Analysis of RPL Objective Functions with Security Perspective

Cansu Dogan

1 a

, Selim Yilmaz

1,2 b

and Sevil Sen

1 c

WISE Lab., Department of Computer Engineering, Hacettepe University, Ankara, Turkey

Department of Software Engineering, Mu

gla Sıtkı Koc¸man University, Mu

gla, Turkey

Keywords:

Internet of Things, Routing Protocol, RPL, Objective Functions, RPL Security, RPL Attacks.

Abstract:

The IPv6 Routing Protocol for Low Power Lossy Networks (RPL) is one of the standardized routing pro-

tocols for lossy networks consisting of resource-constrained Internet of Things (IoT) devices. RPL allows

to use different objective functions based on different routing metrics such as expected transmission count

(ETX), hop count, and energy to determine effective routes. In the literature, the performance of two objec-

tive functions namely Objective Function Zero (OF0), Minimum Rank with Hysteresis Objective Function

(MRHOF) are evaluated thoroughly, since they are accepted as standard objective functions in RPL. However

their performance under attack has not been evaluated comprehensively yet. Although RPL has deﬁned some

speciﬁcations for its security, it is still vulnerable to insider attacks, which could dramatically affect the net-

work performance. Therefore, this study investigates how the performance of objective functions are affected

by RPL speciﬁc attacks. Version number, DIS ﬂooding, and worst parent attacks are analyzed by using the

following performance metrics: packet delivery ratio, overhead, latency, and power consumption. Moreover,

how they are affected by the number of attackers in the network are analyzed. To the best of the authors’

knowledge, this is the ﬁrst study that comprehensively explores RPL objective functions on networks under

attacks.

1 INTRODUCTION

The recent progress in embedded system technol-

ogy has led to the emergence of numerous sensor

devices having different architectures today. These

resource-constrained devices having small on-board

memory, low power, and low computational capabil-

ity can communicate with each other and connect to

the Internet in order to complete speciﬁc tasks. IoT

networks consisting of such sensors have grown so

rapidly in a very short time (Statistica, 2016). They

are used in various IoT applications in every aspect

of our daily life such as healthcare, agriculture, trans-

portation, security.

The Low Power and Lossy Networks (LLN) are

a special type of IoT, in which resource constrained

devices are connected over lossy links that have high

packet loss and so low throughput. In order to meet

the special requirements of such lossy and resource-

constrained networks, a new routing protocol called

RPL (Routing Protocol for Low Power and Lossy

https://orcid.org/0000-0002-3806-657X

https://orcid.org/0000-0002-9516-6892

https://orcid.org/0000-0001-5814-9973

Networks) is developed by IETF-ROLL (Winter et al.,

2012). Due to its efﬁcient routing capability, RPL

has now become a standard routing protocol. The

optimal route between two end-points is found by

selecting the most appropriate communication links

between every node. To ensure that, RPL employs

different objective functions that are based on dif-

ferent routing metrics such as ETX, hop count, and

energy. Although, a number of objective functions

have been proposed till now; OF0 (Thubert, 2012)

and MRHOF (Gnawali and Levis, 2012) are regarded

as standard objective functions in RPL by the Internet

Engineering Task Force Routing over Low power and

Lossy networks (IETF-ROLL) working group and are

widely implemented in the most popular IoT applica-

tions today (Onwuegbuzie et al., 2020; Pradittasnee,

2017). OF0 considers the hop count, and hence it tar-

gets the minimum hop count between sender nodes

and the root node in the network. MRHOF, how-

ever, uses link- or node-based routing metrics. There-

fore, it provides an optimal path based on the met-

ric used. The selection of objective functions could

change routing performance, so the selection of the

appropriate objective function is usually based on the

requirements of the application at hand.

Dogan, C., Yilmaz, S. and Sen, S.

Analysis of RPL Objective Functions with Security Perspective.

