## 2 Healthcare Projects Using Wireless Medical Sensor Networks
The medical sensors timely sense the patient's body data and broadcast it to the MobiCare client. The MobiCare client aggregates the body data and sends them using GPRS/UMTS or CDMA cellular link to the MobiCare server. In this research, MobiCare client makes use of application layer standard HTTP POST protocol for sending BSN data to the server. The MobiCare server supports to the medical staffs for offline physiological analysis, and for patient care [16]. Although, [PERSON] acknowledged the security issues in MobiCare, but only addressing security issues are not sufficient for real-time healthcare applications. In fact author suggested that the wireless application protocol (WAP), which is based on wireless transport layer security (WTLS) protocol could be used to provide the patient privacy, data integrity and authentication. Thus, security and privacy is still not implemented in MobiCare healthcare monitoring, or may have been left out for future work. Figure 6: MobiCare patient monitoring architecture. In [53] authors proposed a personal ambient monitoring (PAM) project for mental health monitoring. The project aims to monitor the activity signatures of patients with bipolar disorder (BP). The PAM has two levels, namely, a personal ambient monitoring infrastructure (PAM-I) and a personal ambient monitoring programming architecture (PAM-A). PAM-I is composed of body and environmental sensors, mobile phones, and personal computers. The medical sensors are planted on an individual's body and environmental sensors are placed in the home environment. The Bluetooth protocol used to connect the body sensors and mobile phones; further Bluetooth also connects the mobile phones and personal computers. Mobile phones are responsible for data aggregation from body sensors and send it to the PC for storage and analysis. It (the mobile phone) controls sensor collection, _i.e._, mental illness, using rule-oriented applications. The environmental sensors transmits data to the PC using multiple communication protocols (_i.e._, IEEE 802.11 b/g, X10, Bluetooth, _etc._) [53]. On the other hand PAM-A is composed of custom applications that handle inter-device network communication (_i.e._, control and record the data, and transfer data offsite for analysis). PAM-A supports the mobile phone and the PC, further PAM-A applications are written in Java and Prolog (_i.e._, rule-based programming). This is the first attempt to obtain activity signatures from the mentally ill patient using worn and environmental sensors networks. Although, authors blaze a new research trail for mental illness monitoring, they focused mainly on reliability and acceptability issues. Although the authors are mainly concerned about wireless mental health monitoring applications where patient privacy is an imperative requirement for such applications, they did not address patient privacy, which is unacceptable for such healthcare applications. Recently, a new system designed at Johns Hopkins University named MEDiSN, especially designed for patients' monitoring in hospital and during disaster events was reported [54]. It comprises multiple physiological monitors (called PMs), which are battery powered motes (_i.e._, Telos boards [51]) and equipped with medical sensors for collecting patients' physiological health information's (e.g., blood oxygenation, pulse rate, electrocardiogram signals, _etc._), as shown in Figure 7.  
Figure 7: Healthcare architecture of MEDiSN. The PMs are mobile, temporarily storing sensed data and transmitting it (after encrypting and signing the sensed data) to the relay points (RPs). MEDiSN incorporates different stationary RPs that are self-organized into a bidirectional routing tree, and forwards PM data to the gateways and _vice versa_. The RPs uses a collection tree routing protocol (CTP) to forward their measurements to the gateway. Moreover, MEDiSN is connected with a back-end database that constantly stores medical data and presents them to authenticated GUI clients.