Further Developments on Router Nodes Positioning for Wireless

Networks using Artificial Immune Systems

Pedro Henrique Gouvêa Coelho, J. L. M. do Amaral, J. F. M. do Amaral, L. F. de A. Barreira

and A. V. de Barros

State Univ. of Rio de Janeiro, FEN/DETEL, R. S. Francisco Xavier, 524/Sala 5001E, Maracanã, RJ, 20550-900, Brazil

Keywords: Artificial Immune Systems, Artificial Intelligence Applications, Node Positioning, Wireless Networks.

Abstract: This paper shows further developments on the positioning of intermediate router nodes using artificial

immune systems for use in industrial wireless sensor networks. These nodes are responsible for the

transmission of data from sensors to the gateway in order to meet criteria, especially those that lead to a low

degree of failure and reducing the number of retransmissions by routers. In the present paper positioning

configurations on environments in presence of obstacles is included. Affinity functions which roles are

similar to optimization functions are explained in details and case studies are included to illustrate the

procedure. As was done in previous papers, positioning is performed in two stages, the first uses elements of

two types of immune networks, SSAIS (Self-Stabilising Artificial Immune System) and AINET (Artificial

Immune Network), and the second uses potential fields for positioning the routers such that the critical

sensors attract them while obstacles and other routers repel them.

1 INTRODUCTION

Wireless industrial sensor network is an emerging

field including a great deal of research work

involving hardware and system design, networking,

security factor and distributed algorithms (Coelho et

al., 2013), (Coelho et al., 2014), (Dai and Li, 2005),

(Akyildiz et al., 2002). Sensor nodes usually sense

the data packet and transfer it to the gateway via

some intermediate nodes. The sensor nodes consist

of low cost, low power and short transmission range

(Coelho et al., 2014). Main advantages are reduced

installation time of devices, no need of cabling

structure, cost saving projects, infrastructure

savings, device configuration flexibility, cost

savings in installation, flexibility in changing the

existing architectures, possibility of installing

sensors in hard-to-access locations and others.

Safety, reliability, availability, robustness and

performance are key issues in the area of industrial

automation. Data transmission in a wireless network

may suffer the problem of interference generated by

other electrical networks and electrical equipment,

moving obstacles (trucks, cranes, etc.) and fixed

ones (buildings, pipelines, tanks, etc.). In an attempt

to minimize these effects, frequency scattering

techniques and mesh or tree topologies are used, in

which a message can be transmitted from one node

to another with the aid of other nodes, which act as

intermediate routers, directing messages to other

nodes until it reaches its final destination. This

allows the network to get a longer range and to be

nearly fault tolerant, because if an intermediate node

fails or cannot receive a message, that message

could be routed to another node. However, a mesh

network also requires careful placement of these

intermediate nodes, since they are responsible for

doing the forwarding of the data generated by the

sensor nodes in the network to the gateway directly

or indirectly, through hops. Those intermediate

nodes are responsible for meeting the criteria of

safety, reliability and robustness of the network and

are also of paramount importance in the forwarding

of data transmission. They could leave part or all the

network dead, if they display any fault (Hoffert et

al., 2007). Most solutions to the routers placement

solve this problem with optimization algorithms that

minimize the number of intermediate router nodes to

meet the criteria for coverage, network connectivity

and longevity of the network and data fidelity.

(Youssef and Younis, 2007), (Molina et al., 2008).

Recently, (Lanza-Gutiérrez and Pulido, 2016)

considered router nodes deployment in wireless

sensor networks with the purpose of optimizing the

Coelho, P., Amaral, J., Amaral, J., Barreira, L. and Barros, A.

Further Developments on Router Nodes Positioning for Wireless Networks using Artiﬁcial Immune Systems.

In Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016) - Volume 2, pages 99-105

ISBN: 978-989-758-187-8

average energy consumption of the sensors and

average sensitivity area provided by the network.

This paper considers further developments to a

previous paper by the authors (Coelho et al., 2014)

which used Artificial Immune Networks for node

positioning. The algorithms based on immune

networks have very desirable characteristics in the

solution of this problem, among which we can

mention: scalability, self-organization, learning

ability and continuous treatment of noisy data

(Coelho et al., 2013). The improvements done

included modifications in the affinity function to

consider obstacles and case studies for different

configurations. This paper is divided into four

sections. Section 2 does a brief discussion of

artificial immune systems. Section 3 presents the

application of artificial immune systems to the

problem of node positioning where the affinity

function is discussed in details. Section 4 ends the

paper by presenting results and conclusions.

