Detection and avoidance of black-hole attack in mobile adhoc network using bee-ad-hoc on-demand distance vector

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 14, No. 1, Febuary 2025, pp. 822~832
ISSN: 2252-8938, DOI: 10.11591/ijai.v14.i1.pp822-832  822
Journal homepage: http://ijai.iaescore.com
Detection and avoidance of black-hole attack in mobile adhoc
network using bee-ad-hoc on-demand distance vector
Srikanth Pala, Prasad Maddula, Kiran Sree Pokkuluri, Sunil Pattem, Ramachandra Rao Kurada,
Ramu Yadavalli
Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women, Bhimavaram, India
Article Info ABSTRACT
Article history:
Received Jan 15, 2024
Revised Oct 18, 2024
Accepted Oct 23, 2024
Mobile adhoc networks (MANETs) are self-configuring networks with a
dynamic infrastructure suit for real world applications. Due to the exponential
increase in the network devices an efficient routing algorithm for dynamic
network adhering the security issues is a critical challenge needs to be
addressed. This article attempts to address this issue with the implemention of
ad-hoc on-demand distance vector (AODV) routing approach, which is the
best of its kind in the dynamic network design of MANETs. The primary goal
is to address security attack weaknesses through the implementation of
dynamic topologies and reactive routing. To this end, a bio-inspired swarm
intelligence algorithm called Bees algorithm is used to emulate the AODV
technique. In order to provide a lightweight solution that integrates the Bee
algorithm and AODV routing, this study presents a unique algorithm called
Bee-AODC. The proposed Bee-AODC algorithm possess the both the
AODV's dynamic topology construction capabilities and the Bee algorithm's
foraging strategy which effectively address security weaknesses by creating a
dynamic network topology for ad hoc routing. By using the suggested
Bee-AODC algorithm instead of the traditional AODV routing method,
throughput is increased by 12.87% while packet loss, latency, and energy
consumption are reduced by 20%, 40%, and 18%, respectively.
Keywords:
Ad-hoc on-demand distance
vector
Bee- ad-hoc on-demand
distance vector
Bee algorithm
Black-hole attack
Mobile adhoc network
Quality of service
This is an open access article under the CC BY-SA license.
Corresponding Author:
Srikanth Pala
Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women
Bhimavaram, Andhra Pradesh, India
Email: sreekanth.pala@gmail.com
1. INTRODUCTION
The global market is marching towards the digitization due to the emerging information and
communication technology (ICT) tools [1] where the data seamlessly needs to be transferred digital format for
ease of accessibility over the smart devices connected in a wired or wireless approach. In the current scenario
the global market is focusing on data transfer using the wireless medium. Wireless ad-hoc networks (WANETs)
uses the wireless medium for the data transfer among the heterogeneous static nodes in a network using a
centralized infrastructure framework. Mobile adhoc network (MANETs) are the extension of WANETs
incorporates the heterogeneous nodes with mobile nature and doesn’t bind to the centralized frameworks.
MANETs are independent and infrastructure less networks which doesn’t maintain a centralized node for
controlling and coordinating the different networking nodes. Routing of data from the sournce to receiver node,
a route needs to be established among the nodes by vicinity and coverage between the nodes using the
interference range. Routing of data from a source to destination in the MANETs needs to adopt for network
topology in a dynamic approach [2].

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Detection and avoidance of black-hole attack in mobile adhoc network using bee-ad-hoc … (Srikanth Pala)
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MANETs are basically deployed in any personal area network for the disaster management, military
bases, defense system for monitoring system. MANETs are low power energy equipped devices with limited
bandwidth, less computational capabilities with limited hardware resources makes data transfer a challenging
issue [3], [4]. An effective routing protocol must handle establishing network link between nodes so that data
may be traversed from the source to the destination while utilizing energy resources and avoiding network
security flaws. A routing protocol that is effective must convey data in response to dynamic topological changes
by modifying the network connection without depleting the battery. An attacker using a passive attack on a
network will listen in and take material from the data flow. An attacker engaged in an active attack [5] is eager
to collect content that compromises network integrity and attempts to bring the network to a complete stop by
reducing its functionality. The black hole attack can use the routing protocol; this issue can be resolved by
integrating the bioinspired bee algorithm with the current ad-hoc on-demand distance vector (AODV).
