Fengye Hu

A game theoretic approach for inter-network interference mitigation in wireless body area networks

Wireless Body Area Network (WBAN) is an emerging technology to provide real-time health monitoring and ubiquitous healthcare services. In many applications, multiple wireless body area networks have to coexist in a small area, resulting in serious inter-network interference. This not only reduces network reliability that is especially important in emergency medical applications, but also consumes more power of WBANs. In this paper, an inter-network interference mitigation approach based on a power control algorithm is proposed. Power control is modeled as a non-cooperative game, in which both inter-network interference and energy efficiency of WBANs are considered. The existence and uniqueness of Nash Equilibrium in the game are proved, and an optimal scheme based on best response is proposed to find its Nash Equilibrium. By coordinating the transmission power levels among networks under interference environment, the total system throughput can be increased with minimum power consumed. The effectiveness of the proposed method has been illustrated by simulation results, where the performance of the proposed approach is evaluated in terms of overall utility and power efficiency and convergence speed.

A human body posture recognition algorithm based on BP neural network for wireless body area networks

Human body posture recognition has attracted considerable attention in recent years in wireless body area networks (WBAN). In order to precisely recognize human body posture, many recognition algorithms have been proposed. However, the recognition rate is relatively low. In this paper, we apply back propagation (BP) neural network as a classifier to recognizing human body posture, where signals are collected from VG350 acceleration sensor and a posture signal collection system based on WBAN is designed. Human body signal vector magnitude (SVM) and tri-axial acceleration sensor data are used to describe the human body postures. We are able to recognize 4 postures: Walk, Run, Squat and Sit. Our posture recognition rate is up to 91.67%. Furthermore, we find an implied relationship between hidden layer neurons and the posture recognition rate. The proposed human body posture recognition algorithm lays the foundation for the subsequent applications.

Balancing Long Lifetime and Satisfying Fairness in WBAN Using a Constrained Markov Decision Process

As an important part of the Internet of Things (IOT) and the special case of device-to-device (D2D) communication, wireless body area network (WBAN) gradually becomes the focus of attention. Since WBAN is a body-centered network, the energy of sensor nodes is strictly restrained since they are supplied by battery with limited power. In each data collection, only one sensor node is scheduled to transmit its measurements directly to the access point (AP) through the fading channel. We formulate the problem of dynamically choosing which sensor should communicate with the AP to maximize network lifetime under the constraint of fairness as a constrained markov decision process (CMDP). The optimal lifetime and optimal policy are obtained by Bellman equation in dynamic programming. The proposed algorithm defines the limiting performance in WBAN lifetime under different degrees of fairness constraints. Due to the defect of large implementation overhead in acquiring global channel state information (CSI), we put forward a distributed scheduling algorithm that adopts local CSI, which saves the network overhead and simplifies the algorithm. It was demonstrated via simulation that this scheduling algorithm can allocate time slot reasonably under different channel conditions to balance the performances of network lifetime and fairness.

Energy-efficient medium access approach for wireless body area network based on body posture

Energy efficiency has become one of the most important issues in wireless body area network (WBAN). In this paper, an energy-efficient medium access control (MAC) protocol for WBAN is proposed based on human body posture under walking scenery. Due to persons movements, WBAN is a dynamic network, which means that traditional static protocols are no more suitable for it. For solving this problem, firstly, the feature of human walking at a constant speed is analyzed and we divide a spell of movements into a sequence of key frames just like a video constituted by numbers of continuous frames. As a result, the dynamic walking process is translated into several static postures, which the static MAC protocol could be used for. Secondly, concerning the performance of network lifetime, we design a posture-aware approach for lifetime maximization (PA-DPLM). With analytical and simulation results provided, we demonstrate that PA-DPLM protocol is energy-efficient and can be used under constant speed walking scenery.