2 IMMUNE SYSTEM BASICS

The immune system is a biological mechanism for

identifying and destroying pathogens within a larger

organism (Amaral, 2006). Pathogens are agents that

cause disease such as bacteria, viruses, fungi,

worms, etc. Anything that causes an immune

response is known as an antigen. An antigen may be

harmless, such as grass pollen, or harmful, such as

the flu virus. In other words disease-causing

antigens are called pathogens. So the immune

system is designed to protect the body from

pathogens. In humans, the immune system begins to

develop in the embryo. The immune system begins

with hematopoietic, (i.e. blood-making from Greek)

stem cells. These stem cells differentiate into the

major players in the immune system e.g.

granulocytes, monocytes, and lymphocytes. These

stems cells also differentiate into cells in the blood

that are not connected to immune function, such as

erythrocytes e.g. red blood cells, and

megakaryocytes for blood clotting. Stem cells

continue to be produced and differentiate throughout

our lifetime. The immune system is usually divided

into two categories--innate and adaptive--although

these distinctions are not mutually exclusive. The

innate subsystem is similar in all individuals of the

same species, whereas the adaptive subsystem

depends on the experience of each individual i.e.

exposure to infectious agents. The innate immune

response is able to prevent and control many

infections. Nevertheless, many pathogenic microbes

have evolved to overcome innate immune defenses,

and so to protect ourselves against these infections,

we have to call in the more powerful mechanisms of

adaptive immunity. Adaptive immunity is normally

silent, and responds or adapts to the presence of

infectious microbes by becoming active, expanding,

and generating potent mechanisms for neutralizing

and eliminating the microbes. The components of

the adaptive immune subsystem are lymphocytes

and their products. The most notable cells of

adaptive immunity are lymphocytes. There are two

main classes of lymphocytes. B lymphocytes, named

so, because they mature in the bone marrow, secrete

proteins called antibodies, which bind to and

eliminate extracellular microbes. T lymphocytes,

which mature in the thymus, and function mainly to

combat microbes that have learned to live inside

cells where they are inaccessible to antibodies. The

normal immune system has to be capable of

recognizing virtually any microbe and foreign

substance that one might encounter, and the

response to each microbe has to be directed against

that microbe. The substances that are recognized by

these lymphocytes are called antigens. The immune

system recognizes and directs responses against a

massive number of antigens by generating a large

number of lymphocytes, each with a single antigen

receptor. Therefore, there are about 10

lymphocytes in an adult, and it is estimated that

these are able to recognize at least 10

– 10

different

antigens (Silva, 2001). Thus, only a few thousand

lymphocytes express identical antigen receptors and

recognize the same antigen. The antigen receptors of

B cells are membrane-bound antibodies, also called

immunoglobulins, or Ig. Antibodies are Y-shaped

structures (Jerne, 1974). The tops of the Y recognize

the antigen and, in B cells, the tail of the Y anchors

the molecule in the plasma membrane. Antibodies

are capable of recognizing whole microbes and

macromolecules as well as small chemicals. These

could be in the circulation e.g. a bacterial toxin, or

attached to cells (e.g. a microbial cell wall

component. The antigen receptors of T cells are

structurally similar to antibodies, but T cell receptors

(TCRs) recognize only small peptides that are

displayed on specialized peptide-display molecules

(Castro and Von Zuben, 1999). Although the

immune system is capable of recognizing millions of

foreign antigens, it usually does not react against

one’s own, i.e. self, antigenic substances. This is

because lymphocytes that happen to express

receptors for self-antigens are killed or shut off

when they recognize these antigens. This

phenomenon is called self-tolerance, implying that

ICEIS 2016 - 18th International Conference on Enterprise Information Systems

100

we tolerate our own antigens and the breakdown of

this process yields in autoimmune diseases. When

one antibody binds to other material, the lymphocyte

carrying it, is stimulated to reproduce by cloning,

this is known as Clonal selection principle. Genes

coding lymphocytes have a mutation rate above

normal, one mutation per cell division, on average,

leading to what is known as somatic hypermutation.

