Smart Cities and M³: Rapid Research, Meaningful Metrics and Co-Design

The research described in this paper is undertaken under the banner of the smart city, a concept that captures the way urban spaces are re-made by the incursion of new technology. Much of smart is centred on converting everyday activities into data, and using this data to generate knowledge mediated by technology. Ordinary citizens, those that may have their lives impacted by the technology, usually are not properly involved in the ‘smartification’ process. Their perceptions, concerns and expectations should inform the conception and development of smart technologies at the same extent. How to engage general public with smart cities research is the central challenge for the Making Metrics Meaningful (MMM) project. Applying a rapid participatory method, ‘Imagine’ over a five-month period (March – July) the research sought to gain insights from the general public into novel forms of information system innovation. This brief paper describes the nature of the accelerated research undertaken and explores some of the themes which emerged in the analysis. Generic themes, beyond the remit of an explicit transport focus, are developed and pointers towards further research directions are discussed. Participatory methods, including engaging with self- selected transport users actively through both picture creation and programmatically specific musical ‘signatures’ as well as group discussion, were found to be effective in eliciting users’ own concerns, needs and ideas for novel information systems

Greener and Smarter Phones for Future Cities: Characterizing the Impact of GPS Signal Strength on Power Consumption

Smart cities appear as the next stage of urbanization aiming to not only exploit physical and digital infrastructure for urban development but also the intellectual and social capital as its core ingredient for urbanization. Smart cities harness the power of data from sensors in order to understand and manage city systems. The most important of these sensing devices are smartphones as they provide the most important means to connect the smart city systems with its citizens, allowing personalization n and cocreation. The battery lifetime of smartphones is one of the most important parameters in achieving good user experience for the device. Therefore, the management and the optimization of handheld device applications in relation to their power consumption are an important area of research. This paper investigates the relationship between the energy consumption of a localization application and the strength of the global positioning system (GPS) signal. This is an important focus, because location-based applications are among the top power-hungry applications. We conduct experiments on two android location-based applications, one developed by us, and the other one, off the shelf. We use the results from the measurements of the two applications to derive a mathematical model that describes the power consumption in smartphones in terms of SNR and the time to first fix. The results from this study show that higher SNR values of GPS signals do consume less energy, while low GPS signals causing faster battery drain (38% as compared with 13%). To the best of our knowledge, this is the first study that provides a quantitative understanding of how the poor strength (SNR) of satellite signals will cause relatively higher power drain from a smartphone\u27s battery

Anchor-Assisted and Vote-Based Trustworthiness Assurance in Smart City Crowdsensing

Smart city sensing calls for crowdsensing via mobile devices that are equipped with various built-in sensors. As incentivizing users to participate in distributed sensing is still an open research issue, the trustworthiness of crowdsensed data is expected to be a grand challenge if this cloud-inspired recruitment of sensing services is to be adopted. Recent research proposes reputation-based user recruitment models for crowdsensing; however, there is no standard way of identifying adversaries in smart city crowdsensing. This paper adopts previously proposed vote-based approaches, and presents a thorough performance study of vote-based trustworthiness with trusted entities that are basically a subset of the participating smartphone users. Those entities are called trustworthy anchors of the crowdsensing system. Thus, an anchor user is fully trustworthy and is fully capable of voting for the trustworthiness of other users, who participate in sensing of the same set of phenomena. Besides the anchors, the reputations of regular users are determined based on vote-based (distributed) reputation. We present a detailed performance study of the anchor-based trustworthiness assurance in smart city crowdsensing through simulations, and compare it with the purely vote-based trustworthiness approach without anchors, and a reputation-unaware crowdsensing approach, where user reputations are discarded. Through simulation findings, we aim at providing specifications regarding the impact of anchor and adversary populations on crowdsensing and user utilities under various environmental settings. We show that significant improvement can be achieved in terms of usefulness and trustworthiness of the crowdsensed data if the size of the anchor population is set properl

Performance evaluation of the cloud computing application for IoT-based public transport systems

The object of research is cloud computing as an element of the server infrastructure for intelligent public transport systems. Given the increasing complexity and requirements for modern transportation, the application of the Internet of Things concept has a high potential to improve efficiency and passenger comfort. Since the load generated in IoT systems is dynamic and difficult to predict, the use of traditional infrastructure with dedicated servers is suboptimal. This study considers the use of cloud computing as the main server infrastructure for the above systems. The paper investigates the main cloud platforms that can be used to develop such systems and evaluates their advantages and disadvantages. The authors developed the overall architecture of the system and evaluated the performance and scalability of individual components of the server infrastructure. To test the system, a software emulator was developed that simulates the controller module installed in vehicles. Using the developed emulator, stress tests were conducted to analyze and confirm the ability to scale and process input data by the proposed architecture. The test scenarios were developed and conducted on the basis of the existing public transportation system in Kyiv, Ukraine. The experimental results showed that the proposed IoT architecture is able to scale efficiently according to the load generated by the connected devices. It has been found that when the number of incoming messages increases from 40 to 6000, the average message processing time remains unchanged, and the error rate does not increase, which is an indicator of stable system operation. The obtained results can be used in the development of modern public transport systems, as well as for the modernization of existing one