Black-hole attack is an extensive active attack which deteriorates the performance of the network and
transforms the existing network to unreliable. This attack cosnsists of a malicious node, which creates an
illusion to the network a reliable node for a best route towards the destination node by constantly sending
route-reply (R-REP) with highest sequence number. Due to the highest sequence if the data traverses through
this node, will never reach the destination node and tends to decrease the throughput and increase the network
delay. Real-world applications are well-suited for MANETs, which are self-configuring networks with a
dynamic architecture. An effective routing algorithm for dynamic networks that adheres to security problems
is a significant topic that has to be solved due to the exponential rise in network devices. By applying the
AODV routing technique, the best of its type in the dynamic network architecture of MANETs, this paper tries
to solve this problem. The main objective is to mitigate security attack vulnerabilities by utilizing reactive
routing and dynamic topologies. In order to do this, the AODV method is imitated by the Bees system, a bio-
inspired swarm intelligence system. This work introduces a novel method called Bee-AODC, which combines
the Bee algorithm and AODV routing to produce a lightweight solution. The suggested Bee-AODC algorithm
successfully addresses security flaws by generating a dynamic network topology for ad hoc routing. It does
this by combining the AODV's dynamic topology creation capabilities with the foraging approach of the Bee
algorithm.
The overall content of the paper is as mentioned. Section 2 explains the operational functioning
AODV routing protocol with black-hole attack. Section 3 describes the review of similar works for this work.
Section 4 emphasizes on the methodological implementation. Section 5 focuses on the results obtained. Section
6 illustrates the conclusion with it its future scope.
2. FUNCTIONING OF AODV ROUTING PROTOCOL WITH BLACK-HOLE ATTACK
Black-hole attack is an attack which is deliberately active in nature where the node tries to deceives
the other nodes in the network as an active functional node with minimal distance for the operational destination
node. In-order to deceive the source node the black hole node send its routing-table to a source node as a
reliable intermediate node. The nodes adjacent to source is drawn into an illusion as the data is traversing to
reliable node with minimal distance to the destination node. Subsequently the illusion is created for the
operational-nodes in this network, try to send the data through this node but needs to identified as a black hole
node. Black-hole node receives all the data packets from the distinguished nodes in that network acting as
reliable intermediate node for their data traversal from its source to the destination. Black-hole node instead of
forwarding the packets, discards the packets in the network, may lead to the increase in network traffic and
creating a congested network will collapse total network. Data-transmission from source node to the destination
node actually initiates with a request message from the source node to neighboring nodes with in its vicinity
range. Source node after receiving an acknowledgement for its request by the routing tables from its
corresponding neighbor nodes in regards the destination node. Source node forwards the data packets to its
corresponding neighbor nodes to traverse to its appropriate destination. Black-hole node creates an illusion to
its corresponding nodes as a reliable node and receive the packets from its neighbor and discards the received
packets from the source node. Ideally the categorization of the black-hole is based upon its functional operation,
single attacker node operating individually and co-operative black-hole attack where attacker is operated
collectively with other functionally active nodes. Black-hole attack degrades the performance of the network
throughput [6], [7] losses the reliability of the network and finally communication system it totally collapsed.
The Figure 1 specifies the black-hole attack in MANETs, data packets need to be routed from the source ‘S’
to the destination ‘D’ but the intermediate node ‘B’ will be a black hole node and it drops all the packet before
reaching to the destination.
2.1. Ad-hoc on-demand distance vector routing protocol
AODV is reactive routing protocol used in MANETs, combines the functioning of re-active and
pro-active routing protocols like dynamic source routing (DSR) and destination sequenced distance vector

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(DSDV). AODV is having better performance metrics [6], [7] when compared to the other reactive routing
protocol DSR. AODV [8] uses two control messages route-request (R-REQ) and R-REP for the connection
establishment from the source to the destination. In the context of connection establishment, the source node
broadcasts the R-REQ control message to all of its adjacent nodes and the intermediate nodes forwards the
control messages to destination node. The destination node acknowledges the intermediate nodes with reply
message R-REP for the request message it has received. Source node upon receiving the reply message from
the destination node stops broadcasting of the request messages to its adjacent nodes. The performance of the
protocol degrades as the intermediate node are vulnerable for attacks like black-hole attack. Black-hole attack
significantly affects the network performance metrics like the packet-delivery-ratio, throughput, end-to-end
delay.
