**10. Simulation**

Our distributed network is formed of a *V*-sized Ultrawideband (UWB) sensor cluster and a *V*-sized Zigbee sensor cluster that are connected to a central controlling station via an *M*-sized overlay mesh network. The network spans a 1 *km* x 1 *km* campus area for which *V* = 20 sensor nodes (excluding the cluster-head) and *M* = 7 mesh nodes. We simulate a small network size without loss of generality. The UWB cluster is focused in a 10 *m* x 10 *m* area for a video feedback application, while the Zigbee cluster performs temperature sensing in a 75 *m* x 75 *m* area such as a computer server room.

Table 3 summarizes our selection of the *α*, *β*, *γ*, *δ* and *η* parameters that have been calibrated for our network scenario.


**Table 3.** Selection of Optimization Parameters in the Heterogeneous WSN

In terms of radio design, each Zigbee and UWB nodes uses a single transmit and receive antenna with gains of 0 dBi and 3 dBi, respectively. WiMax mesh nodes have transmit and receive antenna gains of 13 dBi and 16 dBi, respectively. For our operating parameters, we choose *F*<sup>1</sup> = *F*<sup>2</sup> = *Fc* = 5 as the number of sub-channels in the spectrum band with corresponding bandwidths of *w*<sup>1</sup> = 75 MHz, *w*<sup>2</sup> = 12.5 kHz and *wc* = 6.25 MHz for UWB, Zigbee and WiMax, respectively. As can be seen in Table 4, UWB sensors also transmit four times the information per transmission than Zigbee sensors.


**Table 4.** Communication Parameters for Three Sub-Networks

We also select the maximum number of hops *K*� = 4 for a single candidate path and the energy consumption coefficient *ζ* = 0.5. According to Oppermann et al., "the amount of energy consumed while listening, receiving, and transitioning to receive mode is similar to that of transmitting, and cannot be ignored" and, as such, *ζ* is selected to divide the total energy consumption evenly between the transmitter and receiver [3]. The power consumption attributed to transmitting is higher than receiving in the communication between a transmitter and receiver; however, it should be noted that the energy consumed by listening for a transmission may be a dominant source of energy dissipation in these networks [16, 17].

### **10.1. Energy modeling**

18 Wireless Sensor Networks / Book 1

206 Wireless Sensor Networks – Technology and Protocols Cross-Layer Design for Smart Routing in

While transmissions from different clusters may be of different priorities, typically transmissions from a given sensor cluster all have the same priority at any given time. Hence,

Our distributed network is formed of a *V*-sized Ultrawideband (UWB) sensor cluster and a *V*-sized Zigbee sensor cluster that are connected to a central controlling station via an *M*-sized overlay mesh network. The network spans a 1 *km* x 1 *km* campus area for which *V* = 20 sensor nodes (excluding the cluster-head) and *M* = 7 mesh nodes. We simulate a small network size without loss of generality. The UWB cluster is focused in a 10 *m* x 10 *m* area for a video feedback application, while the Zigbee cluster performs temperature sensing in a 75 *m* x 75 *m*

Table 3 summarizes our selection of the *α*, *β*, *γ*, *δ* and *η* parameters that have been calibrated

Sub-Network *α β γηδ* UWB 47,000 5.22 409.2 1,000 - ZigBee 10,000 -7x10−<sup>7</sup> 1267.8 1,000 0.0517 WiMax 100,000 4.17 125.81 0 -

In terms of radio design, each Zigbee and UWB nodes uses a single transmit and receive antenna with gains of 0 dBi and 3 dBi, respectively. WiMax mesh nodes have transmit and receive antenna gains of 13 dBi and 16 dBi, respectively. For our operating parameters, we choose *F*<sup>1</sup> = *F*<sup>2</sup> = *Fc* = 5 as the number of sub-channels in the spectrum band with corresponding bandwidths of *w*<sup>1</sup> = 75 MHz, *w*<sup>2</sup> = 12.5 kHz and *wc* = 6.25 MHz for UWB, Zigbee and WiMax, respectively. As can be seen in Table 4, UWB sensors also transmit four

