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A Model for QoS-Aware Wireless Communication in Hospitals

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JMSS/ Jan 2012, Vol 2, No1 15 A Model for QoS-aware Wireless Communication in Hospitals Zahra Alavikia * , Pejman khadivi, Masoud Reza Hashemi Electrical & Computer Engineering Dept., Isfahan Univ. of Technology, Isfahan, IRAN Emails: {z.alavikia, pkhadivi, hashemim}@ec.iut.ac.ir ABSTRACT In recent decade, the research regarding wireless applications in electronic health (e-Health) services has been increased. The main benefits of using wireless technologies in e-Health ap
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  JMSS/ Jan 2012, Vol 2, No115  A Model for QoS-aware Wireless Communication in Hospitals Zahra Alavikia * , Pejman khadivi, Masoud Reza Hashemi Electrical & Computer Engineering Dept., Isfahan Univ. of Technology, Isfahan, IRANEmails: {z.alavikia, pkhadivi, hashemim}@ec.iut.ac.ir  A  BSTRACT    In recent decade, the research regarding wireless applications in electronic health (e-Health) services hasbeen increased. The main benefits of using wireless technologies in e-Health applications are simplecommunications, fast delivery of medical information, reducing treatment cost, and also reducing themedical workers error rate. However, using wireless communications in sensitive healthcare environmentraises electromagnetic interference (EMI). One of the most effective methods to avoid the EMI problem ispower management. To this end, some of methods have been proposed in the literature to reduce EMIeffects in health care environments. However, using these methods may result in non-accurate interferenceavoidance and also may increase network complexity. To overcome these problems, we introduce twoapproaches based on per-user location and hospital sectoring for power management in sensitive healthcareenvironments. Although reducing transmission power could avoid EMI, it causes the number of successfulmessage deliveries to the access point (AP) to decrease, and hence, the Quality of Service (QoS)requirements cannot be meet. In this paper, we propose the use of relays for decreasing the probability of outage in aforementioned scenario. Relay placement is the main factor to enjoy the usefulness of relaystation benefits in the network, so we use the genetic algorithm to compute the optimum positions of a fixednumber of relays. We have considered delay and maximum blind point coverage as two main criteria inrelay station problem. The performance of proposed method in outage reduction is investigated throughsimulations .    K   EYWORDS   Wireless Communications, EMI Problem, Power Management, Outage Reduction.   1. I NTRODUCTION   Electronic health (E-Health) is the application of datacommunications and information technology in the healthsector. In the recent decade, e-Health services areacquiring popularity due to the reduced cost andprovisioning advanced healthcare services [1,2].The use of wireless technology has an important influenceon different e-Health applications. The main aim in thehealthcare networks is to provide accurate medicalinformation, anytime and anywhere. This may result indramatic reduction of errors by physicians and otherhealthcare personnel and also an improved Quality of Service (QoS) [3, 4].However, electromagnetic interference (EMI) betweenwireless transmitters and critical medical equipments suchas ventilators is a growing problem in the healthcareindustry that should be addressed carefully. The maineffects of the interference are unexpected automaticshutdown, automatic restart, and waveform distortion of sensitive medical devices that can imperil patients who areusing those devices [1, 2]. The immunity level of critical-care medical devices to the EMI has been defined inInternational Electrotechnical Commission (IEC) 60601-1-2 standard [1, 2]. Immunity level is the minimumelectric field at which the performance of a medical devicedegrades [1]. As Tikkanen indicates in [5], Electromagnetic compatibility (EMC) means that “ thedevice is compatible with its Electromagnetic (EM)environment, and it does not emit levels of EM energy that cause EMI in other devices in the vicinity ” [5].  The most critical issues in designing wireless networks fore-Health environments such as a hospital are how todesign an effective network to provide guaranteed QoSand to consider the EMI problem. The transmission powerof users in the network must be limited to avoid the EMIeffect on the medical devices in the vicinity, and thiscauses the outage probability to increase. As a result of this, the QoS requirements cannot be met. If the receivedsignal strength (RSS) at a specific receiver is less than apre-determined threshold, that receiver is faced with anoutage [1]. This threshold, is the minimum required RSSthat makes the received signal detectable.In this paper, to avoid EMI effects, we first investigate theproblem of power management in transmitting controldata, then propose two approaches for power managementto alleviate EMI problem. After that, we evaluate theoutage reduction by using fixed relay stations. These relaystations are optimally placed in healthcare environments Received: 16-09-2011Accepted: 16-01-2012    16such as hospitals. We investigate delay and maximumblind point coverage as two main criteria in the relaystation placement.The rest of the paper is organized as follows: first anoverview of the requirements and challenges in usingwireless technology for the e-Health application arepresented then in the next section, the related work inusing wireless LAN (WLAN) in health environments willbe reviewed. In Section 4 the system architecture isintroduced. The simulation results as well as analysis of two approaches for power management in controltransmission messages are given in Section 5. Theevaluation and simulation results of our outage reductionmethod in hospital environment are presented in Section 6.Paper concludes in Section 7. 