## 9 Conclusion and Future Work  
We have presented the design and implementation of a comprehensive security solution for medical sensor networks. Our initial results on the patient authentication using ECG data are promising, while our implementation on the Tmote Sky demonstrates reasonable computational overhead for our mechanisms.  
Our future plans include improvements to both the theoretical and implementation aspects of the system. We have not yet addressed some security requirements that would be important in an actual deployment. In particular, these security issues deserve attention:  
**Base stations** - Our work assumes that the base stations in the architecture are secure. Although we feel that base stations are less likely than motes to be compromised, we should nonetheless develop a contingency plan should an attacker gain access to one or more base stations. Furthermore, we currently assume that base stations' public keys are _pre-configured_ in the patient motes. However, we have not yet developed a scheme to allow scalable and secure updates to these public keys.  
**Denial-of-service attacks** - An attacker may simply flood the wireless channel with meaningless transmissions, rendering the motes unable to send any data. Some work has already been done on addressing DoS attacks in wireless sensor networks; [PERSON] and [PERSON] [35] provide a survey. We hope to integrate some techniques from the literature into our system.  
**Attacks on the relay nodes** - Rather than targeting base stations or patient motes, an attacker might focus on the relay nodes in the system. This might include conducting a DoS attack against or physically capturing relay nodes, both of which would result in a routing \"black hole\" in the system. We note that this might be addressed by selecting an appropriate flexible routing protocol, which (as mentioned in Section 5) we do not consider in this work.  
**Random number generation** - In our scheme, the session keys are derived from a secret \(S=rQ\), where \(Q\) is the public key of the base station and \(r\) is a random number generated by the sending mote. Even with no knowledge of \(r\) itself, an attacker who physically captures a mote might gain knowledge of the seed used to generate \(r\). To combat this, we might have the sending mote use patient biometric data in the random number generation, but this is not yet something that we have analyzed in detail.  
Implementation-wise, we plan to evaluate the base station's neural network mechanism using live data streams from the motes, optimize our neural network performance, integrate motes with actual medical sensors and fingerprint readers, and possibly consider a more powerful mote platform such as the Intel iMote2. The iMote2's processor is about an order of magnitude faster than that of the Tmote Sky, which may potentially result in a more efficient system (as the processor can quickly perform its work and resume a low-power sleep mode). Ultimately, we hope to conduct a study on actual patients to demonstrate the real-world feasibility of our system. Even further in the future, we may consider using a body sensor network (which can include implanted as well as wearable wireless sensors) on the patient rather than connecting all sensors to a single mote.