DOI: 10.5220/0011011900003118

In Proceedings of the 11th International Conference on Sensor Networks (SENSORNETS 2022), pages 71-80

ISBN: 978-989-758-551-7; ISSN: 2184-4380

Although RPL ensures an efﬁcient routing perfor-

mance for such constrained networks, it is vulnerable

to insider attacks. The insecure nature of this pro-

tocol is given one of the main obstacles for the fur-

ther development IoT applications (Verma and Ranga,

2019; Abhishek and Virender, 2020). RPL attacks can

easily harm the network they are incorporated by ex-

ploiting and exhausting the resources of constrained

nodes. The degree to which the network is affected

changes according to the objective functions because

they rely on different routing metrics. In this study,

we investigate how objective functions are sensitive

to different attack types and explore if any correla-

tion exists between number of attackers and perfor-

mance degradation when different objective functions

are used. In this regard, to the best of our knowledge,

this is the ﬁrst study in the literature that compre-

hensively analyzes objective functions from security

point of view. Three RPL speciﬁc attacks, namely;

version number, DIS ﬂooding, and worst parent at-

tacks are implemented on a large number of simulated

networks with varying topologies in order to ensure a

better evaluation. The simulation results are evaluated

by using the following performance metrics: packet

delivery ratio (PDR), overhead, latency, and power

consumption. A large number of experimental envi-

ronments are set in order to ensure a better evaluation.

The experimental results show that RPL attacks, espe-

cially the ones aiming to consume network resources

(i.e., version number and DIS ﬂooding attacks) affect

the performance of networks considerably even in the

presence of a single attacker. Moreover, they affect

networks that use different objective functions differ-

ently. While OF0 is more robust to attacks, MRHOF-

ENERGY shows the worst performance in almost all

performance metrics due to the overhead caused by

attackers.

The rest of the paper is organized as follows. The

background information that covers an overview of

RPL, the standard/default objective functions used in

RPL, and attacks targeting RPL is given in Section 2.

Section 3 discusses analysis studies in the literature

that focus on objective functions and attacks in RPL

separately. Experimental settings are introduced and

the experimental results are discussed in Section 4.

Finally, Section 5 concludes the study.

2 BACKGROUND

2.1 Overview of RPL

RPL is a distance vector routing protocol. Nodes in a

RPL-based IoT network are connected to each other

through a special topology that is the combination of

tree and mesh topologies called Destination Oriented

Directed Acyclic Graphs (DODAG). A DODAG com-

prises of a root (or sink) node, which is responsible for

the initiation of DODAG building, and a number of

sensor nodes. RPL enables multipoint-to-point com-

munication (MP2P) from sensor nodes to root node,

point-to-multipoint communication (P2MP) from the

root node to sensor nodes, and point-to-point commu-

nication (P2P) between sensor nodes.

A network can operate on one or more RPL in-

stances where multiple DODAGs can take part. The

role of each instance is to deﬁne an objective function

to calculate the optimum path within the DODAG. A

DODAG is built by using the following RPL control

packets:

• DODAG Information Object (DIO): It is initiated

and broadcast by only the root node. DIO pack-

ets carry network information (e.g., instance ID,

version number). Each of the receiving node adds

the sender to its parent list, calculates its own rank

value, which states its position in the graph with

respect to the root node, and ﬁnally it forwards

DIO to its neighbors. DIO packets are relayed

throughout the graph and play a major role in con-

structing the default upward routes.

• DODAG Information Solicitation (DIS): It is used

as a solicitation for having DIO information when

a new node is to join the DODAG. DIS packets

are broadcast by the new node to its neighbors.

• Destination Advertisement Object (DAO): It is

used for the construction of the downward routes

from the root to sensor nodes. Based on the mode

of operation, the child nodes unicast DAO packets

either to root node (in non-storing mode) or to its

selected parent (in storing mode) so that it records

downward routes in its routing table for the sub-

DODAG.

• Destination Advertisement Object Acknowledge-

ment (DAO-ACK): Upon receiving DAO packets

from a parent node, DAO-ACK packets are sent

to the the sender node as an acknowledgement.

2.2 Objective Functions in RPL

Objective function (OF) is used to select optimal

routes within a DODAG by affecting the calculation

of the rank values of each participating node. As

stated earlier, it differs with respect to the RPL in-

stances, hence different objective functions could be

simultaneously used within an RPL network by dif-

ferent instances. For example, one can take hop count

into consideration to build routes of a DODAG graph,

SENSORNETS 2022 - 11th International Conference on Sensor Networks

the residual energy of the nodes could be used for

ﬁnding the routes of another DODAG in the same net-

work. Therefore, the selection of appropriate objec-

tive functions is crucially important, and it changes in

accordance with the requirements of the application.

