Non-Parametric Bayesian Rejuvenation of Smart-City Participation through Context-aware Internet-of-Things (IoT) Management
📝 Abstract
Tweaking citizen participation is vital in promoting Smart City services. However, conventional practices deficit sufficient realization of personal traits despite socio-economic promise. The recent trend of IoT-enabled smart-objects/things and personalized services pave the way for context-aware services. Eventually, the aim of this paper is to develop a context-aware model in predicting participation of smart city service. Hence, major requirements are identified for citizen participation, namely (a) unwrapping of contexts, which are relevant, (b) scaling up (over time) of participation. However, paramount challenges are imposed on this stipulation, such as, un-observability, independence and composite relationship of contexts. Therefore, a Non-parametric Bayesian model is proposed to address scalability of contexts and its relationship with participation.
💡 Analysis
Tweaking citizen participation is vital in promoting Smart City services. However, conventional practices deficit sufficient realization of personal traits despite socio-economic promise. The recent trend of IoT-enabled smart-objects/things and personalized services pave the way for context-aware services. Eventually, the aim of this paper is to develop a context-aware model in predicting participation of smart city service. Hence, major requirements are identified for citizen participation, namely (a) unwrapping of contexts, which are relevant, (b) scaling up (over time) of participation. However, paramount challenges are imposed on this stipulation, such as, un-observability, independence and composite relationship of contexts. Therefore, a Non-parametric Bayesian model is proposed to address scalability of contexts and its relationship with participation.
📄 Content
Non-Parametric Bayesian Rejuvenation of Smart-City Participation through Context-aware Internet-of-Things (IoT) Management Rossi Kamal, Choong Seon Hong Department of Computer Engineering, Kyung Hee University, South Korea E-mail:rossikamal@rossikamal.info,cshong@khu.ac.kr (Submitted to IEEE/IFIP NOMS 2016 Workshop- PASC 2016) Abstract Tweaking citizen participation is vital in promoting Smart City services. However, conventional practices deficit sufficient realization of personal traits despite socio-economic promise. The recent trend of IoT-enabled smart-objects/things and personalized services pave the way for context-aware services. Eventually, the aim of this paper is to develop a context-aware model in predicting participation of smart city service. Hence, major requirements are identified for citizen participation, namely (a) unwrapping of contexts, which are relevant, (b) scaling up (over time) of participation. However, paramount challenges are imposed on this stipulation, such as, un-observability, independence and composite relationship of contexts. Therefore, a Non-parametric Bayesian model is proposed to address scalability of contexts and its relationship with participation. Finally, developed systematic prototype pinpoints major goals of context-aware application from participants’ opinions, usage and feedback. Introduction World has speculated a huge penetration on urbanization over last decade, which is anticipated to be continued, even with a higher rate. This global trend of urbanization is noticed from the statistical exposition, ’Half of world population is living in cities in 2013, whereas the ratio will be reached by in Asia and Africa around 2020 and 2035, respectively. Last but not the least, global urban population will be nearly doubled (i.e. 3.6 to 6.3 million) by 2050’[1] . Consequently, there is an ever increasing demand of sustainable city, with improved city-governance and quality of urban-life[2]. Cities are reminiscent of verdict, culture and finance from primeval ages. They have been medial plane for cultural or commercial-interchange and public-governance. However, contemporary urbanization- trend are striving us to apply technology for the amelioration of socio-economic conjecture. Hence, recent Information Communication Technologies (ICT) with the advent of information and operational management are going to play a pivotal role in urbanization.[1] [2] Smart City is aimed at undertaking networked information for operational management of an urban- life. It comprises of services that use, among others, Smart-devices, wearable sensors, intelligent vehicles around the city, enabling accumulation of monitoring information, to react autonomously in real time, with less or without human intervention. Operational management comprises of configuration management, green computing, load balancing, quality-of-protection, disaster-response, to name a few. However, information is regarded as vital factor in multidimensional challenges, such as sharing, transfer and analysis of knowledge-base.[3][4][5] An abrupt paradigm shift is noticed in urban space among internet-business community with the replacement of infrastructure providers by operators and service providers. Accordingly, groundbreaking promise of crowd-intelligence pings a transition from straightforward data pipe towards optimized Big data. ec not the least, contextual aspects, stemmed from increasing demand of personalization, necessitate leveraging advanced operations, such as, fusion, cleansing and quality-of- protection. Recently the proliferation of IoT-enabled devices[6] and large-scale adoption of personalized services[7] are striving innovative solutions towards societal challenges with everenhanced capacity of Smart-devices. Having strong potential impact on both formal and informal spaces, such as quality of experience and urban-provisioning, technology is being considered as key player to bring Smartness in community. However, intelligence, sensitivity and responsiveness embedded in things or objects, or ubiquitous surrounding, are demanding redefinition of urban space, ecosystem, and even measurement approaches. Enabling smart-city information as a utility back social ecosystem. It includes exploration of value in massive data, which is collected from heterogeneous sources, such as agencies(i.e. public or private) or social interaction (i.e. Facebook or Twitter) or regular-usage (i.e. YouTube). Since most data is location based, geography is regarded as common platform for inferring value from multiple sources. However, sometimes, not only geography, but also other essences of data, such as mobility, consumer-behavior, usage-hour(peak/off-peak), are regarded important. Uncertainties and privacy concerns over data from heterogeneous sources necessitate high control over overall monitoring information. In this context, analytic is indispensab
This content is AI-processed based on ArXiv data.