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Enhanced Bio-inspired Trust
and Reputation Model for Wireless
Sensor Networks
Vivek Arya, Sita Rani, and Nilam Choudhary
Abstract Today, WSNs are spread in both industry and academia; they are focusing
their research efforts in order to enhance their appliances. One of the first concerns
to solve in order to acquire that expected enrichment is to assure relieve a minimum
level of security in such a prohibitive environment. This study concentrates on trust
and reputation system management. The proposed approach titled enhanced bio-
inspired trust and reputation model (EBTRM) is Bio-inspired extending Trust and
Reputation Model. The aim of the proposed algorithm is to provide an adequate
security solution to collusion network of BTRM, which can provide a high level of
security and energy preserving ability
Keywords Wireless sensor networks ·Security ·Trust and reputation system ·
BTRM ·Accuracy ·Path length and energy consumption
1 Introduction
In last few years, researchers and scientists pay more attention to the area of WSNs
[1]. WSNs are composed of large number of sensor nodes. These sensor nodes are
small in size and battery powered [2,3]. In WSNs, sensor node senses the data,
collect, process and transmit the data to other nodes to complete a task in distributed
manner. In WSNs [4,5], result is based on sensor nodes cooperation. WSNs use
wide variety of applications, for example, industrial process control, ecological and
habitat monitoring, home automation, health care system, weather forecasting, traffic
control, etc. Generally, WSNs are deployed in an outdoor environment, where the
V. A r y a ( B)
Department of ECE (FET), Gurukula Kangri (Deemed To Be University), Haridwar 249404, India
S. Rani
Department of Computer Science and Engineering, Gulzar Group of Institutes, Khanna, Punjab
141401, India
N. Choudhary
Department of CSE, JECRC, Jaipur, India
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022
D. Gupta et al. (eds.), Proceedings of Second Doctoral Symposium on Computational
Intelligence, Advances in Intelligent Systems and Computing 1374,
https://doi.org/10.1007/978-981- 16-3346- 1_46
569
570 V. Arya et al.
possibility of an adversary [6] always more than in an indoor environment. These
malevolent nodes may transmit wrong information in the network; due to malevolent
nodes the performance of the system is decreased. There are a lot of techniques or
method to detect a malicious nodes in the WSNs, and cryptography is one of the tech-
niques which protect our network from attacks or malevolent nodes. Major drawback
of cryptography technique is complex computation [7]. Trust and reputation model
(TRM) is a creative solution for sustaining a lowest security level between two
objects having transactions and interactions with in dispersed system. Many trust
and reputation models were introduced in the past. Some models provide cluster
head selection, secure routing, data aggregation and synchronized trust management
[8–10]. In the fraudulent environment, malevolent node assigns maximum value to
the malevolent node and minimum value to the benevolent node [11]. In current
era, the need of hour is to enhance the data rate and security over the network. For
data rate enhancement, we generally apply data compression techniques [12–17].
We proposed enhanced bio-inspired trust and reputation system which increases the
security level. Our proposed approach is presented, and a performance comparison
between our model and the original one is carried out, followed by conclusion and
future work.
2 Trust and Reputation System
Trust and reputation system (TRS) management is a creative solution for sustaining a
lowest security level between two objects having transactions or interactions within
a distributed system. Trust is a particular level of the subjective possibility with
which an agent will perform a particular action, while a reputation is an expectation
about behavior of an agent based on information about it or considerations of its
prior behavior. In most cases, these two conditions are distinguished definitely and
could be used changeably. In WSNs transactions, if we define the sensors asking for
services as client sensors and sensors providing services as server sensors, then the
client sensors will find out whether to have transactions with a server sensor based
on its trustworthiness or reputation. Trust and reputation model is usually composed
of five components: gathering info, scoring and ranking, selecting objects, having
transaction and reward or punishment. Gathering information, the first element of a
trust and reputation system, is responsible for collecting behavioral information about
other objects, for example, peers, agents or paths. The information collected might
come from different objects. It could be absolute observation or own experience or
information provided by nodes. Once information about an object has been perfectly
assemble and weighed, and a reputation score is then estimated and given base on
certain algorithm. The main aim of this process is to provide the clients a determinable
approach to decide which server node is most trustworthy. The next step is that a
client selects the most trustworthy or reputable server object in the society providing
certain applicability, and then, adequately has intercommunication with it. After
receiving the service provided, the client will access the result and give a score of
Enhanced Bio-inspired Trust and Reputation Model … 571
satisfaction. Based on the satisfaction occurred, the final step, punishing or rewarding,
is carried out. If a server node is unsuccessful in making the client satisfied with
service provider, its reputation score will be affected, and the client is less likely to
have transaction with it again.
