# Energy-Optimized Content Refreshing of Age-of-Information-Aware Edge Caches in IoT Systems
## 5 Performance Evaluation  
_Exemplary Use Case: LWM2M_  
IoT devices generate complex and heterogeneous M2M systems that need to be configured, monitored, and maintained, so there is a need for a standard platform for management. For example, the Open Mobile Alliance (OMA) specified the LightweightM2M (LWM2M) protocol for device management and service enablement. LWM2M defines an application layer protocol between an LWM2M Server, i.e., the IoT application, and the LWM2M Client, i.e., the IoT device. It is designed for constrained devices and networks and provides a set of REST-based resource models where each information made available by the device is a Resource. Resources are then organized into Objects. This object model is easily extensible, and the Object registry is open to the industry. So, LWM2M is becoming broadly used in industry: in this context, the IIoT network manager aims at reducing the energy consumption in the device to prolong its lifetime and reduce the operating costs of the network while satisfying the AoI requirements of the applications. Hence, a possible solution is using a caching system at the edge. To assess the performance of our solution in this use case, we emulate an IoT system where we use the LWM2M protocol to manage the IoT devices and we consider a scenario consisting of an IoT network, an LWM2M Server that runs the IoT application and a cache-enabled LWM2M Proxy that implements our proposed cache-management scheme. The IoT network is emulated using the COOJA network emulator [33] and uses the 6 LoWPAN protocol [34] on top of the IEEE 802.15.4 MAC [35] operating in the 2.4 GHz band, and the RPL routing protocol [36]. The wireless devices of the IoT network run the Contiki-NG operating system [37] and are connected to the Internet through the 6 LoWPAN Border Router. One of the devices runs the LWM2M Client, that in this case is the device application, exposing an LWM2M Object representing the sensor, and is located three hops away from the 6 LoWPAN Border Router. The LWM2M Proxy is located outside the IoT network and manages the requests for the device application sent by the IoT application using the proposed cache-management scheme. The LWM2M Server and the LWM2M Proxy are implemented using the Eclipse Leshan library [38]. Each experiment lasted 400,000 s, the frame size of a response message is 82 bytes and the resulting average service delay, i.e., the time between when a request is issued by the application and the time its response is received, is 326.5 ms (95% CI [322.6, 330.4]), and it is negligible compared to the chosen value of \(AoI_{a}\), i.e., 420 s.  
In our first experiment, \(\alpha\) is 0.9 and the generation of application-requests follows a Poisson distribution with a cumulative rate \(\lambda=1/180\) s\({}^{-1}\)(as in the examples shown in Section 4.1.3). Figure 11 shows the empirical CDFs and the CDFs obtained through the model for the following values of \(\overline{\beta}\): \(\overline{\beta}=0.5\), \(\overline{\beta}=0.97\), \(\overline{\beta}=1\). Table 3 shows the values of \(w\) and \(s\) chosen by the optimizer. We can see that the empirical and the theoretical results are very close to each other, so the empirical CDFs obtained with the values of \(w\) and \(s\) chosen by the model always satisfy the AoI requirements. Moreover, Table 3 also shows the values of \(\overline{Ao  
In our second experiment, we consider the same configuration as the previous experiment, but now multiple IoT applications send periodic requests for the state of the IoT device.