J Med Syst (2014) 38:121 DOI 10.1007/s10916-014-0121-2

SYSTEMS-LEVEL QUALITY IMPROVEMENT

QoS-Aware Health Monitoring System Using Cloud-Based WBANs Ghada Almashaqbeh · Thaier Hayajneh · Athanasios V. Vasilakos · Bassam J. Mohd

Received: 14 May 2014 / Accepted: 28 July 2014 © Springer Science+Business Media New York 2014

Abstract Wireless Body Area Networks (WBANs) are amongst the best options for remote health monitoring. However, as standalone systems WBANs have many limitations due to the large amount of processed data, mobility of monitored users, and the network coverage area. Integrating WBANs with cloud computing provides effective solutions to these problems and promotes the performance of WBANs based systems. Accordingly, in this paper we propose a cloud-based real-time remote health monitoring system for tracking the health status of non-hospitalized patients while practicing their daily activities. Compared with existing cloud-based WBAN frameworks, we divide the cloud into local one, that includes the monitored users and local medical staff, and a global one that includes the

This article is part of the Topical Collection on Systems-Level Quality Improvement G. Almashaqbeh () Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA e-mail: [email protected] T. Hayajneh New York Institute of Technology, New York, NY, USA e-mail: [email protected] A. V. Vasilakos Computer Science Department, Kuwait University, Kuwait City, Kuwait e-mail: [email protected] B. J. Mohd Computer Engineering Department, The Hashemite University, Zarqa, Jordan e-mail: [email protected]

outer world. The performance of the proposed framework is optimized by reducing congestion, interference, and data delivery delay while supporting users’ mobility. Several novel techniques and algorithms are proposed to accomplish our objective. First, the concept of data classification and aggregation is utilized to avoid clogging the network with unnecessary data traffic. Second, a dynamic channel assignment policy is developed to distribute the WBANs associated with the users on the available frequency channels to manage interference. Third, a delay-aware routing metric is proposed to be used by the local cloud in its multihop communication to speed up the reporting process of the health-related data. Fourth, the delay-aware metric is further utilized by the association protocols used by the WBANs to connect with the local cloud. Finally, the system with all the proposed techniques and algorithms is evaluated using extensive ns-2 simulations. The simulation results show superior performance of the proposed architecture in optimizing the end-to-end delay, handling the increased interference levels, maximizing the network capacity, and tracking user’s mobility. Keywords E-Health · Body area networks · Cloud computing · Medical sensors · Multi-radio Introduction Given that being hospitalized is usually costly, inconvenient, and commonly unfavorable by the patients, distance health monitoring becomes inevitable. People prefer to stay home rather than being hospitalized and attached to medical equipment, devices, or escorts that usually restrict their functional mobility and daily activities. The recent advances in computers, electronic devices, and telecommunications made such preferences possible.

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WBANs [33, 46] are pioneering technology that provide convenient distance health monitoring. In WBANs, electronic sensors are attached in, on, or around the human body while constantly reporting information on vital biological signs. Typically, these data are transferred to medical units for processing and utilized for medical decision making. One major limitation of this approach is that the collected data are usually processed and stored at local medical units which makes extended accessibility to such data highly restricted to the surrounding community [30]. Moreover, these centralized telemedicine data require a complicated hardware and dedicated network for data collection and processing which makes the interpretation of this technology incomprehensible to the average user. Cloud computing is a new promising model that has the potential to help in storing, processing, analyzing, delivering, distributing, and securing critical data [16, 24, 45]. It provides a promising solution to address big data storage and analysis due to the cloud abundant resources. Bioinformatics clouds are classified into four sources: data as a service, software as a service, platform as a service, and infrastructure as a service [18]. Hence, combining cloud computing with WBANs has become an innovative approach and has recently received extensive attention [6, 21]. Figure 1 presents a general architecture of a cloudbased WBAN. As shown in the figure, the monitored patients are at their homes while being continuously monitored by the medical staff through the Internet cloud. The cloud is referred to as the “global cloud” which is accessed by intended Internet users for the various applications they run. It is also considered the bridge connecting the WBANs users with one or multiple hospitals and medical centers per the user needs. Merging WBANs with cloud computing will have potentially numerous advantages. Using clouds may imply that organizations are less likely to need their own servers or software which will eventually save energy, physical space, and technical staff [48]. Moreover, [6] showed that a cloud computing-based mobile health monitoring is almost 20 times faster and 10 times more energy-efficient compared to a standalone mobile health monitoring application. On the other hand, several challenging unresolved issues may hinder the success of the marriage between these technologies [26, 38, 49]. Some of these issues are related to the communication standard in WBANs, the integration between WBANs with hybrid clouds, and the authorization of social networks. The integration of cloud computing with mobile services is another challenging issue including the mobile cloud computing architecture, applications, and approaches [22, 40]. Healthcare applications is one important area that can benefit from mobile cloud computing, but with two concomitant challenges: QoS and security.

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Examples of such applications include [22]: complete health monitoring system, smart emergency management system, health-aware mobile devices, pervasive access to healthcare information, seamless connection to cloud storage, patient health record management, and image viewing support. Accordingly, in this paper we propose a cloud-based realtime remote health monitoring system (CHMS) for tracking the health status of non-hospitalized patients while performing their daily activities. In this system, we aim to provide high QoS and focus on connectivity-related issues between the patients and the global cloud. The literature on most cloud-enabled WBAN systems, e.g. [6, 49], assumed that the patients are connected to the global cloud using a gateway that eventually reaches the medical staff. However, we expand this concept by having a local cloud for WBANs, i.e. monitored users and local medical, staff that is also connected to the global cloud itself. For example, if we are conducting the system in a university campus we may have students who are monitored using WBANs that are connected to the campus medical center. Hence, the reported data is available to the local medical staff using the university network that contains multiple routers to cover the whole campus. Simultaneously, the students can be connected, if necessary, to other hospitals since the university local network, which we refer to it as local cloud, is connected with the global cloud. Particularly, we present an architecture and prototype of the local cloud along with its detailed operation. The objective is to optimize its performance by reducing congestion, interference, and data delivery delay while supporting the mobility of the users. We employed several novel techniques and algorithms in CHMS to accomplish its objectives. First, we utilize the concept of data classification and aggregation to reduce the flow of data traffic in the network. The reported health-related data by the WBANs are classified into urgent and non-urgent data. The former is sent instantly to the medical staff while the latter is aggregated and sent less frequently. Second, we propose a dynamic channel assignment policy to distribute the WBANs among the available frequency channels with the objective of reducing mutual interference. Third, we propose a delay-aware routing metric that is used by the local cloud in its multihop communication to reach the medical staff. The goal is to find routes with low end-to-end delay as the speed of reporting health related data is a critical issue in health monitoring systems. Fourth, a delay-aware association protocol is developed to connect the users with the routers in the local cloud. The association protocols are required to track users’ mobility where the router with which the WBAN is associated may change based on the new location of the mobile user. The aforementioned techniques contribute to maximizing the capacity of the local cloud to withstand the

