Enhancing the Performance of Mobile Healthcare ...

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE INFOCOM 2008 proceedings.

Enhancing the Performance of Mobile Healthcare Systems Based on Task-Redistribution Hailiang Mei, Bert-Jan van Beijnum, Ing Widya, Val Jones and Hermie Hermens Faculty of Electrical Engineering, Mathematics and Computer Science University of Twente Enschede, The Netherlands {meih, v.m.jones, beijnum, widya, hermens}@ewi.utwente.nl

Abstract — Mobile healthcare (m-health) systems have attracted a great deal of attention due to their potential to improve the quality of diagnosis, reduce medical costs and help address the challenges posed by the aging society. A generic mhealth service platform has been developed and specialized to deal with emergency settings such as epileptic seizure detection, cardiac care and trauma care. The m-health infrastructure and services can be in this case described as both mission-critical and safety critical. Dynamic context-aware adaptation mechanisms are required in order to meet the stringent requirements on such mission critical applications. The use of mobile devices means that attention must be paid to resource optimization and in emergency situations timeliness of response is also a critical factor. These factors lead to critical performance requirements on the system across several dimensions. This paper presents an m-health application scenario requiring rapid response and identifies the system performance measures that are key to the success of such m-health solutions. As an extension to the m-health service platform we propose an adaptive middleware framework based on dynamic task redistribution. In particular, we present a computational model to estimate the QoS of mhealth system given a particular task assignment and further to select the optimal assignment. Keywords-component: M-health; Performance optimization; task assignment

INTRODUCTION Telemedicine has been receiving more and more attention due to its potential to improve the quality of diagnosis and treatment, to reduce medical costs and to address the challenge of providing services in future despite the demographic changes caused by the aging I.

society [2]. Advances in medical sensor technologies and the rapid adoption of advanced mobile systems into our daily life mean that mobile healthcare (m-health) is emerging as a promising growth area within telemedicine [3]. The high level system architecture of mobile healthcare is depicted in Fig.1. At the core, a distributed m-health system captures biomedical and personal information from a patient while they pursue normal daily life activities. The system processes the data and forwards the result to a decision point, e.g. a doctor in a healthcare center. By reference to a knowledge base, the decision point can plan the appropriate response, e.g. sending an emergency team to a patient in case the system detects a sudden medical emergency.

Fig.1. A system architecture for mobile healthcare The m-health system consists of a distributed and mobile platform including a patient body-area network (e.g. BAN developed at University of Twente) [4] and a number of other intermediate and back-end service nodes. On top of the platform, multiple tele-monitoring and tele-treatment applications can run to provide continuous (24/7) services. These applications include a set of bio-signal data processing tasks distributed across a set of networked nodes. When m-health systems perform critical services, e.g. detection of medical emergencies and initiation of critical, possibly life-saving, interventions, success is heavily dependent on the performance of the m-health system. However, as with most systems running in a mobile environment,

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m-health system performance can be seriously affected by context changes and resource scarcity, for example network bandwidth, battery power and computational power of handhelds[3, 5]. The fundamental model underlying mobile monitoring systems consists of a set of bio-signal data processing tasks distributed across a set of networked nodes. Different assignments of application tasks to platform nodes are possible and each results in a different system configuration, In turn each configuration exhibits different performance characteristic. Therefore, a possible adaptation scenario is to exploit the distributed processing paradigm and adjust the assignment of tasks across available nodes based on context changes. The rationale is that if one node cannot support a task on computation or data communication at a certain moment, some other nodes with richer resources can take over this task. The advantage over other approaches, e.g. task adjustment inside one isolated node [6], is that the user requirements would be less compromised and distributed resources could be better utilized. The goal of this context-aware adaptation mechanism is to drive the system from an outdated configuration into a more suitable configuration by means of task redistribution at runtime. This paper is thus organized as follows. Section 2 studies one safety critical m-health application, i.e. epilepsy detection. Section 3 identifies several QoS measures that are critical to the success of m-health applications and that can be improved by task redistribution. We also present a computational model used to determine the optimal task assignment given the latest context information. Section 4 describes a middleware framework to support the adaptation based on task redistribution. Section 5 presents conclusions and plans for future work. MISSION CRITICAL M-HEALTH APPLICATION SCENARIO – EPILEPSY DETECTION Epilepsy is a serious chronic neurological condition characterized by recurrent unprovoked seizures. Seizures may happen anywhere and at any time. If detection or even prediction of seizures by a few seconds were possible this would give the patient a chance to prepare and for appropriate medical assistance and/or advice to be given. The scenario below illustrates one variant of the health Body Area Network (BAN) and its supporting service infrastructure to provide mobile monitoring services for epilepsy and shows why the system needs to adapt to changing context. This application scenario is based on the epilepsy application and epilepsy BAN II.

