This paper presents a location-based interactive model of Internet of Things (IoT) and cloud integration (IoT-cloud) for mobile cloud computing applications, in comparison with the periodic sensing model. In the latter, sensing collections are performed without awareness of sensing demands. Sensors are required to report their sensing data periodically regardless of whether or not there are demands for their sensing services. This leads to unnecessary energy loss due to redundant transmission. In the proposed model, IoT-cloud provides sensing services on demand based on interest and location of mobile users. By taking advantages of the cloud as a coordinator, sensing scheduling of sensors is controlled by the cloud, which knows when and where mobile users request for sensing services. Therefore, when there is no demand, sensors are put into an inactive mode to save energy. Through extensive analysis and experimental results, we show that the location-based model achieves a significant improvement in terms of network lifetime compared to the periodic model.
With the fast proliferation of mobile Internet, the wireless community has been increasingly looking for a framework that can provide seamless mobility. In this paper, we propose a fast cross-layer handover scheme based on movement prediction in mobile WiMAX environment. Prediction is achieved by linear regression model with keeping track of the signal strength of mobile users. With the help of the prediction, layer-3 handover activities are able to occur prior to layer-2 handover, and therefore, total handover latency can be reduced. The experiments conducted with system parameters and propagation model defined by WiMAX Forum demonstrate that the proposed method predicts the future signal level accurately and reduces the total handover latency.
Low‐cost global positioning system (GPS) receivers installed in most vehicles and smart devices typically display tens of meters of error in their location data. A large amount of research effort has been made to increase the positioning accuracy of GPS receivers mostly by reducing errors with the help of auxiliary devices and/or methodologies such as differential GPSs, assisted GPSs, real‐time kinematic positioning, computer vision, etc, which result in a certain amount of cost increase. In this paper, we propose a new cooperative vehicular localization scheme based on vehicle‐to‐vehicle communication. The proposed scheme first estimates the distances between neighboring vehicles using the weighted least square of double difference scheme and uses a novel machine learning technique called constrained self‐organizing map (C‐SOM) with a set of adjusted GPS fixes to generate the final estimates of GPS locations with considerably lower errors. We also propose a new method of presenting training samples to address the issue of convergence failure that arises from the constraints imposed on the inter‐vehicular distances. We present simulation results that demonstrate the superior performance of the proposed scheme over both the conventional SOM and the distributed location estimate algorithm.
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