Even though there have been a number of ob-

jective functions proposed in the literature so far as

discussed in Section 3, OF0 (Thubert, 2012) and

MRHOF (Gnawali and Levis, 2012) are proposed as

the default objective functions for RPL-based IoT net-

works:

2.2.1 OF0

OF0 takes the hop count between the root node and

a sensor node into account for the calculation of rank

value of that node. Therefore, it aims to minimize the

number of hops to reach to the root node by choosing

the node that has the lowest rank from its reachable

neighbors as its parent. When OF0 is used as the ob-

jective function in the network, for a given node n, the

rank of this node can be calculated by using (1).

R(n) = R(p) + RI (1)

R(n) is the new rank of node n, R(p) is the rank of

the preferred parent node, and RI stands for the rank

increase metric that is calculated by using (2).

RI = (R f × Sp +Sr) × MHRI (2)

R f is a conﬁgurable rank factor and it uses 1 as the

default value. Sp is the step of the rank, and Sr is

the maximum value assigned to the rank level. MHRI

stands for MinHopRankIncrease which is a constant

value that is deﬁned as 256 in RFC6550 (Winter et al.,

2012).

2.2.2 MRHOF

Unlike to OF0, a number of link- and node-based ad-

ditive routing metrics can easily be integrated into

MRHOF. The rank value (R(n) in Eq. 1), and hence

the routing path, is determined according to the em-

ployed routing metric which is stored in Metric Con-

tainer suboption in the DIO packet.

While ETX, latency, packet loss rate, received sig-

nal strength indication are example to the link-based

routing metrics; the remaining energy, maximum life

time, and trustworthiness are examples to the node-

based routing metrics for LLNs. By using one of

these routing metrics, MRHOF ensures the lowest

cost path in the LLN. Two metrics are integrated into

MRHOF in this study: MRHOF with ETX (MRHOF-

ETX), which is a link-based metric, and MRHOF

with energy (MRHOF-ENERGY), which is a node-

based metric.

MRHOF-ETX chooses the paths with the lowest

number of transmission value by considering ETX

values of the links. The ETX value of the links is

calculated using (3).

ET X =

D f × Dr

(3)

D f is the probability of the packet being reached by

the neighbor, and Dr is the probability of the acknowl-

edgment packet being received.

MRHOF-ENERGY chooses the path that pro-

vides maximum remaining energy for the RPL nodes.

Energy metric of the nodes is calculated using (4).

ENERGY =

max

now

(4)

where P

max

is deﬁned as the targeted maximum

power, and it is calculated from the initial energy of

node divided by the targeted lifetime; P

now

, however,

is the actual power of node.

2.3 RPL Speciﬁc Attacks

One of the main drawbacks of the RPL protocol is

that it is vulnerable to a variety of attacks due to its

nature. Moreover, resource-constraint characteristic

of LLNs makes them vulnerable to Denial-of-Service

(DoS) attacks. RPL speciﬁc attacks can vary accord-

ing to what they primarily target, and these attacks are

divided into three categories: attacks targeting net-

work resources, network topology, and network traf-

ﬁc (Mayzaud et al., 2016). In this study, we have stud-

ied version number, DIS ﬂooding, and worst parent

attacks:

• Version Number: Version number in DIO packets

is used by the DODAG root in order to perform

global repair, and it is increased by only the root

node. In the attack scenario, the malicious node il-

legitimately increases the incoming version num-

ber, causing unnecessary rebuilding of DODAG.

• DIS Flooding: It is a typical RPL speciﬁc DoS at-

tack that targets consuming network resources. In

order to make nodes or links unavailable in LLNs,

attacker continuously sends large amount of con-

trol packets. This attack is often performed by

sending broadcast or unicast DIS packets after re-

ceiving a DIO packet from a node. By doing so,

the DIS ﬂooding attack brings about network con-

gestion or overloading of RPL nodes.