Clonal selection and hypermutation increases

affinity between antibodies and antigens. There are

three steps for an Artificial Immune System (AIS).

First, find a representation of the components i.e.

artificial equivalents to cells and antigens. Second,

define affinity functions between components in

order to quantify interaction among them. Third,

write a set of immune algorithms that control system

behavior. Why would a computer scientist get the

trouble to study immune systems? Immune systems

are massively parallel information processing

mechanisms and are incredibly effective examples

of distributed systems built from diverse

components which are constantly being renewed. So

that may inspire better computer security systems,

for example, because of their adaptiveness, they can

train themselves to react to new threats. Moreover

they are error—tolerant, so that small mistakes are

not fatal, and also self-protecting.

3 ROUTER NODE POSITIONING

USING ARTIFICIAL IMMUNE

SYSTEMS

Node positioning based on artificial immune

networks presented in this paper aims to establish

two or more disjoint paths from the sensor nodes to

the gateway by removing, cloning and reconfiguring

intermediate router nodes. In addition, the method is

also able to meet the criteria of low fault degree and

low number of relay routers. These criteria can be

enabled individually or combined with equal or

different weights at user's discretion. The positioning

algorithm is made on two modules: (i) Immune

Network - combines elements of two models of

immune networks: Self-Stabilizing Artificial

Immune System - SSAIS (Neal, 2002) and Artificial

Immune Network - Ainet (Castro and Von Zuben,

1999); (ii) Potential Fields - positions router nodes

by potential fields where the critical sensors attract

them while obstacles and other routers repel them.

The use of wireless sensor network in industrial

automation is still a matter of concern with respect

to the data reliability and security by users. Thus, an

appropriate node positioning is of paramount

importance for the wireless network to meet safety,

reliability and efficiency criteria. Positioning of

nodes is a difficult task, because one should take

into account all the obstacles and interference

present in an industrial environment. The gateway as

well as the sensors generally have a fixed position

near the control room. But the placement of router

nodes, which are responsible for routing the data,

generated by the sensors network to the gateway

directly or indirectly, is determined by the

characteristics of the network. The main

characteristics of wireless sensor networks for

industrial automation differ from traditional ones by

the following aspects: The maximum number of

sensors in a traditional wireless network is on the

order of millions while automation wireless

networks is on the order of tens to hundreds; The

network reliability and latency are essential and

fundamental factors for network wireless

automation. To determine the number of router

nodes and define the position in the network, some

important aspects in industrial automation should be

considered. It should be guaranteed: (1) redundant

paths so that the system be node fault-tolerant; (2)

full connectivity between nodes, both sensors and

routers, so that each node of the network can be

connected to all the others exploring the

collaborative role of routers; (3) node energy

efficiency such that no node is overwhelmed with

many relaying information from the sensors; (4)

low-latency system for better efficiency in response

time; (5) combined attributes for industrial processes

to avoid accidents due to, for example, high

monitored process temperature. (6) self-organization

ability, i.e. the ability of the network to reorganize

the retransmission of data paths when a new sensor

is added to the network or when a sensor stops

working due to lack of power or a problem in

wireless communication channel. All these factors

must be met, always taking into consideration the

prime factor security: the fault tolerance. In the end

of the router nodes placement, the network of

wireless sensors applied to industrial automation

should be robust, reliable, scalable and self-

organizing.

The positioning of router nodes in industrial

wireless sensor networks is a complex and critical

task to the network operation. It is through the final

position of routers that one can determine how

reliable, safe, affordable and robust the network is.

In the application of immune systems to router

nodes positioning reported in this paper, B cells that

make up the immune network will be composed by a

Further Developments on Router Nodes Positioning for Wireless Networks using Artiﬁcial Immune Systems

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set of sensor nodes and a set of router nodes. The

sensor nodes are located in places where the plant

instrumentation is required. These nodes have fixed

coordinates, i.e. they cannot be moved. For security

to be guaranteed it is necessary to have redundant

paths between these nodes and the gateway. The set

of router nodes will be added to allow redundant

paths. The position of these nodes will be changed

during the process of obtaining the final network.

