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A Novel method using Extensible Messaging and presence Protocol (XMPP) was proposed to design environmental monitoring system. Synchronization approaches are based on a pull method, a push method, or a hybrid of both. In this paper, we present an approach builds on pushing change notifications and pulling changed in the intelligent building controls units responsible of sending measurement of sensors integrated on them. When the sensor’s value is updated or more one changed, the intelligent control units send a content ping to a XMPP server considered as a hub service (gateway) using XMPP PubSub protocol. The hub service then retrieves the sensor’s value from intelligent building controls units and consecutively uses XMPP PubSub protocol to push to destination’s user devices. Upon receipt of a notification, the user wherever he is, may decide to obtain the changed sensor’s values. With this push-based synchronization, IoT will enable all the communication devices to push the data generated by or associated with devices or objects globally accessible.
Growth in social networking, smartphone applications, and the Internet of Things (IoT) has resulted in the Internet primarily being used as an information distribution network. This rooted in the recognition that data from IoT devices is the foundation for smart Home. Synchronization approaches are based on a pull method, a push method, or a hybrid of both. In this paper, we present an approach builds on pushing change notifications and pulling changed in the intelligent building controls units responsible of sending measurement of sensors integrated on them. When the sensor’s value is updated or more one changed, the intelligent control units send a content ping to a XMPP server considered as a hub service (gateway) using XMPP PubSub protocol. The hub service then retrieves the sensor’s value from intelligent building controls units and consecutively uses XMPP PubSub protocol to push to destination’s user devices. Upon receipt of a notification, the user wherever he is, may decide to obtain the changed sensor’s values. In order to make this possible, destination’s user have previously subscribed to the hub service and have indicated which IoT data are interested to be notified. With this push-based synchronization, IoT will enable all the communication devices to push the data generated by or associated with devices or objects globally accessible.
A billion of objects can sense, communicate and share information, interconnected over Internet Protocol (IP) networks. However, transmission of data collected by these objects gives rise to a large volume of data. Thus, large internet services must deal with concurrency of billions of objects connect to internet. High traffic demands of sessions initiated by these objects should challenge system design of web services. This paper studies design functionalities to an Internet of Things applications based cloud computing should have. The design consists of defining components of management service in Internet of Things. Therefore, these components are based on the Node.js, a web server architecture based on event loop approach and Hadoop platform responsible of analyzing data regularly collected. The proposed management service based architecture is useful for handling concurrent sessions as the number of objects connects to the Internet services grow and renders the processing information possible through MapReduce framework based analytics.
The growth of mobile devices in Internet of Things (IoT) leads to a number of remote and controlling system related IoT applications. For instance, home automation controlling system uses client system such web apps on smartphone or web service to access the home server by sending control commands. The home server receives the command, then controls for instance the light system. The web service gateway responsible for handling clients’ requests attests an internet latency when an increasing number of end users requests submit toward it. Therefore, this web service gateway fails to detect several commands, slows down predefined actions which should be performed without human intervention. In this paper, we investigate the performance of a web server-side platgorm based event-driven, non-blocking approach called Node.js against traditional thread-based server side approach to handle a large number of client requests simultaneously for remote and controlling system in IoT remote monitoring applications. The Node.JS is 40% faster than the traditional web server side features thread-based approach. The use of Node.js server-side handles a large number of clients’ requests, then therefore, reduces delay in performing predefined actions automatically in IoT environment.
