Open Access

A guide for the selection of routing protocols in WBAN for healthcare applications

Human-centric Computing and Information Sciences20177:24

https://doi.org/10.1186/s13673-017-0105-6

Received: 24 December 2016

Accepted: 25 June 2017

Published: 10 August 2017

Abstract

In the present scenario, the term wireless body area network (WBAN) is becoming an integral part of human day to day life due its wide variety of applications, especially in the field of healthcare systems. To design such a reliable body area network system, there are a number of factors to be considered both in hardware and software levels. One of such factors still developing is the design and the analysis of routing protocols in the network layer. Routing protocols are a set of protocols which can identify and maintain the routes in the network so that the data can be exchanged between the nodes efficiently. Hence, routing protocol plays a vital role in the wireless sensor networks for reliable communication between the sensor nodes. In this paper, different routing protocols for body area networks are surveyed and observed that they are affected by factors like energy, network topology, various quality of services (QoS) in the nodes, node temperature, transmission range of nodes, human posture, etc. An evocative taxonomy of protocols is presented such as cluster-based, cross-layered, postural movement based, QoS aware and temperature-aware routing protocols. From the survey, it is found that the selection of a routing protocol is application dependent. For example, the energy efficient protocols like reinforcement learning based routing with QoS support or wireless autonomous spanning tree protocol can be used for daily health monitoring systems due to its high packet delivery ratio. If the system is for in vivo networks, routing algorithm for network of homogeneous and Id-less biomedical sensor nodes or mobility-supporting adaptive threshold-based Thermal-aware energy-efficient multi-hop protocols are the suitable ones. For critical and emergency cases, where accuracy with little delay is the major concern, the protocols like critical data routing, reliability aware routing, data-centric multi objective QoS-aware routing protocol, etc. can be rightly chosen. This entire survey paper can be used by the researchers as a reference for studying various WBAN routing protocols, especially in the field of medical health care systems.

Keywords

Quality of service Routing protocols Wireless body sensor networks

Introduction

Wireless body area networks (WBANs) include a collection of body sensor nodes that are low power, invasive or non-invasive, light-weight devices which are either worn on the body or implanted inside the body. The architecture of WBAN [1] can be considered as three different tiers, namely: Tier-1 as Intra-BAN, Tier-2 as Inter-BAN and Tier-3 as Extra-BAN as shown in Fig. 1. In Tier-1, the body sensor nodes collect the data and send to the coordinator. In Tier-2, the coordinator processes the received data and sends the information towards the sink node. From the sink node the packets are transmitted to the corresponding health-center through internet or other communication techniques.
Fig. 1

Wireless body area network architecture

According to the survey conducted by the Economic and Social Affairs Department of United Secretariat [2], after 10 years people with age above 65 will be approximately 15% of total world population. As, older people are more exposed to medical health issues, the need for low cost health monitoring devices [3] becomes a major part human life.

WBAN is actually a subset of conventional wireless sensor networks (WSNs), which can be used for early detection of various diseases, for real-time patient monitoring of elderly people, etc. The body sensor node is either worn on the body surface or implanted inside the body. The sensed data is sent to the Base Station and is then forwarded to the sink node. The sink node is responsible for sending the information to its corresponding healthcare center.

The reliability and efficiency of WBAN depend on how the system responds quickly and accurately, to send and receive the data between the nodes, which eventually depends on the selected routing protocols or algorithms. The process of sending information from either an in-body or an on-body sensor node includes the radiation emitted from wireless transceivers which is similar to WSNs.

Although the routing protocols that are used in WSNs have been under study for past few years, these protocols cannot be used for WBANs due to its stringent requirements. For WSNs, the main focus is on minimal routing overhead and maximal throughput than reduced energy consumption [4]. Also, WSNs are mostly homogenous networks, the WBANs are heterogeneous too [5].

In this article, a comprehensive review of the existing recent routing protocols/algorithms is discussed. “The required evaluation metrics for healthcare applications” section discusses the performance metrics that should be considered for WBANs. The classification of the existing routing protocols is explained in “Classification of routing protocols” and “Future challenges and comparative analysis of routing protocols” sections gives the future challenges and a comparative analysis of different selected protocols. Finally, “Conclusions” concludes the survey.

The required evaluation metrics for healthcare applications

In order to identify the important metrics that have to be considered in WBANs during the design process, a general overview about the routing challenges in WBANs should be studied. The certain routing issues and challenges include network topology, postural body movements, limited resources, quality of service metrics, radiation and interference, global network lifetime, heterogeneous environment, etc. By analyzing all these factors we can conclude and list the important performance metrics to be considered while implementing the whole WBAN. The following section defines the metrics:
  • Network lifetime defines the total operation time of the network until the last node is dead.

  • Path loss is the difference between the transmitted power at the source node and received power at sink node.