After the inclusion of the new router nodes, a stop

condition is performed. If the condition is not met,

all routers undergo an action of repulsive forces,

generated by obstacles and routers for other nodes,

followed by attractive forces created by critical

sensor nodes. Those critical nodes are the ones that

do not meet the minimum number of paths necessary

to reach the gateway. The actions of repelling

potential fields have the function of driving them

away from obstacles, to allow direct line of sight for

the router network nodes to increase the reliability of

transmission and also increase the distance among

the routers to increase network coverage. On the

other hand, the attractive potential fields attract

routers to critical sensors, easing the formation of

redundant paths among sensors and the gateway.

After the action of potential fields, from the new

positioning of routers, a new network is established

and the procedure continues until the stopping

criterion is met. The algorithm proposed in this

paper deals with a procedure based on artificial

immune networks, which solves the problem of

positioning the router nodes so that every sensor

device is able to communicate with the gateway

directly and or indirectly by redundant paths. Figure

1 shows the main modules of the algorithm. The first

module is called immune network, and the second,

positioning module is called potential fields

containing elements used in positioning sensor

networks using potential fields (Howard, 2002). The

immune network module performs an algorithm that

can be described by the following steps:

Creation: Creation of an initial set of cells to form a

network.

Evaluation: Determination of the cells affinity to

calculate their stimulation.

Pruning: Performs the resource management and

remove cells that are without resources from the

network.

Selection: Selects the more stimulated cells to be

cloned.

Cloning: Generates a set of clones from the most

stimulated cells.

Mutation: Does the mutation of cloned cells.

Figure 1: The AIS based algorithm.

In the stage of creation, an initial set of routers is

randomly generated to initiate the process of

obtaining the network, and the user can specify how

many routers to place it initially. In the evaluation

phase, a network which is represented by a graph is

formed with sensor nodes and router nodes. From

this graph, values of several variables are obtained

that will be used to calculate the affinity. Examples

of such variables are the number of paths that exist

between each sensor and the gateway, the number of

times that a router is used on the formed paths, etc.

The affinity (Af) is given by equation 1 and consists

of a weighted sum of the three affinities that are

enabled at the discretion and need of the users. Thus,

if a user believes that affinity1 and affinity3, for

example, are critical to his network, he may disable

the other affinities and choose the weights so that the

sum is 1 for the enabled parts.

Af =w1*Afinity1+w2*Afinity2+w3* Afinity3 (1)

Afinity1 takes into account the failure degree of

each router. It is given by the normalized difference

between the total number of paths between the

sensors and the gateway and the number of

remaining paths when the examined router is taken

out. In other words the higher the degree of failure,

the greater the affinity1 and therefore more critical

will be loss of the examined router for the network.

Affinity2 sets the number of times the router is

used as a function of the path. It is calculated by the

ratio of the number of times that the router is used in

the observed paths and the number of paths that

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should exist, according to user specification. The

more the router is used, the greater the affinity2.

Finally, affinity3 is related to the neighbouring

sensors for the examined node. Affinity3 lies between

0 and 1, where 1 is the critical value for the network.

4 RESULTS AND CONCLUSIONS

Case studies were simulated in a 1 x 1 square

scenario. The cloning procedure considered that only

the router with higher affinity would be selected to

produce three clones in each generation. For each

case study 10 experiments were conducted that

demonstrate the algorithm’s ability to create at least

two redundant paths to get the information from any

sensor to the gateway. Two configurations were

considered to demonstrate the proposed router nodes

positioning algorithm in environments with

obstacles. The configurations used in the simulation

were motivated by oil & gas refinery automation

applications. The first configuration (PosA)

comprises two circular obstacles with a radius of

0.1, and five nodes, in which node 1 is the gateway

and the others are sensor nodes.

Figure 2: Sensors and gateway POSA configuration.

(Legend: node 1:gateway; nodes 2 to 5: sensors).

Initially, the gateway has not direct line of sight

with sensor nodes 3 and 5 and is not connected, i.e.

out of range, to any of the network nodes, as

depicted in Figure 2.

The second configuration (PosB) has eight

obstacles: three circular ones have radius of 0.05,

another circular one has radius 0.15 and four

rectangular obstacles with different sizes.

