Research on Sensor Network Spectrum Detection Technology based on Cognitive Radio Network

With the bursting development of computer science and the hardware technology, Internet of Things and wireless sensor networks has been popularly studied in the community of engineering. Under the environment of Internet of Things, we carry out theoretical analysis and numerical simulation on the sensor network spectrum detection technology based on cognitive radio network. As a means of information and intelligence, information service system is an important research hotspot in the field of Internet of things. Wireless sensor network is composed of a large number of micro sensor nodes, which have the function of information collection, data processing, and wireless communication, characterized by the integration of wireless self-organization. However, most of the methodologies proposed by the other institutes are suffering form the high complexity while with the high time-consuming when processing information. Therefore, this study is to assess the economic feasibility of using the optimized multipath protocol availability and the increased bandwidth and several mobile operators through the use of cost-benefit analysis, single path selection model is to develop more path agreement to achieve better performance. To test the robustness, we compare our method with the other state-of-the-art approach in the simulation section and proves the effectiveness of our methodology. The experimental result reflected that our approach could achieve higher accuracy with low time-consuming when dealing with complex sources of information

Coordination and Self-Adaptive Communication Primitives for Low-Power Wireless Networks

The Internet of Things (IoT) is a recent trend where objects are augmented with computing and communication capabilities, often via low-power wireless radios. The Internet of Things is an enabler for a connected and more sustainable modern society: smart grids are deployed to improve energy production and consumption, wireless monitoring systems allow smart factories to detect faults early and reduce waste, while connected vehicles coordinate on the road to ensure our safety and save fuel. Many recent IoT applications have stringent requirements for their wireless communication substrate: devices must cooperate and coordinate, must perform efficiently under varying and sometimes extreme environments, while strict deadlines must be met. Current distributed coordination algorithms have high overheads and are unfit to meet the requirements of today\u27s wireless applications, while current wireless protocols are often best-effort and lack the guarantees provided by well-studied coordination solutions. Further, many communication primitives available today lack the ability to adapt to dynamic environments, and are often tuned during their design phase to reach a target performance, rather than be continuously updated at runtime to adapt to reality.In this thesis, we study the problem of efficient and low-latency consensus in the context of low-power wireless networks, where communication is unreliable and nodes can fail, and we investigate the design of a self-adaptive wireless stack, where the communication substrate is able to adapt to changes to its environment. We propose three new communication primitives: Wireless Paxos brings fault-tolerant consensus to low-power wireless networking, STARC is a middleware for safe vehicular coordination at intersections, while Dimmer builds on reinforcement learning to provide adaptivity to low-power wireless networks. We evaluate in-depth each primitive on testbed deployments and we provide an open-source implementation to enable their use and improvement by the community

On reliability and performance analyses of IEC 61850 for digital SAS

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