaS journey experience, there are four main lessons to be learned: app stickiness, data quality and access, Integrated payments, importance of one-click door-to-door capability for travel experience, and modularized journeys. In conclusion, the main recommendations for MaaS success are: open platforms and data, flexible business models, and reducing friction. These are the key enablers for MaaS to help in the overall reduction of congestion and emissions, encouragement of public health, and improvement of the overall well being for cities and regions.

The simplest version of the sub-Gramian method is based on the spectral decomposition of a square \(H_2\) norm of the transfer function. This case applies when a system can be described as an LTI dynamic one and therefore has a corresponding algebraic Lyapunov equation.

How our society and in particular our mobility will look like within 30 years from now is hard to predict. The authors take a bold position by looking at how mobility could look like in 2050, disregarding of any temporarily constraint and only then, define the way how to get there by analyzing the different steps, needed to pave the way. This methodology allows them to get rid of the famous tunnel-vision what most studies suffer from. It leads to very interesting and refreshing conclusions, in particular on congestion, traffic jams, car accidents and also on the evolution of the automotive industry in general. The key question, that gets an answer herewith, is “how many vehicles will we have on our roads in 2050?”. The answer might be surprising.

The concept of a Smart City is to use the existing resources in an optimal way to provide the greatest convenience to its residents. This requires close integration of all components, for example, street video surveillance, public services, intelligent transport systems and others, on the scale of a megalopolis. Every year, the world's megacities are becoming more comfortable for residents due to the introduction of newest technologies. First, such technologies include intelligent control systems in the transportation field. The main goals of Smart Transportation are the efficient and coordinated movement of people, monitoring the location of objects, fast and reliable interaction of vehicles with each other, as well as guaranteeing road safety. This paper represents examples of artificial intelligence technologies and optimization methods applications to create such smart systems.

An algorithm is developed in order to record a vehicle’s fuel consumption using data from a smartphone’s sensors. Six field tests were conducted: (1) Ford Fiesta car with automatic transmission driven around Coventry, UK, with “passive” and “restless” driving behaviors, (2) Ford Fiesta car with manual transmission under heavy traffic driven around Athens, Greece, (3) Ford Fiesta car with manual transmission driven around Athens, Greece, in heavy traffic, in a highway, with “passive” and “restless” driving behaviors and high variation in altitude during the trip, (4) Ford Fiesta car with manual transmission driven around Athens, Greece, with “passive” and “restless” driving behaviors and even higher variation in altitude during the trip, (5) Suzuki Swift car with manual transmission, a route including highway, streets and alleys, in the west Attica in the surrounding area of the capital of Greece, and (6) Suzuki Swift car with manual transmission, with “passive”, “normal” and “restless” driving behaviors in West Attica, in a place with a small hill with a very high slope that we “climbed” three times in a row. The results show that the proposed algorithm improves the smartphone-recorded GPS data so that they show high accuracy when compared to the GPS data extracted from each vehicle’s on-board diagnostic system.

Active suspension systems allow one to improve the performance of a vehicle, to reduce the fuel (energy) consumption and exhaust emissions. This in turn allows one to improve the transport traffic and well-being in cities. The analysis and design of several types of semi-active and active suspension systems is provided in this paper.