  • Stability period is the time before first node die.

  • Residual energy is the difference between initial energy and used energy during the operation of the network.

  • End-to-end delay is the average time taken by a data packet to reach the sink from the source node.

  • Packet delivery ratio is determined by number packets obtained at the sink divided by the number of packets send from the source.

Classification of routing protocols

The classification of routing protocols can be done in different categories that correlate with the routing challenges of WBAN. The following section gives an overview about the existing protocols, which can be categorized as Cluster-based, Cross-layered, Postural movement based, quality of services (QoS) aware and Temperature-aware routing algorithms [6] as shown in Fig. 2.
Fig. 2

Classification of routing protocols

Cluster-based routing protocols

In both WSNs and WBANs, the limited energy source is the main constraint to be analyzed. Hence, several efficient cluster based schemes are proposed for both networks to minimize the power consumption and maximize the network lifetime [7]. While comparing hybrid indirect transmission [8] to power-efficient gathering in sensor information systems [9] and low-energy adaptive clustering hierarchy (LEACH) [10], it consumes less amount of energy if the number of nodes are small. However, AnyBody [11] protocol is better than LEACH, as the numbers of clusters remain constant with an increase in the number of nodes, but LEACH does not. Also, the installation cost is also less with AnyBody. The Table 1 shows an overview of the existing cluster-based protocols.
Table 1

clustered routing protocols in body area networks

Sl. no.

Protocol [ref. no.]

Goal

Performance metrics

Compared with

1

Cluster based body area protocol (CBBAP) [12]

To enhance the overall energy efficiency of WBAN

Throughput, residual energy, number of live nodes

LEACH

2

AnyBody protocol [11]

To divide the network into clusters and to efficiently send packets from source to sink

Number of clusters, average cluster size, number of transmitted messages to setup

LEACH

3

Hybrid indirect transmission (HIT) [8]

To maintain longer network life

Network longevity, network delay, average energy dissipation, average energy dissipation × average delay product

LEACH, PEGASIS, direct

Cross-layered routing protocols

These protocols use the concept of cross layering [13] which is already addressed in WSNs, where each layer (adjacent or non-adjacent) in the protocol stack shares their information unlike in the strict layered model. In WBANs, we can utilize the cross layering concept between network and medium access control (MAC) layers for routing and thereby can upgrade the overall network performance. Table 2 shows the different cross-layered routing protocols. Considering the detailed analysis of cross layered protocols, Cascading Information retrieval by controlling access with distributed slot assignment (CICADA) [14] and time zone coordinated sleep scheduling [15] have less power consumption. Wireless autonomous spanning tree protocol (WASP) [16] has better packet delivery ratio and CICADA has less delay.
Table 2

Cross layered protocols in body area networks

Sl. no.

Protocol [ref. no.]

Goal

Performance metrics

Compared with

1

Biocomm and Biocomm-D [17]

To optimize the overall performance of the in vivo network

Maximum temperature rise, average energy consumption, packet delivery delay, percentage of injected packets dropped, cumulative network throughput

Shortest hop routing, hotspot preventive routing

2

Cascading information retrieval by controlling access with distributed slot assignment (CICADA) [14]

To introduce less delay and low energy consumption

End to end delay

3

TImezone coordinated sleep scheduling (TICOSS) [15]

To improve 802.15.4 through the division of the network into time zones

Network lifetime

IEEE 802.15.4 without TICOSS

4

Wireless autonomous spanning tree protocol (WASP) [16]

To reduce power consumption, network delay and packet loss rate

End to end delay

CSMA with fixed routing

Postural movement based routing protocols

The body postural movements affect the network topology of the network, which results in link disconnection. The researchers introduced a cost function that is periodically updated for choosing the best route to forward packets to the sink. The protocols listed in Table 3 are the existing postural movement based routing protocols. Among the discussed protocols, on-body store and flood routing (OBSFR) [18] has better performance in reducing the packet delivery delay, but opportunistic postural movement based routing protocol has lower energy consumption than others.
Table 3

Postural routing protocols in body area networks

Sl. no.

Protocol [ref. no.]

Goal

Performance metrics

Compared with

1

Energy efficient thermal and power aware routing (ETPA) [19]

To reduce the node’s temperature and to prevent the hotspot formation

Packet hop count, packet delivery ratio, packet delay, average temperature rise of nodes in the network, scalability, depletion time of the first node mobility

PRPLC

2

Prediction based secure and reliable routing framework (PSR) [20]

To provide secure and reliable routing from various data attacks

Packet dropping rate, authentication cost

Backbone protocol

3

Opportunistic routing [21]

To increase the network lifetime from the motion of body parts

Average energy consumption per bit

Single hop, multi hop

4

Distance vector routing with postural link costs (DVRPLC) [22]

