https://doi.org/10.1016/j.eij.2022.03.003

Smart cities: Fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques

An integrated fire detection system using IoT and image processing technique for smart cities

Road traffic information has become indispensable for routine vehicular communication but user location privacy an important issue which did not well addressed. An adversary may attack a user by tracking location in routine vehicular communication. Although, continuously changing pseudonyms is a promising solution to attain location privacy in road networks, it has been observed that changing pseudonym at improper time or location may again become a threat for location preservation. As a result, a number of techniques for pseudonym-change have been proposed to achieve location privacy on road networks but most of location based services depend upon speed, GPS position and direction angle services. Hence, sensitive information is periodically broadcasted in every 100-300 ms providing an opportunity to adversaries for accessing critical information and easily tracking vehicles. Moreover, existing methods such as RPCLP, EPCS and MODP for attaining location privacy in mix-zones environment have severely suffered due to large number of pseudonym-changes. To cope with these issues, we presented a Dynamic Pseudonym based Multiple Mix-zones (DPMM) strategy to acquire the highest level of accuracy and privacy. The concept of executing dynamic pseudonym change has been forwarded with respect to pseudonyms, velocity and direction of moving objects. We performed our simulations by using one SUMO simulator and analyzed results compared with existing pseudonym-changing techniques. Our simulation results outperformed various existing techniques and provided better results for achieving high privacy rate, requiring small number of pseudonym-change as well as providing best performance.

DPMM: dynamic pseudonym-based multiple mix-zones generation for mobile traveler

Nowadays communication by using mobile vehicle getting popularity and user experience map services like Google maps to reach at the destination. It is also clear that map services user have to reveal important information like GPS coordinates or some other personal information during this process. According to current mix-zones privacy techniques, it is revealed that, these methods are much generalized and cannot be applied to secure map services users. In this paper, we have given a novel MMLPP technique, which is specifically constructed for map services users over road network. According to this, mobile vehicle’s users to ask a path among two end points defined on the map, however not to disclose secret location and query statistics. The main concept is to place end points in such a way that, they must be near to each other, alternatively: 1) Actual statistics defined inside these end points (like GPS coordinates) must not be altered i.e. location privacy ensured. 2) The path reverted by the map services change a little, i.e. service usability must be ensured. We can re-iterate in such a way that, firstly , a mobile client extract point of interest , that is close to original end points, and then finally consider two POIs considered as shifted end points which satisfy the condition of Geo-Indistinguishability. We analyzed MMLPP technique for road network application for GTMobiSim on multiple scales of map services and experiments are conducted using real traces. It has been revealed that MMLPP has outperformed all other techniques and shows balance among location privacy and service usability.

Location monitoring approach: multiple mix-zones with location privacy protection based on traffic flow over road networks

In recent times, social and commercial interests in location-based services (LBS) are significantly increasing due to the rise in smart devices and technologies. The global navigation satellite systems (GNSS) have long been employed for LBS to navigate and determine accurate and reliable location information in outdoor environments. However, the GNSS signals are too weak to penetrate buildings and unable to provide reliable indoor LBS. Hence, GNSS's incompetence in the indoor environment invites extensive research and development of an indoor positioning system (IPS). Various technologies and techniques have been studied for IPS development. This paper provides an overview of the available smartphone-based indoor localization solutions that rely on radio frequency technologies. As fingerprinting localization is mostly accepted for IPS development owing to its good localization accuracy, we discuss fingerprinting localization in detail. In particular, our analysis is more focused on practical IPS that are realized using a smartphone and Wi-Fi/Bluetooth Low Energy (BLE) as a signal source. Furthermore, we elaborate on the challenges of practical IPS, the available solutions and comprehensive performance comparison, and present some future trends in IPS development.

https://www.mdpi.com/1424-8220/20/24/7230/pdf

A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies.

The increasing number of vehicles on the road along with the mismanagement of available parking space leads to the parking related problems as well as increased traffic congestion in urban areas. Thus it is highly required to develop an automated smart parking management system that would help the driver to find out some suitable parking space for his/her vehicle very quickly. Although ample amount of research works on the development of smart parking system exist in literature, but most of them have not addressed the problem of real-time detection of improper parking and automatic collection of parking charges. In this paper, a prototype of internet-of-thing based E-parking system is proposed. The proposed E-parking system uses an integrated component called parking meter to address the above-mentioned issues as well as to provide smart parking management throughout the city.

