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Table 7 Pros and cons and the application domain of routing protocols

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

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