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Fig. 1 General architecture of a cloud-based WBAN

largest possible number of monitored patients without significant degradation in performance. Finally, the performance of CHMS is evaluated using extensive simulation experiments under various conditions. The efficiency of CHMS is tested against several factors that affect wireless networks in general and remote health monitoring systems in particular. We studied the effect of mutual interference, users’ mobility, cloud density, and WBANs’ traffic generation pattern. We used ns-2 simulator after adding all the required modules to support CHMS’s local cloud design. The results are reported in terms of packet delivery rate, end-to-end delay, and the average routing overhead. Motivation WBANs are argued to be the most effective solution for remote health monitoring systems that enable the tracking of non-hospitalized patients. However, using WBANs have many limitations such as: energy, capabilities to run complicated software, storing and processing large amount of data, ... etc. Merging WBANs with cloud computing is a promising solution to overcome these limitations. The resulted WBAN cloud-based system may be restricted by two main issues. The first issue is to provide good QoS level that can guarantee a real-time response of the system. The second is providing adequate security for the communicated data. In this paper, we address the first issue and propose an architecture for a WBAN cloud-based health monitoring system

that is optimized to provide high performance delivery of the critical health-related data. Major contributions The main contributions provided in this paper can be summarized as follows: 1) proposed an architecture of a cloudbased distributed health monitoring system which utilizes the concept of local cloud and global Internet cloud. 2) optimized the design of the WBANs by assigning new roles to the master nodes and the personal digital devices. 3) managed the congestion of the local cloud traffic by data aggregation and classification. 4) proposed a novel delayaware routing metric to optimize the routing process and the data delivery delay. 5) developed an interference mitigation solution that includes a layered and dynamic channel assignment policy for the local cloud and the WBANs. 6) proposed a delay-aware association protocol to connect the WBANs with the local cloud which handles the mobility of the users. 7) performed extensive simulations using ns-2 with several real-life scenarios to evaluate the impact of interference, congestion, mobility, traffic pattern, and network topology on the proposed system. The rest of the paper is organized as follows. Section “Related work” gives a brief overview of the related work found in the literature. Section “CHMS system model” presents CHMS model and its architecture while the details of its operation and modules are introduced in section

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“CHMS operation”. The simulation experiments setup and the results are discussed thoroughly in section “Performance evaluation”. Finally, section “Conclusions” concludes the paper.

Related work As mentioned earlier, building efficient remote health monitoring systems was investigated by the research community. Many solutions and techniques were proposed to solve the main design challenges associated with these systems. In this section, a brief overview of the research efforts in this field is presented. A cloud-based framework for Wireless Sensor Networks (WSNs) was proposed in [5], mainly, to improve the data transfer process from the WSN using cloud computing. A similar attempt was presented in [37] in which the cloud acts as a virtual sink for the sensed data from the WSN. A survey of Sensor-Cloud infrastructure was summarized in [7] which discussed the definition, architecture and applications of these platforms. In [49] the researchers presented a cloud-enabled WBANs design that can support several types of scenarios for home, hospital, and outdoor environments. Another system to support effective health monitoring for in-home users with a Web-based graphical interface was presented in [23]. Jacob et al. in [30] proposed a low-cost remote patient monitoring system based on a reduced platform computer technology. The researchers in [6] proposed mHealthMon which is a cloud computing-based energy-efficient and distributed mobile health monitoring. The idea is to run some parts of the application components, possibly in a parallel manner, on the cloud to avoid draining the batteries of the mobile devices. Karthikeyan et al. in [31] presented a system for electronic medical profiles storage and retrieval including medical images using a cloud based Palm vein recognition. Another electronic health record cloud-based sharing system was introduced in [17] where the authors considered the security and privacy issues of the shared profiles. Lin et al. in [35] utilized WLANs and the television cable network to build a system to monitor the health status of patients at homes and public places to create electronic profiles. The authors in [36] presented a study of the main criteria needed to evaluate cloud-based hospital systems. A survey of health monitoring systems that defines the main design challenges and open research issues is found in [10]. Patients’ health status prioritization, QoS support, multi-interface design, and multi-hop routing in WBANs systems had been considered in many research works [9, 11, 14, 27, 34, 39, 48]. A cloud-based

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service-oriented architecture that is capable to handle emergency care service was proposed in [39]. The developed system can identify patients emergency information, guide the medical personnel to the most appropriate management of the emergency case, prioritize the emergency case, and identify the most appropriate ambulance and healthcare settings. A priority based and interference aware patients’ health monitoring and tracking system was introduced in [14]. The system schedules the transmissions of the vital signs of patients based on their current health status and the network current conditions in terms of congestion, interference, multi-channel availability, and offered delay. The proposed work in [11] and [34] had investigated multi-hop routing and crosslayer design in WBANs based systems. To mitigate fading and shadowing effects in health monitoring systems, the researchers in [9] proposed a multiradio/multi-channel communication framework for WBAN devices. The objective is to increase the data rates to support multimedia information and to improve the overall network throughput. In [48] the authors presented the design of an e-Health cloud platform with QoS guarantees. The cloud platform system is modeled as an M/M/m queue and is composed of two parts: Front-end which consists of the software components and Back-end which includes the servers with their database system. To support patients’ mobility a handoff protocol for WBANs was proposed in [27]. The health monitoring system is divided into multiple tiers where each patient is served by two APs: basic and temporary one. The designed protocol relied on monitoring the signal strength from the basic AP, in case of receiving a week signal the operation is switched to the temporary AP to avoid disconnection. Compared to the previously presented studies, our proposed CHMS system presents a framework that integrates WBAN with cloud computing. However, the main objective is to address QoS support in a light weight manner which is one of the most challenging issues in such integration. Particularly, CHMS optimizes the operation of the local cloud and WBANs by proposing a delay-aware routing metric and association protocols to support real time response. In addition, CHMS presents a dynamic channel assignment policy to mitigate mutual interference between nearby patients. These techniques contribute to promote QoS support and enhance the system scalability.