3 Bio-inspired Trust and Reputation Model (BTRM)
BTRM-WSN [18] carries out the selection of the most trustworthy node through the
most reputable path offering a certain service. It is based on bio-inspired algorithm
called ant colony system (ACS), where ants form paths in order to fulfill some condi-
tions graphically. Pheromone traces of ants that help coming ants to discover and
come from those paths. These pheromone values will help ants to discover the optimal
path solutions since the optimal path will have the maximum amount of pheromone
value. When we apply this ACS algorithm on to trust and reputation system, trust-
worthiness of sensors is represented by pheromone value. In this BTRM-WSN, each
sensor node holds contains pheromone traces for its neighbors (τ ∈[0,1]), which
find out possibility for an ant to select a path as well as the sensor the path leading
to as a solution. In other words, τcan be considered as the trust that a sensor gives
another. The steps of algorithm of BTRM are as follows:
3.1 Gathering Information
A set of imitation ants are developed, and then, they leave the client sensor. When
an ant proceeds from a node ito node j, it gives an instruction for these two sensor
nodes to improve the pheromone value of the path between them through Eqs. (1)
and (2),
τij =(1−ϕ).τ
ij +ϕ. (1)
=1+(1−ϕ).1−τij.η
ij(2)
τij is the pheromone value of the path between sensor iand sensor j,is the
convergence value of τij and ϕis a parameter controlling the amount of pheromone
left by the ants.
When ant moving in a network searching for the most trustworthy path to the
server providing good service, each ant must decide whether to stop and return the
solution to the client or continue to discover another one, based on the reputability
of the server that is discovered. When ant kreaches at sensors, server situations
may occur. The first is that sensor shas more neighbors not visited by ant k; then,
kestimates average pheromone value (τk)of the path come next by ant kfrom the
572 V. Arya et al.
client until the sensor s.Ifτkis greater than described transition threshold TraTh
(transition threshold), then ant k stops and returns the solution or vice versa. Another
situation is that sdoes not provide any services. If sensor shas more neighbors not
visited by ant k, then k decides the next node to move. If sensor s has visited all the
neighbors, then ant k reaches a dead end. It has to go back to the route that it has
form until it reaches at sensor offering the requested service, a sensor not offering
the requested service but having more neighbors not visited yet [19].
3.2 Score and Rank
Client will test and determine the quality of the solution brought back by each
launched ant. The quality of path could be computed by Eq. (3),
Q(Sk)=τk
Length(Sk)PLF..%Ak(3)
τij is the pheromone value of the path between sensor iand sensor j;is the
convergence value of τij and ϕis an amount of pheromone traces left by ants. Sk
designates the solution brought back by ant k.Q(Sk)defines the quality of path Sk;τk
designates the average path pheromone of path Skfound by ant k; PLF ∈[0,1]define
a path length factor and % Akdenote the percentage of ants that have selected the same
solution as ant k. After estimating the path quality of all solution brought back by ants,
the client selects the path with maximum score and collect it as Current_Best solution.
Then, the client compares the route quality with the best solution (Global_Best) found
by earlier transactions. If Current_Best solution is even better, then the client will
take the place of the previous Global_Best with the Current_Best solution. Then, an
extra ant is sent to improve the pheromone value of the current Global_Best.
3.3 Ants Transaction
After the client selects the Global_Best solution, it will have transaction with the
selected sensor. It with the default service which the client expects to obtain, after
receiving the service. There might be two conditions: first, the selected server sensor
might be completely trustworthy and provide the accurate service as it is assumed to
or it could be totally malicious and provide highly difference service. In the earlier
condition, the client is convinced and will give a satisfaction value (Sat) is find out as
an irregular number between PunTh and 1; while in the last condition, the satisfaction
value (Sat) is found out as an irregular number between 0 and PunTh as the client is
considered as unsatisfiable. PunTh is predefined punishment threshold value.
Enhanced Bio-inspired Trust and Reputation Model … 573
3.4 Punish and Reward
A client will demand the desired service to what it objects to be the most reputable
server through the most trustworthy path. Then, punish or reward will be given to
all connection in this path based on whether the client is satisfied with the service
provided by the server. This is done by increasing or decreasing the pheromone value
of the path [20–23].
4 Enhanced Bio-inspired Trust and Reputation Model
This section introduced an enhanced bio-inspired trust and reputation system inspired
by BTRM tested in prior section. In EBTRM algorithm, we modify the parameters
values of bio-inspired algorithm. Flow chart and improvements in BTRM algorithm
(EBTRM) are as shown in Fig. 1.
4.1 EBTRM Algorithm
As described in earlier, the criterion that BTRM-WSN used to determine whether
a sensor is trustworthy is the value of the solution route from the client sensor to
the selected sensor. Similarly, the quality of each solution is estimated based on the
value of average pheromone. This approach has been proven to be effective, and the
performance of this system may get improved if the condition of the server sensors
could be taken into account and examine modify some aspects of the original system.
In the first modification, we try to make system secure, we should need to increase
path quality of the system, so, we enhance security of system. In EBTRM algorithm,
Q(Sk)=τk
Length (Sk).%Ak
where PLF =1, we have selected those paths, which are as short as possible. In
second modification, we enhance the radio range and take the radio range maximum
because maximum radio range provides security. Suppose, two nodes communicate
with each other, if radio range is maximum then they can directly communicate but
its range is minimum then they cannot communicate directly with each other and
possibility of interference of malicious node is increased. The last modification is
in the value of qo(=0.6335), and the possibility of choosing deterministically the
most trustworthy next node is increased which increases the accuracy of the system.