Beam-Pattern Synthesis for Circular Arrays with Sensor Selection for WBAN via Convex Optimization

Wireless body area network (WBAN) is emerging as a powerful tool for health management which is characterized by a modest number of sensors placed on or around human bodies. Since sensor nodes are generally battery-powered devices, reducing sensor node power consumption to improve the efficiency and save sensor usage costs is crucial. In this paper, a convex optimization method based beam-pattern synthesis with sensor selection is proposed for circular sparse arrays, which can be regarded as the model of the head of human. Our goal is two-fold: minimize the number of sensors while achieving the best peak level of side-lobe. The method can solve uniformly spaced circular arrays with inter-element spacings about half- wavelength in order to satisfy the performance of the beam-pattern. Based on the one circular ring array, we propose two different configuration modes, one is uniform circular array and the other is concentric circular array. Simulations are shown using up to a few hundred sensors to illustrate the practicality of the proposed algorithm.

Sparse array synthesis for WBAN with minimised side lobe via convex optimisation

This study concerns sensor selection problem in wireless body area network (WBAN), which can reduce the burden of the human body and improve the energy efficiency. The authors highlight the sparse sensor array synthesis algorithm for different shapes of sensor arrays via convex optimisation to solve this problem. For simplicity, one regular spherical sensor array and five regular cuboid sensor arrays are considered to simulate the sensor distribution around the human body. As a comparison, the conventional method with one objective method is used to synthesise the sensor array first. Then the proposed algorithm which includes two objective variables: the l 1-norm of weight vector and the peak side-lobe level is used. Simulations demonstrate that the proposed sparse sensor array synthesis algorithm achieves array sparsity with lower side lobe and more concentrated main lobe when operated in spherical and cuboid sensor array. Hence, the sensor selection problem is achieved by the sparse sensor array synthesis.

Framework and challenges for Wireless body area networks based on big data

Big Data is a concept proposed on the basis of cloud computing, referring to the large-scale distributed data processing applications that operate on exceptionally large amounts of data. Wireless body area network (WBAN) is a dynamic network with sensor nodes in, on or around the body to monitor the physical parameters. The data of these parameters is so large which we called WBAN big data that traditional methods can not process them efficiently. In this paper, we propose the overall WBAN big data processing framework, and apply MapReduce and HBase to process, store and update the WBAN big data. Two problems of WBAN big data: Interfence and storage are investigated. Taking the specific feathers of WBAN big data into consideration, the framework will satisfy the goal of processing big data in WBAN and achieve the reasonable results that we hope.

Cuboid sparse array synthesis for sensor selection by convex optimization with constrained beam pattern based on WBAN

To measure the physiological parameter accurately, a plenty of sensors are placed in, on or around the human body, which form the Wireless Body Area Network(WBAN). But for energy saving, some sensors can be chosen to achieve the same goal. So an iterative convex optimization algorithm with constrained beam-pattern synthesis of sensor selection is proposed to solve this problem. In the optimization procedure, the objective is to find a tradeoff between the array weight which denotes the sparsity of the array and the Peak Side lobe Level(PSL) which denotes the radiation performance of the sensor array. The method achieves an excellent tradeoff between the array sparsity and energy efficiency. The cuboid sensor array in three-dimensional space is investigated and implemented on MATLAB. Numerical simulation results demonstrate that the algorithm is efficient to synthesize sparse cuboid sensor array, and at the same time minimize the PSL.

An energy-efficiency index based on death probability for maximizing WBAN reward

ABSTRACT This article presents a general energy-efficiency index aiming at maximising the network reward of energy-limited wireless body area network (WBAN). The energy-efficiency index utilises a probability which is named as death probability. The death probability is a function of channel state information (CSI), residual energy information (REI) and transmission power. Finally, the existing protocols using the death probability are summarised. The performance of the proposed protocol is evaluated through numerical results.

Performance analysis of reliability in wireless body area networks

Reliability is a critical design parameter in wireless body area networks (WBANs). In this study, the authors quantify the reliability of WBAN based on the network lifetime, and derive a general formula of the reliability in terms of the number of sensors and group characteristic parameter (GCP). Upper and lower bounds of the reliability are derived for an arbitrary WBAN. Based on the monotonicity property of reliability, an algorithm by using average GCP is presented to calculate the minimum and maximum number of sensors for a given network lifetime, and the optimal number of sensors for a given reliability. The performance of reliability is evaluated through numerical results.