An adaptive cuckoo search based algorithm for placement of relay nodes in wireless body area networks

Journal of King Saud University – Computer and Information Sciences xxx (xxxx) xxx

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An adaptive cuckoo search based algorithm for placement of relay nodes in wireless body area networks Tushar Kanta Samal ⇑, Sushree Chinmayee Patra, Manas Ranjan Kabat Department of Computer Science and Engineering, VSS University of Technology, Burla, India

a r t i c l e

i n f o

Article history: Received 29 April 2019 Revised 30 September 2019 Accepted 4 November 2019 Available online xxxx Keywords: Wireless body area network Cuckoo search Adaptive cuckoo search Relay node Energy efficiency Load

a b s t r a c t The evolution of wireless body area networks (WBAN) has changed the human life for its applications in the field of healthcare, fitness, entertainment and sports etc. However, two of the major challenges in the design of WBAN are energy efficiency and connectivity. The placement of relay nodes in a wireless body area network (WBAN) plays an important role in design of energy efficient and reliable WBAN. This problem is a joint problem of data routing and placement of relay nodes and formulated as a linear integer programming model. The main objective of the problem is to minimize the cost of relay nodes, energy consumption and distributing the loads uniformly on the relay nodes. Considering the hardness of the problem, we propose an adaptive cuckoo search based algorithm which uses an efficient fitness function and an adaptive step size proportional to the fitness function for placement of relay nodes. The set of relay nodes obtained by our proposed adaptive cuckoo search algorithm compared with cuckoo search as well as other state of the art algorithms via simulation results. The simulation results reveal that the proposed algorithm not only consumes less energy than its counterparts but also distributes the load evenly on the relay nodes. We consider two different postures of the body with 13 biosensors placed in fixed positions and 50–100 candidate sites for placement of relay nodes. Furthermore, we also consider 80 biosensors randomly deployed in a rectangular area with 50–300 candidate sites to study the scalability of our algorithm. Ó 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction A wireless body area network (WBAN) is a special purpose sensor network designed to operate autonomously connecting various medical sensors located inside and outside of a human body. We can easily monitor the patients, independent to their locations. The biosensor in WBAN is a device that can search and find a suitable communication network to transmit data to a remote healthcare center for continuously monitoring the patients. WBAN can connect itself to the Internet to transmit data in a non-invasive manner. One of the major challenges in the design of WBAN is energy efficiency and the other one is delivery of the emergency data such as heart rate, electrocardiogram (ECG) etc. to the health⇑ Corresponding author. E-mail address: [email protected] (T.K. Samal). Peer review under responsibility of King Saud University.

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care center. If the emergency data cannot be delivered within the deadline then it may cause a critical situation for the patient. Therefore, WBAN has to transfer the emergency data according to priority rather than first come first serve. The design, optimization and implementation of a wireless sensor network for healthcare applications are studied in (Elias, 2014; Redondi et al., 2013). The design of network architecture for biosensors is handled in (Natarajan et al., 2007), where the researchers attempt to define the efficient network architecture by identifying the design goals of single hop and multi-hop network topologies. The energy efficient multi-hop routing protocols in WBAN are proposed in (Natarajan et al., 2009; Javaid et al., 2015). Thus, it can be concluded that two of the major challenges in the design of WBAN are energy efficiency and reliability. The placement of relay nodes in a wireless body area network (WBAN) plays an important role in design of energy efficient and reliable WBAN. The vital information of the patients are collected by the sensors and transmitted to sink through multi-hop wireless path. In order to process, special devices called relay nodes can be added to the WBAN to collect all the information from sensors and send it to the sink, thus improving the WSN life time and reliability. Since the biosensors have predetermined positions, it is important to optimize the num-

https://doi.org/10.1016/j.jksuci.2019.11.002 1319-1578/Ó 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Please cite this article as: T. K. Samal, S. C. Patra and M. R. Kabat, An adaptive cuckoo search based algorithm for placement of relay nodes in wireless body area networks, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.11.002

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T.K. Samal et al. / Journal of King Saud University – Computer and Information Sciences xxx (xxxx) xxx

ber of relay nodes to be placed. However, selecting the optimal number of relay nodes from a set of candidate nodes is proven to be NP-complete. In this paper, we propose a cuckoo search based algorithm for optimal relay selection from a set of candidate locations in WBAN. The proposed algorithm selects a set of relay nodes from the candidate sets such that the cost, energy consumption and coverage of sensors shall be optimal. The fitness function used in the algorithm to select the relay nodes is computed on the basis of cost, energy consumption, distance and coverage. Furthermore, we have also extended the algorithm by using adaptive cuckoo search (ACS) for finding the optimal set of relay nodes. The ACS adaptively adjusts the step size with respect to fitness function in contrast to the random step size selection of cuckoo search. The main contributions of the paper are presented below.
The optimal relay placement in WBAN problem is formulated as a linear programming formulation.
We use the cuckoo search based algorithm for relay node selection by using a fitness function based on coverage of sensors, energy consumption, distance and cost.
The performance of the proposed algorithm is studied and compared with some existing state of the art algorithms. The remaining part of the paper is organized as follows. The brief description of the works done on the optimal relay placement and routing is presented in Section 2. In Section3, the system model followed by the problem definition is presented. The proposed cuckoo search algorithm for optimal relay node placement and routing in presented in Section 4. The performance evaluation along with the comparison with other existing state of the art algorithms is presented in Section 5. The summary of conclusion and future scope of the work is presented in Section 6.

2. Literature review In this section, we explore the research works done on optimal relay placement and routing in WBAN. Basically, WBAN consists of two types of biosensors such as wearable and implanted. The wearable biosensors are worn on the body and the implanted sensors are implemented inside the body to sense the vital signs of the body. The WBAN provides communication between a coordinating node present on the body to more than one slave nodes implanted inside or outside the human body. The works done on design and application of wireless body area network are presented in (Elias, 2014; Redondi et al., 2013; Natarajan et al., 2007; Natarajan et al., 2009; Javaid et al., 2015; Ahmed et al., 2015). The researchers have described the state-of-art research on WBAN for improving the quality of day to day life for older and chronically disorder people. There are various projects and technologies provided in (Katayama et al., 2009) in which the sensors in WBAN communicate to the outer world. However, it has various issues regarding power supply, implementation cost, delay, reliability, security and hassle use by enabling intercommunication between WBAN and output device (sink). This helps to receive decrypted message at the coordinating node and provide signal to the users. Recently, enormous survey is going on regarding intelligent monitoring of patients for smart healthcare service. In (Reusens et al., 2009), the researchers have proposed two mechanisms for improving the network lifetime. The first mechanism is the use of relay nodes which has the capacity to handle the traffic relaying without sensing and in the second mechanism, it is assumed that all relay nodes are having fixed positions and not optimized. Therefore, total energy consumed by each and every biosensor and relay nodes increased drastically. Improvement of

