## 5 Performance Evaluation of BSNCare+
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Operations in the System Implementation** & **Execution Time (ms)** \\ \hline
4 times of 96-bit random number generations, i.e., \(N_{i}\), \(N_{j}\), \(N_{s1}\) and \(N_{s2}\) & 0.2783 \\
8 times of bitwise exclusive-or operations & 0.2078 \\
4 times of SHA-3 function with 512-bit input sequence, i.e., \(M_{1}\) and \(M_{2}\) & 0.02 \\
2 times of SHA-3 function with 608-bit input sequence, i.e., \(T\tau\) & 0.802 \\
2 times of SHA-3 function with 704-bit input sequence, i.e., \(V_{2}\) & 1.04 \\
2 times of SHA-3 function with 800-bit input sequence, i.e., \(SK_{js}\) & 1.006 \\
2 times of SHA-3 function with 896-bit input sequence, i.e., \(AID_{i}\) & 1.038 \\
2 times of SHA-3 function with 1024-bit input sequence, i.e., \(V_{3}\) & 0.866 \\
2 times of SHA-3 function with 1120-bit input sequence, i.e., \(SK_{is}\) & 0.928 \\
2 times of SHA-3 function with 2208-bit input sequence, i.e., \(V_{1}\) & 3.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The details of the implementation of the authentication phase with \(T_{seq}\). Performance evaluation is one of the most indispensable issues while designing a robust and efficient communication scheme. In general, the evaluation is able to reflect the practicability and feasibility of deploying the proposed mechanism into the real world. In the above sections, we have demonstrated the practicability and feasibility of our proposed system, i.e., the BSNCare+, through a common IoT-oriented developing platform, i.e. the Raspberry PI 2 platform. To further investigate the advantage of the BSNCare+, we compare it with BSN-Care [9], USM-IoT [10] and scheme in [14] in terms of performance efficiency. The comparisons among BSNCare+ and the other two schemes are listed in Table 7. The evaluation metrics are the one-way hash function (HF), random number generation (RN) and bitwise exclusive-or operation (XOR). Although our proposed BSNCare+ scheme additionally requires at most 3 times the random number generation, at most 2 times the bitwise exclusive-or operation and at most 7 times the one-way hash function, our scheme can achieve the same order of computation complexity as these three schemes do. The efficiency is guaranteed to have better security robustness than BSN-Care [9] and USM-IoT [10]. On the other hand, the BSN-Care [9], USM-IoT [10], scheme in [14] and BSNCare+ utilize an elegant re-synchronization mechanism consisting of a set of un-linkable shadow identities \(\mathit{SID}=\{\mathit{sid}_{1},\mathit{sid}_{2},\dots\}\) to conquer the Denial of Services (DoS) attack. Note that from a sensor point of view, the definition of \(\mathit{DoS}\) attack in the context of IoT-based authentication is that the secrets held by the sensor and the _Server_ are out of synchronization due to abnormal process conditions such as malicious interruption or operation interference [15; 16]. Consequently, in the next authentication session this sensor can no longer be successfully validated by the _Server_. The pre-shared un-linkable shadow identities \(\mathit{SID}\) can be adopted as a fresh and legitimate secret when an abnormal session occurs, and the \(\mathit{DoS}\) attack can thus be prevented. In the proposed BSNCare+ system, we require a cost of performing \(k\) times random numbers and hash functions for the re-synchronization mechanism. The cost is similar to that in BSN-Care [9] and USM-IoT [10], and is lower than scheme in [14]. In our implementation, we have realized our proposed BSNCare+ system on a common IoT-based testbed, i.e., raspberry PI 2 platform, as a proof of concept. What we focus on is the healthcare relevant scenario identified in Sections 3.1 and 3.2. In the applied scenarios, intelligent body sensors are deployed in the field and on the patient, respectively, to support the nurse/doctor in activities of patient care, rehabilitation and even healthcare.