574 V. Arya et al.
Q(Current _Best) >
Q(global _Best)
START
Initialized Number of
Sensor Best Path
Q(Sk) >
Q(Current Best )
NO Wait for Timeout
Expire
Current Best Sk Num Returned Ants <
% of Number of Ants
Pheromone
Global Updating
Pheromone
Local Updating
End
End
Yes
NO
Yes
Fig. 1 Flow chart of EBTRM
5 Simulation Results
In our proposed work, we consider ten networks composed of 10–100 sensor nodes,
each for 10 executions in two-dimensional areas. Sensor nodes in a cluster with
particular radio range transmit the data to the cluster head and then to the base
station within the entire network. In collusion network, every malicious node will give
the maximum rating for every other malicious node and minimum rating for every
benevolent one. We used Java based event driven TRMSim—WSN [24] simulator
version 0.5 for WSNs allowing the researchers to simulate and represent random
network distributions and provide statistics of different data dissemination policies
including the provision to test the different strategies of trust and reputation models.
Many networks like collusion, oscillating and dynamic networks, the percentage
of nodes, malicious nodes and so forth, can be implemented and tested over it. In
our experiment, we concentrated on collusion network and enhance the accuracy of
Enhanced Bio-inspired Trust and Reputation Model … 575
Tabl e 1 Parameters of
BTRM and EBTRM Parameters BTRM values EBTRM values
Phi 0.01 0.01
Rho 0.87 0.87
q0 0.45 0.6335
Num ants 0.35 0.35
Num iterations 0.59 0.59
Alpha 1.0 1.0
Beta 1.0 1.0
Initial pheromone 0.85 0.85
Punishment threshold 0.48 0.48
Path length factor 0.71 1
Transition threshold 0.66 0.66
Radio range 12 m 50 m
BTRM algorithm in collusion network. Table 1shows the simulation parameters of
BTRM and EBTRM algorithms.
TRMSim-0.5 WSN is a Java-based trust and reputation models simulator aiming
at providing easy way to test a trust and reputation model (BTRM) over WSNs
and to compare it with EBTRM. We design a WSN template using the network
parameter settings in TRM-WSN as: clients =15%, number of nodes =100, number
of networks =10, number of executions =10. Then, the simulator will randomly
create WSN for experiments based on this template.
5.1 Accuracy with Varying Number of Malicious Nodes
In our work, we have used the concept of accuracy to evaluate the reliability and
level of security provided by the trust and reputation system is represented by the
percentage that the number of times when it is successfully selected trustworthy
sensors out of the total number of transactions. A better trust and reputation should
have a good control of the negative influence in which the malicious nodes have on
the WSN. Figure 2shows the comparison of accuracy BTRM and EBTRM algorithm
with varying number of malicious nodes.
5.2 Path Length with Varying Number of Malicious Nodes
Path length is the average hops leading to the most trustworthy sensors which are
selected by the client in a WSN applying certain type of trust and reputation system.
It is assumed that less average path indicates a better performance in efficiency and
576 V. Arya et al.
Fig. 2 Graphical representation of accuracy of BTRM and EBTRM
Fig. 3 Graphical representation of path length of BTRM and EBTRM
easiness in searching for trustworthy sensors of a trust and reputation system. Figure 3
represents path length of BTRM and EBTRM algorithm graphically.
5.3 Energy Consumption with Varying Number of Malicious
Nodes
Energy consumption of the network is the overall energy consumed in: client nodes
sending request messages, server nodes sending response services, energy consumed
Enhanced Bio-inspired Trust and Reputation Model … 577
Fig. 4 Graphical representation of energy consumption of BTRM and EBTRM
by malicious node which provides bad services, relay nodes which do not provide
services, the energy to execute the trustworthy sensor searching process of a certain
trust and reputation system. For WSN, researcher’s major problem is how to effec-
tively reduce energy consumption. Figure 4shows EBTRM has lowest energy
consumption.
6 Conclusions
Our proposed EBTRM system successfully increases the accuracy in trust and repu-
tation system. Therefore, the level of security of the original BTRM-WSN without
sacrificing its advantages in finding trustworthy sensors efficiently and the extra
amount of energy for those add-ons is acceptable EBTRM is proven to be able
to accurately distinguish benevolent sensor from malicious sensor and thus protect
WSNs from attackers. And most important thing is level of security it provides not
influenced by the number of attackers as much as its two competitors do. When the
network is in a relatively secured status, it becomes more complicated and less energy
efficient to search for trustworthy sensors because of the extra conditioning and
computation overall the modification in BTRM a successful. Our proposed EBTRM
provides better solution to WSNs, where a high level of security is required while
future work will keep on developing the algorithms searching for trustworthy sensors
to improve the easiness in finding trustworthy sensors as well as energy efficiency.
EBTRM provides a higher level of security for WSNs without sacrificing the effi-
ciency of the original approach and does not require huge amount of energy for the
extra consumption.
578 V. Arya et al.
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