The Internet of Things (IoT) paradigm is expected to bring ubiquitous intelligence through new applications in order to enhance living and other environments. Several research and standardization studies are now focused on the Middleware level of the underlying communication system. For this level, several challenges need to be considered, among them the Quality of Service (QoS) issue. The Autonomic Computing paradigm is now recognized as a promising approach to help communication and other systems to self-adapt when the context is changing. With the aim to promote the vision of an autonomic Middlewarelevel QoS management for IoT-based systems, this paper proposes a set of QoS-oriented mechanisms that can be dynamically executed at the Middleware level to correct QoS degradation. The benefits of the proposed mechanisms are also illustrated for a concrete case of Enhanced Living Environment.

SummaryWireless sensor network (WSN) technologies have enabled ubiquitous sensing to intersect many areas of modern day living. The creation of these devices offers the ability to get, gather, exchange, and consume environmental measurement from the physical world in a communicating‐actuating network, called the Internet of Things (IoT). As the number of physical world objects from heterogeneous network environments grows, the data produced by these objects raise uncontrollably, bringing a delicate challenge into scalability management in the IoT networks. Cloud computing is a much more mature technology, offering unlimited virtual capabilities in terms of storage capacity and processing power. Ostensibly, it seems that cloud computing and IoT are evolving independently on their own paths, but in reality, the integration of clouds with IoT will lead to deal with the inability to scale automatically depending on the overload caused by the drastic growth of the number of connected devices and/or by the huge amount of exchanged data in the IoT networks. In this paper, our objective is to promote the scalability management, using hybrid mechanism that will combine traffic‐oriented mechanism and resources‐oriented mechanism, with adaption actions. By the use of autonomic middleware within IoT systems, we seek to improve the monitoring components's architectural design, based on cloud computing‐oriented scalability solution. The intention is to maximize the number of satisfied requests, while maintaining at an acceptable QoS level of the system performances (RTT of the system, RAM, and CPU of the middleware). In order to evaluate our solution performance, we have performed different scenarios testbed experiments. Generally, our proposed results are better than those mentioned as reference.

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This article proposes a new approach for routing and wavelength assignment (RWA) for permanent and reliable wavelength paths (WP) in wide all-optical WDM networks with wavelength continuity constraint. Given a number of available wavelengths on each optical fiber, for each simple link failure of the network, we seek to maximize the number of satisfied requests for connections. This is known as RWAP problem. In our algorithm, called RWA with Minimum Loaded Link for Permanent and Reliable wavelength paths (MLL-PR), routing is based on the search for the optimal path while trying to minimize the maximum load on the links of the network in order to minimize the maximum link capacity and then minimize the number of dropped lightpaths after any link failure. The wavelength assignment is based on a graph coloring method using tabu-search. A series of experiments using two well-known networks (AR-PANET and NSFNET) have been carried out in order to evaluate the performance of our approach, in terms of the number of blocked demands, for different failure scenarios. Generally, our results are better than those provided by the current solving approaches taken as reference.