**11.3. Network lifetime**

While the smart routing protocol indeed provides throughput performance benefits, we also analyze the ability of the policy to conserve energy and meet a desired network lifetime of one week. This enables us to evaluate whether the policy successfully meets both performance and energy conservation requirements.

Figure 11(a) presents the average remaining energy capacities of Ultrawideband (UWB) sensors, Zigbee sensors and the WiMax mesh nodes. The UWB and Zigbee sensors are able to survive for roughly the one week network lifetime, with the outages occurring just before the end of the simulation. At the end of the simulation, the WiMax mesh network has roughly 34% of its mean battery energy remaining.

These results meet our network lifetime expectations based on the initial energy capacities in Table 5. For example, we would expect that both the UWB and Zigbee clusters would lose connectivity in the last few hours of the network lifetime. We would also expect the mesh network to maintain roughly one-third of its energy capacity at the end of the simulation. This result is significant as it shows that we can indeed design wireless sensor networks (WSNs) to plan for predictable network lifetimes, while achieving significant throughput performance.

Figure 11(b) illustrates the remaining energy capacities in the final twelve hours of the simulation and the first nodes in each cluster to fully lose connectivity. Based on the initial energy capacities selected, the UWB cluster gave us almost two extra hours of connectivity over the Zigbee network. In terms of the first node outages, node *12* from the Zigbee cluster was the first node to lose connectivity; its remaining battery energy was just under that of the mean from the Zigbee cluster at 20:15. For the UWB cluster, node *6* experienced the first node outage and followed the mean battery energy of the UWB cluster quite strictly at 22:00.

First Node Outages

### **11.4. Application criticality**

Lifetime

The impact of application criticality on throughput performance is presented in Table 7 by comparing the performance of the smart routing protocol to minimum energy routing. Smart routing selects candidate nodes that are best able to satisfy both performance and energy conservation requirements given current network conditions. While smart routing is able to achieve total network throughput that varies between 84.4 Mbps and 3.4 Gbps, minimum energy routing only achieves throughputs of 49.2 Mbps to 501.2 Mbps. This is due to minimum energy routing basing its resource allocation decisions solely on ensuring minimum energy consumption; while lower resource consumption certainly has a positive effect on increasing network lifetime, minimum energy routing gives no consideration to the impact of resource allocation on application performance. As we observe in this performance evaluation, applications that have high performance demands require greater resources and, as a result, have shorter network lifetimes; energy-conserving systems, on the other hand, allocate resources to prolong the network lifetime at the expense of application performance.


**Table 7.** Throughput Statistics of Smart Routing vs. Minimum Energy Routing

### **11.5. Blocking probability**

Figure 12(a) illustrates the dependency between the network blocking probability and the number of operating channels for the smart routing protocol. This shows that, as the number of operating channels increases, the blocking probability decreases according to a logarithmic relationship. However, as the traffic intensity *ρ* and the number of channels increases, the blocking probability decreases at a slower rate. Figure 12(a) also illustrates that the blocking probability decreases as the traffic intensity decreases, which is expected. The sharpness of

(a) Network Blocking Probability vs. Number of Operating Channels, *F* (b) Blocking Probability of UWB and Zigbee Clusters vs. Number of Operating Channels, *F*

**Figure 12.** Relationship Between Number of Operating Channels *F* and the Blocking Probability

the drop for a traffic intensity *ρ* = 1E-6 can be attributed to the near-zero blocking probability at extremely low traffic intensities.

Figure 12(b) illustrates the relationship between the blocking probability and traffic intensity separately for Ultrawideband (UWB) and Zigbee for *F* = 5, *F* = 10 and *F* = 20 channels. Given the same traffic intensity and number of operating channels, the UWB cluster has a blocking probability that is approximately 2% lower than Zigbee on average for *F* = 5. For *F* = 10 and *F* = 20, UWB also has a lower blocking probability than Zigbee but the improvement decreases as the number of channels is increased. This bodes well for next-generation commercial applications for wireless sensor networks (WSNs) that use UWB as the communication technology of choice.

