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Internet of Things (IoT) is virtually connecting object in the physical world to the internet, where a huge number of embedded devices relays on communication service offered by the internet protocols and these devices are termed as smart objects. Since the smart devices have low configuration it has constrained communication. During the communication, the data has to be disseminated with reliability. The enormous amount of data generated by heterogeneous devices do not achieve higher delivery ratio. Hence, reliable sensor data collection and appropriately disseminating data to targeted IoT device is critical in IoT environment. In the existing work, selfish behavior of each devices and the packet delivery delay are not considered. By observing this, the core challenge to disseminate the data efficiently under a unique network setting with multiple interest's data types. Two data dissemination schemes, namely the rule based data pull scheme and rule based data push scheme are proposed in this paper. The data pull scheme in IoT devices pull, the data from the data provider device, and in data push scheme the data providers generate data and push them to the intended users. In pull scheme, an effective mechanism known as cooperative game using intelligent agents is implemented. In addition, data sharing plays a crucial role between the devices where cooperativeness has been ensured in this work by cooperative game using intelligent agents and Pareto optimality. In data pushing model, a checking process is done using rules to monitor the receiver with best credits that gets the disseminated data form server which is processed by rule based online auction algorithm when comparing the Cooperative Scheme for Data Dissemination (CSDD) with existing model, this proposed model shows better performance in terms of data delivery ratio and delay in delivering the data to the destination.

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As the Internet-of-Vehicles (IoV) technology becomes an increasingly important trend for transportation, de-signing large-scale IoV systems has become a critical task that aims to process big data uploaded by fleet vehicles and to provide data services. The IoV data, especially high vehicle statuses (e.g., location, engine parameters), are characterized as large volume with a low density of value and low data quality. Such characteristics pose challenges for developing real-time applications based on such data. In this paper, we address the challenges in de-signing a scalable IoV system by describing CarStream, an industrial system of big data processing for chauffeured car services.Photon is deployed within Google Advertising System to join data streams such as web search queries and user clicks on advertisements. It produces joined logs that are used to derive key business metrics, including billing for advertisers. Our production deployment processes millions of events per minute at peak with an average end-to-end latency of less than 10 seconds. We also present challenges and solutions in maintaining large persistent state across geographically distant locations, and highlight the design principles that emerged from our experience.