# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 1 Introduction  
Communications in wireless sensor networks (WSNs) is event-driven (i.e., whenever an event is triggered, wireless sensor (WS) nodes generate bursty traffic). Conventionally, WSNs are designed to work on a single channel with non-rechargeable/irreplaceable batteries and a very low active duty cycle. In a dense network environment, WS nodes deployed in the same area might try to access a channel whenever a new event triggers communications, which results in scarce bandwidth to send data packets from all the nodes in the region. These days, an increasing number of sensitive and critical activities are being monitored and observed using bandwidth-hungry multimedia WSNs. Cognitive techniques are now being used in wireless sensors to circumvent the bandwidth limitations imposed by conventional WSNs. Integration of cognitive devices in WS nodes extends their capability to utilize unused spectrum and provides greater bandwidth. The cognitive radio wireless sensor network (CR-WSN) is a candidate for the next generation of WSN systems [1; 2; 3; 4]. Wireless sensors are normally deployed in inaccessible terrain. Therefore, the self-configuring, self-organizing abilities and the lifetimes of the WS nodes have become very important. Along with these capabilities in sensor nodes, the following are the basic characteristics of cognitive radio wireless sensor (CR-WS) nodes. * Nodes can work with multiple channels. * Nodes listen and follow instructions from the base station. * They can switch channels within a pre-specified time. * They broadcast/unicast spectrum information to neighbors. * They feature spectrum sensing, analyzing, predicting, decision making, and management. Details about CR-WSNs were outlined by [PERSON] et al. [1]. In this paper, we use the terms spectrum and channel, and node clustering and clustering interchangeably. Most of the WSNs that measure physical phenomena, like temperature, humidity, location and movement of objects, etc., are delay-tolerant and require low bandwidth. Therefore, sensors with CRcapabilities are basically not required. CR-WSNs are generally required for monitoring systems where low delay, high throughput, and reliability are essential. These requirements are especially applied to multimedia applications. Logically grouping and consolidating similar sensor nodes in their proximity with certain objectives is called node clustering. A clustered wireless sensor networking architecture is advantageous to a non-cluster-based architecture in various ways. A non-cluster-based architecture is also called a single-tier network architecture and is based on a flat topology (discussed in Section 3). Node clustering enables bandwidth reuse and efficient resource allocation; thus, it can improve system capacity. Particularly, in a large scale and a dense sensor network, a single-tier network can overload the gateway node, leading to congestion and delay in communications. The single-tier network is not scalable for a larger set of sensors deployed over a large area. Clustering in CR-WSNs is still in its infancy. There has been plenty of work done in clustering for mobile ad hoc networks (MANET) [5; 6], WSNs [7], and cognitive radio networks (CRNs) [8; 9]. Although some clustering issues have been addressed in the literature [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28], clustering still remains a vastly unexplored domain in CR-WSNs. This work attempts to explore the requirements of node clustering in CR-WSNs, compares node clustering in CR-WSNs and WSNs, and discusses the issues and challenges of node clustering in CR-WSNs.