Modeling induction and routing to monitor hospitalized patients in multi-hop mobility-aware body area sensor networks

In [22], authors present a survey with respect to current development and future demands on implantable and wearable WBASN systems. The authors focus on state-of-the-art technology to provide patients and elders with quality care. Besides discussion on design considerations like energy efficiency, scalability, unobtrusiveness, and security, the authors also discuss benefits and drawbacks of the wearable and implantable WBASN systems. In [15], a generalized voltage-driven model of an inductive link for medical implants is presented. In addition, different parameters are analyzed with respect to resonating impedances. The series resonating impedances improve in voltage gain, while not causing the link efficiency to alter. In [16], an inductive power system is presented which combines power transfer with data transmission for implantable microdevices. The implanted devices receive power from an external transmitter through an inductive link between an external power transmission coil and the implanted receiving coil. The authors in [17] design a transcutaneous link for medical implants by using inductively coupled coils. They also describe the design of an indigenously developed transcutaneous link from commercial off-the-shelf components to demonstrate the design process. Moreover, their work provides an outline for inductive coils and optimized parameters of class E amplifiers. Their experimental results show approximately 40 % link efficiency at 2.5-MHz operating frequency and an output power of 100 mW. In [23], a medical relevance of the monitoring of deformation of implants is presented. It is a powerful tool to evaluate nursing and rehabilitation exercises for tracing dangerous overloads while anticipating implant failure and observing the healing process. The authors also present two implantable wireless designs which are charged via magnetic induction.

In [13], the authors use relaying and cooperation to prolong the network lifetime. They also investigate path loss while considering different body parts for both single-hop and multi-hop topologies. Similarly, the authors in [24] use topology control to account for access delays due to the underlying medium access control (MAC) layer, however, at the cost of high energy consumption. In [25], the authors set an upper bound to determine the number of relay nodes, sensors, and their respective distances to the sink. Each sensor node performs single-hop communication while relaying nodes perform multi-hop communication to the sink. In [14], J. Elias et al. provide an optimal design for WBASNs by studying the joint data routing and relay positioning problem in order to increase the network lifetime. In this research work, the authors present an inter-based linear programming model which aims for (i) optimized number of relay positions, (ii) minimization of energy consumption of sensors and relays, and (iii) minimization of the installation cost. Simulation results show that this framework has a very short computing time as compared to the other frameworks. In [26], the authors study propagation models subject to network lifetime prolongation. These models reveal that single-hop communication is inefficient for far away nodes from the sink and the multi-hop communication is more suitable. In order to avoid hot spot links, extra nodes in the network, i.e., dedicated relay devices, are introduced. The authors in [19] propose M-ATTEMPT routing protocol in which they use single-hop communication for the delivery critical data and multi-hop communication for the delivery of normal data. In order to prevent damage of body tissues, they also introduce a temperature sensing mechanism to detect the hot spot problem of in-body sensors. In [27], Chen et al. introduce a new interference-aware WBASN that can continuously monitor vital signs of multiple patients and efficiently prioritize data transmission based on patients’ conditions. The authors proposed a solution that is based on an integrated hybrid scheduler which guarantees end-to-end delay with the capability to select the best possible route (best link quality) and minimum generated interference which results in high end-to-end packet reliability. In [28], sensing is considered as a service while improving energy efficiency. Thus, the authors present a unique set of design challenges and propose different solutions which are very helpful for current as well as future researchers. In [29], the authors present anycast routing protocol for monitoring patients vital signs while coping with the end-to-end traffic. To achieve minimum network latency, the protocol chooses a nearest data receiver related to the patient. The wireless network performs fall detection, indoor positioning, and electrocardiogram (ECG) monitoring for the patients. Whenever, a fall is detected, the hospital crew gets intimated of the exact position of the patient. In [30], the authors present a cluster-based self-organization protocol. It focuses on relaying data via cluster heads to improve energy efficiency. Initially, the protocol builds a cluster-based structure and then efficiently transmits packets from source to destination. An interesting feature of this technique is the stability in terms of the selected number of cluster heads per round. In [31], the authors balance load of the sensor nodes by presenting a global routing protocol which is tested against real-time experiments along with computer-based simulations. Similarly, [32] introduces a personal wireless hub to collect personal health information of its user(s) through biomedical sensors. The sensed information is securely routed towards the health care unit if found eligible. In [33], Otto et al. present a prototype system for the health monitoring of people/patients at home. The system consists of an uninterrupted WBASN and a home health server. The WBASN sensors sense heart rate and locomotive activity such that the sensed information is periodically uploaded at the home server. The home server may integrate this information with a local database for user inspection, or it may further be forwarded to a medical server. Similarly, the idea of embedding medical devices with hospital information system is presented in [34]. The integration of ubiquitous echograph with the home information network make it very easy for the doctors to immediately diagnose the patients. In [35], Wang et al. present a distributed WBASN model for medical supervision. The system consists of three tiers: sensor network tier, mobile computing network tier, and remote monitoring network tier. This model provides collection, demonstration, and storage of vital information like ECG, blood oxygen, body temperature, and respiration rate. The system demonstrates many advantages such as low-power, easy configuration, convenient carrying, and real-time reliable data. In [36], the authors use wearable sensors to monitor daily activities of humans which they perform during different activities. The correct monitoring of these complex actions is challenging. For this purpose, they introduce activity recognition with the help of wearable sensing devices.

