Abstract- The Internet of Things is used in many sectors for different applications in an efficient way. However,

sharing huge data to distant destinations is a difficult task such as sending large data from remote area for healthcare

application. So that the Delay Tolerant Network is used but, in some cases, the presence of selfish nodes in Delay

Tolerant Network may drop all the packets. To overcome this problem the current research affords a novel African

Buffalo Delay Tolerant Network with Risk Packet Jump (ABDTN –RPJ) mechanism to improve the communication

channel by predicting the selfish and misbehavior node in an earlier stage. Besides, RPJ is introduced to split the

huge data to the other neighboring nodes to reduce packet load on the node if the load is greater than the capacity of

the node. Finally, the ABDTN –RPJ is implemented and evaluated using the NS2 simulator. The comparison results

proved the efficiency of the proposed model by reducing the delay and drop rate.

Keywords: IoT, communication, DTN, African Buffalo optimization, Risk transfer

Received: May 8, 2021. Revised: July 21, 2022. Accepted: August 16, 2022. Published: September 15, 2022.

1. Introduction

The Internet of Things (IoT) accompanied by sensor

technology [1] is a trending topic in today's network world [2].

Especially in the healthcare industry is attained more benefits

from this technology [3]. Without sensor technology, one

could not imagine an appliance [4] because sensor technology

plays a significant role in all fields. Moreover, the IoT is more

adaptable in the health care system, to collect the specified

body condition in which the sensor is inserted. The

assimilation of the IoT strategy in the clinical industry [5] has

reverted many researchers around the world to develop smart

appliances [6] such as mobile healthcare [7], intelligent model,

health-aware suggestions [8], and so on. IoT is the clustered

communication system, with a huge number of host functions

at a time for the specified work [9]. Besides, IoT devices are

consist of sensors and storage space to store the sensed or

gathered information [10]. Thus, the IoT is utilized in various

sectors and processed in different operations to achieve the

desired task [11]. The information gathered by sensors is

important for any application to perform the desired task.

Therefore, it becomes critical not to lose any data during the

communication. In an IoT Delay-tolerant environment, service

establishment is a difficult task because each host in the entire

network has a huge burden of loads to carry on [12]. Satisfying

the customer needs with a limited resource becomes more

critical [13]. Using the Delay Tolerant Network (DTN) in the

IoT environment can enhance communication by controlling

devices more efficiently. The DTN is used in many

applications including military, space communication,

vehicular communication, communication in remote areas,

tracking and monitoring of wild animals, and many more. The

link in DTN is eager to maintain end-to-end connectivity of the

network. To achieve this, the DTN is processed with a store-

carry-forward mechanism to transfer the message to a

particular location that is challenging to reach [14]. If a node

aims to broadcast the data to the target node, then the

intermediate nodes should be within the range of contact, if not

a node waits for the intermediate nodes to arise for

communication opportunity.

In today's digital world, communications in the IoT network

are embedded with the Internet or any other cloud server [15].

The IoT devices (non-mobile) are worked through the internet

by doing the task like control, monitor, etc. further IoT devices

effectively include automated data collection. The sensors

attached to the IoT devices sense the information of particular

events that are critical for the application and send data to the

root (base station) node. However, the time taken by the IoT

devices to transfer the information in the DTN framework is

not enough because of the short contact time to transfer the

information. The heavy load on the IoT devices may cause link

failure. Further, the problem becomes more challenging in

presence of selfish/malicious nodes. The selfish nodes take the

opportunity of forwarding their information by utilizing the

resource of their neighbor nodes while not providing its

resource for neighbors to forward their information. These

may cause link failure as there will not be a communication

path established in presence of selfish nodes. To overcome

such problems many researchers developed some novel

Enhancing the Communication of IoT Using African Buffalo

Delay Tolerant and Risk Packet Jump Approach

1SHRIDHAR SANSHI, 2PRAMODH KRISHNA D., 3RAMESH VATAMBETI*

1Department of CSE, National Institute of Technology Puducherry, Karaikal, INDIA

2Department of CSE, Narayana Engineering College, Guduru, INDIA

3School of Computer Science and Engineering, VIT - AP University, Amaravati, Andhra Pradesh,

INDIA.