relay node placement in WBAN is also given in (Ehyaie et al., 2009)where the relay node placement is quite similar to multihop approach in which number of relay nodes is determined based on path-loss coefficient of the body and total number of sensor nodes deployed in body and their distance to the sink. Here, the authors don’t focus on minimization of number of relay nodes deployment which implies the increase in total network cost. They described that relay nodes are added to the network until all the bio-sensors implanted in WBAN have at least one relay node present in line of sight which implies that dramatically improvement of relay nodes. However, it has a great impact on highly discomfort and mobility of the user. Optimal design of energy and cost effective WBAN is described in (Elias, 2014). Here, authors have proposed energy-aware WBAN Design (EAWD) model by integrating both routing and relay positioning problem. The relay node placement problem is formulated as mixed integer linear programming model (MILP). It helps to both minimizing network installation cost and energy consumption by bio-sensors and relay nodes with minimum computation time. In this model, the authors concluded that total energy and energy consumption by sensor nodes reduced with respect to single-hop and multi-hop approach by selecting optimal number of relay nodes from a set of candidate sites. Link-Quality of the network plays important role for which some researchers proposed a Link-Quality awareness of resource allocation and load balancing in WBANs (Samanta et al., 2015). The authors described a distributed allocation of sub channel which helps to balance the load and improve the performance of network. Here the bandwidth of the network is sub divided into various subchannels for allocation in WBAN. However, the implementation of this method may increase the overall cost of the network which is difficult to use for rural users and most importantly they have not considered security issue while allocating sub-channels to different WBANs. Uniform energy consumption of network and optimization is most important for designing of routing and clustering protocol in wireless body area network. The authors in (Gupta and Jha, 2018) described integrated clustering and routing protocol for WBANs using various meta-heuristic techniques like Harmony and Cuckoo search based solution. Here they discussed two well-known optimization problems; the first one is selection of Cluster-Heads and secondly routing between sensors and the sink node through the Cluster-Heads. Authors used Cuckoo search for finding the Cluster-Heads according to the fitness function and Harmony search algorithm to aggregate data collected from sensors or cluster heads to sink. In (D’Andreagiovanni and Nardin, 2015), authors described the fast and robust optimal design of wireless body area network which helps to increase the efficient energy utilization of the sensors deployed in human body. The authors in (Zhou et al., 2017) design a topology and proposed a cross-layer optimization for WBAN. Here author described i) Mathematical programming model used for optimization of single-path routing and relay placement in WBAN. ii) Robust optimization design for controlling the traffic in WBANs. iii) Proposed optimization algorithm is combination of heuristic ideas from randomized algorithm (Motwani and Raghavan, 1995), ANTS- Approximate Nondeterministic Tree Search (Maniezzo, 1999), optimization of Ant Colony (Dorigo et al., 1999)and lastly Binary Search Linear Programming which is solved by using the State-Of-Art Optimization Technique (D’Andreagiovanni, 2014). iv) Optimization algorithms first compute the solution of better Optimality and quality with respect to State-Of-Art Optimization by using optimization model. Main issue of these proposed works is low performance and various traffic uncertainties by the given model which used single path routing protocol. Proposed work is multilevel primal and dual decomposition method which used binary search approach to transform the original Non-conventional problem to Conventional problem.

Please cite this article as: T. K. Samal, S. C. Patra and M. R. Kabat, An adaptive cuckoo search based algorithm for placement of relay nodes in wireless body area networks, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.11.002

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Cuckoo Search based clustering algorithm for Wireless Sensor Network (Chepkwony, 2013) describes hierarchical clustering algorithm based on energy awareness. The cost function of the proposed work is defined by using Cluster head algorithm which defines as distance between highest distance of non-cluster head to the related cluster head and the residual power of selected cluster head. It also provide better network lifetime and capable of forward more data to the sink. But the major issue is cross layer optimization between routing and query, multi-hop communication of cluster heads to improve efficiency of energy. The characteristics and challenges associated with healthcare applications by using WBANs is presented in Boulis et al. (2012). The energy consumption for WBAN is more than WSN because the volume and area of in-body or on-body sensor nodes are different and the sensors are very hard to recharge. Thus they presented some MAC techniques based on studies of the WBAN channel that could be used to address these challenges. In order to deal with unstable channel, relay nodes are placed in the network to increase the life time of WBAN. During last decade, various research works have been done on application of Machine learning in healthcare which also plays important role in future Medical application and helps to reduce the use of regular dependency of pathologies (Shailaja et al., 2018). Therefore, after thorough study on number of literatures related to optimal placement of relay nodes in WBAN, it is observed that the relay node placement for optimizing the energy consumption, delay, coverage and reliability is still a challenging problem.

3. System model and problem definition The WBAN considered in our proposed framework is modeled as a undirected graph G=(S, R, E, D) where S = {s1, s2, s3,. . .,sn} is the set of biosensor source nodes, R={r1, r2 r3 . . ...rp} is the set of relay nodes, E is the set of edges and D is the on-body destination or sink node. The links can be established between one sensor node and one relay node or between one relay node and another relay node. The candidate locations of the relay nodes are CS {l1, l2, l3. . . lz} and the distance between the last relay node and the destination node is drD. All the biosensors have the same capabilities in terms of initial energy and transmission range. The bio-sensors near to the sink node shall communicate directly with the sink node. Furthermore, it is also assumed that the sink node is having unlimited power a capable of handling multiple packets simultaneously. The routing path is represented by a series of nodes in which the first one represent the source, the last node represents the sink and the intermediate nodes is represented as relay nodes. Thus the path consists of a sequence of non-negative integers that denote the IDs of the relay nodes placed in WBAN for optimal design of WBAN. However, the number of intermediate nodes must not be greater than the number of relay nodes placed in the networks. A number of partial routes from the source to the destination (sink) node are generated by using greedy approach. Then the route with high residual energy, link quality, Minimum delay and minimum number of intermediate nodes is selected as the final route. The optimal placement of relay nodes and routing is formulated as a linear programming based mathematical model with the objective of minimization of energy consumption, maximization of throughput, delay minimization and improvised balancing of load. We assume that each bio-sensor only used to transmit the data and never receiving data from other biosensors. Similarly relay nodes are used to receive and transmit the data between sensor nodes to relay node or relay node to relay node or relay node to sink node. The sink node is intended to only receive the data and never transmit. So we have to calculate each and every module

3

of energy consumption so that it should be minimized by optimizing the relay node and distance between each and every nodes. The basic notations used in this paper are presented in Table 1. We assume each bio-sensor only used to transmit the data and never receiving data from other sensors. Similarly relay nodes are used to receive the data from sensors or other relay nodes and transmit the data to next relay node or sink node. The sink nodes are intended to only receive the data and never transmit. Therefore, we need to calculate energy consumption in each and every sensor node and relay node so that it should be minimized by optimizing the placement of relay nodes in WBAN.