An IoT-based E-parking system for smart cities

Traffic congestion is one of the major issues with the public transportation system in recent time. The traffic congestion has a negative impact on the productivity, competitiveness and economic growth of a country. Hence traffic congestion control has become an important area of research and significant number of solutions to this problem came out of various research efforts in the said field over the past few decades. Among these, vehicle-to-vehicle (V2V) communication based approaches cannot accurately estimate the density of traffic congestion. On the other hand, the traffic signaling systems having predetermined fixed operation time cannot manage the traffic volume changing over time and thus, long traffic queues are generated at the road crossings. To address the above mentioned issue, this paper proposes an internet-of-things (IoT) based intelligent traffic congestion control system that dynamically sets the signal operation time based on the measured values of traffic congestion density. Moreover, a novel technique of measuring the density of traffic congestion created at the road crossings is also presented in this paper.

An IoT based Intelligent Traffic Congestion Control System for Road Crossings

Location estimation is essential to the success of location based services. Since GPS does not work well in indoor and the urban areas, several indoor localization systems have been proposed in the literature. Among these, the fingerprinting-based localization systems involving two phases: training phase and positioning phase, are used mostly. In the training phase, a radio map is constructed by collecting the received signal strength (RSS) measurements at a set of known training locations. In the positioning phase, the training location whose corresponding RSS pattern matches best with the currently observed RSS pattern is selected as the estimated location of the object. The positioning accuracy of such systems depends on the grain size of the training locations, i.e., better localization accuracy can be achieved with increasing number of training locations, which in turn, increases the comparison cost as well as the searching time in the positioning phase. Several clustering strategies have been proposed in the literature to reduce the searching time by grouping several training locations into a cluster and selecting the right cluster in the positioning phase followed by searching within the selected cluster to localize an object. However, selection of some false cluster degrades the positioning accuracy of the localization system. Thus, this paper aims at devising some novel clustering strategy that would reduce the searching time without compromising the positioning accuracy.

A novel clustering strategy for fingerprinting-based localization system to reduce the searching time

Localization is highly required to develop the smart-phone based pervasive computing applications. Because of very poor signal strength of global positioning system in indoor areas, various indoor localization systems have been proposed in literature. Among these, received signal strength (RSS) based fingerprinting localization systems are very popular. However, these localization systems at first, need to construct a fingerprint database by collecting RSS patterns at a set of known training locations and then determine the location of an object by comparing the currently observed RSS pattern with all the RSS patterns stored in the fingerprint database. Thus, such localization systems can provide better positioning accuracy by including large number of training data, which in turn, increase the searching overhead. To resolve this issue, several clustering strategies, which restrict the search within a smaller subset of the whole fingerprint database for such localization systems, have been proposed in the literature over the past decade. This paper presents an extensive comparative performance analysis of various clustering-based fingerprinting localization systems to demonstrate their effectiveness on the large-scale positioning system in the presence of radio irregularities and wall attenuation in the wireless environment.

Performance analysis of clustering-based fingerprinting localization systems

Smart city is an emerging concept that aims to improve the quality of city life, to enhance the efficiency of urban operations and services as well as to create a sustainable economic growth of the city. Internet-of-Things (IoT) and big data have emerged as key enabling technologies for smart city services infrastructure. A major issue in the design of smart city services is the provision of Internet connectivity anywhere within the cities through some suitable network infrastructure. Although the use cases for various smart city services and business models for delivering smart city services have been proposed in the literature over the past few years, none of them attempts to address the problem of dealing with heterogeneous technologies required for collecting and processing data to offer such services. Thus, this paper proposes a suitable framework for smart city services based on IoT to address the above-mentioned issues.

Pampa Sadhukhan

Papers

An IoT-based E-parking system for smart cities

An IoT based Intelligent Traffic Congestion Control System for Road Crossings

A novel clustering strategy for fingerprinting-based localization system to reduce the searching time

Performance analysis of clustering-based fingerprinting localization systems

An IoT based Framework for Smart City Services