2.2. Challenges
The basic issue needs to be addressed in any network is the scalability [9] problem. If the nodes of the
the network ‘n’ increases, then the the throughput of the network decreases in the 1/√n percentage. Here the
basic ‘n’ value depends upon the the simulation conducted squared area. AODV is one of the efficient reactive
routing protocols for the connection establishment from the source to destination. It finds a shortest optimal
path for the communication by maintaining the decent security standards. Black hole is the one of the popular
attacks aims to prevent the communication between the nodes. AODV aims on connection establishment for
effective routing between the nodes but not focuses on identifying the black hole attack in the network. This
paper focuses on the improving the AODV algorithm in detection and prevention of the black hole attack using
the bio-inspired techniques like Bee’s algorithm [10]. A swarm intelligence algorithm like Bee-AODV used
for developing a dynamic network topology aims on black hole attack.
Figure 1. Black-hole attack in a network
3. LITEREATURE REVIEW
This section discusses about the detection and prevention of black-hole attack in the MANETs. The
characteristics of the node and deployment of the nodes in the network exploits the vulnerability of the network
are liable for the black-hole attack. This section clearly emphasizes the role of bio-inspired algorithms for
detection and prevention of black-hole attack in MANETs. Nodes in MANETs are low power energy equipped
devices, there is a need for the time synchronization process among the nodes for the proper end-to-end delay
among the nodes. A secure synchronization protocol is always robust from the attacks. AODV to be robust
there is higher importance for time-to-live (TTL) for the synchronization among the control messages R-REQ
and R-REP. Kalia and Sharma [11] specifies a new baiting technique developed by the source routing node
itself. RREQ control message consisting of the source-id and source sequence number (SSN) is broadcasted to
all adjacent nodes in the respective network. The black node in the corresponding network responds with R-
REP with a destination sequence number (DSN) greater than the SSN but there is no such node in this group
greater the specifed SSN which the source-node is aware of it. Then source node signals alert message by
raising an alarm to all the neighboring nodes regarding the effected node. Initial limitation with this technique
is, there is a possiblility assuming that effected node is not smart enough. If it is a smart black hole node, then
it recreates the source node as attacker node and source node itself blacklisted by all the other nodes in the
network.
Alotaibi [12] proposed a co-operative bait detection scheme (CBDS) consisting of the three phases
baiting-phase, reversing trace and defending reactively. In the phase of baiting, the source-node randomly
identifies an adjacent node and send a request using it id. In the second phase source basing upon R-REP

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received for its corresponding R-REQ then it creates a list of suspicious nodes in the network and source
converts into promiscuous node for identification of the attacker nodes. The source node in the promiscuous
node raise a black hole alarm all of its neighboring nodes of the attacker node, as it is in the promiscuous mode
neighbor nodes may not receive the alarm from it. In the third phase source node will check the throughput of
the suspicious node, if less than the threshold then once again the baiting phase is applied. A smart black-hole
node will raise a false alarm message and makes authenticated nodes also to isolate from the network. The
source nodes send a fabricated request to assuming that node as a black-hole node, if that corresponding node
responds the R-REQ. Source node matains the average value of DSN, if any node acknowledges with R-REP
for corresponding R-REQ for the source node verifies the DSN value it has obtained. The source node checks
the DSN if it closes to average DSN the source node treats as black-hole node or else as a normal node. Here
the it uses digital signature for authentication in identifying the black-hole node [13].
Dhende et al. [14] proposes a SAODV protocol for identifying the black-hole and gray-hole node
basing on opinion of the neighboring nodes. In this technique each no maintains two tables neighbor-list (NL)
and opinion-list (OL). Here the source node generatates R-REQ message for connection establishment to the
destination node. Source node upon receiving the reply message for the corresponding request message from
any of its neighboring node then source node broadcast opinion message claiming that this node shortest path
to the destination. The rest of neighboring nodes responds with YES or NO message. If remaining nodes
responds with NO message then source node claims that node as a black-hole node and raises a black-hole
alarm in the network, if responded YES message it is claimed as a normal node, else responded with both YES
and NO messages then it is claimed as a Gray-Hole node [15], [16].
Due to the excessive trasmission of control messages between the source anf neighboring nodes the
control overhead increases in the network, leads to congestion in the nework which affect the quality of service
(QoS) metrics. Detection and isolation of the black-hole node in network using the AODV routing protocol
can be achieved by the various techniques [11]–[14]. Mostly all the techniques use the DSN for the effective
route establishment from the source to the destinaton, but always a black-hole node claim with a highest DSN.