Each patient is equipped with seven on-body sensors, a body relay (BR), and an in-body pacemaker as shown in Fig. 2. This topology is kept the same for all the patients in the entire ward such that the total number of sensors are 56 body sensors, 8 body relays, and a pacemaker. All these sensors are equipped with limited energy resources. It is worth noting that the sensors are of two types: threshold monitoring sensors (given black color in Fig. 2) and data monitoring sensors (given blue color in Fig. 2). The first type is triggered by a threshold level to transmit data and second type continuously transmits sensed data. In order to reduce the energy consumption of sensors, a main sensor (MS) is also attached to the bed in one of the scenarios. The MS and sink are assumed to have very high power source as compared to the other sensors. After collecting the sensed data of sensors, the MS forwards these data to the sink which transmits the final information to the external network. From this external network, the doctor is then able to completely know about the patient’s conditions. The combination of body sensors and BRs exchange the data in a multi-hop manner.

The sensors measure ailments either by continuously monitoring on the basis of time-driven events or on an event-driven basis. The glucose and temperature level monitoring sensors transmit the data whenever the respective levels of these readings either fall below the lower limit or exceed the upper limit (indicating an alarming condition). The low and high temperature levels are set at 35 and 40 °C, respectively. Similarly, the values for the glucose level are 110 and 125 mg/dL, respectively. The rest of the sensors continuously monitor the parameters. Monitoring of variable data types makes this protocol feasible for a wide range of applications like remote monitoring of patients in hospital ward(s) or home(s) suffering from heart diseases, diabetes, drug addiction, etc.

Arrythmia refers to an abnormal heart rhythm due to changes in the conduction of electrical impulses through the heart that are life threatening. The heart may not be able to pump enough blood (least amount of blood needed for proper functioning of different body parts) to the body causing damage to the brain, heart itself, and other organs. Arrythmia can be well controlled by using a pacemaker that creates forced rhythms according to natural human heartbeats which are required for the normal functioning of the heart. The circuitry of a pacemaker consists of a small battery, a generator, and wires attached to the sensor. It senses a heartbeat and sends the signals to the generator through wires. If the heartbeat is not normal, it generates small electrical signals to regulate the heartbeat. The working mechanism of a pacemaker is shown in Fig. 3.

Figure 4 shows the parts of the heart which are attached with the probes of the pacemaker.

An inductive link consists of two coils, primary and secondary. The primary circuit is powered by a voltage source which generates magnetic flux in order to induce power in the secondary side which is inside the human body as shown in Fig. 5. The skin acts as an interface between the two circuits. The coupling coefficient, k, denotes the degree of coupling between the two circuits. In order to avoid damage to the body tissues, the value of k should be less than 0.45. The parameters considered for the validation of the link efficiency are as follows:

Both voltage gain and η depend upon k, while quality factor depends upon frequency and load resistor at the secondary side.

The equivalent circuits are discussed below in the following two subsections.

Information sharing is necessary for WBASNs because the newly implanted/affixed sensors are in a state of missing reliable communication infrastructure. Therefore, the network configurations are upgraded periodically. We use a HELLO message exchange technique to inform each sensor with the IDs of other sensors, coordinates of all other nodes, status of links, and received signal strength. Sensor node with initial energy greater than zero is considered as alive. The node types are determined via their IDs. During the centralized protocol operation, eight patients are independently monitored. For each patient, each of the body sensors conveys information to the corresponding in range body relay which is equipped with higher energy resources. The received data is finally transmitted to the MS. The protocol’s flow chart is shown in Fig. 9.

The algorithm for patient monitoring is as follows:

The DARE protocol also considers mobility of the patient’s legs, arms, and head to account for bed patients. We have considered four different positions for each arm, three different positions for each leg, and head for all the patients as shown in Fig. 10 b–e. In Fig. 10 b–e, the dashed arrows and dashed spheres illustrate the relative movement horizon in each posture for each arm, each leg, and head (note: spheres are replaced by circles/ellipses only for the sake of simplicity in drawing).

The continuous data monitoring sensors monitor/scan the parameter of interest regularly. Since not all the scanned data is necessarily important to be transmitted, i.e., the scanned data if similar to the previously sent data does not contain useful information and is considered as redundant data. Transmission of redundant data leads to surplus energy consumption cost which ultimately leads to network lifetime degradation. Thus, the objective here is to improve the network lifetime of DARE protocol by removing this redundancy. In order to do this, we use the MI-based technique here.

MI tells us how one random variable (sensor reading) is close to another. Let I be the scanned input of sensor node at time t ₁, and J be the scanned input of sensor node at time t ₂. We calculate the MI between current value say J with its previous value say I as follows [37]:

where p(I _i ,J _j ) is the joint probability distribution function between I _i and J _j and p(I _i ) and p(J _j ) are the independent probability functions of I _i and J _j , respectively. This equation gives us very useful information: (i) the MI value is high if I and J have similar information content, (ii) the MI value is zero if I and J are independent, and (iii) the MI is neither high nor zero if the information content between I and J is loosely related. Based on this calculation, the proposed MI-DARE protocol checks the information content of the current sensed data of continuous data monitoring sensor. If the information content of current sensed data is found similar to that of the previously sensed data, then the current sensed data is not transmitted. In this way, surplus energy consumption cost due to redundant transmissions of continuous data monitoring sensors is prevented which leads to prolongation of the network lifetime.

In order to evaluate the inductive link model in Section 6, we choose three performance metrics: voltage gain, link efficiency, and quality factor.

DARE, MI-DARE, and M-ATTEMPT protocols are compared by taking mobility of the patients into consideration. We investigate different parameters including stability period, network lifetime, number of packets sent, number of received packets at sink, number of dropped packets, and propagation delay. The simulation parameters are listed in Table 2. During the simulation setup, nodes cease transmitting data packets when their energy is completely depleted. The similarities and differences between the two protocols in terms of topology, technical parameters, communication type, etc. are tabulated in Table 4.