3.1. Energy consumption in WBAN In this section, we present the energy consumption model of WBANs having a set of biosensors, relay nodes and a sink node. The energy consumption of WBAN is calculated in three different modules that are by considering data transmission between sensors to relay nodes, relay nodes to other relay nodes and relay nodes to sink.

Table 1 Notation Definition. Notation

Meaning

S R CS D l dsr drr drD Asr ArD ERxeelec ETxeelec Eamp ETxe ErTxe ErRxe Einf Elink Eseloss Ereloss Erloss Estotal Ertotal N w ErTxen=w

Set of Bio-sensors in the WBAN Set or Relay nodes placed in WBAN Set of candidate sites for placement of relay nodes Sink Node Size of the packet Distance from biosensor to relay node Distance from relay node to next relay node Distance from last relay node to sink node Connectivity parameter from sensor to relay node Connectivity parameter from relay node to sink Per-bit energy consumption for reception Per-bit energy consumption for transmission Amplification energy Per packet energy consumption for transmission Per packet energy consumption for transmission by relay node Per packet energy consumption for reception by relay node Loss co-efficient Energy consumption due to Interference Energy consumption due to path-loss Energy loss of Sensor node Energy loss of Relay node Total Energy loss for all relay nodes Total energy consumption of all sensor nodes Total energy consumption of all relay nodes Total number of nodes (sensor nodes + relay nodes) in WBAN Data aggregation parameter Transmission energy of all relay node

ErRxen=w

Reception energy of all relay node

Etotal
 LQ si ; r j

Link Quality of ith Sensor to jth relay node

SPjRec SPMax dr jSnj j

Received Signal power of jth relay node Maximum Signal Power Average distance of sensors to its respective relay node Number of Sensor nodes covered by jth relay node

Rcov D

Total coverage of Relay nodes Average delay of packet transmission between Sensor to Relay mode Flow from Sensor to Relay node

g

t

f sr t

f rD C sr C rD kr C Lrload Lav g Lov erall

Total energy consumption of WBAN

Flow from Relay to Sink node Capacity from Sensor to Relay Capacity from Relay to Sink Data generation rate over the time period of t Installation cost of relay node Standard Relay Load Average Load Overall Load of Relay

Please cite this article as: T. K. Samal, S. C. Patra and M. R. Kabat, An adaptive cuckoo search based algorithm for placement of relay nodes in wireless body area networks, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.11.002

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3.1.1. Energy transmission of sensor nodes The main objective of optimal design of WBAN is not only to minimize the total energy consumption of biosensors and relay nodes but also to balance the load on relay nodes so that the energy consumption of the relay nodes shall be uniform and the nodes can live for a long time. The energy costs for transmission and amplification of data are calculated according to the given equation below

  2 ETxe ðl; dsr Þ ¼ l ETxeelec þ Eamp dsr

ð1Þ

where, l is the packet length and dsr is the distance between sensor and relay node. ETxe is the per packet energy consumption by sensor node. Similarly, ETxeelec is per bit energy consumption for transmitting data in body and Eamp is the energy required by the amplifier. In other hand, if there are some external interferences or link failure during data transmission then it causes energy loss. Therefore, we have to calculate energy consumption of WBANs at the time of any interferences or link failure which is given in Eq. (2).

Eseloss ¼ gkEinf þ aElink

ð2Þ

Where g is the loss coefficient and kEinf and aElink are denotes the rate of energy consumption due to interferences and link failure/path loss at the time of transmission and receiving in WBANS respectively. Then total energy consumption by a single sensor node is calculated as

ETxe ðl; dsr ; gÞ ¼ ETxe þ Eseloss

ð3Þ

Similarly, total energy consumption of each sensor node over the time period of t implanted in human body calculated as

Z Estotal ¼

t

ETxe ðl; dsr ; gÞdt

ð4Þ

The relay node receives data from bio-sensors or other relay nodes for which it consumes energy for reception that depends on number of nodes from which it receives data in a network and amount of data it receives. Therefore, reception energy of a relay node is calculated as

ErRxe ¼ ðN  1ÞERxe ðlÞ

where, ERxe ðlÞ ¼ ERxeelec ðlÞ Here, is the per packet energy consumptions of the receiver node i.e. either a sensor or a relay node and ERxeelec is the per bit energy consumption values for receiver electronic circuits. Similarly, Reception energy of all relay nodes over the time period of t in entire life time is calculated as

Z ErRxen=w ¼


 ErTxe ¼ mWETxe l; drr=rD þ mlWETxeelec

ð5Þ

Where ErTxe is the transmission energy of a relay node, m is total number of relay nodes used in WBAN and packet size isl. W is the data aggregation factor which is received from the various sensors or relay node and drr=rD is the distance of relay node to relay node or relay node to sink node. Similarly, Transmission energy of each relay node over the time period of t in its network life time is calculated as

Z ErTxen=w ¼

t

ErTxe dt

ð6Þ

t

ErRxe dt

ð8Þ

However, when a relay node is transmitting and receiving the data there is some interferences or link failures in WBAN, then energy loss by a single relay node is calculated as

Ereloss ¼ nkEinf þ aElink

ð9Þ

Similarly total energy loss for all the relay nodes deployed in WBAN is calculated as

Z

t

Erloss ¼

Ereloss dt

ð10Þ

Therefore, all the nodes in WBAN have mostly same energy consumption according to the cost function. Suppose N is the total number of node deployed in human body which include both sensor nodes and relay nodes, then connectivity parameters from biosensor to relay and relay to sink are given below