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DOI: 10.37394/23209.2022.19.20

Shridhar Sanshi, Pramodh Krishna D.,

Ramesh Vatambeti

E-ISSN: 2224-3402

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approaches like adaptive spray [16], Fuzzy based approach

[17], intrusion detection [18], and so on. However, the

problem persists because of the DTN flexibility and IoT broad

usage. Therefore, in this paper, a hybrid routing and risk

migration model has been designed to reduce the risk factor in

the DTN-IoT communication channel. The proposed idea can

be applied to the healthcare applications that requires large

medical data to be transmitted from remote area. The key

contribution of this current research is summarized as follows:

 Initially construct the African Buffalo Delay Tolerant

Network (ABDTN) by the use of the network

simulator.

 Before transferring the data, monitor all the IoT nodes

for the malicious and any selfish activities.

 Consequently, the message is transferred and then

checks the communication or packet load and nodes

capacity.

 If the packet load is more than the capacity of the node

then the Risk Packet Jump (RPJ) model is initiated to

transfer the packet to other free nodes.

 Finally, evaluate the data transmission rate.

The remainder of this research article is itemized as follows,

section 2 describes recent literature related to IoT and DTN,

section 3 defines problem statement, section 4 deals with the

proposed methodology, section 5 enumerate the result and

discussion, and section 6 concludes the paper.

2. Related Work

Some of the recent literature's related to the detection of a

malicious node, distribution of load, and delay tolerant

described as follows:

The author Tuan Le [19] proposed a routing strategy to route

large data by dividing it into smaller chunks. The smaller

chunks were further forwarded to the destination based on

several successive nodes. The paper also proposed a delivery

model by considering crucial parameters like contact time,

data size, contact frequency and build a contact graph for

delivery. The proposed model was evaluated using simulations

by collecting real-world mobility traces. Results of simulation

showed a 53% improved delivery rate compared to other

works.

The authors in [20] have proposed a new hybrid protocol for

IoT DTN by taking the advantages of DTN routing strategies

namely flooding strategy and forwarding strategy. The

modified Prophet metrics were used in the new protocol to

improve the delivery rate. Also, the protocol uses replication

bundles to maintain the quality of service in IoT. The

simulation results conducted on the ONE simulator showed an

18% improvement in the delivery rate.

The authors Yosra Zguira et al [21] have proposed a

lightweight protocol for bike-sharing applications in urban

areas and called it Internet of Bikes for DTN (IoB-DTN). In

the IoB-DTN protocol, three buffer management policies were

discussed so that which packet to be discarded when the buffer

is full. The simulation results were conducted on the Omnet++

simulator and the results showed the impact of redundancy

packets on the overall efficiency.

In the wireless medium, identifying the estimated location is a

significant task for process distribution and resource usage. So

AhmadAlZubi et al [22] proposed a location-assisted service

to reduce the transmission delay and to maximize resource

utilization. Finally, the projected technique minimized the

request broadcasting delay. Also, the comparison analysis

proved the efficiency of the proposed model.

The crowdsourcing paradigm is a trending field in the IoT

environment, especially in the online job allocation model.

Chongyu Zhou et al [23] proposed an online scheme auction to

allocate the late tolerant job. Besides, the system utility also

increased by categorizing the trustful user in the network

channel.

The essential part of IoT is WSN, which can perform both

sensing and message broadcasting processes. But the severe

threat against IoT technology is denial attacks which consume

and collapses a large amount of data in a hybrid manner. To

overcome this Zubair A. Baiget et al [24] proposed a hybrid

intelligent denial attack detection model to protect the IoT

device. The proposed model is tested with different kinds of

attacks also achieved better results compared with other work.

The authors Solmaz et al [25] have proposed a hybrid protocol

for detecting selfish nodes by combining the methods of

reputation-based method and game theory-based method. The

hybrid protocol has three phases to monitor the behavior of

neighboring nodes. If the activity of the neighbor node was

found to be malicious then a penalty was assigned to the node

as a punishment. The simulation results of the proposed

protocol showed an improvement of 12% for detecting the

selfish nodes compared to other works.

Sybil attacker makes and controls more than one characteristic

on any system. These illegal characteristics of the Sybil

attacker cause several malicious exercises without the dread of

being distinguished and hence responsible for submitted

malign activities. To overcome this problem, Sohail Abbas

[26] projected a recognition system that notices both intended

and unintended Sybil characteristics using one-time

positioning devoid of causing time positioning information

overhead.

