Recent open-data movements give access to large datasets derived from real-world observations. This data can be utilized to enhance energy systems modeling in terms of heterogeneity, confidence, and transparency. Furthermore, it allows to shift away from the common practice of considering average values towards probability distributions. In turn, heterogeneity and randomness of the real-world can be captured that are usually found in large samples of real-world data. This paper presents a methodological framework for an empirical deterministic–stochastic modeling approach to utilize large real-world datasets in long-term energy systems modeling. A new software system—OSeMOSYS-PuLP—was developed and is available now.It adds the feature of Monte Carlo simulations to the existing open-source energy modeling system (the OSeMOSYS modeling framework). An application example is given, in which the initial application example of OSeMOSYS is used and modified to include real-world operation data from a public bus transport system.

OSeMOSYS-PuLP : A Stochastic Modeling Framework for Long-Term Energy Systems Modeling

Smartness in smart cities is achieved by sensing phenomena of interest and using them to make smart decisions. Because the decision makers may not own all the necessary sensing infrastructures, crowdsourced sensing can help collect important information about the city in near real-time. However, involving people brings the risk of exposing their private information. This chapter explores crowdsensing in smart city applications and its privacy implications.

Crowdsensing and privacy in smart city applications

The topical issue of climate change has increasingly become important as scenarios indicate an increase of 2.5–7.8°C in the global mean temperature by the end of this century, if no greenhouse gas ...

Transition Technologies for Electrification and Optimisation of Bus Transport Systems

Mobile crowd-sensing (MCS) has gained significant momentum in recent years for sensory data acquisition through non-dedicated sensors. Even though most of the participants are assumed to be willing to participate in the sensing campaigns, some smartphone users may be reluctant to grant access to particular sensors in their devices. Besides this, the presence of adversaries in the participant pool makes the participant selection problem further challenging. With these in mind, we introduce Reputation and Comfort Level Aware Participant Selection (RACLAPS)which allows participants to modify their available set of sensors that can be accessed by central platform/task publisher i.e. participants can turn-off sensors according to their comfort. To demonstrate the effect of RA-CLAPS, we utilize Selective and Reputation-aware Recruitment (SRR) in which participants are selective in choosing their task to sense solely to improve income. To enrich the discussion, Non-Selective and Reputation-aware Recruitment (NSR) is also considered in which participants are given no choice regarding selectiveness, sensor configuration. Simulation results show that RA-CLAPS improves average user utility by 7.6% compared to its predecessor, SRR. We also mention that average discomfort per participant is reduced by 6.4% under RA-CLAPS when compared to non-selective and selective recruitment approaches.

Trustworthiness and Comfort-Aware Participant Recruitment for Mobile Crowd-Sensing in Smart Environments

The emergence of resource-rich mobile devices and smart vehicles has paved the way for Urban Sensing. In this new paradigm, users sense their environment and become part of an unprecedented large-scale network of sensors, with extensive spatial and temporal coverage, that enables the collection and dissemination of real-time information, practically, from anywhere. Urban sensing can facilitate the deployment of innovative applications that can address the ever-growing concerns for citizens’ well-being. Nevertheless, the openness of such systems (ideally anyone can participate) and the richness of the data users contribute unavoidably raise significant concerns for both the security of urban sensing applications and the privacy of the participating users. In this thesis we consider different urban sensing application domains: vehicular communication networks, intelligent transportation systems and environmental monitoring applications. We begin with a detailed analysis of the security and privacy requirements of these applications domains. Our objective is to protect users from the system (by ensuring their anonymity and privacy) and urban sensing systems from malicious users (by holding malicious users accountable of their actions). This is not straight-forward; anonymity may tempt malicious user behavior, compromising the reliability of the entire urban sensing system.Towards that, we design and implement secure and privacy-preserving identity management systems that can accommodate these requirements. We demonstrate their efficiency, practicality, and scalability through extensive experimental evaluations. Furthermore, we formally evaluate formally their security and privacy preserving properties.

SPPEAR : Security a Privacy-Preserving Architecture for Mobile Crowd-Sensing Applications

Smart City concept is being adopted by many governments to implement sustainable solutions and improve the quality of life of the citizens. This paper presents a data oriented view of a smart city by studying the characteristics of data generated by various sources in the city. This data would be used to build smart city components related to transport, healthcare, energy and environment. Datafication of smart city reveals that the data to be managed is Big Data. Hence, we further review how big data can be used for smart city solutions, and provide a process flow and mapping for some of the smart city components.

Data oriented view of a smart city: A big data approach

Urban computing is an exciting area of research with huge amount of urban data being generated every day. Citizens however put their privacy at stake, while generously trying to share data with the society. Nowadays, smart phone is the most common and convenient method of data capture, which has given rise to an emerging paradigm called Mobile Crowdsensing. This work discusses the features of mobile crowdsensing and focuses on the privacy issues in this method of data capture. The work studies privacy threats arising from different sensors and analyses the requirements for privacy. The main contribution of this paper is detailed analysis of how different sensors in the smartphone can unknowingly compromise the user’s privacy and reveal his/her lifestyle and routine activities.

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Prajakta Joglekar

Papers

Data oriented view of a smart city: A big data approach

Privacy Issues in Urban Computing using Mobile Crowdsensing