Besides, the gateway is node 1 and nodes 2 to 8

are the seven sensor nodes. The second

Figure 3: Sensors and gateway POSB configuration.

(Legend: node 1:gateway; nodes 2 to 8 :sensors).

configuration (PosB) has eight obstacles: three

circular ones have radius of 0.05, another circular

one has radius 0.15 and four rectangular obstacles

with different sizes. Besides, the gateway is node 1

and nodes 2 to 8 are the seven sensor nodes.

Initially, the gateway has not direct line of sight to

any of the sensor nodes and is not connected to any

network node as it is out range to the other nodes.

Moreover, sensor nodes do not have a direct line of

sight with each other and are not connected as they

are out of range with each other too. Figure 3 shows

the PosB configuration. In this section, case studies

1 and 2 are considered for configurations PosA and

PosB. For case study 1, the network configuration is

cross-shaped, the operating range of the network

nodes is 0.2 and the positioning procedure led to two

disjoint paths for the sensors send data to the

gateway. Case study 2 uses configuration PosB and

considers the same operating range as in the case

study 1, 0.2, and now three disjoint paths are

required.

Tables 1 and 3 show the used parameters for case

studies 1 and 2. Figure 4 shows the best

configuration obtained from the 10 experiments.

Table 2 shows the network performance for case

study 1.

Table 1: Case study 1 – POSA configuration parameters.

Simulation Parameters Values Method

Number of

generations

30 -

Initial number of

Routers

10 -

Affinity -

Failure Degree,

Number of Times

A router is used

and Number of

neighbour sensors

Further Developments on Router Nodes Positioning for Wireless Networks using Artiﬁcial Immune Systems

103

Table 2: Network performance for case study 1.

Network Min. Av. Max. St. Dev.

No. of nodes 19 19.9 22 0.99

No. of routers 14 14.9 17 0.99

No. of critical sensors 0 0 0 -

No. a router is used 2 2 2 0

It can be seen in Figure 4 that the sensor nodes 3

and 5 in the paths 3-16-17-20-1, 3-19-7-10-1, 5-13-

11-18-1 and 5-12-14-15-1 show four jumps to the

gateway. This means the data sent by these devices

suffer a delay when received by the gateway, since it

will need to be relayed through three intermediate

nodes.

Figure 4: Node positioning for case study 1 in POSA

configuration.

Table 3: Case study 2 – POSB configuration parameters.

Simulation Parameters Values Method

Number of generations 100 -

Initial number of

Routers

10 -

Affinity -

Failure Degree, No.

of times a router is

used and No. of

neighbour sensors

Table 4: Network performance for case study 2.

Network Min. Av. Max. St. Dev.

No. of nodes 59 60.5 63 1.18

No. of routers 51 52.5 55 1.18

No. of critical sensors 0 0 0 -

No. a router is used 5 5.4 8 0.97

With respect to the failure degree, the

intermediate nodes 7, 10, 11, 12, 13, 14, 16, 17 and

19 have a 30% failure degree, and the other router

nodes have an index lower than 30%. So 70% of the

paths from the sensors to the gateway, continue to

exist even after the removal of a node. Figure 5

shows the best configuration out of ten experiments

for case study 2 and table 4 shows the network

performance for case study 2. Figure 4 indicates that

for sensor nodes 3 and 6, the paths 3-20-22-24-7-40-

63-53-36-1, 3-50-49-61-4-52-15-56-39-1 and 6-58-

61-4-60-31-15-37-26-1 show nine hops to the

gateway. This means that the data sent by these

devices suffer a delay in the gateway, since it will

need to be relayed by eight intermediate nodes. With

respect to the failure degree, the router node 32 have

21% failure degree, and the other router nodes have

an index lower than 21%. This means that 79% of

the paths from the sensors are still present even after

a node removal.

Figure 5: Node positioning for case study 1 in POSB

configuration.

This work proposed a positioning algorithm for

router nodes in wireless network using immune

systems techniques. The algorithm creates redundant

paths to the data collected by the sensors to be sent

to the gateway by any two or more paths, meeting

the criteria of degree of failure, the number of

retransmission by routers and number of sensors to

neighbouring routers. The algorithm allows each

criterion is enabled at a time or that they be

combined with weights. The affinity function, which

works as an objective function, is multi-objective, so

several other objectives could be jointly considered.