Sub- Payload Length Packet Length Data Length Packets to Number of Channel Band of Network *l* (bytes) *m* (bytes) (bytes) Transmit, *n* Channels Bandwidth Operation UWB 32 38 128 4 5 75 MHz 2.4 - 2.5 GHz ZigBee 32 38 32 1 5 12.5 kHz 3.0 - 11 GHz WiMax 48 256 128 or 32 4 or 1 5 6.25 MHz 5.25 - 5.725 GHz

We also select the maximum number of hops *K*� = 4 for a single candidate path and the energy consumption coefficient *ζ* = 0.5. According to Oppermann et al., "the amount of energy consumed while listening, receiving, and transitioning to receive mode is similar to that of transmitting, and cannot be ignored" and, as such, *ζ* is selected to divide the total energy consumption evenly between the transmitter and receiver [3]. The power consumption attributed to transmitting is higher than receiving in the communication between a transmitter and receiver; however, it should be noted that the energy consumed by listening for a transmission may be a dominant source of energy dissipation in these networks [16, 17].

it is optional and configurable for the network scenario in question.

**Table 3.** Selection of Optimization Parameters in the Heterogeneous WSN

times the information per transmission than Zigbee sensors.

**Table 4.** Communication Parameters for Three Sub-Networks

**10. Simulation**

area such as a computer server room.

for our network scenario.

Initial energy capacities depend heavily on the energy efficiencies of the communication technology. This is intuitive since each wireless transmission depends on the amount of energy consumed during communication. As a result, we equip sensor and mesh nodes with different energy capacities. The energy efficiencies of Ultrawideband (UWB), Zigbee and WiMax are presented in Table 5, along with the expected data rates of the technologies. We also test our network with arrival rates of sensor node requests of *λ*<sup>1</sup> = *λ*<sup>2</sup> = 0.1 requests/minute.


**Table 5.** Energy Efficiencies and Data Rates for Various Standards [3] [18]

By considering each hop as a two-stage pipeline, we also implement holding times of 1/*μ*<sup>1</sup> = 3.04 *μ*sec and 1/*μ*<sup>2</sup> = 2.43 msec per transmission. Hence, our network models traffic intensities of *a*<sup>1</sup> = *λ*1/*μ*<sup>1</sup> and *a*<sup>2</sup> = *λ*2/*μ*2.

Table 5 presents the implemented energy capacities in the network for sensors, cluster-heads and mesh relay nodes given our system parameters. These energy capacities are determined as those to reach a one week network lifetime. Hence, we equip UWB and Zigbee nodes with *E*<sup>1</sup> = 17.5 *J* with *E*<sup>2</sup> = 69.5 *J*, respectively. The energy capacity of a mesh node is chosen as *Em* = 1 *kJ*. We implement sensor energies slightly under the required levels to analyze the performance of these nodes in the final stages of the network lifetime.


**Table 5.** Required and Applied Energy Capacities for Various Node Types

### **10.2. Energy harvesting**

The most challenging factor facing the widespread deployment of wireless sensor networks (WSNs) is the power constraint faced by sensors that affects network lifetime and performance. Energy harvesting technologies are a significant enabler for smart routing because they relax the critical power constraint by replenishing energy reserves of sensors over time. This can be achieved by converting energy sources such as kinetic, solar or heat energy into usable battery energy.

The impact of energy harvesting can be observed by comparing the rate of energy replenishment to the rate of energy consumption in the sensor network. Define the rate of energy replenishment as *rh* in joules per second, or watt. Given an energy consumption rate *rc* of a sensor, the effect of energy replenishment on network lifetime fits into one of three categories:


From the perspective of resource allocation, the effect of energy harvesting can also be observed by analyzing the network lifetime. The network is able to operate at peak performance as long as sufficient transmission resources are available; this occurs until nodes are unable to maintain a high level of performance because remaining energy capacities are insufficient. Since energy harvesting enables us to prolong the network, it also increases the period of time that the network operates at high levels of throughput performance. We will analyze the impact of the replenishment rate *rh* on the network lifetime for smart routing.