Watch-dog techniques have been employed for the forwarding of the packets and followed by some truth-based
algorithms in identifying the normal nodes and black hole nodes. So, there is need for the proper corelation
between the sender request message with neighbor’s response message. Therefore, in all the above-mentioned
techniques there is need of proper time synchronization techniques are needed for the request and response
packets in identifying the path as well as the malicious nodes black-hole nodes in that network.
Bio-inspired algorithms for optimization take reference from the networked aggregate behavior of
living species, namely insects and animals, in addition to the principles of natural evolutionary processes in
order to determine the most effective approaches for challenging and complex optimization problems [17].
From the work of [18] researchers are driven to seek for and develop efficient methods for discovering and
enhancing the solutions of complex and optimization problems by the increasing complexity of real-world
problems. In computing, one of the more renowned evolutionary-based techniques is the genetic algorithm
(GA). Stochastic search techniques referred to as evolutionary-based algorithms (EA) simulate the communal
dynamics and natural evolutionary processes of living things, encompassing recombination, mutation, and
adaptation in reproduction. Massive optimization problems can go above the reach of standard mathematical
techniques; in such instances, EA have been created to identify the optimal or nearly optimal response of swarm
intelligence is concerned with developing intelligent, dynamic systems with multiple agents that cooperate to
achieve a common goal that is outside the abilities of a single agent. Especially comparing to other traditional
methods, bio-inspired optimization algorithms exhibit outstanding variance, resilience, flexibility, level of
complexity, and unique events, which have contributed to their developing are attractive in the realm of
computation. The basic steps for identifying the malicious node in the network using the literature review
conducted as follows:
a) Initially a back-bone network is created, the source node raises a request for unused restricted IP.
Backbone networks searches for the new unused network IP and forwards it to the source node [11], [12].
b) Request intiated from the source-node for the data transmission to its respective destination using a
restricted IP generated by the back bone network.
c) Destination node upon receiving the request from the source node, it enters IP address in the routing table
send back to source node. Upon establishing the link, the source will initiate the further transmission of
data between the nodes [19].
d) If the node accepting the data is not destination node, then it forwards neighboring nodes by making it IP
entry in the routing table. Source node only responds and sends the data only if it is a destination node.
But if response obtained from the destination having a restricted IP address, then it starts sending the
dummy messages for identifying the malicious node.
e) Sender node intiaties a caution alert notes to its neighbors to enter into a safe zone and to keep a track of
the maliciously effected node. If the source node obtains a DSN value greater than the threshold value for
the dummy message it has generated [11]. The source nodes generate a black hole alarm to all the

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neighboring nodes and tries to determine a new safe and secure path to the destination. Thereby increasing
the throughput of the network and decreasing the end-to-end delay.
4. PROPOSED RESEARCH
The initial random population is utilized by the bee life algorithms to populate the area of search. A
colony of bees includes ‘W’ workers, ‘D’ drones, and ‘1’ queen. The two distinct bee behaviors which make
up every algorithm cycle include searching for food and reproduction. In reproduction, the fitness of the broods
is determined after generation ‘N’ broods by mutation and crossover. The fittest brood succeeds the queen for
a subsequent population if it is a better fit than the queen. Then, using the ‘D’ fittest drones and broods of the
current colony, the ‘D’ best bees are chosen to produce the next generation of drones. ‘W’ best bees are then
chosen from the ‘W’ fittest surviving hovers and workers of the current community to ensure food gathering;
if otherwise, the algorithm is aborted. When it comes to food foraging behavior, ‘W’ workers in ‘W’ regions
look for sources of food first. subsequently bees are chosen for neighborhood searches in each location. The
fittest bees in each location will be chosen to create the following bee population, and its fitness will be
evaluated. If the halting requirement is not met after these two bee behaviors, a new bee life cycle is carried
out; if not, the algorithm is terminated. The protocols use the Bee’s behavior in solving the surviving the fittest
function in identifying the black-hole attack. BeeAdhoc [20] routing algorithm, that borrows its clues from
honey bee foraging behavior, is intended for use in mobile ad hoc networks. It functions as an energy-efficient
reactive source routing algorithm. To find new pathways and move data from source to destination, the
algorithm uses scouts and foragers, respectively involves the following steps as shown.
Step 1 Route-discovery: A forward-looking scout is broadcast to each neighbor of a node with an
increasing TTL when a route for a destination is needed. Down until the point of destination,
intermediate nodes append their addresses to the scout's source route.
Step 2 Backward-scout: The target node gives back the scout to source route for configuring the
backward scout after the forward scout reaches at the destination. The source receives this
backward scout afterwards.
