Sensing as a Service in Cloud-Centric Internet of Things Architecture

Sensing as a Service in Cloud-Centric Internet of Things Architecture

Burak Kantarci (Clarkson University, USA) and Hussein T. Mouftah (University of Ottawa, Canada)
DOI: 10.4018/978-1-4666-8662-5.ch003
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Abstract

Sensing-as-a-Service (S2aaS) is a cloud-inspired service model which enables access to the Internet of Things (IoT) architecture. The IoT denotes virtually interconnected objects that are uniquely identifiable, and are capable of sensing, computing and communicating. Built-in sensors in mobile devices can leverage the performance of IoT applications in terms of energy and communication overhead savings by sending their data to the cloud servers. Sensed data from mobile devices can be accessed by IoT applications on a pay-as-you-go fashion. Efficient sensing service provider search techniques are emerging components of this architecture, and they should be accompanied with effective sensing provider recruitment algorithms. Furthermore, reliability and trustworthiness of participatory sensed data appears as a big challenge. This chapter provides an overview of the state of the art in S2aaS systems, and reports recent proposals to address the most crucial challenges. Furthermore, the chapter points out the open issues and future directions for the researchers in this field.
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Introduction

The Internet of Things (IoT) paradigm denotes the pervasive and ubiquitous interconnection of billions of embedded devices that can be uniquely identified, localized and communicated (Aggarwal, C., Ashish, N. & Sheth, A., 2013). Sensors, RFID tags, smart phones, and various other devices are interconnected in a scalable manner in the IoT architecture. Application areas of IoT are various such as healthcare, smart environments, transportation, social networking, personal safety and several futuristic applications such as robot taxi (Atzori, A., Andlera, L. & Morabito, G., 2010; Miorandi, D., Sicari, S., De Pellegrini, F. & Chlamtac, I., 2012). IoT architecture can be implemented as either Internet centric or object-centric. Internet centric architecture of IoT aims at provisioning services within the Internet where data are contributed by the objects. On the other hand, object-centric architecture aims at provisioning services via network of smart objects. Scalability and cost-efficient service provisioning of IoT services can be achieved by the integration of cloud-computing into the IoT architecture, i.e., cloud-centric IoT (Gubbi, J., Buyya, R., Marusic, S. & Palaniswami, M., 2013) as illustrated in Figure 1.

Figure 1.

Minimalist illustration of cloud-centric IoT architecture

As future Internet is expected to offer everything-as-a-service (XaaS) such as CPU, network, memory and so on (Moreno-Vozmediano, R., Montero, R. S. & Llorent, I. M., 2013), sensing, as well, can be offered as a service within the cloud. Furthermore, cloud computing enables on demand access to the information and/or knowledge obtained from sensor data providers based on the pay-as-you-go fashion and providing software/platform/infrastructure as a service (SaaS/PaaS/IaaS).

The requirements of sensing objects driving the integration of cloud computing and IoT are summarized as huge computing and storage capacity, web-based interfaces for data exchange and integration, real-time processing of big data, web-based programming platforms, inter-operability between the sensing objects, cost-efficient and scalable on-demand access to the information technology (IT) resources, and security and privacy assurance. Therefore, Zhou et al. (Zhou, J., Leppanen, T., Harjula, E., Ylianttila, M., Ojala, T., Yu, C., Jin, H. & Yang, L. T., 2013) propose deployment, development and management of the IoT applications over the cloud, namely the CloudThings architecture.

Applications that can be improved by the integration of IoT with cloud computing are various; such as pervasive healthcare (Doukas, C. & Maglogiannis, I., 2012) where cloud platform enables efficient management of mobile and wearable body sensors; smart homes where appliance recognition via sensor data and energy usage profile of the household owners are performed within the cloud (Chen, S-Y., Lai, C-F., Huang, Y-M. & Jeng, Y-L., 2013); smart cities where distributed cloud services are deployed to manage and control the IoT devices (Suciu, G., Vulpe, A., Halunga, S., Fratu, O., Todoran, G. & Suciu, V., 2013), future transportation systems where in-vehicle smart phones, roadside sensors and/or cameras are connected to a cloud-based IoT platform for monitoring road condition and alert generation and so on (Ghose, A., Biswas, P., Bhaumik, C., Sharma, M., Pal, A. & Jha, A., 2012; Yu, X., Sun, F. & Cheng, X., 2012). Furthermore, public safety in smart city management can be efficiently addressed by taking advantage of cloud and IoT integration (Li, W., Chao, J. & Ping, Z., 2012). A travel recommendation system is proposed by Yerva et al. (Yerva, S. R., Saltarin, J., Hoyoung, J.& Aberer, K., 2012) where mood information of a particular user is extracted from the tweets of the corresponding user on Twitter, and it is associated with the weather information for a travel destination on a given date, which is obtained via sensors in that particular region. In the corresponding study, sensor data are not collected through smart phones but via sensors that are already deployed for an online weather report service.

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