# Distributed Routing and Channel Selection for Multi-Channel Wireless Sensor Networks +
## 3 Motivation Behind This Work
Figure 1: (**a**) Experimental setup to assess the activities of the radio; and (**b**) comparison of radio activities in a wireless sensor node performing data collection. Figure 2: (**a**) View of the Paradise substation, where the _ParadiseNet_ was deployed; (**b**) One of the wireless sensor nodes for circuit-breaker monitoring. In addition to variations in energy consumption, rechargeable WSNs are also characterized by significant variations in terms of energy availability from the renewable energy sources. Consider a network of solar energy harvesting rechargeable sensor nodes that are deployed in an outdoor location. Since node placement is driven by sensing needs, it is likely that individual nodes will see variations resulting from the effects of shading, reflections, and orientation. Changes of weather and solar irradiance also change solar power intake over time. To illustrate such spatio-temporal variations from a real network setting, we developed a WSN testbed named _EPIC-RoofNet_, which comprises of a set of pyranometer sensor nodes [43] that are used to obtain solar irradiance data from wireless sensor nodes placed in outdoor settings. EPIC-RoofNet was deployed in the roof of the Energy Production and Infrastructure Center (EPIC) building at UNC Charlotte to provide an experimental platform for this research. In Figure 4 we show the positions of two motes along with their irradiance measurements in three different days, illustrating high variations in the amount of irradiance over both time and space. As shown in Figure 4, node 153 has an unobstructed view of the sky, whereas node 157 is placed between two large exhaust vents that provide significant shading throughout much of the day. Furthermore, the latter node is oriented vertically, meaning that it receives reduced contribution from the direct normal component of the radiant flux. Figure 4c-f show the irradiance measurements of these two nodes as well as their accumulated solar energy over different times of the day. Note that the peak irradiance value observed by node 153 is approximately 1000 W/m\({}^{2}\), which is expected to be the peak irradiance value at the surface of the Earth [44]. However, the vertical orientation and shading cause a significantly different pattern to be observed at node 157. Figure 3: (**a**) Illustration of the layout of _ParadiseNet_[42], a 122-node WSN deployed for equipment health monitoring from a power substation; and (**b**) the average battery usage of nodes in different geographical zones over a period of five months. In addition to the spatial effects noted above, individual nodes are also subject to various temporal variations. This phenomena can be classified into high and low frequency categories. Weather patterns can cause high frequency fluctuations as passing clouds can momentarily reduce direct irradiance. Figure 4c-f demonstrate this effect by presenting the observed irradiance at the nodes on three different days. Where as weather variations have high frequency, seasonal variations are considered to be low frequency. Seasonal variations are caused by fluctuations due to slow changes within the Earth's rotation around the sun. Furthermore, it can be observed that the amount of irradiance to be present within a day is directly related to the time at which the sun rises and sets. All these observations  
Figure 4: Samples of nodes deployed in _EPIC-RoofNet_: (**a**) Node 153 (**b**) Node 157. Irradiance measurement of node 153 (**c**) and node 157 (**d**) for two sunny days (8 and 15 February 2013) and a cloudy day (8 March 2013). Node 157 is kept in the shaded region, whereas 153 receives sunlight most of the time. Accumulated solar energy of these two nodes for three different days (**e**,**f**). motivate the need for developing adaptive schemes that address the spatio-temporal variations of energy availability.