Cognitive Radio Networks with Wireless Sensor Applications

无线传感器网络(WSN)，FAST的一部分 新兴的物联网将在转变我们的 活着。大量这样的传感器可能很快就会被部署到 执行从简单到复杂的任务，如自动控制热量和 智能家居中的灯光可以引导自动驾驶车辆和导弹。查找 这些潜在的众多无线传感器的足够的无线电频谱是一个 这是个大问题。虽然加拿大工业部正在努力分配更多的频率 波段，最高可达毫米波段，许多研究人员的实验研究表明 已经分配的频谱通常未得到充分利用25%-30%。 认知无线电(CR)网络是一种新的 智能无线网络中，认知(次级)用户是 配置为与许可(主要)用户共存，而不会造成危害 通过机会主义的频谱接入对他们进行电磁干扰。那里 是成功实施CR周期的几个步骤。也就是说， 频谱感知(通过感知各种频谱参数和 频谱占用)、基于观测的分析和决策，以及 频谱开发(适应)。需要实现这些基本功能 在认知无线电网络中反复发出。 无线传感器网络的另一个挑战是提供能量。 这些传感器的电池管理和维护可能会令人望而却步 由于它们的丰富，运动费用很高。传感器的性能将 随着剩余的能量级别恶化，失效的传感器节点将影响 整个网络的性能，尤其是在多跳网络中。 电池处理不当还会造成环境污染和健康 从长远来看是危险的。 能源问题可以通过能源来缓解。 收割。环境具有光、热、机械等形式的能量。 振动和电磁辐射。特别是，认知型WSN可以收获 来自主网络的能量。这种能量必须与 适当的传感器，可靠地存储并用于无线数据 传输通常是突发性的。的零星和不可靠的性质 电子元器件的能量采集过程及特性 必须对其进行适当的建模，以设计可行的能量收集无线传感器网络。 当频谱窃取CR功能是 再加上采集能量的无线传感器，研究问题非常多 很有挑战性。但是，有必要调查这种情况，因为 迫在眉睫。此外，传统的无线要求 通信，例如适当的服务质量、满足延迟、 吞吐量、公平性和可靠性仍需满足未来认知 能量采集无线传感器网络。拟议的研究计划将 关注这一方向，研究认知无线领域的紧迫问题 传感器网络和寻找新的解决方案。

Wireless sensor networks (WSNs), part of the fast emerging Internet of Things will be playing vital role in transforming our lives. Potentially huge number of these sensors will soon be deployed to perform simple to complex tasks such as automatically controlling heat and light in smart homes to guiding autonomous vehicles and missiles. Finding adequate radio spectrum of these potentially numerous wireless sensors is a major issue. While Industry Canada is struggling to allocate more frequency bands, up to mm wave bands, many researchers’ experimental studies show that already allocated spectrum is typically underused by 25-30%. Cognitive radio (CR) networks are a new form of intelligent wireless network where, the cognitive (secondary) users are configured to coexist with licensed (primary) users without causing harmful electromagnetic interference to them by opportunistic spectrum access. There are a few steps in the successful implementation of a CR cycle. Namely, spectrum awareness (achieved through sensing various spectrum parameters and spectrum occupancy), analysis and decision making based on the observation, and spectrum exploitation (adaptation). These basic functions need to be carried out repeatedly in a cognitive radio network. Another challenge in WSN is providing energy. Battery management and maintenance of these sensors can be a prohibitively expensive exercise because of their abundance. Sensors’ performance will deteriorate with the remaining energy level and dead sensor nodes will affect the performance of an entire network, especially in multi-hop networks. Improper battery disposal will also cause environmental pollution and health hazardous in the long run. The energy issue can be alleviated by energy harvesting. The ambience has energy in the forms of light, heat, mechanical vibrations and electromagnetic radiations. Especially, cognitive WSN can harvest energy from the primary network. This energy has to be collected with appropriate transducers, stored reliably and used for wireless data transmission which is typically bursty. The sporadic and unreliable nature of the energy harvesting process as well as characteristics of electronic elements has to be appropriately modeled to design viable energy harvesting WSN. When the spectrum stealing CR functionality is added to the energy harvesting wireless sensors, the research problem is very challenging. However, it is essential to investigate this scenario due to the pressing necessity. In addition, traditional requirements of wireless communication such as appropriate quality of service, meeting latency, throughput, fairness and reliability still have to be met by future cognitive energy harvesting wireless sensor network. The proposed research program will focus on this direction, investigating pressing issues in cognitive wireless sensor networks and finding novel solutions.