To decrease end to end delay

Average delay, packet delivery ratio, packet hop count

Probabilistic protocol (PROPHET), opportunistic protocol, UTILITY

5

On-body store and flood routing (OBSFR) [18]

To provide better routing delay and hop count

End-to-end packet delay (PD), number of transmissions per packet (NTPP), packet delivery ratio (PDR)

PROPHET, on-body store and flood routing (OBSFR)

6

Probabilistic routing with postural link costs (PRPLC) [23]

To reduce end to end delay

Average delay, packet delivery ratio

PROPHET, OBSFR

QoS aware routing protocols

Presently, there are a number of diverse QoS aware protocols available in WSNs, which cannot be as such implemented in WBANs, but by considering its unique curbs it can. In WBANs different data types require different QoS [24]. Hence the proposed protocols should be aware of different types of QoS metrics for various types of data. The various QoS aware routing protocols are shortlisted as given in Table 4.
Table 4

QoS aware routing protocols in body area networks

Sl. no.

Protocol [ref. no.]

Goal

Performance metrics

Compared with

1

Efficient next hop selection algorithm (ENSA-BAN) [25]

To improve the overall QoS performance of the network using link cost function

Energy consumption, packets forwarded, end-to-end delay, packet delivery ratio

DMQoS

2

Energy efficient routing algorithm [26]

To maximize the working lifetime of the network

Coverage distance, residual energy, communication count node criticality

3

Multi-hop protocol using cost function [27]

To boost the network performance and lifetime by optimum residual energy and distance

Number of dead nodes, residual energy, data packets sent and received to sink, delay

Old energy aware multi-hop

4

Critical data routing (CDR) [28]

To forward the critical data packets with better reliability along with reduction in temperature rise of the in-body sensor nodes

Packet loss ratio, packet success ratio, on-time packet delivery ratio, energy consumption

TMQoS, LTRT

5

Threshold sensitive energy efficient sensor network protocol (TEEN) [29]

To monitor and record critical data of the patient’s body parameters

Energy consumption, false acceptance rate, false rejection rate, time served

6

Adaptive routing and bandwidth allocation protocol (ARBA) [30]

To enhance bandwidth utilization and routing in BAN, better network lifetime

Residual energy, throughput

Optimal solution

7

Relay based routing protocol [31]

For network lifetime maximization and end-to-end-delay (E2ED) minimization

Remaining energy, no. of dead nodes, no. of dropped packets, packet arrival rate

Single-hop

Multi-hop

CH-rotate

8

Link-aware and energy efficient scheme for body area networks (LAEEBA) [32]

To route data with minimum path-loss over the link in WBAN

Stability period, residual energy, network lifetime, path-loss, delay spread, throughput

SIMPLE, M-ATTEMPT

9

Cooperative link-aware and energy efficient protocol for WBAN (Co-LAEEBA) [33]

To select better routing path with minimum path-loss in cooperative links in WBAN

Stability period, residual energy, path-loss, throughput

LAEEBA, SIMPLE, M-ATTEMPT

10

Two-hop transmission scheme [34]

To extend network lifetime and to improve the network stability of WBAN

Average residual energy, number of packets per priority level, total energy, number of dead nodes

Direct transmission, TPDS

11

Modified LAEEBA: link aware and energy efficient scheme for BAN (MLEEBA) [35]

To upgrade the LEEBA protocol by increasing throughput and decreasing delay

PDR, end to end delay, throughput

LEEBA

12

Reliability aware routing (RAR) [36]

To enhance reliability for reliability constraint data packets

Packet loss ratio, average PDR, average energy consumption

RAR with relay nodes, TMQoS

13

Zahoor energy and QoS-aware routing protocol (ZEQoS) [37]

To provide better QoS by selecting the best routing paths

Energy consumption, successful transmission rate, packets forwarded and received

DMQoS, NoRouting

14

QoS aware peering routing protocol for reliability sensitive data (QPRR) [38]

To enhance the reliable delivery of emergency BAN data for indoor hospital communication

Successful transmission rate, network traffic load, overall energy consumption, latency

DMQos, NoRouting

15

Distance aware relaying energy efficient protocol (DARE) [39]

To achieve better network lifetime for monitoring patients in multi-hop body area networks

Residual energy, PDR, number of packets sent to sink

M-ATTEMPT

16

Stable increased-throughput multi-hop protocol for link efficiency (SIMPLE) [40]

To boost the network stability period and packet delivered to sink

Network lifetime, stability period, throughput, residual energy, path loss

ATTEMPT

17

QoS-aware peering routing protocol for delay sensitive data (QPRD) [41]

To lessen the end to end delay

Traffic load, successful transmission rate, number of packets timeout

DMQoS

18

Q-learning based routing protocol (QRP) [42]

To design a power efficient and reduced hop count body sensor networks

Residual energy, average hop count

PSR, EBRAR

19

Adaptive multihop tree-based routing (AMR) protocol [43]