Future work will try to consider a comparison of this

work among the several related works existing in the

literature taking into account the different scenarios

and the objectives of each approach. A suitable

benchmark problem would be important for the

comparisons. Due to the distinct objectives assumed

in each work the comparison task will not be an easy

one.

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REFERENCES

Dai S., Jing X., and. Li L, 2005. Research and analysis on

routing protocols for wireless sensor networks. In

Proc. International Conference on Communications,

Circuits and Systems, Volume 1, pp. 407-411.

Akyildiz F., Su W., Sankarasubramaniam, Y. and Cayirci,

E., 2002. Wireless Sensor Networks: A Survey,

Computer Networks, 38, pp. 393-422.

Coelho, P. H. G., Amaral, J. L. M., Amaral, J. F. M.,

Barreira, L.F.A. and, Barros, A. V., 2013. Deploying

Nodes for Industrial Wireless Networks by Artificial

Immune Systems Techniques. In 15

International

Conference on Enterprise Information Systems,

Angers, France.

Coelho, P. H. G., Amaral, J. L. M., Amaral, J. F. M.,

Barreira, L.F.A. and, Barros, A. V., 2014. Router

Nodes Positioning for Wireless Networks Using

Artificial Immune Systems. In 16

International

Conference on Enterprise Information Systems,

Lisbon, Portugal.

Hoffert, J., Klues, K., and Orjih, O., 2007. Configuring the

IEEE 802.15.4 MAC Layer for Single-sink Wireless

Sensor Network Applications, Technical Report

http://www.dre.vanderbilt.edu/~jhoffert/802_15_4_Ev

al_Report.pdf.

Youssef, W., and Younis, M., 2007. Intelligent Gateways

Placement for Reduced Data Latency in Wireless

Sensor Networks. In ICC’07 International Conference

on Communications, Glasgow, pp.3805-3810.

Molina, G., Alba, E., and Talbi, E. G., 2008. Optimal

Sensor Network Layout Using MultiObjective

Metaheuristics. Journal of Universal Computer

Science, Vol.15, No. 15, pp.2549-2565.

Lanza-Gutiérrez, J. M., and Pulido, J. A. G., 2016.

Studying the multiobjective variable neighbourhood

search algorithm when solving the relay node

placement problem in Wireless Sensor Networks. Soft

Computing , Vol. 20 pp. 67-86.

Silva, L. N. C., 2001. Immune Engineering: Development

and Application of Computational Tools Inspired by

Artificial Immune Systems, Ph. D. Thesis, State

University of Campinas, Campinas, in portuguese.

Amaral, J. L. M., 2006. Artificial Immune Systems Applied

to Fault Detection, Ph. D. Thesis, Pontifical Catholic

University of Rio de Janeiro, Rio de Janeiro, in

portuguese.

Jerne, N. K., 1974. Towards a Network Theory of the

Immune System. Ann. Immunol. (Inst. Pasteur), 125C,

pp. 373-389.

Howard, A., Mataric, M. J., and Sukhatme, G. S., 2002.

Mobile Sensor Network Deployment using Potential

Fields: A Distributed, Scalable Solution to the Area

Coverage Problem heuristics. In DARS’02, 6th

International Symposium on Distributed Autonomous

Robotics Systems, Fukuoka, Japan.

Howard, A., Mataric, M. J., Sukhatme, G. S., 2002.

Mobile Sensor Network Deployment using Potential

Fields: A Distributed, Scalable Solution to the Area

Coverage Problem. Proceedings of the 6th

International Symposium on Distributed Autonomous

Robotics Systems (DARS02), Fukuoka, Japan, pp. 299-

308.

Timmis, J., and Neal, M. , 2001. A Resource Limited

Artificial Immune System for Data Analysis.

Knowledge Based Systems, Vol.3-4, No. 14, pp.121-

130.

Neal, M., 2002. An Artificial Immune System for

Continuous Analysis of Time-Varying Data. In 1

ICARIS.

Castro, L. N., Von Zuben, F. J., 1999. Artificial Immune

Systems: Part I – Basic Theory and Applications.

Technical Report TR-DCA 01/99.

Further Developments on Router Nodes Positioning for Wireless Networks using Artiﬁcial Immune Systems

105