The DTN enables efficient communication through all wireless

communication channels but securing the communication

medium is difficult. To overcome this problem Naveed Ahma

et al [27] proposed a pseudonyms approach to secure the

DTN. When comparing this strategy with security protocols

the pseudonyms mechanism attained a better security rate by

protecting the DTN communication channel against malicious

activities.

3. Problem Statement

DTN is more suitable for IoT environments as it allows the

information to reach the destination through successive nodes.

In the DTN network if the nodes are IoT sensors then it carries

much more gathered and sensed information and that has to be

transferred to a certain base station where the processing and

decision will take place. However, in such DTN, transferring

the huge gathered information is challenging. Further, it

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complicates in presence of malicious and selfish nodes. Hence

to resolve the huge burden on IoT nodes and to increase the

transmission facilities in presence of selfish nodes, some of the

mitigation strategies should be followed.

The problem in the DTN IoT network is shown in fig. 1. In fig.

1, the source node 1 forwarding its gathered information to the

destination node 11 via nodes 8, 9, and 10. If any disruption in

the link then the information is stored in the previous node's

buffer and wait for the connection establishment. A malicious

node joins the network to establish a connection. During that

period a malicious or selfish node can get the information or

purposely drop the packets.

810 9

21 2 4

5 6 7

Buffer storage

It waits for the

connection

source

destination

After the connection establishment it sends

huge amount of data, on that time traffic will

occur and attacker tries to hack the

information, packet drop also take place.

If the connection is lost

then all data is stored in

previous buffer layer

Figure 1. Problem model

4. Proposed Model: ABDTN-RPJ

Considering the conventional network system DTN has as

many facilities because it tolerates the link failure by delaying

the packets. Therefore, it is utilized in several communication

applications. To improve the performance of such applications

a new ABDTN is designed. Initially, the ABDTN is developed

for the communication process, and then the fitness function of

the African buffalo is used to predict the malicious and selfish

node in an earlier stage. To forward more packets in the IoT

environment a novel risk transfer mechanism is developed.

Simultaneously, the RPJ model is also developed to maintain

path stability.

The proposed approach is diagrammatically shown in

fig.2.

ABDTN

Securing the data from

malicious and selfish

node

Packets

Predicting selfish and malicious node by

AB mode fitness

Re routing in secure

path

Evaluating the node

capacity and packets

weight.

Load less free IOT node

Secure and efficient

communication channel

Figure 2. Proposed model

4.1 African Buffalo Waiting Network

The ABDTN model is designed for providing a tolerant

mechanism to the network paradigm. It is an improvement

over the DTN which supports the transfer of information to a

distant destination. ABDTN has many advantages over the

conventional network which does not have the ability for

detecting and preventing malicious activities.

African buffalo is a heuristic mechanism; here it is utilized

to predict and remove the selfish and malicious node by its

fitness function. Here all the nodes are considered as IoT

nodes thus the IoT nodes have more sensitive information that

is kept very secret so the prevention and detection of attack are

quite important. In the ABDTN model, every group has an

elective member that is the head of the group which is

represented as



.Here, the head of the buffalo is considered

to be a monitoring node. The working of the proposed network

is elaborated in algorithm. 1. The head buffalo monitors all the

buffalos in the group by monitoring the fitness function of

each buffalo. The Algorithm 1 uses eqn. 1 for estimating the

radio frequency of entire node links and eqn. 2 for searching

malicious node.

Algorithm.1 ABDTN

int

node

nY ...3,2,1

*

is the set of node present in

network channel

Begin: searching for less energy nodes (malicious and selfish

node)

Estimate the radio frequency of entire node links

)()( .max22.max111



mrxlmrxlff pg 



(1)

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Where



and



represents the link and node frequency,

respectively.



is frequency evaluating factor of each node

)....,.........2,1( N



;

and

are the learning factors;

and

are random node selection to calculate its frequency

between [0,1];



.maxg

is the maximum radio range and



.maxp

represents the maximum frequency of link rate to transfer the

information.

Search the selfish and malicious node



in relation to

(



.maxp

and



.maxg

) using Eqn. (2) [37]

5.0

)(

1











(2)



.maxg

updating. Yes go to step 6. No go to step 2

If the stopping criteria isn't met, return to eqn. (1) [37], else

stop the process

Output: medium frequency value for embedding

The significant parameters of ABDTN is

 Earlier prediction of malicious and selfish node

 Frequently monitoring the IoT node for any fault

These both important function of the newly developed network

model is utilized to carry on the communication channel in the

better way.