To assess several node and network parameters in order to enhance network performance using fuzzy logic

Network lifetime, PDR, normalized residual energy

Shortest path tree, received signal strength indicator, battery

20

Gateway selection algorithm [44]

To adaptively select the gateway node for balancing the load among the nodes

Network lifetime

No energy and independent energy harvesting device

21

Energy-aware peering routing protocol (EPR) [45]

To enhance BAN reliability and to reduce network traffic and power consumption

Traffic load, energy consumed and saved, buffer overflow, packets forwarded and received

DMQoS

22

Energy-aware topology design (EAWD) [46]

To reduce the total energy consumption and installation cost by wireless sensors and relays

Total energy consumed, installation cost

23

Energy-balanced rate assignment and routing protocol (EBRAR) [47]

To lessen the total energy consumed in the network at the expense of high network utility, adaptive resource allocation

Normalized residual energy, routing tree size

EBRAR-SP, EBRAR-PD, EBRAR-PE

24

Energy-efficient routing scheme (EERS) [48]

To provide adaptive transmission power for sensor nodes, establish an energy-efficient path

Packet reception ratio, average hop count, collection delay, average number of transmissions per packet, energy consumption per packet, per hop, overhead

Collection tree protocol (CTP)

25

Modified Dijkstra’s global routing algorithm [49]

To yield better network lifetime in WBAN

Network lifetime ratio, energy per bit ratio

Opportunistic routing, transmit power adaptation, min. energy packet forwarding, use of dedicated relays

26

Data-centric multi objective QoS-aware routing protocol (DMQoS) [50]

To achieve best QoS services for different data types

Average end-to-end delay, on-PDR, average energy consumption per packet, operation energy overhead

MMSPEED

LOCALMOR

DARA

27

Random contention-based resource allocation protocol (RACOON) [51]

To provide better the quality of service for multi-user mobile wireless body area networks

Packet latency, power consumption, packet collision, user capacity

BodyQoS

28

Heuristic adaptive routing algorithm [52]

To make multi-hop WBAN energy efficient

Lifetime, standard deviation of remaining power, average end to end delay, packet loss

Optimal scheme

29

Environment-adaptive routing algorithm (EAR) [53]

To achieve better network lifetime and reliable communication for heterogeneous networks

Number of alive nodes, amount of collected data in the coordinator

Hop-count based method, energy-base method

30

Localized multi-objective routing protocol (LOCALMOR) [54]

To consider the traffic diversity typical for biomedical applications and to provide a differentiation routing for different quality of service (QoS) metrics

Packet reception ratio, end to end delay, packets receiving within deadline

SPEED, MMSPEED, GFW, EAGFS

31

Reinforcement learning based routing with QoS support (RL-QRP) protocol [55]

To attain desirable QoS in respect of throughput and end to end delay

Average end to end delay, average PDR, node mobility, network traffic load

QoS-AODV

32

QoS aware routing service

To provide service with prioritized routing, user specific QoS

End to end delay, packet delivery ratio

The comparative analysis has shown that energy-aware peering routing protocol (EPR) [45], QoS-aware peering routing protocol for delay sensitive data (QPRD) [41] and QoS aware peering routing protocol for reliability sensitive data (QPRR) [38] have less power consumption when compared to other protocols. Some protocols do not consider energy consumption, while others. One of the most used QoS aware protocols is data-centric multi objective QoS-aware routing protocol (DMQoS) [50] because it can decrease the delay for delay-sensitive information, and similarly, it can provide reliable routing for reliable-sensitive information. The other QoS-aware protocols are used or selected for a particular network, according to the data type and its QoS requirements.

Temperature-aware routing protocols

The antenna radiation, its absorption and interference are the major challenges to be considered while designing a body sensor network, since the radiated fields cause a temperature rise of node’s electronic circuitry. The field of radiation also has a strong impact on the human body [57] that may damage the human tissues due to its continuous exposure. The goal of all temperature-aware protocols is to decrease the temperature rise of in-body sensor nodes by avoiding routing through hotspots. Table 5 discusses the existing temperature-aware protocols. From the comparison of different protocols, it is seen that LTRT [58] performs much better than others while TARA [59] performs worst. In terms of temperature rise, HPR [60] shows less temperature rise in comparison with others. Finally, the latest M2E2 protocol [61] has proved that it is the best one among all thermal aware protocols, suitable for heterogeneous, multimode, energy efficient body sensor networks.
Table 5

Temperature aware routing protocols in body area networks

Sl. no.

Protocol [ref. no.]

