The recent emergence and success of cloud-based services has empowered remote sensing and made it very possible. Cloud-assisted remote sensing (CARS) enables distributed sensory data collection, global resource and data sharing, remote and realtime data access, elastic resource provisioning and scaling, and payas- you-go pricing models. CARS have great potentials for enabling the so-called Internet of Everything (IoE), thereby promoting smart cloud services.
In this paper, we survey CARS. First, we describe its benefits and capabilities through real-world applications. Second, we present a multilayer architecture of CARS by describing each layer’s functionalities and responsibilities, as well as its interactions and interfaces with its upper and lower layers. Third, we discuss the sensing services models offered by CARS. Fourth, we discuss some popular commercial cloud platforms that have already been developed and deployed in recent years. Finally, we present and discuss major design requirements and challenges of CARS. Enabling Smart Cloud Services Through Remote Sensing An Internet of Everything Enabler
Existing models for cloud computing are mostly on a per-reservation basis, where cloud service customers reserve resources for some time, and pay for them regardless of whether they have or have not been used fully. Although this pricing approach may not the best option for cloud service customers, it eases resource dimensioning and pricing logistics for cloud providers. In a network constituted of billions of sensory devices, finding and locating the right sensors and/or SANs to perform specific sensing tasks is very challenging. Search, selection, and ranking algorithms that locates and design appropriate SANs need to be developed. Different algorithms are to be developed for different cloud layers, which depend on the functionalities that are specific to the layers (e.g., an alto-cumulus cloud needs to search for suitable stratus clouds while a stratus cloud needs to search for physical SANs) develop models that capture the impact of stochastic and correlated failures on cloud reliability. These models can be used to develop dynamic task re-allocation policies while maximizing reliability of data cloud centers in the context of (SANs).
We proposed a cloud-assisted remote sensing (CARS) now enables: 1) decentralization of data
sensing and collection, where sensory data can now be sensed and collected from everywhere instead of being restricted to limited areas; 2) sharing of information and cloud resources, where data and resources can be shared and used globally by all users; 3) remote access to global sensed information and its analytics, where sensed data can easily be accessed and analyzed online from everywhere; 4) elastic provisioning of cloud resources, where users can scale up and down their requested resources in real time based on demand; and 5) pay-as-you-go pricing models, where users can request (and hence pay for) only resources that they need based on their demand. CARS can then be viewed and foreseen as an emerging technology that has great potential for enabling the so-called Internet of Everything (IoE), thereby enabling smart cloud services.