4.2 Risk packet jump (RPJ)

If one node overloaded with more number of packets than its

capacity then packet drop may occur. To avoid that risk

transfer model is adopted. The working of RPJ is to split the

huge data to the other nodes.

Algorithm.2 RPJ

int packets

fetch

)( packets

calculate the available packets in the network medium

)( *

kp 

// here

is the packet size and

is the bit rate

Cpacket 

is the other free

node

// packet is forward to the other free nodes

CkY  )( **

packetsgainedc 

else (search for other free hubs)

Stop

If the IoT is utilized in different sectors such as military or

space applications then there is a huge amount of data, so

distributing the data is a difficult task in DTN. For that, the

risk transfer model is proposed to reduce the burden of the

node. When the load is minimized or shared equally to the

entire nodes then the packet drop will be reduced.

Monitoring node

ABDTN

IoT nodes

Selfish or

malicious node

//If the selfish node is

predicted then it is neglected

from the network

Packet size evaluating for

Each node

If the load or packet is greater than the threshold

// search for free IoT

nodes using

CkY  )( **

Less load IoT

Packet is transferred

Cloud or server

RPJ model

Figure 3. Proposed work flow model

The entire process of the projected strategy is elaborated in

fig. 3. In the beginning stage itself, the monitoring process is

initialized by the AB fitness function. The RPJ is activated to

detect the load on each of the nodes. If the load is greater than

the threshold then searches for the node whose value is less

than the threshold. If a node is found then the load is

transferred or else it will wait for other nodes.

5. Results and Discussion

The proposed approach is implemented in NS2 and running on

the Windows 7 platform. Ns2 is the simulator tool that is

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popularly used to simulate the network model. The comparison

of the proposed strategy is compared with recent existing

approaches to determine the efficiency of the proposed work.

This research work aims to enhance the transmission channel

for IoT communication so that the DTN model is adopted.

Here the ABDTN network is constructed with 30 IoT nodes,

before message broadcasting, the energy of the node should be

determined otherwise cause’s huge data loss.

5.1 Case study

Nowadays, IoT application is advanced in many fields to

evaluate the successful measure of the proposed model the

case study is processed.

Let us consider, there are IoT nodes that contain set of sensed

medical information and medical records. It would be

transferred to the server, thus the server is located in far

distances. So before forwarding the packets the present nodes

in network should be analyzed and monitored, whether it is

good condition to receive the packets or not. Here, 5 is the

total number of nodes, 0.2 is the link frequency, 0.1 is the node

frequency. While substituting these values in eqn. (1) eqn. (3)

is obtained

1)1.05.0(1)1.05.0(12.0

1





(3)

Now the 1 is above 0.5 then it is good nodes if the node

energy is below the range of 0.5 then its go the next eqn. (2) to

check wheather it is malicious or not. The gathered

infromation from the patient with the help of IoT is detailed in

tabular form.

ABDTN

ABDTN IoT network

Medical data

Hospital

Selfish node

To eliminate the selfish

node it reroutes to the

other IoT device

If the selfish node is

identified then the

previous node break

the connection

Huge amount of data

Shared it to other free

node

RPJ model

Figure 4. Interior work flow of the proposed approach

The interior work flow of the proposed approach is detailed in

fig.4. In the other model the high weight packet is distributed

to the other node which is in the Free State condition by the

method of Risk packet jump. For that one of the node is

initiated and monitor all the node in RPJ method it sets the

maximum threshold of 2000 bits for an each node. Then the

evaluate is processed by eqn.(4).

)( *

kp 

)20003000(

(4)

Let us consider 3000 is the bits of packet size which is handled

by a single node and 2000 is the maximum threshold range. If

the packet size is higher than the maximum threshold then it

forward to the other node.

5.2 Performance metrics

To evaluate the effeteness of the proposed strategy some of the

key metrics should be validated in standard way such as

routing overhead, packet delivery ratio, delay, packet drop

ratio and node life time. For that some of the recent existing

works are adopted such as precedence and Reliability base

Routing in Delay Tolerant model (PRiDE) [25], Online

auction scheduling (OAS) [21], locality assisted delay service

detection (LADS) [20].

Routing overhead

Routing overhead is calculated in terms of packet size divided

by node capacity, it is well evaluated by calculating the traffic

analysis in the network link. Each node has own buffer to store

the data when the connection is interrupted.