CHMS system model In this section, the components of CHMS and the relations between them are described in details. As dis-

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Fig. 2 CHMS design

cussed earlier, the core idea of CHMS is to have a distributed, flexible, reliable, and real-time health monitoring system for subject categories including: healthy people, athletes, people with chronic conditions, or recovered patients. The general design of CHMS architecture (depicted in Fig. 2) includes four basic components: the wireless routers, the gateways, the users’ WBANs, and the medical staff. The wireless routers, referred to as local routers throughout this paper, represent the infrastructure of the local cloud. They are responsible of the routing process by constructing multi-hop routes to deliver the WBANs’ reported data to the gateways. The number of local routers and their underlying wireless standard specifications determine the coverage area of the local cloud and the number of supported users. The gateways are a subset of the local routers with the privilege of having a connection, possibly wired, with the global cloud. Thus, they represent the connection points through which the medical staff is reached. As shown in Fig. 2, few gateways are used, compared to the number of

local routers, and they serve the entire local cloud. Therefore, load balancing and efficient routing are required to guarantee high performance level in terms of delay and packet delivery rate. Each user in CHMS is represented by his/her own WBAN referred to as local WBAN. The local WBAN contains the health monitoring units which are sensor nodes used to measure the vital signs of the body health status. These units are under the control of a special node called the master node. A magnified view of these local WBANs is provided in Fig. 3. Examples of the monitored body health indicators include: the electrical activity of the heart (ECG), the electrical activity of the brain (EEG), the heart beat rate [32], ... etc. The user’s, or the patient, health state determines the required types of sensors. In addition to the traditional WBAN design, CHMS assumes the existence of another node that supports the operation of the master node. This node is a digital device which is used by the patient on regular basis such as a tablet, a laptop, or a smart phone. This device

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Fig. 3 Local WBAN design

installs special software which has the ability of analyzing and aggregating the reported data by the WBAN nodes to create a full electronic profile for the user. The master node represents the central control unit of the local WBAN. It controls the operation of the sensor nodes, receives their periodical measured data, and (in CHMS system) it is responsible of processing and classifying the reported data to be urgent or regular (non-urgent) data. The term urgent data refers to an early notification about changes in the user’s health state. Such notifications are utilized as early alarms to the medical staff to follow up the user and take the appropriate actions to avoid any future serious health complications. As shown in both Figs. 2 and 3, the urgent data, represented as a dashed wireless connection, is sent immediately by the master to the medical staff across the local cloud network, if the medical staff is local, or delivered to the global cloud to reach the global medical staff. On the other hand, the regular data, represented as a solid wireless connection in the figures, is sent to the user’s digital device. This data is used by CHMS software to create an electronic profile for each user. The digital device sends an aggregated data packets using the local cloud on periodical basis. This is done to enable sharing the user’s profile with the medical staff across different hospitals and medical centers, if needed, to have a unified profile for the user. A high level description of CHMS operation with data classification is illustrated in the flow chart found in Fig. 4. The design of the local WBANs is subject to many important factors including signal propagation, sensor nodes’

effect on the human body [15], the wireless technology used to build the WBANs [46], sensor nodes’ energy consumption, QoS support especially the delay for the urgent data, congestion at the cloud, and coexistence with other nearby patients or other devices that use the same frequency spectrum of WBANs [29]. The local cloud with its design is the basic building block of CHMS. This cloud represents a framework where the used routing metrics, protocols, and its organization can be varied based on the targeted area, the health-status of the users, the available resources, and the objective of the monitoring system. The technical details and operation of this architecture are thoroughly discussed in the following section.

CHMS operation The interference caused by the simultaneous operation of CHMS components (i.e. local WBANs, local routers, and the digital devices) has large effect on the medium congestion and the data delivery delay. A congested medium leads to larger data loss and longer end-to-end delay of both urgent and non-urgent data. In CHMS, as a health monitoring system, delay-aware operation with high packet delivery rate is essential. Hence, we utilize the architecture described in the previous section along with multichannel/multi-interface hardware design to propose a delay and interference aware operation (DIAop) of CHMS. DIAop includes three modules: a dynamic channel assignment policy, a delay-aware routing metric, and a

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Fig. 4 CHMS basic operation

delay-aware association protocol. The dynamic channel assignment policy distributes the co-located local WBANs among the available frequency channels. Hence, the assignment policy balances the load among the channels and reduces congestion effects by separating the collision domains. The delay-aware routing metric is utilized in constructing reliable routes toward the gateways that offer low data delivery delay. In addition, this metric is adopted by the association protocol that is used by the master nodes to associate with the most suitable local router. The operation of these modules is described in details in this section. The notations and terminologies that are used in DIAop modules are summarized in Table 1. Dynamic channel assignment policy DIAop divides the components and devices found in the local cloud of CHMS according to the operating frequency channel into two layers, as illustrated in Fig. 5. Layer 1 includes the digital devices, the local routers, and the gateways while Layer 2 includes the sensor nodes of the local WBANs. The connection points between the two layers are the master nodes. Therefore, DIAop assumes that the master nodes are equipped with two radio interfaces, one for each layer.