 3.1.2. Energy consumption of relay nodes In our proposed model, we assume that the sensor nodes are placed in fixed positions for measuring the physical parameters of the body and transmit data to the sink. Sink is either a computer or a mobile or any kind of data reading device who can receive the data from sensor nodes through relay nodes. However, if the body is moving from here and there and the sink is out of the radio range of the sensor node then packets are dropped aggressively which leads to maximize the energy consumption. Therefore, it is required to deploy relay nodes on different suitable locations of the body to minimize the energy consumption of the sensor nodes. The relay nodes are wireless devices that act as intermediate nodes between sink and sensor nodes. Relay nodes receive data from sensors and then aggregate them in to various packets according to the protocol and then transmit it to another relay node or sink. Energy consumption of a relay node depends on amount of data it receives from the sensor nodes, data aggregation in to various packet format and most importantly distance between relay nodes to the sink node. Transmission energy of relay node is calculated as

ð7Þ ErRxe

Asr ¼

1; if there is a link between sensor ðsÞto relay ðrÞ 0; otherwise

 ArD ¼

1; if there is a link between relayðr Þto sinkðDÞ 0; Otherwise

Therefore, total energy consumption of the relay node is summation of transmission energy, reception energy and loss energy due to path loss or any interference.

Ertotal ¼

X r R DN

ArD ðErTxen=w Þ þ

X r R s2S

Asr ðErRxen=w Þ þ EW þ Erloss

ð11Þ

Total energy required to data transmission from sensor to sink through relay node is calculated as

Etotal ¼

X sS

Estotal þ

X r R

Ertotal

ð12Þ

3.2. Load calculation of node in WBAN There are enormous amount of heterogeneous sensor nodes deployed in human body area network. Each body sensor differs from one another according to the communication and computational load. So, for this reason the link quality of WBAN nodes gets affected, which degrades the network performance. If there are irregular link failures then it also affects to the load on each relay node. This causes non-uniform energy consumption in the rely nodes present in the WBAN and degrades the overall network performance as well. Therefore, it is important to balance the load across the relay nodes over the WBAN so that the network will last longer.

Please cite this article as: T. K. Samal, S. C. Patra and M. R. Kabat, An adaptive cuckoo search based algorithm for placement of relay nodes in wireless body area networks, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.11.002

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We define the link quality in WBAN which can be mathematically calculated as


 SPj LQ si ; rj ¼ Rec ½0; 1 SPMax

ð13Þ


 whereLQ si ; r j is the link quality of ith sensor node to jth relay node,

SPjRec is denoted as received signal power of the relay node j measured at node i while receiving the data packet from sensor node and SPMax is maximum signal power. The Link-Quality is defined between 0 and 1 which is based on the value shows 1 and poor otherwise. Link- Quality also depends on the distance between sensors to relay node. It is defined as average distance from a relay node j to group of sensor nods (Sj ) which is subset of all sensor nodes deployed in body area network (S) i.e. Sj # S. This is also defined as the quality of service (QoS) provided by relay node to sensors connecting to.

dr ¼

m X

2 4

3

PjSj j

 r k2 i¼1 ks  i j 5

ð14Þ

Sj 

j¼1

where dr is the average distance of relay node to the group sensors node (Sj ), n is the total number of relay nodes present in WBAN, jSj j is the number of sensor node connected by a single relay node, ksi  r j k2 is the Euclidean distance between jth relay node and ith sensor node. To provide a cost effective and reliable backbone to the biosensors for transmitting data to the sink node through relay nodes. It is requested to optimize the number of relay nodes which covers maximum number of sensor nodes leads to maximize the throughput, minimize the energy consumption, minimize the delay of communication and most importantly minimize the left out sensor nodes in the selected group in coverage of relay node. So we can mathematically calculate the coverage of a relay node (Rcov Þ is

Rcov ¼

jSj 

Pm   j¼1 Sj jSj

ð15Þ

  where, m is total number of relay node and Sj  is denoted as numth

ber of sensor nodes in j group of Sensor. Delay plays an important role in network because as much as delay is minimizing then energy consumption is minimize also

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uX m
2 1 u t Lsd ¼ Lj  Lav g jRj j¼1

ð18Þ

P Here, LAv g is average load which is ni¼1 Lsi =jRj, Lsi is the load of ith sensor node. Lj is the overall load of relay node. 3.3. Optimal placement of relay (OPR) problem definition In the previous subsections, we have presented various notations and variables of energy consumption, cost, delay, load, coverage and distance in WBAN. In this section, we formulate the problem of optimal placement of relay node (OPR) for energy efficient cost effective routing in WBAN. The problem is to place the relay nodes in suitable locations out of a set given candidate locations such that energy consumption, overall network installation cost of the model shall be minimized, load shall be minimized balanced among the relay nodes, delay will be minimize and most importantly optimization of relay node placement will covered maximum number of bio-sensors in WBAN by using effective routing protocol which helps to transfer data from sensor to sink node. The problem statement is defined as follows

(

min

X

Estotal þ

s2S

X

!

Ertotal

)

þ Cost þ Lrload þ De þ Rcov þ dr

ð19Þ

r2R

Subject to

 Etotal ; 8s 2 N

ð19:1Þ

Ertotal  Etotal ; 8r 2 N

ð19:2Þ

Estotal  0; Eseloss  0

ð19:3Þ

Ertotal  0; Ereloss  0

ð19:4Þ

Estotal

X

t

f sr  C sr ; 8s 2 S

ð19:5Þ

s2S t

f rD  C rD ; 8r 2 R X

ð19:6Þ

lsr þ kr t  LtrD ; 8s; r 2 N

ð19:7Þ

s2S

th

which is more reliable. Delay should minimum between i sensor th

nodes to j relay node, which is mathematically represented as

D¼

S X R 1X kaj  di k2 S i¼1 j¼1

ð16Þ

Here,D is the average delay between sensor to relay packet transmission time and reception time. di is the departure time of th

th

a packet at i sensors and aj is the arrival at j relay node. The average link quality of all the individual bio-sensors to their respective relay nodes can be numerically defined as