Goal

Performance metrics

Compared with

1

M2E2 protocol [61]

To reduce energy consumption and increase life time, to reduce link hotspot network in heterogeneous WBSNs

Packets sent to BS, number of dead nodes, total energy of network, number of cluster head

M-ATTEMPT

2

Reliability enhanced-adaptive threshold based thermal unaware energy-efficient multi-hop protocol (RE-ATTEMPT) [62]

To maximize the network lifetime and to remove the deficiencies of ATTEMPT protocol

Average rate of dead nodes, number of packets sent to sink, packets dropped and throughput

ATTEMPT

3

Thermal-aware multi constrained intra body QoS routing protocol (TMQoS) [63]

To ensure the required QoS demands along with maintaining the temperature of each node to a desirable level

Energy efficiency, average temperature rise, delay reliability, maximum temperature rise, packets meeting deadline

LTRT, TARA

4

Mobility-supporting adaptive threshold-based thermal-aware energy-efficient multi-hop protocol (M-ATTEMPT), ATTEMPT [64]

To sense the link hot-spot, to forward the data away from such links

Number of dead nodes, throughput, number of cluster heads per round, total energy of network

Multi-hop

5

Thermal-aware shortest hop routing algorithm (THSR) [65]

To reduce the node’s temperature and to prevent the formation of hotspot

Maximum temperature rise, average delay, packets dropped

LTR, SHR, HPR, TARA

6

Hotspot preventing routing (HPR) algorithm [60]

To prevent the formation of hotspots and to reduce the average packet delivery delay

Maximum temperature rise of any node, average packet delivery delay, total number of packets dropped

TARA, SHR

7

Routing algorithm for network of homogeneous and Id-less biomedical sensor nodes (RAIN) [66]

To be used in an in vivo network of homogeneous and id-less biomedical sensor nodes

Maximum temperature rise of nodes, average energy consumption per node, percentage packet delivery, average packet delivery delay

Controlled-FLOOD

8

Least total-route temperature routing protocol (LTRT) [58]

To reduce temperature caused by biomedical sensors implanted in human bodies

Average temperature rise and hop count per arrival packet

LTR, ALTR

9

Least temperature routing protocol (LTR), adaptive least temperature routing protocol (ALTR) [67]

To reduce the amount of heat produced in the network

Average temperature rise, delay, power consumption, packets dropped, lifetime of networks

TARA, SHR

10

Thermal-aware routing algorithm (TARA) [59]

To estimate the temperature variation of neighbor nodes and direct packets nearby the hotspot region

Maximum temperature rise, average temperature rise, delay performance

Shortest hop

Future challenges and comparative analysis of routing protocols

The scope of this article is to open up new research areas in WBAN domain for routing protocol designs. Among all the routing protocols, the cluster based protocol HIT [8] aims at maximizing the network lifetime, but it does not consider the packet delivery ratio which is an important QoS metric. The second one, AnyBody [11] protocol considers the delivery ratio, but does not consider average delay, mobility and energy consumption. It leads to poor security measures. In order to optimize the performance of sensor networks for some specific applications, it is necessary to include the aforementioned metrics also in the design considerations.

The cross layered concept is attaining great significance and interest among researchers due to its flexibility and effectiveness in sensor networks. The future research work aims at improving the reliability of CICADA [14] which performs better in terms of energy efficiency and average delay when compared to other cross layered protocols. When IEEE 802.15.4 standard networks are used, it will be good if the TICOSS [15] protocol is redesigned for reducing the average delay which is not considered in the existing one. If the entire network performance is to be optimized, the choice will be the Biocomm protocols along with new techniques to reduce the node temperature. Hence, the scope for research in this area is very much wide enough to work with. The comparative analysis of postural movement protocols has shown that, none of them considered the thermal effects of nodes and QoS issues together. Therefore, the future protocols could be proposed in such a way that, it could achieve better QoS parameters along with techniques to reduce the node temperature rise and methods to counter security attacks. The survey on QoS aware protocols unveils various research areas for future work because of its importance. Every new protocol, which has been designed, is meant for addressing the limitation of the previous one. For example, in routing service framework [56] and reinforcement learning based routing with QoS support [55] the energy consumption is not considered, but it is taken into account in the remaining protocols.

In almost, all the existing QoS aware protocols, only the QoS metrics are examined, without concentrating on the human body movements and temperature rise of implanted devices. The proposed temperature aware protocols perform better by reducing the temperature rise due to radiation from antenna and other node circuitry. Along with the thermal issue and power consumption, it will be better if these protocols can also address the routing issues like shortest path as in QoS aware.

Table 6 summarizes the comparison between some of the routing protocols used in WBAN. From the analysis, it is seen that almost all the protocols have considered different QoS metrics for their performance analysis. Hence, choosing the protocol for a WBAN system depends on the particular application of the system; whether it should be energy efficient, good reliable one or it should reduce the temperature of the node circuitry. Table 7 lists the pros and cons and the application domain of each protocols used in body area networks. This table helps to choose a particular protocol based on the QoS requirements. For example, if the application of the proposed system is patient monitoring in hospitals, then the protocols like WASP [16] or TICOSS [15] can be selected due to its high packet delivery ratio and low average delay. If the sensors are implanted within the body, then the protocols like Co-LEEBA [33], TARA [59], or routing algorithm for network of homogeneous and Id-less biomedical sensor nodes (RAIN) [66] can be chosen. If the network is heterogeneous one, then M-ATTEMPT or M2E2 can be used.
Table 6

Comparative analysis of routing protocols

Sl. no.