Figure 5. Routing overhead

Here the routing overhead is validated under the TTL

condition; the metrics time to alive of nodes is defined as the

remaining energy of the IoT node after transferring the

message. The attained routing overhead by the NS2 is shown

in fig.5 and its comparison result is validated in figh.6.

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Figure 6. Routing overhead comparison

Table 1. Routing overhead comparison

Routing overhead comparison

IoT

nodes

LADS

OAS

PRiDE

Proposed

Packet delivery ratio

It is validated by calculating the number of packets which

delivered in limited interval of time. In DTN the packet

delivery ratio is improved by developing the high routing

protocols. The packet broadcasting is defined as the amount of

packets delivered by source and the amount of data received

by the target node in eqn.(5).

Packet delivery ratio=

packetsreceivedofamount

packetsdeliveredofamount

(5)

The obtained packet delivery ratio by the network simulator is

shown in fig.7 and its comparison is elaborated in fig.8 and

table.2.

Figure 8. Packet Delivery Ratio Vs IoT nodes

Table 2. Packet delivery ratio comparison

packet delivery ratio

IoT

nodes

LADS

OAS

PRiDE

Proposed

99.2

98.5

Delay

While broadcasting the packet, the approximation of receiving

time is noted. The delay is validated as the extra time taken to

complete the process. The obtained delay rate is mentioned in

fig.9 and its comparison is evaluated in fig.10 and table.3. As

shown in fig.10 the proposed idea showed better delay as

compared to other techniques especially in high density of

node. In the proposed technique, intermediate nodes are

selected immediately and forwarded as compared to others

techniques.

Figure 9. Delay rate

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The proposed mode shows the delay rate as 3 ms for 40 IoT

nodes, while comparing it with the recent existing approached

it proved the efficency by attaing very less delay rate.

Figure 10. Delay comparison

The statics of the delay comparison with exing approach is

drawn in table.3, also here the delay is calculated as milli

second.

Table 3. Delay comparison

Delay (ms) comparison

IoT

nodes

LADS

OAS

PRiDE

Proposed

5.3 Node life time

In wireless communication medium the awareness of node is

more important before transfering the data or information. So

the life time of the node is estinated by evaluating the energy

of nodes. The comaparison of node life time is detailed in

fig.11 and in table.4.

Figure 11. Node life time

After the each data transmission the life time of the node

should be evaluated for the better communication channel.

Without the awareness of node life time the efficient

transmmision in wireless medium is impossible.

Table 4. Node life time

Methods

Node life time (s)

LADS

OAS

PRiDE

Proposed

5.4 Packet drop

Packet drop is validated by taking the difference between the

amount of received packet by the total number of original

packet in eqn.(6).

packetsTotal

packetsreceived

droppacket 

(6)

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Figure 12. Packet drop ratio comparison

The comparison of packet drop ratio with existing approach is

validated in fig. 12 and its statistics is drawn in Table 5

Table 5. Packet drop ratio

Methods

Packet drop ratio

LADS

OAS

PRiDE

Proposed

5.5 Discussion

From all the comparison validation results, the proposed

approach proved its efficiency and which is applicable in all

DTN wireless environment without much complexity. Aslo it

assured the security communication in the wireless channel.

6. Conclusion

To afford the network channel for an IoT device is some more

a critical issue, so that the present research work proposed a

novel strategy which is ABDTN to construct an efficient

network channel. The fitness ofn function of african buffalo is

utilized here to predict the malicious and selfish node in earlier

stage. Then a novel RPJ mechanism is introduced to tranfer the

high load packet to the other noder duirin g the distribution

process. Thus thecommunication channel of IoT is enhanced

and spport to foprward the huge data to the far distances.

Finnally, the proposed model is compared with existing

approached and gained better resut by attaining the high

packet delivey ratio as 99% and reduced packet drop ratio as

3%.

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Contribution of Individual Authors to the Creation of a

Scientific Article (Ghostwriting Policy)

Shridhar Sanshi – Proposed Algorithm, Coding and simulation

of proposed approach.

Pramodh Krishna – Literature review and identification of

related approaches for comparison.

Ramesh Vatambeti – Results and discussion

Creative Commons Attribution License 4.0 (Attribution

4.0 International, CC BY 4.0)

This article is published under the terms of the Creative

Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en_US

WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS

DOI: 10.37394/23209.2022.19.20

Shridhar Sanshi, Pramodh Krishna D.,

Ramesh Vatambeti

E-ISSN: 2224-3402

201

Volume 19, 2022