Vol. 41 · No. 2

There has been a swift rise in the development of smart cities. This evolution has been prompted by the rise in emerging technologies such as edge computing, IoT, data science, and analytics. Combining these technologies has paved the way for new, automated systems for managing and monitoring procedures and industries, resulting in increased efficiency and improved quality of life. While these interconnected services assist in managing the growing population in the urban environments through efficient service delivery and increased operational efficiency, they also increase the risk of adversary threats, security, and privacy challenges to smart cities. This paper presents the holistic view of the security landscape and highlights the security threats, challenges, and risks to the smart city environment.

Blockchain has recently emerged as an auspicious technology for enabling vulnerable data to be exchanged anonymously and securely within Intelligent Transportation System (ITS). Furthermore, Blockchain can be used as an access control mechanism to present a decentralized solution to the distributed authentication problem in the Internet of Bikes (IoB). Although several Blockchain access control mechanisms have been proposed to address the security concerns in IoB, most of them are still vulnerable to some active attacks, especially the cloning attacks. Therefore, this paper proposes a new Trust-Based Access Control Blockchain System (TBACS) to address the cloning attack based on using a secure Trusted Digital Ticket (DGT). The simulations of our solution through the Hyperledger Fabric are showing relevant results in terms of communication overhead and the detection probability of cloning attacks.

Analysis and automation of business processes are a relevant topic in Industry 4.0. This document describes a framework called BP-M* for the analysis, restructuring and implementation of business processes, starting with the creation of a process model and ending with the implementation of the process itself on a workflow management system. The BP-M* framework has been applied to a real case study, the production of fabrics for the collection that will be distributed worldwide by an Italian woolen mill. This process was analyzed and automated, providing the company with useful information to simplify processes and support human operators.

Early waning signals methods have been introduced in the field of ecological sciences and widely used in other domains. However, while these methods have proven effective for deterministic dynamics governed by differential equations or smooth maps – both on synthetic and real data – their application in the social sciences is more complex. A series of protests started in Iraq on 1 October 2019 and farmers' protests in India in September 2020. We investigate in this work how these waves could have been anticipated using early warning signals for the time series of daily occurrences of protests. We use to this end metric-based indicators (autocorrelation at-lag-1, standard deviation and skewness), analyse trends using Kendall rank correlation and use bootstrapping methods to implement a statistical test exhibiting a regime shift (tipping points) in the dynamics of protests. We moreover highlight the importance of the standard deviation as an indicator.

Emergent technologies are being adopted at all the stages of smart building lifecycles. More specifically, mobile, pervasive, and communication technologies are being deployed to achieve a wide range of functions that improve the building performance (including ventilation, air conditioning, heating, lighting, and security) and reduce their related costs. Augmented Reality (AR) has arisen as a promising tool to achieve these goals. However, in spite of the multiple solutions that have integrated AR within smart buildings, several shortcomings are yet to be solved. In addition to the limited user experience and the lack of AR content, current solutions do not provide effective collaborations between construction stakeholders as well as do not include intelligent mechanisms for the management of inspection activities. In order to address some of the smart building challenges, we are proposing in this paper a new framework for intelligent collaborative self-inspection buildings based on the concept of awareness wheel as well as the multi-agent system paradigm.

Connected and autonomous vehicles (CAV) have recently attracted policymakers, manufacturers, and customers' attention. Despite their numerous benefits, CAVs still have to overcome many challenges related to the implementation and market penetration. When not dominated by financial constraints, the CAV adoption heavily depends on how policymakers and the government address the other challenges, including public perception, rules, and regulations. This study aims at formulating recommendations to support decision-makers in choosing the most appropriate and sustainable strategy to implement CAV technology. To do so, key barriers were first identified based on the literature review and discussions with decision-makers. Moreover, long-term adoption of CAV technologies in alternative future scenarios is developed. Multicriteria decision-making analysis was conducted to weigh these barriers and rank different strategies of CAV implementation. The transportation system of the Sultanate of Oman was used as a study case. It was found that the lack of technical skills and policies/regulations are the main barriers to the adoption of CAV technologies. To overcome these barriers, suggested strategies include establishing low-cost and short-term solutions, providing training to transportation professionals, and investing in statewide radio communications/IoT for emergency responses.

In this paper, we address the problem of managing driving behaviours within the context of Connected Vehicles (CVs). In contrast with the existing related solutions, we are proposing a Cyber Physical System (CPS) architecture that ultimately enables the continuous acquisition and processing of driving data and then the assessment and classification of driving performance according to a welldefined set of driving states. The transitions between these states are decided based on current and previous driving records. In addition to their use for the generation of the appropriate feedback to the driver, the driving states could be used to identify relevant data to be shared with the CVs in the vicinity. They could also be used to recommend personalized trainings to the driver based on his/her driving performance.