Step 3 Route-advertisement: The backward scout notifies subsequent foragers regarding the route after
finding its way returning to the initial node.
Step 4 Data-transport: Data routed to the target node by foragers using a dance metaphor as an aid. To
determine the dancing number, suggesting standards of the routing path, alongside gathering the
routing data
Mazhar and Farooq [21] have specified the above framework, BeeSec, a secure alternative of
BeeAdHoc which enables the use of digital signatures based on asymmetric cryptography. Scouts and foragers
in BeeSec rely on digital signatures which have been generated using various parameters such as source and
destination addresses, packet IDs, and routing information. Additionally, the source route's integrity is
preserved to make sure that malevolent nodes can't eliminate legitimate nodes from the path. As a result,
BeeSec successfully thwarts attempts to tamper with or fabricate attacks in BeeAdHoc and counters attacks
directed towards the routing. The pseudocode of the basic Bee-algorithm.
Pseudo-Code for Basic Bee-Algorithm
Initialization: Popualation is assigned with Random Solutions
Fitness function evaluation
Repeat-until (Stopping criteria is met)
1. Choose the sites for neighborhood-search
2. Recruit the Bees for chosen sites
3. Choose the fittest Bee from each site
4. Assign the Bees for random search
End-repeat
Detection mechanism needs to protect the data both in rest and transmission state, data protection in
the rest state is a likely approach. If malicious node enters into the network, it is likely to destroy the data and
manipulate the network heuristics to increase the network traffic. So, the there is a need for the network
surveillance system to defend the forthcoming attacks in the network. Identification of the effected node in the
MANETs is shown in Figure 2 basing on the network incoming traffic module, root node for increasing in the
network load with traffic inception module, analysis module with event generation for identification of the
node. On simulating the Bees approach to the network for identifying the malicious node, whenever food is

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needed, the scout bees from each node are sent to the nectar area to search for it. Scout bees were sent out to
collect data in the nectar area, which resulted in obtaining of the node's routing information. The scout bees
gather data regarding the nodes they have visited, including the distance and time delay. The movements that
the scout bees conducted in the hive upon getting back are shared along with this information. Besides having
access to information from the paths they have already walked, the scout bees also carry information from
neighbor nodes to the next node. Research refers to the scout bees that transmit this data as "accumulator scout
bees." The pseudode of the elimination of malicious node.
Figure 2. Approach for identifying black-hole attack
Pseudo-code of elimination of Malicious Node
Step 1: Identification of Malicious Node
1.1: Adding Malicious Node to the Cluster Head in the routing table
Step 2: Broadcasting the Malicious node to all the Cluster Heads in the MANET Network
Step 3: Cluster Head Broadcast list to all the Nodes in its Region
Step 4: Drops the Packets Received from the Malicious Node
The neighborhood search of every element is obtained by the solution vector consisting of routing
table for each node Xi , Xpi is the solution vector and ‘ngh’ is the radius where the neighborhood is obtained is
range of the transmission of the node is shown in the (1).
𝑋𝑝𝑖 = (𝑥𝑖 − 𝑛𝑔ℎ) + 2 ∗ 𝑖 ∗ 𝑛𝑔ℎ (1)
On reaching the specific condition the loop gets terminated through the neighborhood searches signifies it. The
propose Bee-AODV algorithm.
Algorithm: Bee-AODV
Initialize: n - number of paths available
Repeat -Until (termination condition)
1. Select ‘s’ number of paths from the source to the destination
2. Choose ‘e’ from the ‘s’ having minimum distance from the source to destination
3. nsp: Number neighborhood searches performed around ‘s’ in identifying the
black-hole nodes
4. nep: Number neighborhood searches performed around ‘e’ in identifying the
black-hole nodes
5. Identify the best path among the ‘s’ and ‘e’ i.e nep>nsp
6. Select the remaining ‘n-s’ paths and verify whether the paths exists from source
to destination and just cumulate to ‘s’
End-Repeat
5. RESULTS AND DISCUSSION
The discussion can be made in several sub-sections. The research findings consist of the qualitative
and quantitative research metrics [22] that involves the three techniques from mathematical, experimental and
simulation approaches [23], [24]. Here the simulation approach is chosen for the inferential conditions.
Quantitative approach involves a series of experiments to be conducted at different situations consisting various
network sizes, transmission range, interference range, number of black-holes in determining the efficient route
from the transmitter to its respective delivery node. All these factors have a greater impact on the QoS metrics

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like throughput, network delay, packet-delivery ratio and energy utilized by the nodes in the network-region.
