e-Healthcare promises to be the next big wave in healthcare. It offers all the advantages and benefits imaginable by both the patient and the user. However, current e-Healthcare systems are not yet fully developed and mature, and thus lack the degree of confidentiality, integrity, privacy and user trust necessary to be widely implemented. Two primary aspects of any operational healthcare enterprise are the quality of healthcare services and patient trust over the healthcare enterprise. Trust is intertwined with issues like confidentiality, integrity, accountability, authenticity, identity and data management, to name a few. Privacy remains one of the biggest obstacles to ensuring the success of e-Healthcare solutions in winning patient trust as it indirectly covers most security concerns. Addressing privacy concerns requires addressing security issues like access control, authentication, non-repudiation, and accountability, without which end to end privacy cannot be ensured. Achieving privacy from the point of data collection in wireless sensor networks (WSN), to incorporating the internet of things (IoT), to communication links, to data storage and access, is a huge undertaking and requires extensive work. Privacy requirements are further compounded by the fact that the data handled in an enterprise is of an extremely personal and private nature, and its mismanagement, either intentionally or unintentionally, could seriously hurt both the patient and the future prospects of an e-Healthcare enterprise. Research carried out in order to address privacy concerns is not homogenous in nature. It focuses on the failure of certain parts of the e-Healthcare enterprise to fully address all aspects of privacy. In the middle of this ongoing research and implementation, a gradual shift has occurred, moving e-Healthcare enterprise controls away from an organizational level towards the level of patients. This is intended to give patients more control and authority over decision making regarding their PHI/EHR. A lot of work and effort is necessary in order to better assess the feasibility of this major shift in e-Healthcare enterprises. Existing research can be naturally divided on the basis of techniques used. These include data anonymization/ pseudonymization and access control mechanisms primarily for stored data privacy. This, however, results in giving a back seat to certain privacy requirements (accountability, integrity, non-repudiation, identity management). This paper reviews research carried out in this regard and explores whether this research offers any possible solutions to either patient privacy requirements for e-Healthcare or possibilities for addressing the (technical as well as psychological) privacy concerns of the users.

Traditionally employed human-to-human (H2H) and human-to-machine (H2M) communication has recently been replaced by a new trend known as the Internet of things (IoT). IoT enables device-to-device (D2D) communication without any human intervention, hence, offers many challenges. In this paradigm, the machine’s self-sustainability due to limited energy capabilities presents a great challenge. Therefore, this paper proposed a low-cost energy harvesting device using rectenna to mitigate the problem in the areas, where battery constraint issues arise. So, an energy harvester is designed, optimized, fabricated and characterized for energy harvesting and IoT applications which simply recycles RF energy at 2.4GHz, from nearby Wi-Fi/WLAN devices and converts them to useful DC power. The physical model comprises of antenna, filters, and rectifier, etc. A rectangular patch antenna is designed and optimized to resonate at 2.4GHz using the well-known transmission-line model while the band-pass and low-pass filters are designed using lumped components. Schottky diode (HSMS-2820) is used for rectification. The circuit is designed and fabricated using the low-cost FR4 substrate h=1.6mm and εr = 4.6) having the fabricated dimensions of 285mm x 90mm. Universal software radio peripheral (USRP) and GNU Radio is employed to measure the received RF power, while similar measurements are carried out using R&S spectrum analyzer for validation. The received measured power is -64.4dBm at the output port of the rectenna circuit. Hence, our design enables a pervasive deployment of self-operable next generation IoT devices.

Millimeter-Wave (mm-Wave) frequencies are a front runner contender for the next generation body-centric wireless communications. In this paper, the design of a very low-profile antenna is presented for body-centric applications operating in the mm-Wave frequency band centered at 60 GHz. The antenna has an overall size of 14 × 10.5 × 1.15 mm ³ and is printed on a flexible printed circuit board. The performance of the antenna is evaluated in off-body, on-body, and bodyto-body communication scenarios using a realistic numerical phantom and verified through measurements. The antenna has a bandwidth of 9.8 GHz and offers a gain of 10.6 dBi in off-body (free space) configuration, while 12.1 dBi in on-body configuration. It also achieves an efficiency of 74% in off-body and 63% in on-body scenario. The small and flexible structure of the antenna along with excellent impedance matching, broad bandwidth, high gain, and good efficiency makes it a suitable candidate to attain simultaneous data transmission/reception at mm-Wave frequencies for the 5G body-centric applications.

The motivation of this work is to help outpatients find their corresponding departments or clinics, thus, it needs to provide indoor positioning services with a room-level accuracy. Unlike wireless outdoor localization that is dominated by the global positioning system (GPS), wireless indoor localization is still an open issue. Many different schemes are being developed to meet the increasing demand for indoor localization services. In this paper, we investigated the AoA-based wireless indoor localization for outpatients’ wayfinding in a hospital, where Wi-Fi access points (APs) are deployed, in line, on the ceiling. The target position can be determined by a mobile device, like a smartphone, through an efficient geometric calculation with two known APs coordinates and the angles of the incident radios. All possible positions in which the target may appear have been comprehensively investigated, and the corresponding solutions were proven to be the same. Experimental results show that localization error was less than 2.5 m, about 80% of the time, which can satisfy the outpatients’ requirements for wayfinding.

A novel study on monitoring and analysis of debilitating condition of patients suffering from neurological disorder is presented. Parkinson’s disease is characterized by limited motor-ability of a patient. Freezing-of-gait is a major non-motor condition among ageing patients and its evaluation can reduce the chances of any secondary disorders. In this work, amplitude and phase information of the radio signals observed for a fixed period of time are used to differentiate the motor and non-motor symptoms. The amplitude information is classified using Support Vector Machine while linear transformation is applied to obtain sanitized phase information for detection. The proposed method is very handy with minimum deployment overhead. The analysis shows that this method also offers a high accuracy level of around 99% based on the observation of a number of patients. These features make it an attractive solution for real-time patient monitoring systems.