2. R EQUIREMENTS AND C HALLENGES IN U SING W IRELESS T ECHNOLOGY FOR E -H EALTH A PPLICATIONS   Advanced information technologies, especially wirelesscommunications have been considered for deliveringmedical data and enhancing clinical activities. Severalresearches have been proposed in the contexts of improving QoS in healthcare environments.In different contexts, for using wirelesscommunications in healthcare networking, three differentscenarios have been proposed: hospital integratednetworks, residential/home-care networks, and anytime-anywhere healthcare networks. Each of these scenarios hasvarious applications [6].In order to simplify investigations in such scenarios, theapplications in each category, according to their QoSrequirements, have been classified into office/medical ITapplications, real-time non critical applications, and real-time critical applications [6]. The requirements of eachcategory are summarized in Table 1.As illustrated in Table 1, each category has distinctiverequirements. For example, real time critical applications(e.g. patient monitoring) have strict requirements, as theyare both delay sensitive and loss sensitive applications. Incontrast, office/medical IT applications are just losssensitive, and some packet loss is usually acceptable. Thedifference between medical data and other types of trafficis the on-time delivery requirement of medical informationaccording to their QoS requirements.Some challenges have been proposed in using wirelesscommunications such as wireless personal area network (WPAN), WLAN, and wireless metropolitan area network (WMAN) for e-Health applications [1, 3, 6].There are a number of issues that must be addressedwhen wireless communication is used in e-health. Someexamples are electromagnetic compatibility and EMIrequirements [5], QoS provisioning [4], coexistence of different wireless technologies [7], seamless connectivity,and security [8]. 3. W IRELESS L OCAL A REA N ETWORKS FOR E -H EALTH   In this section, we study WLAN applications in thehealthcare systems. We classify different research worksin two categories: research works considering QoSrequirements and research works considering the EMIissue.  A. Research works considering the QoS provisioning In [4] the application of IEEE 802.11 wireless standardfor QoS provisioning within e-Health services have beeninvestigated. Zvikhachevskaya et al. indicate in [4] thattransferring medical information between a clinic and anambulance, which is moving through different e-Healthareas, requires guaranteed QoS. To guarantee QoSbetween distinctive traffics (patient’s emer  gency data andmedical IT data) a priority scheme for telemedicine/e-Health service is also proposed [4].The concept of differentiated services in telemedicinehas been introduced in [9]. Differentiated Services(DiffServ) can support e-Health applications with differenttraffic requirements and QoS guarantees. In the introducedDiffserv architecture, medical data corresponds to serviceclasses that include expedited forwarding, assuredforwarding, and best-effort service classes. T ABLE .1   M EDICAL A PPLICATIONS R EQUIREMENTS  [6].    17Chigan and Oberoi [10] proposed a resource-efficientmechanism for QoS provisioning in unpredictableemergency data transmission with minimum delaylimitation.An integrated and ubiquitous network for medicalenvironments has been introduced in [6] that have usedWLAN and WPAN technologies to meet QoSrequirements. Besides that, an adaptive WLAN andBluetooth coexistence mechanism with QoS provisioningfor interference management has been investigated in [7].  B. Research works considering the EMI problem The EMI control methods can be classified in twogroups: policy-based approaches and technology-basedapproaches.In policy-based approaches such as what was proposedin [11], the exceeding risk of immunity level of medicaldevices has been estimated. This risk can be reduced withan appropriate management policy. The advantages of amanagement policy are investigated with a quantitativeapproach in [11] by comparing three different policies:unrestricted use of wireless devices, restricted use of them,and a ban on wireless devices.In technology-based approaches, the infrared LAN andilluminating networks was proposed in [12] and [13] fortransferring data in a hospital. In [13] the authors take theadvantage of optical modulation in which high brightnesslight-emitting diodes (HB-LED) are used as anilluminating source. The information is modulated on thevisible light emitted by HB-LED. However, datatransmission by visible/invisible light does not allowseamless mobility through a lot of obstacles in thehospital. Hence, in [1] an EMI aware scheme has beenproposed for transferring medical information in a hospitalwith considering QoS requirements based on radiofrequency (RF) wireless systems. 4. S YSTEM A RCHITECTURE   The system architecture and the communication modelused in this paper are similar to the models introduced in[1] and [2]. The main idea of this system is to avoidharmful EMI to the medical devices and providingdifferentiated QoS to different e-Health applications.Particularly, two e-Health applications, a real timeapplication and a non-real time hospital informationsystem have been considered. Real time applications (e.g.remote consultation, remote diagnosis, cliniciannotification applications, and patient information transfer)are sensitive to delay and packet loss and theircorresponding users are named high-priority users. On theother hand, the hospital information systems (e.g., medicalIT applications) are only sensitive to packet loss, and theircorresponding users are named low priority users.The location of users can be changed dynamicallywhile the location of sensitive medical devices is assumedto be fixed.The controller (i.e. access point) manages effectivechannel allocation and controls wireless access of theusers according to a time slotted request to send/clear tosend (RTS/CTS) based mechanism. Every time, a userwho has some data ready for transmission must competewith other users who have data to transmit. The user cantransmit its data, if successfully received CTS from thecontroller, otherwise the