P /ðS; RÞ ¼

si 2S r j 2R

hP

 i


 m   j¼1 LQ si ; r j = Sj

ð17Þ

jRj

  where, Sj is the number of sensor nodes connected to relay node r j . Thus, for the m relay nodes and n number of sensor nodes covered by that selected relay nodes Ri , the standard deviation of relay load is calculated as

dsr ^ drD ! dmin


ð19:8Þ



LQ si ; r j 2 ½0; 1

ð19:9Þ

where, total energy of sensor node and relay node must be less than equal to the total energy required by all node present over the network. Similarly sensor node and relay node energy consumption are always positive and energy loss due to any interferences or path loss should also positive. Here in constraint (19.5) and (19.6) total t

flow from bio-sensor (b) to relay(r); f sr over the time period t and t f rD

from relay(r) to sink(s); over the time period t always less than or equal with their physical link capacities denoted as C sr and C rD respectively. Constraint (19.7) denoted the flow conservation where lsr is the packet length which bio-sensor sends to relay nodes. kr tis data generation rate over the time period t. LtrD is the total data transmission from relay nodes to sink node within the time period t. Eq. (19.8) implies that we have to select minimum distance to minimize delay, cost and energy consumption so here dmin is the minimum communication distance between sensor and sink. In

Please cite this article as: T. K. Samal, S. C. Patra and M. R. Kabat, An adaptive cuckoo search based algorithm for placement of relay nodes in wireless body area networks, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.11.002

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constraint (21.9) Link quality of ith sensor to jth relay node is best if its selection probability is 1 otherwise 0 having worst link quality. 4. Adaptive cuckoo search optimization to solve OPR problem In this section, we present the cuckoo search optimization algorithm for optimal relay node placement. First, we present the basic cuckoo search algorithm with Lévy flights and then the modification of cuckoo search to make it suitable for application to OPR problem solving. 4.1. Basic cuckoo search

i. Each cuckoo lays one egg randomly at a time in the host nest. ii. A fraction of the eggs that are similar to host bird’s eggs grow and show the suitability of nests in that area. iii. The eggs that are discovered as alien eggs by the host bird are destroyed or the host bird builds a new nest in another location. Step 1: Initialization: In this phase a set of host nest is created and occupied with eggs. Each egg in a host nest has a unique ID. After populating the host with eggs the fitness of the nest is calculated. The host with best quality eggs (best fitness value) is represented cbest . After this phase, the iteration process is conducted and the solutions are generated by Lévy flights. Step 2: Iteration process by Lévy flights: This process is repeated till the terminating criteria are met. The CSO method can make a well balance between local and global movement by using a switching parameter pa (Elias, 2014). The pais the probability of identifying an alien egg by the host bird. The local random walk by the cuckoo is formulated as

ð20Þ

where, X tk and X tl are two different solutions elected randomly andHis the heavy side function and e is a random number generated by uniform distribution, s is the scaling factor. The global random work is made by Lévy flights using the updating formulae as given in Equation (21)

X tþ1 ¼ X ti þ a  Le0 v y ðkÞ i

ð21Þ

where, X tþ1 and X ti are the nests at time t + 1 and t.  represents i the entry-wise operator. a > 0 is an adjustment operator because of the random behavior of Lévy flight. The following equation shows the formulation of Lévy flight.

Le0 v yð kÞ ¼

l jv j1=b

ð22Þ

where, b is usually taken as 1.5 and the parameters l and v are the random numbers generated by following Normal distribution of zero mean and deviation r2l and r2v respectively. The following equations show the generation of l and v.

(

l Nð0; r2l Þ v Nð0; r2v Þ

where

ð23Þ

r2l and r2v are computed by using Mantegna’s algorithm as

follows

ð24Þ

where, C denotes Gamma function and expressed as Eq. (25).

CðxÞ ¼

Z

1

et tx1 dt

ð25Þ

0

The algorithm of basic cuckoo search is presented in Algorithm 1. Algorithm 1: Cuckoo Search via Lévy Flights

The cuckoo search optimization (CSO) algorithm is one of the most powerful evolutionary algorithms, which has been developed by Yang and Deb in 2009. The CSO algorithm is inspired by the life of cuckoos which uses three basic rules.


 X ti ¼ X ti þ as  HðPa  eÞ  X tk  X tl

9 8 8 = < > > C ð 1þb Þsin ð p b=2 Þ < r2 ¼   l b1 :C ð1þbÞb2 2 ; 2 > > : r2v ¼ 1

begin Objective function f ðxÞ; x ¼ ðx1 ;    ::; xd ÞT with d ¼ dim ðXÞ Generate initial population of N host nests xi ði ¼ 1; 2;    :; NÞ While ðt < MaxGenerationÞ or ðstop criterionÞ Get a cuckoo (say, i) randomly by Lévy Flights Evaluate its fitness F i Choose a nest among N (say, i) randomly if (F i > F j Þ Replace j by the new solution end if A fraction ðpa Þ of worse nests are abandoned and new ones are built via Lévy Flights Keep the best solutions (or nests with quality solutions) Rank the solution and find the current best end while Post process results and visualization end

4.2. Adaptive cuckoo search The adaptive cuckoo search (ACS) (Naik and Panda, 2016; Mareli and Twala, 2018) has been developed to increase the efficiency of cuckoo search by adaptively controlling the step size of the cuckoo search. The cuckoo search generally uses Lévy’s distribution for selecting the step size to explore the search space. The Gauss distribution and Gauss & Cauchy distribution has been used by Zheng and Zhou (2012) and Ho et al. (2014) respectively to determine the step size of cuckoo search. However, these distributions don’t have any mechanism to control the random walk step size of cuckoo search so that the algorithm can be guided to reach at optima. Therefore, in ACS, the step size is incorporated which is proportional to the fitness function. Here the step size is computed as follows.

stepi ðt þ 1Þ ¼

jðfitnessðXtbest ÞfitnessðX ti ÞÞ=ðfitnessðXtbest ÞfitnessðX tworst ÞÞj 1 t ð26Þ

where X tbest , X ti ; X tworst are the best, current and worst solution generated in tth iteration. The step size starts with a high number which subsequently decreases with the increase of number of iterations. This assures the solution to be converged to the global optimal in less time than the cuckoo search. When the step size becomes small the solution converged to the optimal solution. From this, it is evident that the step size is adaptive and the solution generated in (t + 1) iteration of ACS can be modelled as

X tþ1 ¼ X ti þ q stepi ðt þ 1Þ i

ð27Þ

Please cite this article as: T. K. Samal, S. C. Patra and M. R. Kabat, An adaptive cuckoo search based algorithm for placement of relay nodes in wireless body area networks, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.11.002

