Context-Awareness in Location Based Services in the Big Data Era

Grifoni, Patrizia; D’Ulizia, Arianna; Ferri, Fernando

doi:10.1007/978-3-319-67925-9_5

Cited by 16 publications

(17 citation statements)

References 53 publications

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“…The procedure of context-awareness generally consists of three phases. These are context acquisition, context representation, and context reasoning [126].…”

Section: Role and Functionalities Of Roof Computingmentioning

confidence: 99%

Software-Defined Dew, Roof, Fog and Cloud (SD-DRFC) Framework for IoT Ecosystem: The Journey, Novel Framework Architecture, Simulation, and Use Cases

2021

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The Internet of Things (IoT) has become one of the most known terms in present times, reaching new levels and setting a trend in the world. Evidently, it is the future of connectivity which has turned physical objects into intelligent objects. Therefore, there has been a growing curiosity in the IoT field and this leads to the concept of IoT ecosystem. But the contemporary fragmented ecosystem of regulations, technologies, and systems slows IoT deployments. Thus, we consider multi-tiered computational infrastructure which would be feasible to provide services from the nearest possible location of end devices. To mitigate multi-tier infrastructure issues, Software-Defined Networking (SDN) steps in. The journey behind SDN-supported multi-tier computational infrastructure can be understood from this paper's elaborate study. Next, a comparative analysis on dew, roof, fog and cloud is conducted and the impact of internet on these computing paradigms is briefly explained. Then a novel framework termed "SD-DRFC (Software-Defined Dew, Roof, Fog and Cloud computing)" is proposed for today's IoT ecosystem. The role and functionality of each tier of SD-DRFC framework are adequately explained. A use case focused on the SD-DRFC framework is then presented and simulated by utilizing iFogSim simulator. To evaluate the efficiency of the presented SD-DRFC framework, four QoS parameters (Latency, Network Usage, Cost, and Energy Consumption) are considered in this paper. When comparing the simulation results, the presented SD-DRFC framework performs much better than cloud-only implementation. The advantages and suitability of utilizing this proposed framework have been demonstrated by multiple use-cases which range from conceptual visions to existing running systems.

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“…The procedure of context-awareness generally consists of three phases. These are context acquisition, context representation, and context reasoning [126].…”

Section: Role and Functionalities Of Roof Computingmentioning

confidence: 99%

Software-Defined Dew, Roof, Fog and Cloud (SD-DRFC) Framework for IoT Ecosystem: The Journey, Novel Framework Architecture, Simulation, and Use Cases

2021

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“…Relying only on the location sometimes may lead to irrelevant results, as two persons using an LBS at the same location, or even the same person at the same location but within a different context (e.g. time of the day, seasons, with whom), might expect different answers (Grifoni, D'Ulizia, and Ferri 2018). For effectively supporting the user, LBS should provide information and services adapted not only to the user's location, but also her/his other context information (Raper et al 2007a).…”

Section: Towards Context-awarenessmentioning

confidence: 99%

Location based services: ongoing evolution and research agenda

Huang

Gärtner

Krisp

et al. 2018

Journal of Location Based Services

214

121

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We are now living in a mobile information era, which is fundamentally changing science and society. Location Based Services (LBS), which deliver information depending on the location of the (mobile) device and user, play a key role in this mobile information era. This article first reviews the ongoing evolution and research trends of the scientific field of LBS in the past years. To motivate further LBS research and stimulate collective efforts, this article then presents a series of key research challenges that are essential to advance the development of LBS, setting a research agenda for LBS to 'positively' shape the future of our mobile information society. These research challenges cover issues related to the core of LBS development (e.g. positioning, modelling, and communication), evaluation, and analysis of LBS-generated data, as well as social, ethical, and behavioural issues that rise as LBS enter into people's daily lives.

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“…Therefore, it may be tough to resolve these issues with only spatio-temporal data. For detection of the user situation, some articles have reported that various sensors can be promising solutions [18,19]. Our method explored other spatial information, except for location data, to appropriately extract points of staying.…”

Section: Approaches Of Articulated Trajectory Data In Related Workmentioning

confidence: 99%

Articulated Trajectory Mapping for Reviewing Walking Tours

Sasaki

Takahashi

2020

IJGI

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This paper addresses how to enrich a map-based representation for reviewing walking tours with the features of trajectory mapping and tracing animation. Generally, a trajectory generated by raw GPS data can often be difficult to browse through on a map. To resolve this issue, we first illustrated tangled trajectory lines, inaccurate indoor positioning, and unstable trajectory lines as problems encountered when mapping raw trajectory data. Then, we proposed a new framework that focuses on GPS horizontal accuracy to locate indoor location points and find stopping points on an accelerometer. We also applied a conventional line simplification algorithm to make the trajectory cleaner and then integrated the extracted points with the clean trajectory line. Furthermore, our experiments with some actual logs of walking tours demonstrated that articulated trajectory mapping, which comprises simplification and characterization methods, sufficiently reliable and effective for better reviewing experiences. The paper contributes to the research on cleaning up map-based displays and tracing animations of raw trajectory GPS data by using not only location data but also sensor data that smartphones can collect.

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Context-Awareness in Location Based Services in the Big Data Era

Cited by 16 publications

References 53 publications

Software-Defined Dew, Roof, Fog and Cloud (SD-DRFC) Framework for IoT Ecosystem: The Journey, Novel Framework Architecture, Simulation, and Use Cases

Software-Defined Dew, Roof, Fog and Cloud (SD-DRFC) Framework for IoT Ecosystem: The Journey, Novel Framework Architecture, Simulation, and Use Cases

Location based services: ongoing evolution and research agenda

Articulated Trajectory Mapping for Reviewing Walking Tours

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