# Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches +
## 5 Performance Evaluation  
We study the performance of the presented methods in two different scenarios, namely, either with variable number of nodes and fixed percentage of both producers and consumers, or with fixed number of nodes and variable number of consumers. In all simulations, the producers generate new data according to a uniform distribution probability with a period of 3 s. Each consumer generates 400 requests; generation begins with 30 s of delay from the simulation start in order to allow producers to produce some data. Each simulation reproduces 40 min of system operation. Every experiment is run five times, with different random seeds, and results are averaged; in the plots, confidence intervals are also shown. Some preliminary tuning was needed in order to optimize system behavior. We set the queue length at MAC layer to 500, in order to minimize the message loss probability when the network is overloaded. As far as CSMA/CA is concerned, preliminary experiments were allowed to reach 100% message delivery when up to seven retransmissions could be performed in order to overcome collisions. As far as the proxy selection algorithm is concerned, we set the average data access latency threshold to 60 ms. The main considered performance metrics are: _success rate,_ defined as the percentage of requests from consumers that obtained a response; and _latency_, defined as the time elapsed between a request generation by a consumer and the reception of the corresponding response. In the first scenario, the number of nodes varies between 20 and 180. For all cases, both producers and consumers amount to 10% of the total number of nodes. In Figure 3(a), the success rate is shown. The use of proxies in method D2 allows to maintain a success rate very close to 100%, thus indicating that the proxy selection algorithm effectively characterizes appropriate number and locations for the nodes selected to cache data items. D1 mimics quite well this behavior thanks to the proxies placed in the LCAs of the producer-consumer pairs; however, it is penalized by traffic congestion at the LCAs and along the tree branches, which causes a higher number of collisions. We recall that the paths used in D1 and D2 are different (see Section 4.1 and Figure 2): while D1 uses a CNC-centered tree, D2 uses optimal paths between producers/consumers and their proxies, thus achieving a better traffic distribution and load balancing in the network, which reduces the probability of collisions. By contrast, centralized solutions are heavily penalized with the growing number  
Figure 3: Superframe slot numbers. of nodes and therefore of both consumers and producers: this leads to a higher number of generated messages that converge toward the CNC. In the case of C1 (RPL in non-storing mode), an increasing number of losses due to collisions is observed as a consequence. In the case of C2 (WirelessHART), which uses time slotting, collisions do not occur. Rather, messages accumulate in the queues and are lost due to buffer overflow, while nodes wait the appropriate slot for transmitting; this effect is more evident as nodes are nearer to the CNC. This also explains the discontinuity for 100 nodes: with increasing number of nodes, the tree produced by the [PERSON]'s algorithm changes, and buffer congestion distributes differently across nodes. Results for C2 with 180 nodes are not available as the model was computationally too heavy for the simulator, which ran out of memory. Figure 4: Performance for variable number of nodes. (**a**) Success rate; (**b**) Average latency; (**c**) Maximum latency; (**d**) Maximum average latency; (**e**) Number of proxies. The average latency, maximum latency and maximum average latency, which are shown in Figure 4b-d, respectively, confirm that the adoption of a distributed approach allows for reducing latency.