Data driven wireless network design : a multi-level modeling approach

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Enabling stream processing for people-centric IoT based on the fog computing paradigm

The world of machine-to-machine (M2M) communication is gradually moving from vertical single purpose solutions to multi-purpose and collaborative applications interacting across industry verticals, organizations and people - A world of Internet of Things (IoT). The dominant approach for delivering IoT applications relies on the development of cloud-based IoT platforms that collect all the data generated by the sensing elements and centrally process the information to create real business value. In this paper, we present a system that follows the Fog Computing paradigm where the sensor resources, as well as the intermediate layers between embedded devices and cloud computing datacenters, participate by providing computational, storage, and control. We discuss the design aspects of our system and present a pilot deployment for the evaluating the performance in a real-world environment. Our findings indicate that Fog Computing can address the ever-increasing amount of data that is inherent in an IoT world by effective communication among all elements of the architecture

Joint topology optimization, power control and spectrum allocation for intra-vehicular multi-hop sensor networks using dandelion-encoded heuristics

In the last years the interest in multi-hop communications has gained momentum within the research community due to the challenging characteristics of the intra-vehicular radio environment and the stringent robustness imposed on critical sensors within the vehicle. As opposed to point-to-point network topologies, multi-hop networking allows for an enhanced communication reliability at the cost of an additional processing overhead. In this context this manuscript poses a novel bi-objective optimization problem aimed at jointly minimizing (1) the average Bit Error Rate (BER) of sensing nodes under a majority fusion rule at the central data collection unit; and (2) the mean delay experienced by packets forwarded by such nodes due to multi-hop networking, frequency channel switching time multiplexing at intermediate nodes. The formulated paradigm is shown to be computationally tractable via a combination of evolutionary meta-heuristic algorithms and Dandelion codes, the latter capable of representing tree-like structures like those modeling the multi-hop routing approach. Simulations are carried out for realistic values of intra-vehicular radio channels and co-channel interference due to nearby IEEE 802.11 signals. The obtained results are promising and pave the way towards assessing the practical performance of the proposed scheme in real setups

Mapping SysML to modelica to validate wireless sensor networks non-functional requirements

International audienceWireless Sensor Networks (WSN) have registered a large success in the scientific and industrial communities for their broad application domains. Furthermore, the WSN specification is a complex task considering to their distributed and embedded nature and the strong interactions between their hardware and software parts. Moreover, most of approaches use semi-formal methods to design systems and generally simulation to validate their properties in order to produce models without errors and conform to the system specifications. In this context, we propose a Model Driven Architecture (MDA) approach to improve the verification of the WSN properties. This approach combines the advantages of the System Modeling Language (SysML) and the Modelica language which promote the reusability and improve the development process. In this work, we specify a model transformation from SysML static, dynamic and requirement diagrams to their corresponding elements in Modelica. Thanks to the SysML requirement diagram which is transformed into Modelica properties (constraints), we propose a technique using dynamic tests to verify WSN properties. We have used the Topcased platform to implement our approach 1 and chosen a crossroads monitoring system which is based on wireless sensors to illustrate it. Besides, we have verified and validated some wireless sensors properties of the studied system

Ant Colony Optimization for Network Aggregation in a Fully Connected Network

Ants Colony exhibit complex structure called ant streets. We were observed in several situations such as highway or may have placed some obstacle in the way and see ant’s reaction to such disturbances. One of the most surprising behavioral patterns by ants is the ability to find shortest path which is the challenge of today’s computer scientists. Ant Colony Optimization is a probabilistic technique for solving computational problems. This paper explains Ant Colony Optimization is an effective approach for finding a shortest path between two nodes on a network. DOI: 10.17762/ijritcc2321-8169.15067

A Survey on Behavioral Pattern Mining from Sensor Data in Internet of Things

The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. © 2013 IEEE

Model-Driven Methodology for Rapid Deployment of Smart Spaces based on Resource-Oriented Architectures

Advances in electronics nowadays facilitate the design of smart spaces based on physical mash-ups of sensor and actuator devices. At the same time, software paradigms such as Internet of Things (IoT) and Web of Things (WoT) are motivating the creation of technology to support the development and deployment of web-enabled embedded sensor and actuator devices with two major objectives: (i) to integrate sensing and actuating functionalities into everyday objects, and (ii) to easily allow a diversity of devices to plug into the Internet. Currently, developers who are applying this Internet-oriented approach need to have solid understanding about specific platforms and web technologies. In order to alleviate this development process, this research proposes a Resource-Oriented and Ontology-Driven Development (ROOD) methodology based on the Model Driven Architecture (MDA). This methodology aims at enabling the development of smart spaces through a set of modeling tools and semantic technologies that support the definition of the smart space and the automatic generation of code at hardware level. ROOD feasibility is demonstrated by building an adaptive health monitoring service for a Smart Gym