Successful rheological characterization of mineral slurries is fraught with many problems and the need to pump higher concentrations, especially in tailings disposal, has meant that more and more slurries exhibit non-Newtonian behaviour. Capillary tube viscometry is the preferred method of testing and apparently credible results obtained from stable "homogeneous" suspensions, containing particles with diameters of 10s and 100s of microns, are the rule rather than the exception. Settleable solids suspended in visco-plastic fluids may be stable in the un-sheared condition, but will stratify when the fluid is sheared. This behaviour results in an unobserved stratified flow within the conveying line. For small pipes, such as those used for rheological characterization, the stratified bed flow effect is small and is masked by the viscous nature of the suspending medium. Unfortunately, the stratified bed flow dominates the transport pressure gradients in larger pipes, resulting in often gross under-prediction of full size behaviour. To illustrate this effect, tests conducted with a non-Newtonian carrier fluid in 12 and 25NB (nominal bore) pipes were found to be insensitive to the addition of large (-1mm) particles at concentrations up to 20% w/w. Conversely, transport characteristics for these suspensions are a strong function of solids concentration in larger pipes, e.g 100NB. Analysis presented in the paper shows that such behaviour is consistent with the behaviour of high viscosity stratified flows. In practice, tests would not be attempted with such large particles, which make the results obtained even more surprising. The behaviour casts doubts upon the validity of much of the capillary tube data obtained with "normal" slurry size distributions. This phenomenon needs to be understood, if the design of high concentration pumping systems for industrial slurries and pastes is to be performed with any certainty.
With cloud computing, cloud-based vehicular networks can support many unprecedented applications. However, deploying applications of cloud-based applications with low latency is not a trivial task. It creates a burden to the cloud-based vehicular network"s designers because rapid development and communication technologies, gradual changes in cloudbased vehicular networks evolve into a revolution in the process. In this paper, we propose a new cloud-based vehicular networks architecture called Vehicular Edge-SDN which combines two emergent computing and network paradigm Software Defined Networking(SDN) and Fog Computing as a prospective solution.
The use of material processing numerical simulation allows a strategy of trial and error to improve virtual processes without incurring material costs or interrupting production and therefore save a lot of money, but it requires user time to analyze the results, adjust the operating conditions and restart the simulation. Automatic optimization is the perfect complement to simulation. Evolutionary Algorithm coupled with metamodelling makes it possible to obtain industrially relevant results on a very large range of applications within a few tens of simulations and without any specific automatic optimization technique knowledge. Ten industrial partners have been selected to cover the different area of the mechanical forging industry and provide different examples of the forming simulation tools. It aims to demonstrate that it is possible to obtain industrially relevant results on a very large range of applications within a few tens of simulations and without any specific automatic optimization technique knowledge. The large computational time is handled by a metamodel approach. It allows interpolating the objective function on the entire parameter space by only knowing the exact function values at a reduced number of “master points”. Two algorithms are used: an evolution strategy combined with a Kriging metamodel and a genetic algorithm combined with a Meshless Finite Difference Method. The later approach is extended to multi-objective optimization. The set of solutions, which corresponds to the best possible compromises between the different objectives, is then computed in the same way. The population based approach allows using the parallel capabilities of the utilized computer with a high efficiency. An optimization module, fully embedded within the Forge2009 IHM, makes possible to cover all the defined examples, and the use of new multi-core hardware to compute several simulations at the same time reduces the needed time dramatically. The presented examples demonstrate the method versatility. They include billet shape optimization of a common rail, the cogging of a bar and a wire drawing problem.
Software Defined Network (SDN) and edge computing are emerging technologies in information and communication technology to minimize data collection latency on the Internet of Vehicles (IoV). The car has the potential to contribute to the development of smart cities by accessing urban data that are collected and reported to a remote data centers through wireless communication This paper presents the study on intelligent transport data collection strategy in a Software- Defined Edge vehicular networking. The two sensing data collection schemes are cooperative vehicular and edge prediction mode based Software Defined Vehicular Network (SDVN). We prove that urban data collection cost through a novel algorithm called car sensing platform for urban data collection algorithm to find an optimal strategy. We set up a simulation scenario based on realistic traffic and communication features and demonstrate the scalability of the proposed intelligent transport data collection strategy. It can be seen that the edge cell predictive trajectory decision-based SDEVN mode scheme reduces greatly the cost when the road seems to have a high number of the vehicle.