The objective of having different layers is to distribute the available frequency channels among the incorporated devices to reduce interference. DIAop assigns a single channel for Layer 1. On the other hand, layer 2 uses the rest of the available channels for the local WBANs. For the connection points, i.e. the master nodes, the first radio interface is assigned to the same channel used by layer 1, where the second one is assigned to the same channel used by its local WBAN. The multi-radio master node design supports standards diversity in CHMS operation. In other words, if the WBANs implement a different networking standard from the one used by the infrastructure the master nodes will be equipped with two different radios according to the used standards. This feature allows interoperability without any degradation in the system performance where the hybrid communication between the two radio interfaces is carried out by the master node itself. The master node is responsible of finding the most suitable channel for its local WBAN. It uses the delivery delay of the health status related data packets to decide if switching to a new channel is needed. The flowchart found in Fig. 6 gives a high level description of the channel selection and switching policy implemented by the master nodes while the details are found in Algorithm 1.

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Algorithm 1 Dynamic channel assignment policy

J Med Syst (2014) 38:121 Table 1 DIAop notations Notation

Meaning

W BAN i Si CL1 Ci

The i th local WBAN in CHMS. The number of sensor nodes in the i th local WBAN. The frequency channel assigned to Layer 1. The frequency channel assigned to the i th local WBAN. The end-to-end delay of the k th data packet. Total number of periodic packets received at the master from the i th local WBAN. Measurement time window for channel switching policy. Average end-to-end delay of the i th local WBAN.

P dk Pi Tw i W BANdelay

P dstd α, β a, b CountCr avgDelayCr WCr Tm

When a new WBAN is constructed, i.e. a new user decides to use CHMS, it is assigned a channel randomly from the available set of channels in Layer 2. The master node monitors the delay of all packets received from every node in its local WBAN over a specific time window Tw . The delay of each packet includes the queuing delay, the time needed to reserve the medium by the WBAN node, and the packet transmission time. For this purpose, we added an additional field in the data packet header to be used by the WBAN node to store the sum of the queuing delay and medium reservation time experienced by the packet. The master node extracts this data and adds the transmission time to compute the final delay value. At the end of each monitoring period an average delay value is computed for all packets. If the computed average is found to be greater than a threshold value then the channel selection phase starts. The threshold value is the multiplication of P dstd by a factor of α, where P dstd is the minimum delay needed for a packet to be sent from the sensor node towards its master according to the used WBAN standard. During the channel selection phase the master node checks the rest of the available channels at Layer 2 including its current channel to find a lightly loaded one. This is done by

Li,j ET Xi.j i Pcount Tetx τ P DR R Tassoc RSSRi DARRi RSS NRi DAR NRi WRi γ, θ δ

Standard value of packets end-to-end delay. Multipliers where β > α > 1 Weighting factors where a + b = 1 Total number of WBANs on the r th channel. Average packet delay for all WBANs on the r th channel. Weight value assigned to the r th channel. Measurement time window for recording end-to-end delay of data packets exchange at the MAC layer. The link between nodes i and j . ETX metric [20] of the link between nodes i and j . Total number of data packets sent by node i. Measurement time window for ETX metric. Periodic time of sending ETX probes. Packet delivery rate. Set of discovered local routers around a master node. Periodic time of the association process. Received signal strength of the i th local router. Total delay-aware routing metric value of the best route of the i th local router. Normalized RSS value of the i th local router. Normalized DAR value of the i th local router. Weight value assigned to the i th local router. Weighting factors where γ + θ = 1 Urgent data packets percentage.

broadcasting a WBAN DISCOVER control packet on the interface that uses the common channel of Layer 1. All close master nodes, i.e. one-hop neighbors, respond by sending a WBAN DISCOVER REP control packet that includes the channel ID used by the neighbor’s local WBAN, and the last recorded average packet delay on that channel. Next, the master node which initiated the channel selection process assigns to each channel a weight value WCr . This weight is based on the number of WBANs that are currently using the channel and the delay reported by their

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Fig. 5 Frequency channels layers in CHMS

master nodes. The aim is to have an estimation of the channel congestion, represented by the number of WBANs, and the channel quality, represented by the measured delay. Initially, the average delay of channel Cr is computed as follows: CountCr k W BANdelay k=1 avgDelayCr = (1) CountCr WCr is based on combining avgDelayCr and CountCr with each other. Therefore, the normalized values of these quantities are used and combined by weighting factors a and b using the following equation: WCr = a ∗

avgDelayCr CountCr +b∗ max∀r avgDelayCr max∀r CountCr

(2)

The newly selected channel is the one with the minimum weight value. After the new channel is selected the master decides whether to switch to the new channel or not. If the value of the monitored average delay on the used channel increases to be larger than P dstd by a factor of β, then the master node informs its local WBAN nodes to switch to the new channel. This is done by broadcasting a CHANNEL SET control packet. Upon the reception of this packet all WBAN nodes switch to operate on the new channel. In this paper, we assume that the channel switching process at the nodes can be performed instantly and reliably. In other words, we assume that all WBAN nodes receive the CHANNEL SET without any loss and the channel switching delay is negligible. Delay-aware routing Health monitoring systems with urgent data or notifications require real-time operation. Hence, delay optimization

while routing is needed to guarantee fast response. Moreover, finding routes with good quality is essential to reduce data loss and deliver to the medical staff a complete profile for the user. For this purpose, CHMS implements a novel delay-aware routing metric, referred to as DAR, that can be integrated in any routing protocol to construct the needed routes. The proposed metric depends on the cross interaction between the medium access layer (MAC) and the routing layer, i.e. cross-layer design. The MAC layer records the total delay of sending a packet to a specific neighbor. It monitors the delay for both successfully delivered packets and unsuccessful ones. The former corresponds to packets with received ACK, while the latter corresponds to the dropped packets after exceeding the packet retransmissions limit. The recorded delay includes the time of reserving the medium, transmitting the packet, and receiving the ACK. High congestion level means that longer time is required to reserve the medium. The packets transmission time captures the effect of the packet size and the used data rate. Exceeding the retransmission counter at the MAC layer leads to high packet error rate and packets collision. Thus, DAR tries to capture the effect of all these factors. The MAC layer is able to record delay values only for used links. In other words, packets must be transmitted on a link to record the delay values. Consequently, there is a need for a metric to evaluate the delay of the unused links. Moreover, the recorded delay depends on the packet size. Large delay value implies poor channel quality for small data packets, however, it may indicate good channel quality and successful transmission from the first attempt for large packets. This is due to the difference in the transmission time in both cases. Hence, monitored delay for different pack-