developed during the Dutch Freeband AWARENESS project [7]. John is an epileptic patient who had been seizurefree for several years and, following a medical, he has been certified fit to drive again. He wears a mobile monitoring system which monitors his health state and can give him a few seconds’ advance warning of an upcoming seizure. This monitoring system is called an epilepsy BAN (Body Area Network). The BAN incorporates electrodes (for measuring ECG) and an activity sensor, which communicate wirelessly with John’s PDA via a sensor front-end. The PDA sends the BAN data to a mobile health service provider in John’s home town over GPRS or, when available, UMTS. The m-health monitoring service constantly runs a seizure detection algorithm (Fig. 1) which analyses the ECG signals in combination with context information about John’s current activity levels derived from the activity sensor. One Friday morning John is driving to visit a client in another city. The weather is fine and he is early so he takes the scenic route through the countryside. As he enters the city, his PDA gives a warning beep and tells him to stop driving. John pulls over and parks the car. The epilepsy detection algorithm has detected a possible upcoming seizure. At the m-health service centre a notification is received and the duty medic examines John’s bio-signals which are displayed in real time on his PC screen. At the same time the system makes an automatic call to the ambulance service in the city where John has just arrived giving John’s location information and his emergency dataset. Back in the car John waits for a few moments. He knows that the system can sometimes give false positives and since he feels fine he cancels the alarm. The medic at the mhealth call centre is satisfied that the bio-signals are normal and after calling John to make sure he is OK he cancels the ambulance. If John had really suffered a seizure, he would not have cancelled the alarm and the emergency services would have arrived to give assistance. If the event had happened earlier when John was in a rural location he would have been outside UMTS coverage. GPRS has lower bandwidth than UMTS and so the system would have adapted to optimize the usage of bandwidth whilst taking into account other factors such as battery status of the mobile devices, data availability to support correct remote assessment and the time criticality of this potential medical emergency. Given the distributed processing paradigm in m-health system, a possible adaptation is to adjust the assignment of tasks across available nodes based on context changes, e.g. handover from UMTS to GPRS.


Fig.2. Distributed tasks for seizure detection studied in the AWARENESS project [1] III.

TASK ASSIGNMENT IN M-HEALTH SYSTEM FOR QOS OPTIMIZATION

The m-health system consists of a set of bio-signal data processing tasks distributed across a set of networked nodes. Therefore, it is possible to adapt the system, i.e. redistribute tasks, according to the optimal task assignment calculated based on latest context information. The goal of this adaptation is to maintain the system with the optimal QoS performance. A. Influential QoS characteristics

From the perspective of the decision point (c.f. Fig.1.), we focus on the following QoS characteristics of m-health system that are critical to the success of mhealth missions. There are certainly other critical performance measures not covered here, in particular data security and privacy. We study here only the measures that can be improved based on the task redistribution approach. (1) “End-to-end delay”- D: it is defined as the elapsed time between receiving a frame of patient’s bio-signal information by the m-health system and sending the corresponding processed result of this particular bio-signal frame to the decision point. This parameter indicates how quick the bio-signal and processed result can be delivered by the m-health system. The faster the real-time bio-signal information is delivered, the higher the chance that the patient’s emergency situation can be dealt with in time, e.g. the epilepsy prediction/detection application (c.f. Section 2). (2) “M-health system availability level” - A: The availability measure we consider here is the steady state availability as defined in [8], that is the system mean uptime divided by the sum of the mean uptime and mean downtime. Failures may potentially occur during either data processing or communication. (3) “System battery lifetime” - L: it is defined as the minimum battery lifetime of all the battery powered nodes in m-health system. Some nodes in the m-health system are powered by batteries; if the remaining battery energy is lower than a certain level, the node

cannot perform bio-signal processing operations anymore, and thus, the m-health system cannot provide the necessary information to the decision point. This parameter indicates the maximum operating time period of m-health system. B. Graph based model for task assignment

