Wireless Body Area Network Coordinator Research Articles

Novel Data Transmission Schemes for Inter-WBAN Networks Using Markov Decision Process.

This work proposes a stochastic model of the coordinator units of each wireless body area network (WBAN) in a multi-WBAN scenario. In a Smart Home environment, multiple patients can come into the vicinity of each other while each of them is wearing a WBAN configuration for monitoring body vitals. Thus, while multiple WBANs coexist, the individual WBAN coordinators require adaptive transmission strategies in order to balance between maximizing the likelihood of data transmission and minimizing the chances of packet loss due to inter-BAN interference. Accordingly, the proposed work is divided into two phases. In the offline phase, each WBAN coordinator is modeled stochastically and the problem of their transmission strategy has been modeled as a Markov Decision Process(MDP). The channel conditions and buffer status that influence the transmission decision are taken to be the state parameters in MDP. The formulation is solved offline, prior to deployment of the network to find out the optimal transmission strategies for various input conditions. Such transmission policies for inter-WBAN communication are then incorporated into the coordinator nodes in the post-deployment phase. The work is simulated using Castalia and the results demonstrate the robustness of the proposed scheme in handling both favorable and unfavorable operating conditions.

Interference Mitigation for Coexisting Wireless Body Area Networks: Distributed Learning Solutions

When multiple wireless body area networks (WBANs) exist in close proximity to each other, the inter-user interference considerably degrades the signal to interference plus noise ratio of the packets arriving at each WBAN coordinator. Also, the propagation paths within each WBAN experience fading due to the continuous changes in the body posture and mobility of the human body. The most preferred coexisting mechanisms specified in the IEEE 802.15.6 standard is the channel hopping mechanism, which fails to consider the varying radio environment and obtained reward in its channel selection. Thus, our paper investigates this channel selection problem for interference mitigation in a time-varying environment. We formulate this channel selection problem as a finite repeated potential game and propose two learning algorithms, Stochastic Learning Algorithm (SLA) and Stochastic Estimator Learning Algorithm (SELA) to achieve the Nash Equilibrium (NE) of the game. Numerical results show the convergence of the learning algorithms to the NE point of the game. The performance evaluation and impact of parameters on these two algorithms are also analyzed in our paper.

Efficient medium access arbitration among interfering WBANs using Latin rectangles

The overlap of transmission ranges among multiple Wireless Body Area Networks (WBANs) is referred to as coexistence. The interference is most likely to affect the communication links and degrade the performance when sensors of different WBANs simultaneously transmit using the same channel. In this paper, we propose a distributed approach that adapts to the size of the network, i.e., the number of coexisting WBANs, and to the density of sensors forming each individual WBAN in order to minimize the impact of co-channel interference through dynamic channel hopping based on Latin rectangles. Furthermore, the proposed approach opts to reduce the overhead resulting from channel hopping, and lowers the transmission delay, and saves the power resource at both sensor- and WBAN-levels. Specifically, we propose two schemes for channel allocation and medium access scheduling to diminish the probability of inter-WBAN interference. The first scheme, namely, Distributed Interference Avoidance using Latin rectangles (DAIL), assigns channel and time-slot combination that reduces the probability of medium access collision. DAIL suits crowded areas, e.g., high density of coexisting WBANs, and involves overhead due to frequent channel hopping at the WBAN coordinator and sensors. The second scheme, namely, CHIM, takes advantage of the relatively lower density of collocated WBANs to save power by hopping among channels only when interference is detected at the level of the individual nodes. We present an analytical model that derives the collision probability and network throughput. The performance of DAIL and CHIM is further validated through simulations.

Reinforcement Learning Based Power Control for In-Body Sensors in WBANs Against Jamming

Wireless body area networks (WBANs) have to address jamming attacks to support health-care applications. In this paper, we present a reinforcement learning-based power control scheme for the communication between the in-body sensors and the WBAN coordinator to resist jamming attacks. This scheme applies Q-learning to guide the coordinator to achieve an optimal power control strategy without being aware of the in-body sensor’s transmission parameters and the WBAN model of the other sensors in the dynamic anti-jamming transmission. In addition, a transfer learning method is adopted to accelerate the learning speed. Stackelberg equilibria and their existence conditions are deduced in a single time slot to upper bound the performance of the learning-based sensor power control scheme. Simulation results show that the proposed scheme can efficiently increase the utilities and decrease the transmission energy consumptions for the in-body sensors and the WBAN coordinator, and simultaneously reduce the attack possibility of the jammer compared with a standard Q-learning-based sensor power control scheme.

A multimedia healthcare data sharing approach through cloud-based body area network

Wireless Body Area Network (WBAN), as a dramatic platform for pervasive computing and communication, has been widely applied in healthcare domains. Since the patient-related data in the form of text, image, voice, etc. is significant in the process of healthcare services, efficiently managing these media data from various WBAN is vital for various applications. Recently, Cloud-assisted WBAN has become popular that can supply massive computing, flexible storage and various software services to WBAN. Still, there are some challenging issues exist in this platform to deliver and share the huge media healthcare data to remote terminals timely with guaranteed QoS support. In the paper, we propose an efficient network model that combines WBAN and Cloud for valid data sharing. The proposed network architecture is designed as four layers: perception layer, network layer, cloud computing layer, and application layer. In the network, the integration of TCP/IP and Zigbee in the coordinator devices is utilized. Consequently, WBAN coordinators can compatibility inter-operate with various local networks such as WiFi and LTE network to support high mobility of users. Besides, we integrate Content Centric Networking (CCN) with our proposed architecture to improve the ability of the WBAN coordinator. Thus, it can support uninterrupted media healthcare content delivery. In addition, adaptive streaming technique was also utilized to reduce packet loss. Various simulations were conducted using OPNET simulator to show the feasibility of the proposed architecture in terms of transmitting a huge amount of media healthcare data in real-time under traditional IP-based network.

Chemical sensor platform for non-invasive monitoring of activity and dehydration.

A non-invasive solution for monitoring of the activity and dehydration of organisms is proposed in the work. For this purpose, a wireless standalone chemical sensor platform using two separate measurement techniques has been developed. The first approach for activity monitoring is based on humidity measurement. Our solution uses new humidity sensor based on a nanostructured TiO2 surface for sweat rate monitoring. The second technique is based on monitoring of potassium concentration in urine. High level of potassium concentration denotes clear occurrence of dehydration. Furthermore, a Wireless Body Area Network (WBAN) was developed for this sensor platform to manage data transfer among devices and the internet. The WBAN coordinator controls the sensor devices and collects and stores the measured data. The collected data is particular to individuals and can be shared with physicians, emergency systems or athletes' coaches. Long-time monitoring of activity and potassium concentration in urine can help maintain the appropriate water intake of elderly people or athletes and to send warning signals in the case of near dehydration. The created sensor system was calibrated and tested in laboratory and real conditions as well. The measurement results are discussed.