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Fig. 6 Dynamic channel assignment policy basic operation

ets sizes has to be recorded separately. In CHMS urgent data packets have higher priority than nonurgent ones, thus, only the delay of urgent packets is monitored. DAR is computed in two different approaches; one for recently used links and one for the links that have not been used during the last time period Tm . However, both approaches report the expected delay for exchanging packets on the links. For a link Li,j between local

routers i and j , DAR is computed based on the following equation: ⎧ Pcount i P dk i ⎪ , Pcount = 0 ⎨ k=1i Pcount DARi,j = (3) ⎪ ⎩ i ET Xi.j ∗ P dstd , Pcount = 0 Where ET Xi.j is the expected number of retransmissions metric [20] measured over Li,j . It is computed by

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measuring the packet delivery rate of both directions on a link, i.e. forward direction over which the data packets are sent and the reverse direction over which the ACKs are received. Each local router broadcasts small packets, called probes, periodically at a rate of one probe every τ second. The number of received probes during a time window Tetx is recorded by each router and is used to compute PDR according to the following equation: P DR =

count (t − Tetx , t) Tetx /τ

(4)

PDR is computed on both directions of a link. To compute the forward direction PDR, denoted as P DRf , a router needs to know how many probes its neighbors have heard. ETX probes are used to exchange the recorded counters between neighbors to allow them to compute P DRf . On the other hand, the counters recorded by the router itself about the probes from its neighbors are used to compute PDR on the reverse direction denoted as P DRr . Finally, the link’s ETX value is computed by combining PDR on both directions using the following equation [20]: ET X =

1 P DRf ∗ P DRr

Association protocols To connect with the local cloud in CHMS the master nodes and the digital devices have to run an association protocol to find the best local router to associate with. The master nodes communicate with the local router whenever an urgent data packet is received. For this purpose, two factors must be considered by the master nodes while selecting the best router to associate with: distance to the router and route quality toward the gateway. Due to users’ mobility, the best local router to associate with may change over time. As a result, in CHMS mobility tracking is a task handled by the association protocols. Algorithm 2 describes the association protocol that is used by the master nodes, which is a modified version of the protocol found in [8].

Algorithm 2 Master nodes’ association protocol

(5)

As a result, the DAR value for the used links is the average delivery delay experienced during the last measurement period. On other hand, for unused links DAR is the expected delay to be experienced. DAR is used by the underlying routing protocol implemented by the local cloud for the route construction toward the gateways. The route weight is the sum of DAR for all links that belong to this route. The route with the minimum total DAR value is designated as the best route that offers the lowest end-to-end delay based on the current medium conditions. The selection of the routing protocol depends on many factors such as the coverage area of the local cloud, the number of nodes, the available resources, the health status of the monitored users, ... etc. Basically two options are available: –

–

Static routing: Where each router maintains one route toward each gateway. These routes are configured during the network setup phase and is fixed during the network operation. This option is suitable for small size and sparse networks with limited number of alternative routes. Dynamic routing: Here the routes are constructed when needed and they change during the network operation time. Any dynamic routing protocol can be used in this case such as AODV [19] and DSR [12]. Instead of using the hop-count metric, which is the basic metric for most of these protocols, the DAR metric is used to assign weights for the network links.

In what follows a detailed description of Algorithm 2 is presented. The master starts by discovering the local routers around it by broadcasting a DISCOVER control packet. Each local router, that exists around, replies by a DISCOVER REP packet that contains the total DAR value for the best route it has toward any gateway. From these replies the master extracts the IP address of the router, the RSS value of the received reply, and the DAR value. For all discovered routers found in router set R, the master computes the normalized RSS and DAR values. WRi is computed by combining these normalized values using the weighting factors γ and θ .

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The master selects the router that has the maximum weight and sends a REGISTER control packet to register. The selected router sends a REGISTER REP packet as a response after which the association process is considered complete. If no reply is received then the master tries to register again. After a specified retries limit is reached the master looks for another router in R and tries to register with. If R is found empty then the master initiates the discovery process again. The association process is repeated periodically every Tassoc to track any location change due to human mobility. To reduce the overhead of the association process, the master nodes utilize the ETX probes broadcasted by the local routers instead of sending DISCOVER packets. As mentioned previously, these probes are broadcasted periodically and can be used to extract the needed information about the local routers. However, if the DAR metric is not adopted in the implementation of the local cloud of CHMS we return to the original association protocol described in Algorithm 2. The digital devices run the same association protocol of the master nodes with the difference in the quantities used for computing WRi . The digital devices need the local routers to deliver regular data packets which are not critical as urgent packets sent by the master nodes. For this purpose, and to privilege urgent data over regular one, the digital devices select the local router with the highest RSS value only to associate with. Similar to the master nodes, the digital devices need to run the association process periodically every Tassoc to assure connectivity.

Performance evaluation

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behavior based on empirical bit error rate and signal to noise ratio (BER vs. SNR) curves for IEEE 802.11b are added based on the patch found in [2]. Finally, we have added a common database between the MAC and the network layers to support cross-layer design. Both layers have full access to this database for storing and retrieving the needed data and parameters required to compute the DAR metric. For the Local WBANs design, as elaborated earlier, these WBANs are based on the usage of low power wireless technologies. Many options are available such as Zigbee [3], IEEE 802.15.6 [4], and more recently the IP-based sensors which implement a low-power version of IEEE 802.11 standard [25, 44]. We assume the usage of one standard for all devices incorporated in CHMS. Therefore, the implemented WBANs are based on IEEE 802.11b but with special settings. We followed the same specifications of IP-based sensors found in [25, 44] for the low power operation. In CHMS only the local WBANs are allowed to move throughout the network where each local WBAN moves as one entity, i.e. represents a patient or user in the system. In addition, we assume that the user holds the digital device wherever he/she goes. As a result, all the nodes that belong to the same local WBAN move at the same speed toward the same destination at the same time. For this purpose, we modified the mobility generation script found in ns-2 to enable all nodes associated with the same user to move as one object. Figure 7 shows the design of the user’s local WBAN used in CHMS simulation. The number and locations of the nodes on the human body are arbitrary. As shown in the figure, the selected local WBAN design consists of seven nodes: five monitoring nodes, one master, and one digital device.