In order to support the task redistribution based adaptation, the m-health system is formulated using a graph based model ([9, 10]). The specification of an m-health application is modeled as a task DAG in which vertices represent tasks and arcs represent the precedence relation of tasks (Fig.3.). This graph comprises two types of vertices, these are: transmission vertex representing the bio-signal data stream and processing vertex representing the processing of a stream. Note a processing vertex having multiple directed predecessor transmission vertices, e.g. “16” in Fig.1, implies that this processing task can only perform correctly when all the input streams are received, i.e. we assume only “AND” semantics in task DAG. A mobile device network can be modeled as a resource DAG that also contains two types of vertices, i.e. device vertex representing the available computing device and (communication) channel vertex representing the network connection between devices. Both task DAGs and resource DAGs are bipartite graphs in which each type of vertex forms a disjoint set, i.e. there is no connection between two vertices of the same type. In a task DAG, each transmission vertex has exactly one direct predecessor processing vertex and one direct successor processing vertex, while a processing vertex may have multiple direct predecessor and successor transmission vertices. The similar property holds for resource DAG. In the task DAG, we further label the processing vertex with a tuple of (np, rr, a) and the transmission vertex with (nc, a) respectively. In the resource DAG, we label the device vertex with (rs, e, h, p) and the channel vertex with (bw, l, s, r). The explanation of these variables is shown in Table 1 and their values can


be obtained by using analytical benchmarking or task profiling [11] Thus, these two graphs are capable of representing various tele-monitoring applications and dynamic changes in m-health platforms. In particular, the fluctuation of a device’s resource can be denoted as the change of label weights in the resource DAG and the dynamism of devices’ presence can be denoted as a topology change in the resource DAG.

ai,

/trans

Fig.3. Distributed bio-signal processing in m-health system Based on this graph model, the task assignment problem can be seen as distributing a set of graph vertices onto another graph (Fig.3.) given the latest context information contained in the graph itself. Each processing vertex must be mapped to one device vertex. Each transmission vertex must be mapped to a device vertex, a channel vertex or a directed path containing several device/channel vertices since we allow a stream to be relayed by devices. Such a task assignment problem in its general form is NP-hard [12], various heuristic methods therefore can be adapted to tackle the challenge, e.g. branch and bound [10], A* [9] and graph partitioning [13]. In special cases, we have proposed polynomial-time algorithms for chain to chain assignment [14] and tree to star assignment [15] inspired by previous work [16]. Notation

@Vert ex

np

Proc

the number of operations per second

rr

Proc

required resource for processing, e.g. minimal CPU, minimal memory, etc.

Meaning

Proc

availability measure of running a task vertex i at a particular resource vertex . the number of transmitted data units per second

nc

Trans

e

Dev

device available battery energy1

h

Dev

power consumption of device’s “housekeeping” activities per second, e.g. CPU, display, powering network interface cards

p

Dev

power consumption per operation on the device

rs

Dev

available resource supply for processing, e.g. CPU type, available memory, etc.

bw

Chan

available bandwidth

l

Chan

current load information at the channel

s

Chan

power consumption for sending one data unit

r

Chan

power consumption for receiving one data unit

Table 1: Notations for labelling the task DAG and resource DAG C. QoS characteristic estimation for a given task

assignment We present the models to estimate system QoS performance based on the assumed known characteristics of individual task (Table 1) given a particular assignment. 1) End-to-end delay