T.K. Samal et al. / Journal of King Saud University – Computer and Information Sciences xxx (xxxx) xxx

where q is a random number chosen from 0 to 1. Furthermore, since we consider the step size modelled as proportional to fitness function. The solution X tþ1 can be defined as shown in Eq. (28). i


 X tþ1 ¼ X ti þ q stepi ðt þ 1Þ X ti  X gbest i

ð28Þ

where X gbest is the global best solution generated so far. 4.3. Operation of adaptive cuckoo search algorithm for optimal relay node placement The operation of selection of optimal locations for relay node placement is presented in Algorithm 2. In this algorithm, we assume that there are n sensors nodes and z candidate locations for relay node placement. In this ACS algorithm, we have the following steps: Step 1: Initialization: The adaptive cuckoo search algorithm for optimal relay node placement starts with finding the Euclidean distance from all the sensor nodes to all the candidate locations of relay node and sink node. The distance from all candidate locations to all other candidate locations are also computed. The algorithm also initializes the candidate locations and selecting r locations randomly for relay node placement. This is represented as one host nest and H such host nests are created by populating each host nest with r eggs. These eggs are the relay locations selected randomly from the set of candidate relay locations. All these relay locations selected in a host nest must have unique ID. Each host nest uses the lower and upper limits on the candidate location variables and uniformly distributed random numbers. For example, the ith nest of the population may be generated as follows


 Nestði; tÞ ¼ xjl þ r ij xju  xjl ; forj ¼ 1topandi ¼ 1toH

ð29Þ

th

where, Nest(i,t) represents the i nest at iteration t, xjl and xju are the lower and upper limits of the jth variable and rij is a uniformly distributed random number between 0 and 1. After creating each Host nest, the fitness of each Host nest is evaluated using Eq. (31).

FitnessðPi Þ ¼ c1

X

costðjÞ xj

j2R

þ c2

X

Ei xij þ

i2S;j2R

X

i2S;j2R

P

i2S;j2R xij

xj E r

j2R

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 P
dij  d0 þ c3

!

P i2S;j2R xij þ c4 S i2S;j2R xij

ð30Þ

The best host nest is selected based on the best value (lowest) of the fitness function. This nest with best quality eggs is selected as best Host nest and is represented as Hbest. After this phase, iterative process of the Cuckoo Search algorithm is initiated. Step 2: Iterative Process: The ACS proposed in this paper makes a balance between local search and global search. An adaptive step size has been incorporated in this algorithm to achieve faster convergence at global optima. The iterative process is repeated until the terminating criteria are satisfied. The new nests are built up in subsequent iterations by using Eqs. (20) and (28) which shows the local and global random walk respectively. The fitness of this new nest X tþ1 is compared with a randomly chosen nest X tj from i the set of host nests generated in the previous iteration. If the

7

compared with global best solution Hbest. If the former is better, then it replaces Hbest. Step 3: Terminating Process: The iterative process continues till the maximum number of iterations or if the solution is not improved after a fixed number of iterations Algorithm 2: Cuckoo Search Based Optimal Placement of Relay Nodes Parameters n=|S| Number of bio-sensors z=|CS| Number of Candidate locations p=|R| Number of relay nodes to be placed H Number of host nests Pa Probability of finding an alien egg MAX_ITER Maximum number of iteration Initializing 1. for i = 1 to n 2. Calculate the distance from bio-sensor i to relay node locations and sink. 3. end for 4. for i = 1 to z 5. Calculate the distance from relay node location i to other relay node locations and sink. 6. end for 7. //Each host nest is populated with p relay nodes randomly selected from candidate sets CS and connect the biosensors to the relay nodes having minimum distance.// 8. t = 1 9. for i = 1 to H
 10. Nestði; t Þ ¼ X ti ¼ xjl þ r ij xju  xjl ; forj ¼ 1top
t 11. F i ¼ Fitness X i //Calculate the fitness of each host nest// 12. end 13. Select best host nest having the high quality of relay nodes based on the fitness function. 14. Best nest is selected with minimum fitness denoted as Hbest .Iterative process for selection of best host 15. while : ((t < MAXITER)or (termination criterion)) 16. Generate a new population again. 17. for i = 1 to H
 fitnessðX tbest ÞfitnessðX ti ÞÞ=ðfitnessðX tbest ÞfitnessðX tworst ÞÞj 18. stepi ðt þ 1Þ ¼ 1t jð   ðtÞ ðtÞ t t 19. X i ¼ X i þ bs  ðP a  eÞ  X k
 X l  ¼ X ti þ q stepi ðt þ 1Þ X ti  X gbest 20. X tþ1 i t 21. X j ¼ Select a random host nest   Þ 22. if ðf X tj > f X tþ1 i ðtÞ ðtþ1Þ 23. Replace X j with X i 24. end if 25. end for 26. A fraction (Pa) of worst nest are discarded and new ones are built via Lévy flights 27. Find the best solutions Hlbest (nest with quality solution) 28. if ðf ðHlbest Þ < f ðHbest ÞÞ 29. Hbest ¼ Hlbest 30. end while Placement of Optimal Relay node:-Select the best host nest having minimum fitness function and this fitness set having best relay nodes.

new nest X tþ1 is better than the randomly selected nest X tj then i

5. Simulation results

replaces the previous one else it continues to the new nest X tþ1 i generate a new population. After each iteration, a fraction pa of worst nests are discarded and a new set of host nests are created to keep the total number of host nests fixed. The best nest in this iteration is generated as is represented as Hlbest. Then the Hlbest is

In this section, we present the comprative analysis of our proposed adaptive cuckoo search (ACS) and cuckoo search (CS) algorithms with the existing state of the art algorithms EAWD (Ehyaie et al., 2009) and improved EAWD (Rostampour et al.,

Please cite this article as: T. K. Samal, S. C. Patra and M. R. Kabat, An adaptive cuckoo search based algorithm for placement of relay nodes in wireless body area networks, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.11.002

8

T.K. Samal et al. / Journal of King Saud University – Computer and Information Sciences xxx (xxxx) xxx

Table 2 Parameter Value. Parameter

Value

Initial Energy of WBAN ETxeelec ERxeelec Eamp (3.38) (LOS) Eamp (5.9) (N-LOS) Number of WBAN Number of relay Nodes Number of Sensor Nodes Number of Sink Nodes Communication range of sensor Candidate sites (Location) Simulation Area of the Network Size of Data Packet Installation Cost