In this section, we evaluate the performance of CHMS using extensive simulation experiments under various conditions. We used ns-2 to implement the simulation scenarios after adding the required modifications and design details of CHMS. The simulation setup, the tested factors, the performance measures, and the obtained results are presented in the following subsections. Simulation setup We have modified the original distribution of ns-2 by adding several modules needed to support the design of CHMS. Some modules are related to the wireless medium operation, while others are related to WBANs’ networking standard, traffic generation, patients’ mobility, and the local cloud design in CHMS. The Multi-interface/multi-channel design is implemented based on [1]. The multi-rate transmission operation, Ricean fading propagation model, and realistic channel

Fig. 7 Simulated local WBAN design

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Another important issue related to local WBANs operation is the data traffic generation. Mainly, WBANs applications are characterized by periodic generation of small data packets. In CHMS the monitoring (sensor) nodes generate small data packets regularly and send them to the master node. Also, the digital devices generate periodic large packets that contain aggregated data less frequently. According to the nature of the monitored body health indicators, the sensor nodes may be heavy traffic sensors such as EEG and ECG with a rate of approximately 10 pps (packet per second), or lower loaded ones such as the heart beat rate and blood pressure sensors with a rate of 1 pps, or even lightly loaded nodes such as temperature sensors which generate on average 1 packet per 5 seconds [43, 50]. To represent a realistic and fair load we selected sensors from all types. Specifically, each local WBAN in CHMS consists of the following: two sensors with a rate of 10 pps, two sensors with a rate of 1 pps, and one sensor with a rate of 0.2 pps. The packet size is selected to be 128 byte for these sensors. For the digital devices the following settings are used: a rate of 1 packet per 5 seconds with a packet size of 1500 byte. Another important aspect that is related to sensor nodes traffic generation is the percentage value, δ, of the generated packets that will be considered as urgent. Practically, this value should be very small most of the time since CHMS is targeting people with fair health condition who practice

Fig. 8 Simulated local cloud topology

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their normal life at home, work, sporting, ...etc. However, to analyze the performance of CHMS under heavy load conditions we selected large value for δ in most of the conducted experiments which reaches 30 % of the total generated data packets. As for the local cloud of CHMS, the simulated local cloud topology, which is depicted in Fig. 8, consists of 36 local routers distributed in a 6×6 grid within an area of 1200×1200 m. The separation distance between the local routers in the grid is set to 150 m. Wired connections to the Internet were added to two local routers to set up the gateways. The combination of wired and wireless environments in ns-2 is called wired-cum-wireless where wired hosts can be added and they are able to communicate with the wireless nodes. We have added one wired host that represents the medical staff in CHMS as shown in Fig. 8. The local WBANs were distributed and allowed to move in the second half of the network located far away from the gateways, i.e. the lower 500×1200 m region. For the multi-hop wireless routing protocol needed by the local cloud we selected AODV protocol [19]. The original implementation of this protocol in ns-2 is for single-radio ad hoc networks with the hop-count as its routing metric. At first, we modified AODV module to support multiinterface/multi-channel operation based on [1]. Then, we added the ability of routing in wired-cum-wireless scenarios based on the work found in [28]. Finally, we implemented

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our DAR routing metric to be used instead of the hop-count metric for the route construction process. The values of the used simulation parameters for each simulation run, unless different settings are specified, are summarized in Table 2. Each simulation scenario has been repeated 25 times for statistical validation.

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Performance metrics

Routing overhead ratio (ROR): is the ratio of the total overhead required for the local cloud operation with respect to number of delivered data packets. The overhead includes the association, routing metric computation, and routes construction overhead. ROR is computed according to the following equation where the received data packets include both urgent and nonurgent data packets:

In the simulations we adopted the following metrics to measure the performance of CHMS:

ROR = 

–

–

Packet delivery ratio (PDR): is the total number of successfully received data packets with respect to the total number of generated packets during simulation. CHMS involves two types of packets with different characteristics, i.e. urgent and regular data packets. For this purpose, we reported PDR for each data type separately. Average end-to-end delay: is the average time needed by the data packets to reach their ultimate destination which is the medical staff. This metric is also reported separately for urgent and non-urgent data.

Table 2 Simulation parameters MAC protocol IEEE 802.11b with RTS/CTS disabled. Number of channels 3 (one assigned for Layer 1 and two available for Layer 2). Propagation model Two ray ground reflection for large-scale path loss, and Ricean fading for small-scale fading. Mobility model Random way-point [13] (1 m/s minimum speed, 5 m/s maximum speed, and 5 s pause time). Transmission range 250 m (routers, master nodes, and digital devices). 50 m (WBANs sensor nodes). Interference range 500 m (routers, master nodes, and digital devices). 100 m (WBANs sensor nodes). Transmission power 30 mW (routers, master nodes, and digital devices). 8 mW (WBANs sensor nodes). Transmission rate 1, 2, 5.5, 11 Mbps (routers, master nodes, and digital devices). 1 Mbps (WBANs sensor nodes). Traffic type CBR over UDP. Packet size 1500 byte (digital devices). 128 byte (WBANs sensor nodes). Interface queue 50 packets. length P dstd 2 ms α, β, a, b, γ , θ, δ 2, 3, 0.5, 0.5, 0.5, 0.5, 0.3 5, 1, 10, 10, 1 s. Tw , Tm , Tassoc , Tetx , τ Simulation time 900 s.