## **11.6. Energy harvesting**

22 Wireless Sensor Networks / Book 1

The impact of application criticality on throughput performance is presented in Table 7 by comparing the performance of the smart routing protocol to minimum energy routing. Smart routing selects candidate nodes that are best able to satisfy both performance and energy conservation requirements given current network conditions. While smart routing is able to achieve total network throughput that varies between 84.4 Mbps and 3.4 Gbps, minimum energy routing only achieves throughputs of 49.2 Mbps to 501.2 Mbps. This is due to minimum energy routing basing its resource allocation decisions solely on ensuring minimum energy consumption; while lower resource consumption certainly has a positive effect on increasing network lifetime, minimum energy routing gives no consideration to the impact of resource allocation on application performance. As we observe in this performance evaluation, applications that have high performance demands require greater resources and, as a result, have shorter network lifetimes; energy-conserving systems, on the other hand, allocate resources to prolong the network lifetime at the expense of application performance. Routing Policy Maximum Total Minimum Total Mean Total Standard Variance Throughput Throughput Throughput Deviation Smart Routing 3.4 Gbps 84.4 Mbps 1.8 Gbps 112.2 Mbps 1.3 x 1016 Minimum Energy 501.2 Mbps 49.2 Mbps 327.3 Mbps 20.8 Mbps 4.3 x 10<sup>14</sup>

Figure 12(a) illustrates the dependency between the network blocking probability and the number of operating channels for the smart routing protocol. This shows that, as the number of operating channels increases, the blocking probability decreases according to a logarithmic relationship. However, as the traffic intensity *ρ* and the number of channels increases, the blocking probability decreases at a slower rate. Figure 12(a) also illustrates that the blocking probability decreases as the traffic intensity decreases, which is expected. The sharpness of

First Node Outages

Percentage of Joules Remaining (%)

12:00 13:30 15:00 16:30 18:00 19:30 21:00 22:30 24:00

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

UWB Cluster UWB Node 6 Zigbee Cluster Zigbee Node 12

Time (Hour)

(b) Remaining Network Energies in Last 12 Hours with

UWB Cluster Zigbee Cluster WiMax Mesh

0 1 2 3 4 5 6 7

(a) Remaining Battery Energies over One Week Network

**11.4. Application criticality**

Routing

**11.5. Blocking probability**

Time (Days)

**Figure 11.** Comparison of Remaining Battery Energies vs. Network Lifetime

**Table 7.** Throughput Statistics of Smart Routing vs. Minimum Energy Routing

0

Lifetime

20

40

60

Percentage of Joules Remaining (%)

80

100

The impact of energy harvesting on energy capacity is illustrated in Figure 13. Figure 13(a) presents the energy dissipation of a single Ultrawideband (UWB) node with no energy harvesting for the one week network lifetime. Two energy states are observed - sleep state and transmission state. In the sleep state, the impact on energy capacity is a regular dissipation of energy due to the sensor operating in a low power state. In the transmission state, we observe a sharp decrease in energy capacity for the duration of the transmission. The energy dissipation during the transmission state is positively correlated to the energy efficiency of the technology. For the given energy capacity *E*<sup>1</sup> = 17.5 *J* for UWB nodes presented in Table 5, we compute the rate of energy consumption as *rc* = 28.6 *μW*.

Figure 13(b) presents the impact of energy harvesting on the UWB node's energy capacity. We compare the energy capacity with *rh* = 0 *μW* with replenishment rates *rh* = 22 *μW*, 25 *μW* and 30 *μW*. For the first two cases, we observe an increase in the energy capacity over time and, hence, a prolonged network lifetime. However, the network lifetime is finite. This is observed for all cases where 0 < *rh* < *rc*. For *rh* = 30 *μW*, however, we seemingly have 100% energy capacity and hence an unlimited network lifetime. This is intuitive since the rate

**Figure 13.** Energy Dissipation and Impact of Energy Harvesting

of energy replenishment is greater than the rate of consumption. In this case, the network is self-sustaining and can theoretically last forever.