5J 16.7 lJ/bit 36.1 lJ/bit 1.97 lJ/bit 7990 lJ/bit 1 40–100 16 1 30 cm 40–100 180 40 cm2 250 kb/s 10 monetary unit/relay

2017). The proposed algorithms along with its counterparts are implemented in MATLAB 2016a installed in an intel-core i7 with CPU operating at 3.4 GHz, 8 GB RAM and 1 TB hard-disk. To study and compare the performance of our proposed algorithm along with other esisting algorithms, each senario is run for 10 iterations and the average of the results is considered as the final result. The parameter settings to study and evalute the performance of our proposed model in comparision to the EAWD (Ehyaie et al., 2009)and improved-EAWD (Rostampour et al., 2017) is shown in Table. 2. First, we consider the WBAN scenario in which the body is in Standing position and 16 bio-sensor nodes are deployed in different positions on the body, one Sink node placed in predetermined and fixed position and number of candidate locations for placement of relay nodes are varied from 40 to 100. The Sensor nodes are involved in sensing the vital signs of the body and transmite to the Sink node directly or via relay nodes. The relay nodes are involved in reception and aggregation of data received from multiple bio sensors. It is also assumed that the sensors may use Time Division Multiple Acess or Carrier sense multiple acess for data transmission. Further, it is assumed that the sensors used in WBAN are having extremely low transmission power for protection of human tissue. The range of each sensor node is 30 cm. The installation cost and maximum capacity of each relay node is assumed to be 10 monetary unit and 250 kb/s respectively (Fig. 1). We study and compare the energy consumption of all the nodes (both relay nodes and sensor nodes) by our proposed ACS and CS algorithm with EAWD (Faheem et al., 2018) and modified EAWD (Shailaja et al., 2018). The Figs. 2 and 3 show the comparison of our proposed algorithms with its counterparts in terms of total energy consumption of all nodes and total energy consumption by the relay nodes with respect to number of candidate sites. It is observed from the figures that total energy consumption of all nodes as well as all relay nodes in our proposed topology of relay nodes designed by ACS and CS are less as compared to EAWD and modified EAWD. The EAWD selects the relay nodes only by considering its installation cost and energy consumption with the objective of minimizing the total energy consumption and installation cost. However, the maintenance cost of relay nodes and energy of each relay node also play significant role in locating the optimal set of relay nodes. This is because when one relay node exhausts its energy, the connectivity of the sensors to this relay node is lost. Therefore, modified EAWD defines the heuristic to make the energy consumption to be uniformly distributed. It examines different candidate sites and selects the relay which has maximum energy utilization. The modified EAWD succeeds in distributing the energy consumption uniformly but the total energy consumption increases. In our cuckoo search approach, the relay node set is selected on the basis of cost, energy as well

Fig. 1. Body on Standing Position.

Fig. 2. Comparison of Energy consumption of all nodes (mJ/bit) by our proposed Cuckoo Search algorithm, EAWD and modified EAWD approach.

as load on each relay node which is computed as proportional to the link quality. Therefore, the cuckoo search algorithm finds an optimal set of relay nodes which not only consumes less energy than its counterparts but also distributes the energy consumption evenly on relay nodes. The cuckoo search optimization algorithm makes a balance between local and global search by using two different functions for random local walk and global walk. However, the step size is chosen randomly. Furthermore, to improve upon the search, the step size of cuckoo search is computed with proportional to fitness function which is called ACS. The ACS finds the optimal set of relay nodes which is found to be better than cuckoo search, EAWD and modified EAWD.

Please cite this article as: T. K. Samal, S. C. Patra and M. R. Kabat, An adaptive cuckoo search based algorithm for placement of relay nodes in wireless body area networks, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.11.002

T.K. Samal et al. / Journal of King Saud University – Computer and Information Sciences xxx (xxxx) xxx

Fig. 3. Comparison of Energy consumption by all relay nodes by our proposed Cuckoo Search algorithm, EAWD and modified EAWD approach.

Furthermore, we consider a scenario with 50 candidate location and the host nest size varied from 10 to 15 to find the optimal number of relay nodes. The average number of relay nodes required covering all sensor nodes with minimum cost and energy consumption is found to be 13. In modified EAWD, it is argued that the total energy consumption is not important as the consistency of energy consumption by all the relay nodes. Therefore, the comparison of the algorithms in terms of energy consumed by each relay node is presented in Fig. 4. It is observed that the energy consumed by the relay nodes are uniform like modified EAWD (Shailaja et al., 2018). This is because the load of transmission is equally distributed among the relay nodes. Our proposed fitness function considers the energy consumption as well as the load on the relay nodes to find out the best set of relay nodes which not only minimizes the energy consumption but also distributes the load uniformly among the relay nodes. Here, we evaluate the computational time of the network by our proposed algorithm as compared to EAWD (Faheem et al., 2018) and improved EAWD (Shailaja et al., 2018), cuckoo search and ACS. The Fig. 5 shows the comparison of our proposed algo-

Fig. 4. Comparison of Energy consumption of each relay nodes (mJ/bit) in our proposed Cuckoo Search algorithm, EAWD and modified EAWD approach.

9

Fig. 5. Comparison of Total computational time of networks (sec) by our proposed Cuckoo Search algorithm, EAWD and modified EAWD approach.

rithms with their counterparts in terms of total computational time with respect to number of candidate sites. It is observed from the figure that as the number of candidate sites increases the computational time increases. In our proposed work, we observed that for optimal solution computational time is maximum 2.5 sec at CS = 60. Then it drastically increases the computational time. However, cuckoo search consumes less time than EAWD and modified EAWD. This is because both EAWD and modified EAWD use traditional approach whereas cuckoo search is a meta-heuristic approach which tries to search the optimal set of relay nodes from the set of candidate sites more exhaustively with minimum number of iterations. Further, ACS starts searching by setting step size long for wide coverage of search space and then reduces the step size. This adaptive step size controlling capability of ACS helps in generating the optimal solution at a less time than cuckoo search. It is not always true that the patient is in one position rather has different postures during his sickness. It will be noteworthy to study and compare our algorithms with its competitors in different postures of a patient. Therefore, to validate and analyze our result with its competitors, we consider the patient on a chair sat position to generate another new topology which is shown in Fig. 6. The Fig. 7, Fig. 8, Figs. 9 and 10 show the energy consumption of all sensor and relay nodes, energy consumption of all relay nodes, energy consumption of each relay node and total computational time respectively. It is observed from Fig. 7, Fig. 8, Figs. 9 and 10 that the performance of our algorithm is better than its counterparts if the position of the body is changed. Furthermore, in order to study the scalability of our proposed algorithm in comparison with the existing algorithms we implement our algorithm with 80 sensor nodes deployed randomly in different locations of the body in an area of 100 *200 cm2. The sensing Range each Sensor is 20 cm. The candidate locations for placement of relay nodes are varied from 50 to 300. The Figs. 11 and 12 show total energy consumption of all nodes (both sensors and relay nodes) and energy consumption of all relay nodes respectively by our proposed algorithms as compared to EAWD (Faheem et al., 2018) and improved EAWD (Shailaja et al., 2018). It is observed from the figures that as the number of candidate sites increases energy consumption gradually decreases. This is because these algorithms have more opportunities to select a better set of relay nodes. It is also observed that our algorithms perform better than modified EAWD (Shailaja et al., 2018) and EAWD (Faheem et al., 2018) in terms of energy consumption of relay nodes. This is because our algorithm searches the candidate sites more exhaus-