–



ControlP ackets ReceivedDataP ackets

(6)

The conducted experiments include studying the effect of several factors that are considered crucial to the operation of remote health monitoring systems in general. These factors include the following: –

–

–

–

Mutual interference: This factor studies the effect of increasing the number of coexisting WBANs within the local cloud of CHMS. More WBANs implies that more interference is introduced causing a more congested medium, more collisions between simultaneous transmissions, and more waiting time to reserve the medium. For this purpose, we evaluate the ability of CHMS in utilizing the availability of multiple channels to reduce the effect of mutual interference. Human mobility: Continuous monitoring of users’ health is a very important requirement that is essential in health monitoring systems. Human mobility affects mutual interference since moving to a new location, especially crowded one, triggers the dynamic channel switching module in CHMS. In addition, the association protocols used by both master nodes and digital devices play a crucial rule in tracking users’ mobility by periodically looking for new local routers to associate with. For this purpose, we study this factor based on different mobility patterns and scenarios. Cloud density: The cloud density represents the distance separation between the local routers that form the local cloud of CHMS. Closer routers means larger density, i.e. greater number of one hop neighbors, and higher level of mutual interference. However, large density has an advantage of having larger number of alternative routes toward the gateway which promotes the reliability of the network. Thus, we study the performance of CHMS under different network topologies with various cloud densities. Urgent data percentage: The amount of urgent data generated by the local WBANs highly affects the performance of the entire system. More urgent data implies more data to be routed by the local cloud toward the gateways. Hence, CHMS is evaluated under different urgent traffic loads to measure its ability to route the

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data on various routes while maintaining acceptable end-to-end delay and data delivery rate. Simulation results In this subsection, the results of the simulation experiments for each of the aforementioned factors are presented and discussed. Impact of mutual interference In this experiment, the number of local WBANs is increased from 6 to 18 with a step of 2 WBANs. Each additional WBAN means that seven nodes are added. Consequently, the total number of nodes in this experiment is in the range from 42 to 126 nodes. We evaluated the performance of CHMS design with and without DIAop module to examine the effect of DIAop in interference mitigation. In the exhibited figures we refer to the first configuration as DIAop while the second one, i.e. without DIAop, is referred to as Hop-SR since the used routing metric is the hop-count metric with single-radio/single-channel design. As shown in Fig. 9a, DIAop achieved higher packet delivery rate for both urgent and non-urgent data which is around twice the rate reached by Hop-SR. For example, at network size of 70 WBAN nodes, DIAop had a PDR of around 80 % while Hop-SR reached only 50 %. Moreover, as the network size grows larger than 70 nodes, Hop-SR had much lower PDR for urgent data than nonurgent one. This is due to the fact that the hop-count metric does not distinguish between the two types of reported data. DIAop, on the other hand, reduced the effect of interference between nearby WBANs using the dynamic channel assignment policy and the layered channel distribution.

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Figure 9b proves that DIAop succeeded in its main objective by reducing the data delivery delay of the urgent data. As depicted in the figure, DIAop had lower delay for urgent data as compared to non-urgent one. This is due to two main reasons. The first one is the usage of DAR metric which is based on monitoring the urgent data delay. While the second one is the implemented association protocol by the master nodes. The figure also shows that DIAop had lower delay for both types of data compared to the Hop-SR for approximately all cases of increased number of WBAN nodes. On the other hand, Hop-SR experienced a severe degradation in its performance beyond 70 nodes especially for the urgent data packets. Finally, ROR of both DIAop and hop-SR is shown in Fig. 10. DIAop has the constant overhead of broadcasting ETX probe control packets which is part of the DAR metric. Additional overhead is introduced as the network size and traffic load increase, as depicted in the figure. However, DIAop has lower ROR compared to Hop-SR. This means that DIAop is more efficient in utilizing the introduced operation overhead with respect to the obtained PDR. Hop-SR degraded in performance as the interference increases where the routes experience frequent breakage and increased number of collisions. As a result, more overhead is needed to construct alternative routes and retransmit the data. Impact of human mobility As discussed earlier, the users in CHMS might be patients in the recovery period, i.e. moving slowly, or healthy people who experienced some irregularities in their vital signs, i.e. moderate speed while moving, or athletes, i.e. moving at high speed. The mobility pattern of the users affects the topology of the network where some locations experience congestion due to the existence of

Fig. 9 Mutual interference: (a) Packet delivery ratio, (b) Average end-to-end delay

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Fig. 10 Mutual interference: routing overhead ratio

multiple users around each other. Furthermore, mobility affects the association of the master nodes and the digital devices with the local routers. Excessive data packets loss may occur as a result of being disassociated due to high mobility. Hence, the objective is to examine the response of CHMS to different mobility patterns. The random way-point mobility model [13] found in ns2 was used to generate the mobility scenarios by varying the average nodes speed and the pause time parameters of this model. In this experiment the number of WBAN nodes is set to 56 and six mobility scenarios were generated as shown in Table 3. As shown in Fig. 11a, CHMS exhibited a stable performance under different mobility patterns where PDR is almost the same for both types of data. This is because of the operation of the dynamic channel assignment policy. It distributes the WBANs among the available channels and separates their collision domains even in crowded regions formed by users moving toward the same destination. Moreover, the association protocols repeat the discovery process periodically which allows tracking the new locations of the master nodes

Table 3 Tested mobility patterns Pattern name

Speed range (m/s)

Pause time (s)

s5p0 s5p5 s10p0 s10p5 s15p0 s15p5

[1, 5] [1, 5] [1, 10] [1, 10] [1, 15] [1, 15]

0 5 0 5 0 5

and the digital devices and reconnect them with the local cloud. The end-to-end delay, found in Fig. 11b, exhibited a different trend compared to PDR where it is affected by mobility. The urgent data delay increased slightly in the range of 140 ms and reached around 180 ms at the highest speed. However, the non-urgent data delay varied in a larger range. It had the lowest delay for the most stable mobility pattern s5p5 with a value of around 340 ms, and reached around 590 ms for high mobility ones. As mentioned previously, the optimization of urgent data delay is a result of the core operation of CHMS association protocols and DAR metric. On the other hand, the main concern with non-urgent data is to be delivered with high PDR even with larger delays. As shown in Fig. 11a, this goal has been achieved where the non-urgent data PDR remained approximately constant regardless of the applied mobility pattern. In terms of ROR, Fig. 12 proves another advantage of CHMS by having approximately constant overhead against the different mobility patterns. One reason is the stable PDR for all the tested mobility scenarios which means that the routes are stable and not experiencing additional link failure due to mobility. Moreover, the association protocols are based on monitoring the ETX probe packets where they are broadcasted periodically every 1 s. As a result, the discovery process period Tassoc can be modified to guarantee continuous coverage by the local routers with no cost. Impact of cloud density In this experiment, we vary the distance between the local routers to change the density of the local cloud infrastructure. Similar to the previous experiment, the reported results here are for 56 WBAN nodes scenario. The distance separation had been varied from 100 m to 200 m with a step of 25 m. Distance larger than 200 m is not studied to avoid disconnections in the network since the used transmission range is 250 m. As shown in Fig. 13a, CHMS with DIAop module achieved a PDR close to 98 % for both urgent and nonurgent data in dense clouds. Hence, the interference-aware part of DIAop succeeded in mitigating the interference effect. The same trend is depicted for the end-to-end delay in Fig. 13b where the delay is minimal for dense clouds especially for urgent data (around 100 ms). In dense clouds there are more routes toward the gateway among which the DAR metric is used to pick the best one. On the other hand, the sparse clouds have limited number of routes and the onehop distance traversed by the packet is larger which leads to larger signal attenuation. This is reflected on both PDR and the end-to-end delay which have degraded in sparse clouds. However, CHMS still has the ability to favor urgent