In this paper we present the preliminary results of simulation-based experiments of an integrated scheme that has been proposed to control taxi supply-demand imbalance in the context of a smart city with multiple taxi operators and using Connected Mobility. We particularly explore the difference between centralized and decentralized implementations of the scheme as well as between collaborative and competitive attitudes of connected taxis. Our results show that by sharing knowledge about supply-demand imbalance and adopting a collaborative attitude, connected taxi systems can improve the performance of the service across a city by achieving a better supply-demand service balancing while improving their profits.

Accurate travel time information enables travellers to plan their journey more wisely and efficiently. This in turn, lessens traffic congestion and improves people's travel experience, particularly in urban areas. Open-source traffic data available from different sources and Google Map API have raised opportunities for analyzing and predicting the traffic more accurately. The purpose of this research work is to analyze bus or car travel time data and showcase different insight and aspect of a society from its traffic pattern. Google Distance Matrix API, Python programming language and machine learning algorithms have been applied in this study to automatically extract, analyze, and visualize traffic data and showcase analysis methodology to improve people's travel experience in Dhaka City and the City of New York. In particular, we apply data analytics to develop an oracle that will give answers to different queries about traffic, such as least congested period and/or least congested route within a day/week/month etc., which in turn would enable people to make informed decisions for travel arrangements. The experimental results and detailed analyses show that there exists a wide fluctuation of travel time during the day in both cities. Furthermore, unlike other works, we accomplish various socio-cultural aspects and behaviour from traffic patterns in those two cities, perform the accessibility analysis and provide recommendations for further research.

Traffic flow at urban intersections varies randomly during the day. It depends on several dynamic factors and requires efficient regulation and flexible control strategies in particular for traffic light regulation. The proposed strategy allows managing the green light time autonomously. The dynamic behavior of traffic signals at intersections can be seen as a discrete event system. Through this paper, a modular Timed Synchronized Petri Net (TSPN) model is developed and a real-time adaptive control strategy of urban signalized intersections is proposed. The control is shared between two communicant actors. The master-slaves approach is adopted in this control strategy. The master (controller) decides the next phase to be served with green light and its duration. While, the slaves (TSPN modules) control the traffic signals displays, phases transitions, and model traffic flow fluctuations. Thanks to the used modularity approach, the developed models reduce the system complexity in terms of combinatorial explosion, and they could be adapted easily for any real intersection. Using the developed models, some interesting properties of the system are checked, and some simulations are performed and analyzed in order to validate the proposed control approach.

Traffic applications such as Google Traffic and Waze have been introduced to let users know about existing congestions on real time. However, this cannot help drivers who are not using these applications or not connected to Internet. Besides, it also suggests that drivers can interact with their smart phones while driving, which is illegal in most countries. The idea of this paper is to use dynamic road signs which can collect real-time data from traffic applications and alert drivers who are heading towards congestions. A proof-of-concept of the dynamic road sign has been developed.

Schizophrenia is characterized by delusions, hallucinations, anhedonia and apathy, and is a chronic mental illness, which still has no cure. It is diagnosed in three main groups, namely positive symptoms, negative symptoms, and cognitive symptoms, and affects the patient in major areas of life, such as work, interpersonal relationships, or self-care. The usual treatment is carried out with the help of antipsychotic medications, which mainly target the positive symptoms of the illness, but have little effect on the negative symptoms of schizophrenia. It is a disease that affects 1

The issue of an ever-increasing ageing population has been the increasing burden on caregivers to care for the elderly population. Caring for elders, especially those diagnosed with dementia, can be challenging. People living with dementia (PWD) require extra care and attention from the caregivers due to the associated behaviours that come with dementia. Wandering is a frequent behaviour exhibited by PWD, which can bring about negative outcomes on the PWD as well as increasing the stress of the caregivers. Though many technological solutions exist, they are not widely deployed. This paper introduces a technological framework, bridging the localisation technologies to the needs of elders and caregivers. The aim is to minimise or eliminate the negative outcomes of dementia wandering and to reduce the burden and stress on the caregivers, thus improving overall well-being. In this paper, we study the application, SingTRACeX, features by considering user needs from the field study with 2 focus group discussions (FGD), comprising of 14 professional caregivers and coordinators. The proposed system features Real-time Location Tracking and Indoor Localisation. The location is determined by GPS location from the Sensor module when outdoors, and estimation using data from the WiFi module, and Bluetooth module when indoors. The indoor navigation provided by the Indoor Localisation module uses an A-star search algorithm. This paper could serve as a foundation that can be built upon over time as the needs of elders and caregivers may change over time, as well as the evolution of technology that may bring about new methods to address needs.