Please cite this article as: T. K. Samal, S. C. Patra and M. R. Kabat, An adaptive cuckoo search based algorithm for placement of relay nodes in wireless body area networks, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.11.002

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T.K. Samal et al. / Journal of King Saud University – Computer and Information Sciences xxx (xxxx) xxx

Fig. 8. Comparison of Energy consumption of all relay nodes (mJ/bit) by our proposed Cuckoo Search algorithm, EAWD and modified EAWD approach.

Fig. 6. Body on a Chair Sat position.

Fig. 9. Comparison of Energy consumption of each relay nodes (mJ/bit) in our proposed Cuckoo Search algorithm, EAWD and modified EAWD approach.

Fig. 7. Comparison of Energy consumption of all nodes (mJ/bit) by our proposed Cuckoo Search algorithm, EAWD and modified EAWD approach.

tively than the existing approaches. The average optimal number of relay nodes required to cover all the sensors for 300 candidate sites is found to be 43 in our proposed algorithm which is shown in Fig. 13. The Fig. 14 shows the comparison of energy consumption of each relay node in our proposed algorithm with its counterparts. It is observed from the figure that, the energy consumption of relay

Fig. 10. Comparison of Total computational time of networks (sec) by our proposed Cuckoo Search algorithm, EAWD and modified EAWD approach.

Please cite this article as: T. K. Samal, S. C. Patra and M. R. Kabat, An adaptive cuckoo search based algorithm for placement of relay nodes in wireless body area networks, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.11.002

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Fig. 11. Comparison of Total Energy consumption of all nodes (mJ/bit) by our proposed Cuckoo Search algorithm, EAWD and modified EAWD approach.

Fig. 12. Comparison of Energy consumption by all relay nodes (mJ/bit) by our proposed Cuckoo Search algorithm, EAWD and modified EAWD approach.

nodes are uniformly distributed in our algorithm and modified EAWD whereas the energy consumption is non uniform incase of EAWD. Therefore, this can be argued that our network shall last longer than its counterparts. The Table 3 shows the comparison of our proposed algorithm with its counterparts in terms of total computational time. We observe that for optimal solution computational time is maximum 15.6sec at CS = 100. Then, it drastically increases the computational time with increase of number of candidate sites. This is because the search space increases exponentially. However, our proposed algorithms take less computational time than EAWD and modified EAWD. The cuckoo search searches the search space efficiently by using a probabilistic function for local search and Levy flight for global search. Thus, it searches with good exploration and exploitation capability to find the near optimal solution quickly as compared to its counter parts. However, the adaptive cuckoo search adaptively determines the step size for which the search starts with a long step size and then reduces it. This helps ACS to search the space more exhaustively at low convergence time as compared to cuckoo search.

Fig. 13. Comparison of Total number of relay node placements in our proposed Cuckoo Search algorithm, EAWD and modified EAWD approach.

Fig. 14. Comparison of Energy consumption of each relay nodes (mJ/bit) in our proposed Cuckoo Search algorithm, EAWD and modified EAWD approach.

Table 3 Total Time consumption of the Different Networks. Candidate Sites (CS)

EAWD (sec)

Modified EAWD (sec)

Cuckoo Search (sec)

Adaptive Cuckoo Search (sec)

50 75 100 125 150 200 250 300

4.8 7.3 18.2 59.7 81.9 149.3 428.4 694

5.3 8.9 20.7 55.4 78.9 153.7 451.6 700

1.8 4.7 15.6 50.7 74.2 134.9 410.8 687

1.5 4.1 14.8 48.2 71.6 128.6 382.4 643.7

6. Conclusion and future scope In this paper, we proposed an adaptive cuckoo search optimization based algorithm for solving optimal relay node placement problem. We proposed an efficient fitness function which considers energy consumption, load and coverage of the relay nodes for

Please cite this article as: T. K. Samal, S. C. Patra and M. R. Kabat, An adaptive cuckoo search based algorithm for placement of relay nodes in wireless body area networks, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.11.002

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selecting the optimal set of relay nodes. The ACS proposed in this paper makes a balance between global search and local search to find out the best set of relay nodes from the set of candidate sites. Further, the ACS uses an adaptive step size to get rid of trapping at local optima and the move of ACS is made dynamic so that the optimal solution can be obtained. As it is evident that the life time of a network not only depends on the total energy consumption of the nodes deployed in the network but also depends on uniform consumption of energy by the nodes, the proposed algorithm selects a set of relay nodes which not only consumes less energy than its counterparts but also energy consumption is uniformly distributed among the relay nodes. The proposed algorithm and algorithm its counterparts are implemented in MATLAB to study and compare their performances in-terms of energy consumption, load and computational time. It is observed that the proposed algorithm performs better than the existing algorithms. However, the drawback of the algorithm is finding an optimal number of relay nodes is time consuming because it starts with a fixed number of relay nodes and then increases to find the optimal number of relay nodes as well as the optimal set of relay nodes. In future, the algorithm can be designed in such a way that the size of the solution can also be adaptively generated in addition to the step size so that the execution time can be reduced. Further, the proposed work can also be integrated with an efficient MAC protocol for energy efficient data transmission over the WBAN.

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Please cite this article as: T. K. Samal, S. C. Patra and M. R. Kabat, An adaptive cuckoo search based algorithm for placement of relay nodes in wireless body area networks, Journal of King Saud University – Computer and Information Sciences, https://doi.org/10.1016/j.jksuci.2019.11.002