Routing protocol

Type of protocol

Average delay

Energy consumption

Packet delivery ratio

Average temperature rise

1

HIT [8]

Clustered

Very Low

Low

*

*

2

AnyBody [11]

Clustered

*

*

Very high

*

3

WASP [16]

Cross-layered

Low

Low

Very high

*

4

CICADA [14]

Cross-layered

Low

Low

*

*

5

TICOSS [15]

Cross-layered

*

Low

High

*

6

Routing service framework [56]

QoS-aware

*

*

Moderate

*

7

RL-QRP [55]

QoS-aware

High

*

High

*

8

ZEQoS [37]

QoS-aware

*

High

High

*

9

RL-QRP [55]

QoS-aware

Low

Low

High

*

10

ENSA-BAN [25]

QoS-aware

Very low

Low

Very high

*

11

Co-LEEBA [33]

QoS-aware

*

Low

High

*

12

DMQoS [50]

QoS-aware

Low

Moderate

High

*

13

LOCALMOR [54]

QoS-aware

Low

Low

High

*

14

TARA [59]

Temperature-aware

Very High

High

Low

Low

15

LTRT [58]

Temperature-aware

*

*

Very high

Low

16

RAIN [66]

Temperature-aware

Moderate

Low

High

Low

17

M-ATTEMPT [64]

Temperature-aware

Low

Low

High

Low

18

M2E2 [61]

Temperature-aware

Low

Very low

Very high

Low

19

TMQoS [63]

Temperature-aware

Very low

Very low

High

Moderate

20

ETPA [19]

Postural

High

Low

High

Low

21

PSR [20]

Postural

Low

High

Moderate

*

* Not applicable

Table 7

Pros and cons and the application domain of routing protocols

Sl. no.

Routing protocol

Pros

Cons

Application domain

1

HIT [8]

Requires only 25% of the time required by PEGASIS and LEACH for data collection, network longevity is 1.05 and 1.44 times that of PEGASIS and LEACH respectively

Not mentioned for a specific medical application, the terms security, fault-tolerance and reliability of the network need to be addressed

Micro sensor networks, bio medical sensing like EEG and EMG signals, bio electric computer interfaces

2

AnyBody [11]

A self-organizing protocol, maintains constant number of clusters with increasing number of nodes. PDR is approximately 100%

The metrics like network delay and energy consumption are not analyzed. Hence cannot considered for critical medical applications

Periodic patient monitoring in hospitals

3

WASP [16]

Minimizes the coordination overhead, throughput obtained is 94%. It can minimize the delay by reducing the number of levels in the spanning tree and also the energy consumption

Mobility is not supported, hence cannot be used for dynamic sensor network applications

For indoor hospital patient monitoring

4

CICADA [14]

Enhanced mobility is supported, generation of the scheme is easier, end to end delay is about 110 ms, nodes wake up only to transmit and receive data, hence dissipation of energy is minimized

It does not support traffic from the sink to the nodes

For sensors where computational resources are scarce

5

TICOSS [15]

Doubles the network lifetime for high traffic scenarios, PDR is higher than 92%. Lifetime of 4 min per Joule for TICOSS with 802.15.4 and 2 min per Joule with 802.15.4 alone, saves energy due to timezone coordinated sleeping mechanism

Not suitable for delay tolerant networks and also it is not an application specific protocol

Continuous vital sign monitoring, for ambient sensor nodes placed throughout a site

6

Routing service FRAMEWORK [56]

Provides prioritized routing service, user specific QoS support for small scale networks

Not considered energy consumption, which is one of the major constraints of sensor networks

Dynamic, small scale wireless body area networks

7

RL-QRP [55]

Uses independent distributed reinforcement learning approach for QoS route calculation, PDR above 90%

Average delay is higher (above 200 ms), energy consumption is not considered, not sufficient for global optimization in large scale networks

Dynamic, small scale wireless body area networks

8

ZEQoS [37]

Suitable and effective for all data types like ordinary, delay and reliability sensitive packets, 84% consistent throughput

No considerable improvement in terms of energy consumption

Hospital BAN communication

9

RL-QRP [55]

Fits well in dynamic environments using optimal routing policy. Good performance during heavy traffic conditions, average delay is less than 200 ms

Not suitable for large scale networks like multi agent systems

Dynamic bio-medical sensor networks

10

ENSA-BAN [25]

