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Several Link Quality Estimators (LQEs) have been proposed for Wireless Sensor Networks. However, their adequacy to smart grid environments has not been properly investigated. This paper addresses the problem of efficient low-power link quality estimation for smart grid environments. The first part of this paper presents a performance study of representative LQEs, namely ETX, four-bit and F-LQE, in three typical smart grid environments. These LQEs are evaluated in terms of reliability, stability and reactivity, by analyzing their statistical behavior. This study shows that F-LQE is more reliable and more stable than ETX and four-bit. However, it is not the most efficient for smart grid due to the lack of reactivity and also its higher complexity. Hence, the second part of this paper introduces Opt-FLQE, an optimized version of F-LQE that overcomes its limitations. The performance analysis of Opt-FLQE shows that it is more reactive than F-LQE while still being more reliable.

In moving toward an interoperability architecture, the concept of network centric is a step in the right direction-all modules connect to the network, not to each other. And a handful of good network citizenship rules provide a syntactical guide for attachment. From the point of view of the network designer this is sufficient-we have enough to build internetworks for the common good. The continued burgeoning of the Internet constitutes an existence proof. But a common networking base is insufficient to reach a goal of cross-system interoperability-the large information system. Many standardization efforts have attempted to solve this problem, but appear to have lacked the necessary scope. For instance, there have been many efforts aimed at standardizing data elements; these efforts, if followed through, yield some gains, but never seem to quite reach the interoperability goal. If we are to truly erect an interoperability architecture, we need to broaden the scope. This problem of cross-program, cross-service and cross-ally interoperability requires that we agree on the what of modularization, not just the how. This paper is aimed at framing the interoperability architecture problem. On modularization The core of architecture-the way things fit together-is a sense of modularization. This is the part of the problem that is perhaps the least mechanical and requires judgment. Experience, no doubt, helps. Architectural conformity must be traded off against other desired characteristics. The objective is that modules become inherently interoperable so we have components delivered by multiple programs that can be assembled for particular tasks. Prerequisite-network centric.

Advances supported by emerging wearable technologies in healthcare promise patients a provision of high quality of care. Wearable computing systems represent one of the most thrust areas used to transform traditional healthcare systems into active systems able to continuously monitor and control the patients' health in order to manage their care at an early stage. However, their proliferation creates challenges related to data management and integration. The diversity and variety of wearable data related to healthcare, their huge volume and their distribution make data processing and analytics more difficult. In this paper, we propose a generic semantic big data architecture based on the "Knowledge as a Service" approach to cope with heterogeneity and scalability challenges. Our main contribution focuses on enriching the NIST Big Data model with semantics in order to smartly understand the collected data, and generate more accurate and valuable information by correlating scattered medical data stemming from multiple wearable devices or/and from other distributed data sources. We have implemented and evaluated a Wearable KaaS platform to smartly manage heterogeneous data coming from wearable devices in order to assist the physicians in supervising the patient health evolution and keep the patient up-to-date about his/her status.

Maintaining the Quality of Service (QoS)

No abstract

Abstract-Wireless Sensor Networks (WSNs) have been recognized as a promising communication technology for smart grid monitoring and control applications. However, the deployment of WSNs in smart grid brought new challenges that pertain to the harsh electrical grid nature, and the different and often contradicting communication requirements of smart grid monitoring applications. MAC protocols play a crucial role to meet the reliability and latency requirements of WSN-based smart grid communications. In particular, the IEEE 802.15.4 TSCH (Time Slotted Channel Hopping), the latest generation of low-power and highly reliable MAC protocols, orchestrates the medium access according to a time-frequency communication schedule. However, TSCH specification does not provide any practical solution for the establishment of the schedule. Orchestra is a recent scheduling solution for TSCH that brings significant advantages such as, the use of simple scheduling rules, the low signaling overhead, and the high delivery ratio. Despite its unique features, Orchestra has the limitation of computing the TSCH schedule at each node independently from its traffic load, which can drastically affect the communication delay. This limitation makes Orchestra not sufficiently convenient for several delaysensitive smart grid applications. Further, the current TSCH specification does not support traffic differentiation (i.e. handle all packets equally regardless of their criticality levels). In this paper, we propose an enhanced Orchestra-based TSCH protocol, called e-TSCH-Orch, that dynamically adjusts time slots assignment according to traffic load and criticality level. The performance analysis of e-TSCH-Orch shows that it significantly reduces the communication delay compared to the original Orchestra-based TSCH, while preserving the low signaling overhead and the high packet delivery ratio.

Due to its abilities to capture real-time data concerning the physical world, the Internet of Things (IoT) phenomenon is fast gaining momentum in different applicative domains. Its benefits are not limited to connecting things, but lean on how the collected data is transformed into insights and interact with domain experts for better decisions. Nonetheless, a set of challenges including the complexity of IoT-based systems and the management of the ensuing big and heterogeneous data and as well as the system scalability; need to be addressed for the development of flexible smart IoT-based systems that drive the business decision-making. Consequently, inspired from the human nervous system and cognitive abilities, we have proposed a set of autonomic cognitive design patterns that alleviate the design complexity of smart IoT-based systems, while taking into consideration big data and scalability management. The ultimate goal of these patterns is providing generic and reusable solutions for elaborating flexible smart IoTbased systems able to perceive the collected data and provide decisions. These patterns are articulated within a model-driven methodology that we have proposed to incrementally refine the system functional and nonfunctional requirements. Following the proposed methodology, we have combined and instantiated a set of patterns for developing a flexible cognitive monitoring system to manage patients' health based on heterogeneous wearable devices. We have highlighted the gained flexibility and demonstrated the ability of our system to integrate and process heterogeneous 2 large scale data streams. Finally, we have evaluated the system performance in terms of response time and scalability management.