This research uses network-simulator version 2 (NS-2) simulator towards setting up the similation environment
of proposed work and evaluation with Qos metrics.
5.1. Network-simulator version 2
NS-2 [25] is an open-source simulator work on discrete event driven approach which is highly
accustom able for the most of the communication protocols in the network stack. The characteristic features of
the NS-2 simulator include:
− As it is discrete event simulator which supports the new designs for the existing communication protocols.
− A sustainable comparative study of the new protocols with the existing protocols in performance metrics
for enhancing the quality of routing in the network.
− NS-2 is a unix based system uses the tool-command-language (TCL) and object oriented tool-command-
language (OTCL). TCL can be flexibly integrated with any of the platform, so the protocol specification
is really flexible.
5.2. Parameter for the simulation
In this study, NS-2 is used for the performance analysis of QoS metrics on the normal AODV [26]
and Bee-AODV routing protocol for the black-hole attack. Simulation is conducted for 10, 20, 30, 35 nodes,
simulation area of 1000*1600 meters, transmission range of 250 meters, and simulation of 150 seconds. The
parameter used in the article is shown in Table 1.
Table 1. Simulation parameters
Specification parameters Standards
Protocol AODV routing protocol
Simulation-time 150 seconds
Mode-speed 40 meters/second
Size of network (nodes) 10, 20, 30, 35 nodes
Area of simulation (quad) 1000 × 1600
Type of traffic Constant-bit-rate (CBR)
MAC-type 802.11
Size of packet 512 Bytes
Simulator Network simulator
The main objective of accumulators, which boost network resource productivity, is to decrease the
number of scout bees that have travelled over the network. The sout bees in the network increases can cause to
the network infringement due to overlapping of the transmission range and interference range of the scout bee
nodes. The control request (HELLO-Req) message from the scout bees leads to the network congestion.
AODV protocol on implementation the HELLO message is broadcasted to neighboring nodes as the
R-REQ will acknowledge R-REP by its neighboring nodes for the corresponding source node. In case if no
notification is obtained for its corresponding for HELLO packet the link failure notification [27] is obtained,
upon obtaining the response then a bi-directional connectivity will be established. Local connection management
will maintain a routing table consisting of the address of the both nodes and identification number. Link failure
notification is determined by TTL for every corresponding HELLO packet given by the source node.
5.3. Parameter for the Simulation
The AODV routing protocol is reasonably affected by QoS metrics, and the black hole attack
deteriorates the routing protocol's performance in MANETs. The throughput, energy consumption, packet loss,
and latency are the elements influencing performance. All of these indicators' performance analyses are
contrasted at networks of 10, 20, 30, and 35 nodes, in varying sizes. After impacted nodes in that network
region are removed, an analysis is conducted comparing the performance of AODV with Bee-AODV.
5.3.1. Packet loss
Packet loss measures the number of the acknowledgement received from the receiver, used to justify
the reliability of the protocol. Loss of packets simply signifies the number of the packets received at receiver
for the number of the packets send by the sender, here we are using the sequence number for every HELLO in
identifying the number of packet. The packet loss ratio [28] is signified as the drop ratio in (2).
𝐷𝑟𝑜𝑝 𝑅𝑎𝑡𝑖𝑜 =
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑃𝑎𝑐𝑘𝑒𝑡𝑠 𝐷𝑟𝑜𝑝𝑝𝑒𝑑
𝑃𝑎𝑐𝑘𝑒𝑡𝑠 𝑆𝑒𝑛𝑑+𝐹𝑜𝑟𝑤𝑎𝑟𝑑𝑒𝑑 𝑝𝑎𝑐𝑘𝑒𝑡𝑠+𝑝𝑎𝑐𝑘𝑒𝑡𝑠 𝑑𝑟𝑜𝑝𝑝𝑒𝑑
(2)

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The Figure 3 packet loss for the AODV routing protocol in the black-hole attack at different intervals in pause time
for every 10 seconds, the bytes/sec specifies that the number of the data in the bytes is lost. The percentage of the
packet loss compared to normal AODV to Bee-AODV is reduced in the network with different range of nodes.
Figure 3. Packet loss comparison in percentage between AODV and Bee-AODV
5.3.2. Throughput
Throughput in routing protocol signifies the number of traffic packets that each node in the network
receives over a certain time interval and is measured in megabytes (Mbps). Every possible delay observed
while transmitting the data packet from the source node to the destination nodes is included in the average
end-to-end latency [29].