Along with all QoS requirements, it considers the energy consumption of nodes to improve the network performance. Approx. 96% PDR compared to DMQoS can be achieved, average delay is less than 16 ms

Although it is a QoS aware routing protocol, the body movement is not considered

Continuous patient monitoring sensor networks

11

Co-LEEBA [33]

It is a link aware routing protocol. Path loss is reduced due to the use of different path loss models. With the discontinuous data transmission, it provides better life time. It maximizes the throughput to 36 Mbps compared to other protocols with a throughput of average 2 Mbps

Maximizes the throughput at the cost of increased delay

Implanted sensors, monitoring of aged people

12

DMQoS [50]

Uses modular architecture for delay critical and reliability critical packets, end to end delay is less than 120 ms when compared to other QoS aware protocols of 260 ms. PDR is above 92% for varying traffic flows

The estimation of several tuning parameters is not analytical. They are fixed through different simulation experiments

Resource-constrained body area networks

13

LOCALMOR [54]

This routing algorithm can be used along with any MAC protocol with ACK mechanism, considered the diversity of data traffic like regular, delay- sensitive, reliability-sensitive and critical traffic, end to end delay is less than 200 ms, Packet reception ration is above 85%

Scalability of the protocol with higher number of sensor nodes should be investigated

Diverse traffic biomedical applications

14

TARA [59]

Handles data transmission in the presence of temperature hot spots, routes packets through low temperature area, has load balancing capability, smaller average temperature rise

Higher packet loss due to larger delay (greater than 400 ms), unique hardware ids for nodes hence this algorithm fails to operate in id-less sensor nodes, homogeneous and not emergency supported

Implanted sensor networks and applications like retinal prosthesis and cancer detection

15

LTRT [58]

Optimization of routing is accomplished, very high packet delivery ratio, which is close to 100%, Smaller average temperature rise

The analysis is done only on the average temperature rise and packet loss rate

Implanted bio -medical networks, cardiac patient monitoring applications

16

RAIN [66]

Routes the data efficiently towards the sink in an id-less biomedical sensor networks, prevents the formation of high temperature zones in the network, maximum temperature rise increases slowly than CFLOOD protocol, PDR is greater than 90%, the average energy consumption is less than 1000 energy units compared to 3000 units of CFLOOD

Average packet delivery delay is slightly higher than CFLOOD protocol, PDR is slightly lower than CFLOOD protocol

In-vivo network of homogeneous and id-less biomedical sensor nodes

17

M-ATTEMPT [64]

Mobility supported, greater network lifetime (29.5%), better stability period (greater than 20%) and 29% better results for successfully received packets when compared to multihop communication, energy efficient and emergency supported

A moving node needs a new parent and the new parent may refuse this request, analysis of average/maximum temperature rise is not included

Heterogeneous and homogeneous wireless body area networks

18

M2E2 [61]

Mobility and multi-mode supported, energy efficient and emergency supported, throughput is above 100Mbps when compared to 50 Mbps of M-ATTEMPT

Requires more hardware than the other protocols

Heterogeneous wireless body sensor networks

19

TMQoS [63]

Table-driven protocol with high network lifetime, low end to end delay which is less than 130 ms, above 85% reliability, can meet the QoS demands along with maintaining the temperature of the nodes to an acceptable level, uses a hotspot avoidance mechanism

Average temperature rise is higher in order to meet the desired QoS demands

In-vivo wireless body area networks

20

ETPA [19]

Mobility supported, It solves the link disconnection problem due to body movements along with a reduction in temperature rise, PDR is up to 95%

The average delay is slightly higher than PRPLC in order to balance the temperature rise in the network

Wireless body area network with long lasting communication and scarce resources

21

PSR [20]

It provides reliable and secure communication against data injection attacks, PDR up to 80%, shorter routing delay

Uses ACK techniques for measuring link quality and if the number of ACKs is large, it may consume a lot of network resources, as a whole, network lifetime is less

Reliable and secure wireless body area networks

Conclusions

Wireless body area network is a part of wireless sensor network, with a number of nodes deployed within and on the surface of human body to measure different biological parameters for a particular application. In this survey article, various existing routing protocols that are used in WBANs are categorized and briefly analyzed from the available articles between the years 2002–2016. It is seen that the routing protocol plays a vital role in the design process of every efficient, reliable, low cost wireless body sensor networks. Based on the structure and nature of networks, the routing protocols for WBANs are categorized as cluster-based, cross-layered, postural movement based QoS aware and temperature-aware protocols. It is observed that there is no strict classification of protocols is possible since most of them aims or results in achieving the challenges of sensor networks. It is also concluded that each protocol is application dependent, i.e., the protocols used for daily monitoring and the critical medical cases are different. The future directions for each group of protocols are also presented which helps the researchers to focus on their interested area. Also, a comparative study of different protocols has been examined so that an appropriate protocol can be selected according to the targeted application. This survey will benefit the researchers to study the existing routing protocols for WBANs in the field of healthcare systems.

