Cognitive Radio Networks with Wireless Sensor Applications

无线传感器网络（WSNs），快速 新兴的物联网将在改变我们的 生活这些传感器的潜在数量将很快部署到 执行简单到复杂的任务，如自动控制热量， 智能家居中的照明，引导自动驾驶汽车和导弹。找到 这些潜在的众多无线传感器的足够的无线电频谱是 主要问题。虽然加拿大工业部正在努力分配更多的频率， 波段，直到毫米波段，许多研究人员的实验研究表明， 已经分配的频谱通常未充分使用25 - 30%。 认知无线电（CR）网络是一种新的形式， 智能无线网络，其中，认知（次要）用户是 配置为与许可（主要）用户共存，而不会造成有害的 通过机会性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（通过感测各种频谱参数和 频谱占用），基于观察进行分析和决策，以及 频谱利用（适应）。这些基本功能需要携带 在认知无线电网络中反复出现。 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是一个令人望而却步的问题。 昂贵的运动，因为他们的丰富。传感器的性能将 随着剩余能量水平的恶化，死亡的传感器节点将影响 整个网络的性能，特别是在多跳网络中。 电池处理不当也会造成环境污染和健康问题 从长远来看是危险的。 能源问题可以通过能源来缓解 收获环境具有光、热、机械等形式的能量 振动和电磁辐射。特别是认知无线传感器网络， 从主网络的能量。这些能量必须通过 适当的传感器，可靠存储并用于无线数据 通常是突发的传输。零星和不可靠的性质， 能量收集过程以及电子元件的特性 必须适当地建模以设计可行的能量收集WSN。 当频谱窃取CR功能被 再加上能量收集无线传感器，研究问题非常 挑战性但是，由于以下原因，必须对这种情况进行调查 迫切的需要。此外，无线通信的传统要求 通信，例如适当的服务质量，会议延迟， 吞吐量、公平性和可靠性仍然必须由未来的认知 能量收集无线传感器网络。拟议的研究计划将 专注于这一方向，研究认知无线电中的紧迫问题 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.