𝑇ℎ𝑟𝑜𝑢𝑔ℎ𝑝𝑢𝑡 = ∑ (
𝑝𝑎𝑐𝑘𝑒𝑡𝑠 𝑑𝑒𝑙𝑖𝑣𝑒𝑟𝑒𝑑∗𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑝𝑎𝑐𝑘𝑒𝑡 𝑠𝑖𝑧𝑒(𝑀𝐵)
𝑇𝑜𝑡𝑎𝑙 𝑃𝑎𝑐𝑘𝑒𝑡𝑠 𝑠𝑒𝑛𝑑
𝑁𝑜𝑑𝑒𝑠
𝑛=1 ) (3)
The throughput at different pause time and simulations for AODV routing protocol in the black-hole attack
with percentage of improvement in throughput when compared with normal AODV routing protocol to the
Bee-AODV routing protocol is shown in the Figure 4.
5.3.3. End-to-end delay
End-to-End delay clearly specifies the average time taken for the transmission of the data packet from
the source to the destination. Delay includes factors like the transmission time, waiting time, receiving time [30].
𝐷𝑒𝑙𝑎𝑦 = ∑ 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑡𝑖𝑚𝑒 + 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝑡𝑖𝑚𝑒 + 𝑊𝑎𝑖𝑡𝑖𝑛𝑔 𝑇𝑖𝑚𝑒
𝑁𝑜𝑑𝑒
𝑖=1 (4)
The percentage improvement in the delay when compared normal AODV with Bee-AODV routing protocol is
shown in the Figure 5. The graph clearly signifies that Bee-AODV clearly reduces the end-to-end delay in
comparison with the normal AODV routing protocol. As Bee-AODV aims in elimination of the malicious
nodes, definitely improves the end-to-end delay between the source to the destination.
Figure 4. Throughput comparison in percentage
between AODV and Bee-AODV
Figure 5. End-to-end delay comparison in percentage
between AODV and proposed Bee-AODV

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5.3.2. Energy-consumed
Energy-consumed [31] is significant factor for enhancing the performance. As the MANETs low
power energy equipped devices there is need for the energy optimization. Energy consumed consists of the
total energy required for dissemination of the data packet from the intial to the target node, receiving energy
for accquirng the data at target node and waiting energy at intermediate nodes.
𝐸𝑛𝑒𝑟𝑔𝑦 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑑 = ∑ 𝑇𝑜𝑡𝑎𝑙 𝐸𝑛𝑒𝑟𝑔𝑦 + 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝐸𝑛𝑒𝑟𝑔𝑦 + 𝑊𝑎𝑖𝑡𝑖𝑛𝑔 𝐸𝑛𝑒𝑟𝑔𝑦
𝑛𝑜𝑑𝑒𝑠
𝑖=1 (5)
The energy consumed in percentage compared to conventional AODV to Bee-AODV routing protocol
is shown in Figure 6. Here also the graph clearly signifies the improvement in the energy consumption when
compared with the conventional AODV to Bee-AODV routing protocol. The overall performance of
parameters on average analysis of the conventional AODV routing protocol compared with the proposed
Bee-AODV routing protocol is shown in Table 2. It is clearly inferring from the performance metric analysis
that the proposed Bee-AODV routing protocol performs better than conventional normal AODV routing
protocol in all the parameters like packet loss, end-to-end delay, and energy-consumption.
Figure 6. Energy consumed comparison in percentage between AODV and proposed Bee-AODV
Table 2. Performance comparison of conventional AODV and proposed Bee-AODV protocol
Parameter protocol Packet loss (%) End-to-end delay (%) Throughput (%) Energy consumption (%)
Conventional AODV 50 56.25 40 23.125
Proposed Bee-AODV 20 40.5 18 12.875
6. CONCLUSION AND FUTURE SCOPE
The research work aims for idenditification of malicious node in the network and to counteract for the
black-hole attack in AODV routing protocol. AODV routing protocol integrated with bioinspired bee algorithm
(Bee-AODV) not only enhances the security but also out rates the performance of normal AODV routing
protocol in the QoS metrics. The proposed Bee-AODV routing algorithm decreases the packet loss, delay, and
energy consumption by 20%, 40.5%, and 12.875%. The throughput is improved on 18% but not a reasonable
improvement compared to normal AODV routing protocol. Thus, the article justifies the use of conventional
AODV protocol mimiced with bee algorithms, identified and removed the number of black-holes while
establishing a safe path by redirecting the attacking nodes. A further research can be carried out in the future
using soft computing techniques for enhancing the QoS metrics of the AODV.