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Fig. 11 Human mobility: (a) Packet delivery ratio, (b) Average end-to-end delay

data even when resources are scarce. Based on the aforementioned results, one can deduce that critical patients or users need to be located in dense cloud-based monitoring systems to provide real time tracking of their health status. Whereas, normal users can be located in sparse clouds where no need for the extra cost of deploying more local routers. For the routing overhead, as shown in Fig. 14, sparse clouds had the highest overhead ratio due to the decreased number of delivered data packets. On the other hand, smaller fixed ratio values are obtained for large to moderate density. As shown in Fig. 13 and Fig. 14, moderate density, e.g. 150 m distance separation, can be a good compromise between overhead, delay, and PDR. Impact of urgent data percentage Finally, we varied the percentage of urgent data with respect to the total reported data by the sensor nodes in each local

WBAN. The tested percentage values started with 5 % till 50 % with a step of 5 %. Again the reported results are for 56 WBAN nodes with the default cloud density of 150 m. Figure 15a shows that CHMS provided a PDR of around 92 %for urgent percentage of 30 % which is considered heavy load. PDR degraded after 30 % where it reached around 65 % for both urgent and non-urgent data at 50 % load. The regular reported data by the digital devices is also affected by the increase in urgent data percentage. This is due to the fact that heavier cloud load affects the delivery of all packets regardless of their type. The same situation is depicted for the end-to-end delay in Fig. 15b, where it shows a value of approximately 100 ms for a load up to 30 % urgent data. After that the delay was degraded noticeably due to the increased congestion. Finally, ROR was found to be larger for small percentage, less than 15 %, as shown in Fig. 16. The reason is the constant overhead needed by CHMS to compute ETX which is independent of the network load. However, as the load increases the number of delivered packets increases and ROR decreases. As shown in the figure, ROR is optimum for loads between 25 % and 35 %, while it is slightly increased for larger loads. CHMS limitations and open research issues

Fig. 12 Human mobility: routing overhead ratio

Since CHMS requires additional computational and communication overhead while WBAN nodes have limited energy source, in our future research we plan to analyze the cost of CHMS operation (e.g., energy consumption) and optimize its algorithms accordingly. Moreover, the complexity of the system due to the large amount of processed data needs to be studied especially for large scale systems. In addition, the data aggregation and classification algorithms used by the digital devices

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Fig. 13 Cloud density: (a) Packet delivery ratio, (b) Average end-to-end delay

and the master nodes need further investigation to be defined and optimized. Another limitation in our system is that we assumed that the dynamic channel switching process is performed instantly without delay. Besides, the control packets announced by the master nodes to switch the channels operation may be lost due to congestion or bad channel quality. As a result, some WBAN nodes may operate on a different channel than the announced one and fail to deliver the reported data. Hence, future research tackling the cost of channel switching (both energy and delay) and reliable communication of control packets is needed. Furthermore, the performance of heterogeneous systems (i.e. have WBANs use different wireless standard other than the one adopted by the local cloud) has to be studied to understand its impact on the entire system performance.

An important issue that requires further analysis is related to the mobility of the human body organs (i.e. moving the arms, legs, and chest, ...etc) which may affect the operation of the WBAN nodes and their connectivity with the master node, the digital device, and the local cloud. Moreover, the mobility model for the patients (or local WBANs) needs to be explored. We used the random waypoint model in our simulations as mentioned previously. However, this mobility model has been proved to be unable to capture the behavior of humans [47]. More accurate models are needed for patients that are residing at hospitals or recovering at home that consider their health status which has a direct effect on the mobility speed, track, and frequency. Security is another critical factor in health monitoring systems where securing the transmitted data, authenticating the users’ WBANs, detecting and avoiding jammers are essential in these systems [41, 42]. In our future work, we plan to consider extending the CHMS system architecture to include securing its operation while maintaining an acceptable data delivery delay and overhead to minimize potential degradation in the system performance.

Conclusions

Fig. 14 Cloud density: routing overhead ratio

Existing design challenging issues encountered by WBANsbased health monitoring systems include: the coverage area, the health-status of the monitored patients, network congestion, mutual interference, real time response, in addition to mobility tracking of the users. Integrating WBANs with cloud computing provides promising solutions to overcome these limitations and make cloud-based health monitoring systems more viable. In this paper, a cloud-based real-time remote health monitoring system (CHMS) for tracking the

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Fig. 15 Urgent percentage: (a) Packet delivery ratio, (b) Average end-to-end delay

health status of individuals while practicing their daily activities is proposed. The system divides the cloud into local and global clouds and optimizes the operation of the local cloud to enhance the system QoS level. Many techniques and algorithms were proposed in this paper to promote the performance of CHMS including a dynamic channel assignment policy, a delay-aware routing metric, and association protocols for the WBANs to connect with the local and global clouds. Moreover, data classification and aggregation are used to enable tracking the patients’ status while minimizing the traffic flow in the entire network to manage congestion and interference. CHMS with all the proposed techniques and modules has been evaluated against traditional solutions using extensive ns-2 simulations. Simulation results proved the efficiency of CHMS in supporting larger number of users while maintaining a high performance level in terms of packet delivery rate, end-to-end delay, and the operation overhead.

Fig. 16 Urgent percentage: routing overhead ratio

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