Contactless ICT transaction model of the urban transport service

The paper examines the problem of the productive functioning of an urban passenger transport system, which has a modular structure for the generation and exploitation of the urban transport services. The research objects consist of conventional, scalable and innovative contactless transaction models of an urban transport services in the case study of the Transport Organization (TO) – Joint Stock Company for Passenger Railway Transport “Serbia Trains” (Srbija Voz a.d.). The urban transport service is defined by invoking users, user expectations and requirements, the input data provided by users to a transport provider, the mechanisms for access and delivery of the service, the resources and roles responsible for delivery, security requirements and other parameters. The communication platform for modeling urban transport services in different transaction contexts is defined by the utilitarian framework with 6W dimensions with situational mapping of the 6 Communication Dynamics Factors (6CDF). The technology-process restructuring was achieved with the scalable In-formation Technology (IT) model by implementing the elements of electronic business in the key activities of the supply of the train tickets. Using the results of the performed research, in the paper has been developed an innovative, non-contact ICT model of urban transport services on the platform for integrating the Internet service into the process-technology and behavioral-context structures. First published online 4 May 202

CDLB: A Cross-Domain Load Balancing Mechanism for Software Defined Networks in Cloud Data Center

Currently, cross-domain load balancing is one of the core issues for software defined networks (SDN) in cloud data centre, which can optimise resource allocation. In this paper, we propose a cross-domain load balancing mechanism, CDLB, based on Extensive Messaging and Presence Protocol (XMPP) for SDN in cloud data centre. Different from poll method, XMPP based push model is introduced in the proposed scheme, which can avoid wasting network and computing resources in large-scale distributed network environment. The proposed scheme enables all the controllers in the flat distributed control plane to share the same consistent global-view network information in real time through XMPP and XMPP publish/subscribe extension. Thus, the problem of non-real time information synchronisation can be resolved and cross-domain load balancing can be realised. The simulations show the efficiency of the proposed scheme

Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing

Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost. However, for the conventional MCS, the large consumption of communication resources for raw data transmission and high requirements on data storage and computing capability hinder potential requestors with limited resources from using MCS. To facilitate the widespread application of MCS, we propose a novel MCS learning framework leveraging on blockchain technology and the new concept of edge intelligence based on federated learning (FL), which involves four major entities, including requestors, blockchain, edge servers and mobile devices as workers. Even though there exist several studies on blockchain-based MCS and blockchain-based FL, they cannot solve the essential challenges of MCS with respect to accommodating resource-constrained requestors or deal with the privacy concerns brought by the involvement of requestors and workers in the learning process. To fill the gaps, four main procedures, i.e., task publication, data sensing and submission, learning to return final results, and payment settlement and allocation, are designed to address major challenges brought by both internal and external threats, such as malicious edge servers and dishonest requestors. Specifically, a mechanism design based data submission rule is proposed to guarantee the data privacy of mobile devices being truthfully preserved at edge servers; consortium blockchain based FL is elaborated to secure the distributed learning process; and a cooperation-enforcing control strategy is devised to elicit full payment from the requestor. Extensive simulations are carried out to evaluate the performance of our designed schemes

Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing

Mobile crowdsensing (MCS) counting on the mobility of massive workers helps the requestor accomplish various sensing tasks with more flexibility and lower cost. However, for the conventional MCS, the large consumption of communication resources for raw data transmission and high requirements on data storage and computing capability hinder potential requestors with limited resources from using MCS. To facilitate the widespread application of MCS, we propose a novel MCS learning framework leveraging on blockchain technology and the new concept of edge intelligence based on federated learning (FL), which involves four major entities, including requestors, blockchain, edge servers and mobile devices as workers. Even though there exist several studies on blockchain-based MCS and blockchain-based FL, they cannot solve the essential challenges of MCS with respect to accommodating resource-constrained requestors or deal with the privacy concerns brought by the involvement of requestors and workers in the learning process. To fill the gaps, four main procedures, i.e., task publication, data sensing and submission, learning to return final results, and payment settlement and allocation, are designed to address major challenges brought by both internal and external threats, such as malicious edge servers and dishonest requestors. Specifically, a mechanism design based data submission rule is proposed to guarantee the data privacy of mobile devices being truthfully preserved at edge servers; consortium blockchain based FL is elaborated to secure the distributed learning process; and a cooperation-enforcing control strategy is devised to elicit full payment from the requestor. Extensive simulations are carried out to evaluate the performance of our designed schemes