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.
Nowadays there are several approaches that enable the smart objects management. @cite_3 and R " o @cite_29 present surveys that describes several architectures, techniques, methods, models, features, systems, applications, and middleware solutions related to the CoT context. In this section, firstly we present some architectures that enable the resource discovery of smart objects and next some works related to sensor discovery techniques.
@cite_29 @cite_3
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To keep systems affordable, there is a trend towards using open standard and commercial off the shelf (COTS) components in the development of dependable real-time systems. However, the use of COTS also introduces the vendor-driven upgrade problem that is relatively new to the dependable real-time computing community. If we refuse to accept the "new and improved" hardware and software components provided by vendors, then the hope that using COTS components will help keep the system modern via market forces will be dashed. If we decide to keep our systems modern, then we have to develop approaches that can introduce new hardware and software components into deployed systems safely, reliably and easily, in spite of the inevitable bugs in some of the new COTS components. In this paper, we give an informal review of the Simplex Architecture, which has been developed to support safe and reliable online upgrade of dependable computing systems. This paper is a revision of the SEI technical report: A Software Architecture for Dependable and Evolvable Industrial Computing Systems. CMU SEI-95-TR-005. Due to the advances in wireless networking and microelectronics, more and more smart objects are equipped with wireless networking as well as high computing capability. Heterogeneity of service protocols inhibits the interoperation among smart objects using different service protocols. Existing works overcome the heterogeneity by assuming that service protocols used are known in advance so that components necessary for interoperations can be deployed before runtime. This assumption prevents existing works from being applied to situations where a user wants to spontaneously configure her smart objects to interoperate with smart objects found nearby without (or with minimal) user intervention. In this paper, we propose a service interoperability gateway that discovers service protocols used in a given environment, finds smart objects using these protocols, and instantiates proxies for interoperability between smart objects. We present a new approach to detection and tracking of moving objects with a 2D laser scanner for autonomous driving applications. Objects are modelled with a set of rigidly attached sample points along their boundaries whose positions are initialized with and updated by raw laser measurements, thus allowing a non-parametric representation that is capable of representing objects independent of their classes and shapes. Detection and tracking of such object models are handled in a theoretically principled manner as a Bayes filter where the motion states and shape information of all objects are represented as a part of a joint state which includes in addition the pose of the sensor and geometry of the static part of the world. We derive the prediction and observation models for the evolution of the joint state, and describe how the knowledge of the static local background helps in identifying dynamic objects from static ones in a principled and straightforward way. Dealing with raw laser points poses a significant challenge to data association. We propose a hierarchical approach, and present a new variant of the well-known Joint Compatibility Branch and Bound algorithm to respect and take advantage of the constraints of the problem introduced through correlations between observations. Finally, we calibrate the system systematically on real world data containing 7,500 labelled object examples and validate on 6,000 test cases. We demonstrate its performance over an existing industry standard targeted at the same problem domain as well as a classical approach to model-free object tracking. Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2 and 97.5 for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.