collided user must wait for arandom time based on exponential backoff [1, 2]. Toprovide priority, high-priority users will wait for a randomtime based on a constant back off window, while low-priority users will wait for a random time based onexponential back off. In these cases the users are assumedto be in a high priority, and low priority orbits,respectively. To avoid harmful interference to sensitivemedical devices, the controller computes the maximumallowable transmit power for each user in the hospitalbased on the information (e.g. location and status of critical medical devices or users) obtained from aninventory system. The maximum transmitted power can becalculated as follows:P max = min {min (P NLS (y)), min (P LS (z)), P transmit } (1)In equation (1), P transmit is the initial transmission powerof the user, min(P LS (z)) and min(P NLS (y)) are the upperbounds on the user transmit powers that non-life-supporting device, y, (e.g., infusion pump,electrocardiograph monitor) and life-supporting device, z,(e.g., incubator, defibrillator) can tolerate. P NLS and P LS  can be calculated from (2) and (3),P NLS (y)   = )7)().( (2  y E  NLS y D NLS (2)P LS (z) = )23)().( (2  z E  LS z D LS (3)in which, radio frequency ranges can be varied from 800MHz to 2.5 GHz. D NLS (y) is the distance between non life-supporting device, y, and the user. D LS (z) is the distancebetween the life-supporting device, z, and the user.E NLS (y) and E LS (z) are the EMI immunity levels of thenon-life-supporting device, y, and life-supporting device,z, respectively (refer to [1] for more details).The controller calculates and notifies the upper boundon user transmit power twice: for RTS transmission andfor data transmission. Because of simultaneous RTStransmissions, the probability of interference in the RTStransmission is more than that of the data transmission.Hence the upper bound in the RTS transmission power isless than the upper bound in the data transmission power. 5. P OWER MANAGEMENT A PPROACHES In this section, we investigate two approaches forestimating effective RTS transmission power.    18  A. Power management based on every user location In the first method the upper bound on the usertransmitted power is calculated and broadcasted to everyuser by the controller. This method is named as power management based on every user’s location. As mentionedin [1] the controller can broadcast this power for everyuser in the control channel. It is assumed that we have twochannels: data channel for data transmission and controlchannel for control message transmission. The controllercan access both channels simultaneously, while the userscan access only one channel at a time. In what follows, anupper bound on RTS transmission power is determined.High-priority and low-priority users will arriveaccording to independent Bernoulli processes with arrivalprobabilities   1 and   2 , respectively. These users thattransmit their RTS massages in the first try are named outof high-priority and out of low-priority orbit users,respectively. The retransmission probability from high-priority orbit and low-priority orbit are shown with   1  and   2 that can be obtained from the backoff windowsizes (in this paper, it is Wmin=32) and the maximumbackoff stage of low-priority users (in this paper, it ism=5) as in [14]. When collision occurs, the users will goto the orbits, the collision probability of high priority andlow priority users are illustrated byPc1 and Pc2 and can bedetermined as in [1] (Fig.1). Figure 1: Imaginary orbit model for EMI-aware prioritizedwireless access system [1]. A discrete-time Markov chain model for estimating thenumber of high priority and low priority users in the orbitsis also used.In order to calculate an upper bound on RTStransmission power, we have to determine the probabilityof aggregate transmitted power when multiple userssimultaneously transmit RTS messages. The maximumtransmission power for RTS transmission by a user can bedetermined by (4). y)}_p(x,Transition  ])1max)1(  )1(  )1(1 )1([  ],1nnoo P )1(  )1(  )1(  )1(1 [(promax_arriv_{ 2121 220222021111101101112121 max2202220211101111011100 2222222211111111222222221111111112  (4)                                        nnoo Pwwo yn yT wwo xn xT wwo yn yT wwo xn xT P o yo yon yT n yT no xo xon xT  xT nno yo yon yT n yT no xo xon xT  xT nnT  xT  y H ctr            where P H ctr  indicates the maximum transmission power of high-priority users, Transition_p(x,y) represents theprobability of the number of users who are in orbits to bex and y, where x and y refer to the number of high-priorityand low-priority users in the orbits, respectively. Themax_arriv_pro represents the maximum arrival probabilityof high-priority orbit and out of high-priority orbit in eachstate of transition matrices. T1 and T2 are the total numberof high-priority and low-priority users, respectively. O1and O2 are the number of high-priority and low-priorityuser in orbits who have some ready data for transmission;n1 and n 2 represent the number of high-priority and low-priority users in out of orbits who have some data fortransmission.The maximum transmission power of low-priority userscan be determined in a way similar to (4).  B. Power management based on hospital sectoring In upper bound power calculation per user method, thecontroller must have accurate and perfect knowledge of the location and the status of all users in the network, andthis information have to be updated with every change inthe network. However, this can increase the complexity of the controller in the network.For simplicity, the hospital area is divided into anumber of areas, and the maximum allowable power foreach area is calculated based on the proposed algorithmand the network parameters. It is assumed that each userknows its own geographical position[15]. In this case,every user can obtain its own maximum allowabletransmission power based on the power of each area andits location.In this approach, the average interference caused byeach area and the mean number of simultaneous
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