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BIOGRAPHIES OF AUTHORS
Srikanth Pala holds a Ph.D. degree in Computer Science Engineering from Andhra
University, Visakhapatnam, 2022. He is currently working as the Associate Professor in
Computer Science Engineering, Shri Vishnu Engineering College for Women’s since 2015. His
research interests include the routing protocols in IoT & MANET’s, soft computing approaches
like fuzzy logic and ANFIS for the time series problems. He can be contacted at email:
sreekanth.pala@gmail.com.

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832
Prasad Maddala working as an Associate Professor in Department of Computer
Science & Engineering at Shri Vishnu Engineering College for Women (Autonomus),
Bhimavaream, West Godavari Dist, Andhra Paradesh, India. He completed his bachelor's,
master's, and doctorate degree in Computer Science and Engineering. He has 19+ years of
teaching experience. He has two years of international teaching experience in Ethiopia. He has
guided more than 25+ projects at UG/PG level. He is guiding two Ph.D. research students also.
His primary areas of interest are machine learning, data mining, cloud computing, mobile
application development using Android, MANET routing, network & system security, visual
data analytics, IoT, and ethical hacking. He can be contacted at email:
prasads.maddula@gmail.com.
Kiran Sree Pokkuluri received his B.Tech. and M.E. in computer science and
engineering from JNTU and Anna University, respectively. He has obtained his Ph.D. degree in
the area of AI from JNTU-Hyderabad. He has authored six textbooks for UG and PG students
of engineering in AI and published more than 100+ research articles in various international
journals and conferences. He has filed and published SIX patents in the areas of deep learning
and AI. His bibliography was listed in Marquis Who’s Who in the World, 29th Edition (2012),
USA. He is the recipient of bharat excellence award. He has got 20+ years of teaching experience
and working as Professor in the Department of Computer Science and Engineering at Shri
Vishnu Engineering College for Women(A), Bhimavaram. He has delivered 100+ technical
talks on deep learning and AI in various international conferences, FDP’S, and webinars. He is
the Faculty Champion of the University Innovation Fellows program by Stanford University,
USA. He can be contacted at email: drkiransree@gmail.com.
Sunil Pattem holds a Master of Science (M.Sc.) in computer science, Master of
Technology (M.Tech.) in computer science and engineering, pursuing Ph.D. in computer
science and systems engineering from Andhra University. He is currently lecturing with the
Department of Computer Science and Engineering at Shri Vishnu Engineering College for
Women, Bhimavaram, Andhra Pradesh. He is a member of CSI and ISTE. His research areas of
interest include image processing and deep learning. He can be contacted at email:
sunilpattem1979@gmail.com.
Ramachandra Rao Kurada holds a Doctor of Computer Science and Engineering
degree from Acharya Nagarjuna University, Nagarjuna Nagar, Guntur, India in 2019. He also
received his M.Tech. in Computer Science and Engineering and M.Sc. (CS) in 2012 (JNTUK)
and 1999 (AU), respectively. He is currently working as Professor at Department of Computer
Science and Engineering, Shri Vishnu Engineering College for Women, Bhimavaram, India. He
has 23 year of teaching experience and his research includes AI, machine learning,
computational intelligence, networking, and blockchain. He has published over 36+ papers in
international journals and conferences. He is life member of CSE, IETE, IEI and reviewer/board
member for reputed journals, and IEEE conferences. He can be contacted at email:
ramachandrarao.kurada@gmail.com.
Ramu Yadavalli holds an M.Tech. in computer science and engineering from
Bharath Institute of Higher Education and Research (2015) and an M.Sc. in computer science
from Andhra University (2000). He is currently an Associate Professor in the Department of
Computer Science & Engineering at Shri Vishnu Engineering College for Women
(Autonomous), Bhimavaram, India. With over 22 years of teaching experience, his expertise
spans AI, data analytics, machine learning, software project management, and blockchain. He
has supervised more than 15 postgraduate and 25 undergraduate projects. He has published over
25 papers in international journals and conferences and serves as a reviewer for IEEE
conferences and textbooks from reputed publishers. He is also a life member of CSI, IETE, and
IEI. He can be contacted at email: yramumail@gmail.com.

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