The Future work includes the design and implementation of a body sensor prototype with a newly designed routing protocol, which will be highly energy efficient and reliable one for rehabilitation of old age people using a microcontroller based system with suitable sensors.

Abbreviations

WBAN: 

wireless body area network

QoS: 

quality of services

RL-QRP: 

reinforcement learning based routing with Qos support

WASP: 

wireless autonomous spanning tree protocol

RAIN: 

routing algorithm for network of homogeneous and Id-less biomedical sensor nodes

M-ATTEMPT: 

mobility-supporting adaptive threshold-based thermal-aware energy-efficient multi-hop protocols

CDR: 

critical data routing

RAR: 

reliability aware routing

DMQoS: 

data-centric multi objective QoS-aware routing protocol

LEACH: 

low-energy adaptive clustering hierarchy

CBBAP: 

cluster based body area protocol

HIT: 

hybrid indirect transmission

PEGASIS: 

power-efficient gathering in sensor information systems

MAC: 

medium access control

CICADA: 

cascading information retrieval by controlling access with distributed slot assignment

TICOSS: 

time zone coordinated sleep scheduling

OBSFR: 

on-body store and flood routing

ETPA: 

energy efficient thermal and power aware routing

PSR: 

prediction based secure and reliable routing framework

DVRPLC: 

distance vector routing with postural link costs

PRPLC: 

probabilistic routing with postural link costs

ENSA-BAN: 

efficient next hop selection algorithm

TEEN: 

threshold sensitive energy efficient sensor network protocol

ARBA: 

adaptive routing and bandwidth allocation protocol

LAEEBA: 

link-aware and energy efficient scheme for body area networks

Co-LAEEBA: 

cooperative link-aware and energy efficient protocol for WBAN

MLEEBA: 

modified LAEEBA: link aware and energy efficient scheme for BAN

ZEQoS: 

Zahoor energy and QoS-aware routing protocol

QPRR: 

QoS aware peering routing protocol for reliability sensitive data

DARE: 

distance aware relaying energy efficient protocol

SIMPLE: 

stable increased-throughput multi-hop protocol for link efficiency

QPRD: 

QoS-aware peering routing protocol for delay sensitive data

QRP: 

Q-learning based routing protocol

AMR: 

adaptive multihop tree-based routing

EPR: 

energy-aware peering routing protocol

EAWD: 

energy-aware topology design

EBRAR: 

energy-balanced rate assignment and routing protocol

EERS: 

energy-efficient routing scheme

MDGRA: 

modified Dijkstra’s global routing algorithm [49]

RACOON: 

random contention-based resource allocation protocol

EAR: 

environment-adaptive routing algorithm

LOCALMOR: 

localized multi-objective routing protocol

RE-ATTEMPT: 

reliability enhanced-adaptive threshold based thermal unaware energy-efficient multi-hop protocol

TMQoS: 

thermal-aware multi constrained intra body QoS routing protocol

M-ATTEMPT: 

mobility-supporting adaptive threshold-based thermal-aware energy-efficient multi-hop protocol

THSR: 

thermal-aware shortest hop routing algorithm

HPR: 

hotspot preventing routing

LTRT: 

least total-route temperature routing protocol

LTR: 

least temperature routing protocol

ALTR: 

adaptive least temperature routing protocol

TARA: 

thermal-aware routing algorithm

Declarations

Authors’ contributions

VB conducted the survey work, analyzed the schemes and drafted the manuscript. CPS participated in literature review and helped to draft the manuscript. Both authors read and approved the final manuscript.

Authors’ information

V. Bhanumathi received the Bachelor Degree in Electronics and Communication Engineering from Madras University, Master Degree in Communication Systems from Anna University, Chennai and Ph.D. in Information and Communication Engineering from Anna University, Chennai. She is currently working as an Assistant Professor in the Department of Electronics and Communication Engineering, Anna University, Regional Campus, Coimbatore. She has published her works in various International Journals and conferences. Her areas of interest are Wireless Communication, VLSI Design, Network Security, and Digital Communication.

C. P. Sangeetha received her Bachelor Degree in Electronics and Communication and Master Degree from Cochin University of Science and Technology, Kerala. She is currently doing her Ph.D. in Information and Communication Engineering, Anna University, Chennai. She has worked as a Lecturer in Electronics and Communication Engineering, Toc H Institute of Science and Technology, Cochin for 8 years. She has published a number of papers in various International Journals and conferences. Her areas of interest include wireless sensor networks and mobile communications.

Acknowledgements

We would like to thank the editors and the reviewers for their noble comments and constructive criticisms for improvement of the manuscript.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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Authors’ Affiliations

(1)
Anna University Regional Campus

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