# Collective Attestation for Manageable IoT Environments
## 6 Evaluation
### Scalability against Infection  
CAMIE and SANA support the identification of compromised devices (bad provers) in different ways. SANA accumulates the public keys and software configurations of compromised devices to the attestation response. Thus, the communication overhead increases linearly as the number of compromised nodes increases. However, the excessively enlarged responses may overuse the overall resource of the swarm network. To prevent the attestation process from being overwhelmed by massive infections, SANA also has a threshold to limit the size of message as a protocol extension [21]. CAMIE takes a different approach to identify the compromised nodes. The attestation information is separated and localized in the multiple region summaries. Hence, in CAMIE, a verifier first learns the compromised regions not individual devices. Then, the verifier can identify the individual device from the region summary. In this way, the communication overhead of CAMIE is not proportional to the number of infections even with supporting bad-prover identification.  
We can define the sensitivity of identified compromised device as \"detection precision.\" to distinguish the different features of the schemes. In other words, the detection precision is how detailed the information is that attestation results give for the individual identification of compromised nodes. SANA has the _exact_ 1-detection precision with linear overhead and SEDA has no detection precision with fixed overhead. CAMIE has the probabilistic detection precision since it depends on the tag size and the region size.  
The scalability against infections means how much an attestation method can maintain the detection precision for a large-sale infection. Thus, we focus on the change of the message size when a certain portion of the network is compromised, since the infection does not impact the computation of devices. Figure 6 shows the change of message size of CAMIE and SANA when the probability of compromised nodes (or the infection rate) increases. In Figure 6, we calculate the proper tag size for the given infection rate to have the false-positive rate under 5% (i.e., 0.95-detection precision). As shown in Figure 6, the message size of SANA increases more rapidly than that of CAMIE when both use the 4-ary attestation tree. Thus, we can see that CAMIE is more scalable than SANA to the large-scale infection (more than 30% infections, in particular).  
Figure 5: Energy cost of CAMIE with different settings.  
**Region size and detection precision.** The probabilistic detection precision of CAMIE is also related to the size of region. Additionally, CAMIE supports target search by adding \(\mathbb{T}\) in the request to localize maintenance targets. For both attestation and target search, smaller regions are helpful for reducing the number of devices to manually inspect. However, when the region size is smaller, the swarm network has more regions and the overhead also increases. Figure 7 shows the change of the energy cost for different region sizes. If the number of regions increases, then more region summaries are accumulated for communication. In Figure 7, the region size is changed from 2 to 256. We can see the clear trade-off relation between region size and energy cost; the average energy cost required for a device decreases rapidly as the region size increases.