Over the last few years, the number of smart objects connected to the Internet has grown exponentially in comparison to the number of services and applications. The integration between Cloud Computing and Internet of Things, named as Cloud of Things, plays a key role in managing the connected things, their data and services. One of the main challenges in Cloud of Things is the resource discovery of the smart objects and their reuse in different contexts. Most of the existent work uses some kind of multi-criteria decision analysis algorithm to perform the resource discovery, but do not evaluate the impact that the user constraints has in the final solution. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi-objective decision analyses algorithms and the impact of user constraints on them. We evaluated the quality of the proposed solutions using the Pareto-optimality concept.
@cite_27 use domain name server as a scalable metadata repository to support the entity discovery using their location. The authors proposes the creation of a new domain, such as , which represent the entities of the real world. Thus, when a smart object becomes available it must register their characteristics and services into the DNS repository.
@cite_27
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Sensor technology is becoming pervasive in our everyday lives, measuring the real world around us. The Internet of Things enables sensor devices to become active citizens of the Internet, while the Web of Things envisions interoperability between these devices and their services. An important problem remains the need for discovering these devices and services globally, ad hoc in real-time, within acceptable time delays. Attempting to solve this problem using the existing Internet infrastructure, we explore the exploitation of the Domain Name System (DNS) as a scalable and ubiquitous directory mechanism for embedded devices. We examine the feasibility of this approach by performing a simulation involving up to one million embedded devices, to test system performance and scalability. Finally, we discuss practical issues and the overall potential of this approach.