The end-to-end delay is a composition of processing delay, dp, and transmission delay, dt. The values of dp and dt can be estimated from the profiling information, e.g. np, nc, rr, rs, bw, l, based on the knowledge from real measurements, e.g. [17, 18]. We define the processing delay for processing task i to process one frame of bio-signal data at device as dpi, and the transmission delay of transmission task i to transfer one frame of bio-signal data at device or channel as dti, . Given a particular assignment, there exist multiple paths connecting a source task and a sink task and each path exhibits a different end-to-end delay, i.e. the summation of dp and dt along the path: 1

If a device has a fixed line power supply, then its E has a value of + .


d (taskpath) =

dp i ,* + i∈taskpath

dt i ,* i∈taskpath

The end-to-end delay of a given assignment, D, is then defined as the maximum of all task paths’ end-toend delay: D = max(d(taskpath)). 2) System availability level

Since we assume only “AND” semantic in task DAG, the availability of the whole system depends on all the included tasks: Only when all tasks perform successfully, the system can perform successfully. Therefore, the system availability level, A, can be computed as: A=

“direction” on end-to-end delay, we define the optimal assignment as the one that has highest score on . A TASK-REDISTRIBUTION BASED MIDDLEWARE TO SUPPORT M-HEALTH SYSTEM ADAPTATION

IV.

To support the context-aware task redistribution in m-health system, we develop a middleware framework named MADE consisting of four functional phases, i.e. Monitoring, Analysis, Decision and Enforcement (Fig.4). "

∏a

The value of ai, can be zero, i.e. meaning it is not possible that running task i on resource due to the violation on hard QoS requirement, e.g. device CPU type and channel’s bandwidth cannot support the to-beassigned task.

$

#

i ,* i∈taskDAG

"

!

%

&

'

(

3) Battery lifetime

Based on the power consumption model of a mobile device [19], we estimate the battery life time T for a specific mobile device given a particular task assignment as follows: T=

e h+P+R+S

where P is the power consumed by local data processing; R is the power consumption of receiving data stream; S is the power consumption of sending data stream. Based on the aforementioned profiling information, these can be calculated per device given all assigned processing tasks and transmission tasks: P = p np S = s nc R = r nc Once the battery lifetime of all devices are estimated, the minimum of all device’s battery lifetime determines the overall system lifetime, thus L = min(T). 4) Overall estimation

Similar to our earlier work [20], the overall QoS measure, , across these three dimensions is defined as: η=

w D2 D2

+ w A2 ⋅ A 2 + w L2 ⋅ L2

where wD, wA and wL are the weighting factors among the three QoS measures and should be tuned based on specific applications. After adjusting the

Fig.4. MADE – an task redistribution adaptation middleware The monitoring phase includes m-health application registration, device discovery and context discovery/registration. All these information can be represented as the change in the two weighted graphs – task DAG capturing information about the m-health application and resource DAG capturing information about the m-health platform (Fig.3). The analysis phase takes these two DAG models as input and runs a task assignment algorithm to determine the optimal assignment with optimal system QoS performance. The decision phase compares the computed optimal assignment with the current system configuration to determine the actual cost of reconfiguration. If the reconfiguration cost can be leveraged by the enhanced performance of the new configuration, the new assignment plan will be executed. The Enforcement phase controls the m-health system to adjust its configuration according to the new assignment. V.

CONCLUSION AND FUTURE WORK

This paper studies the QoS requirements of mission-critical m-health services based on a novel epilepsy detection scenario. End-to-end delay, system


availability level and battery lifetime are identified as the key performance measures that are critical to the success of such m-health solutions. Given the distributed processing paradigm in m-health, we propose a task-redistribution adaptation approach to build the adaptive m-health system. To support the task redistribution, a computational model to estimate the system QoS performance for a particular task assignment is presented. Future research includes: (1) applying various techniques, e.g. A* or branch and bound, to design more efficient task assignment algorithms based on the computational model, (2) proposing performance metrics (including reconfiguration time, power consumed by reconfiguration) on the distribution infrastructure to evaluate the reconfiguration, and (3) experimenting the MADE framework in a real world mobile healthcare system to evaluate the feasibility of task redistribution based adaptation. ACKNOWLEDGMENT This work is part of the Freeband AWARENESS Project. Freeband is sponsored by the Dutch government under contract BSIK 03025. (http://awareness.freeband.nl)

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