• Worst Parent: As stated earlier, an RPL node

chooses its own parent node according to the rank

Analysis of RPL Objective Functions with Security Perspective

value that is determined by the objective func-

tion, which ensures the ‘best parent’ for that RPL

node. However, in this attack scenario, attacker

node contrarily chooses the worst parent, resulting

in non-optimized routing path and hence leading

LLN to show very poor performance.

3 RELATED WORKS

By building efﬁcient routes among sensor nodes, the

objective functions deﬁned on LLNs have a great im-

pact on the Quality of Service (QoS) in an IoT net-

work. That’s why, the attention towards RPL objec-

tive functions has been growing; and until now, there

is a good deal of studies proposed on this research

area in the literature. In this section, we mainly dis-

cuss the studies that analyze the existing standard OFs

and that propose new OFs by integrating different

routing metrics. Lastly, the studies analysing routing

attacks against RPL are summarized and the contribu-

tion of the current study is emphasized.

As stressed earlier, OF0 and MRHOF objective

functions are the basic functions deﬁned in the RPL

protocol. That’s why they have been investigated

by far in the literature. In (Kechiche et al., 2017),

how efﬁcient routing built by OF0, MRHOF-ETX,

and MRHOF-ENERGY as a response to different net-

work densities is investigated. The analysis in this

study shows that MRHOF-ENERGY performs poorly

in high and low trafﬁc, while OF0 and MRHOF-ETX

perform better on low trafﬁc. A very similar analy-

sis that shows the impact of network density on dif-

ferent OFs is given in (Qasem et al., 2015). Unlike

to (Kechiche et al., 2017), the authors here took the

topology (grid and random) and the packet reception

ratio (RX) into consideration as key parameters since

they might play a major role on the performance of

OFs. For both topologies, a higher PDR performance

and lower battery level are obtained as RX increases

and as the network becomes more dense. The routing

performances of OF0 and MRHOF (with ETX and ex-

pected lifetime [ELT] metrics) are analyzed in (Jamil

et al., 2019) according to the distance of sensor nodes

to the root node. The ﬁndings reveal that, for all OFs,

the network performance (PDR, overhead, latency)

gradually decreases with the increase in the average

distance of nodes to the root. The parameters like

node density, transmission range, and time interval

for sending control packets are considered in (Mar-

dini et al., 2018) for the performance analysis of OF0

and MRHOF-ETX. As expected, the authors found

that a positive correlation between the sending inter-

val and PDR; whereas, a negative correlation between

the sending interval and power consumption. The

node density, however, has no major impact on the

performance metrics.

The fact that network requirements could vary ac-

cording to IoT applications or services, the develop-

ment of new objective functions for these applications

by integrating new RPL metrics or combining existing

metrics has become one of the most studied research

areas in RPL (Pancaroglu and Sen, 2021). In (Al-

Kashoash et al., 2016), the authors showed that a great

number of data packets are lost when the data traf-

ﬁc is high. To ensure a reliable and efﬁcient routing

when the network is under congestion, they took the

buffer occupancy into consideration as a routing met-

ric; and hence, Congestion-Aware Objective Func-

tion (CA-OF) is proposed. Even though the perfor-

mance of CA-OF degrades when the trafﬁc is higher,

it still performs superior than OF0, MRHOF-ETX,

and MRHOF-ENERGY in terms of PDR, energy con-

sumption, and packet loss.

Not only a single RPL metric, but also multiple

metrics can be integrated to OFs so that it considers

more than one objective simultaneously. In (Sousa

et al., 2017), Energy Efﬁcient and Path Reliability

Aware Objective Function (ERAOF) is proposed. By

considering two RPL metrics, energy consumed and

ETX, ERAOF disregards the routes with low energy

level and high probability of packet loss. In com-

parison to OF0 and MRHOF-ETX, ERAOF yields

higher PDR, lower number of hops, and comparable

energy consumption. In (Xiao et al., 2014), the au-

thors combined hop count and ETX metrics in order

to improve MRHOF-ETX. However, that the cumula-

tive ETX value can only be calculated along the path

(as in MRHOF-ETX) might be misleading in the se-

lection of appropriate route. Regardless of the quality

of each link, a node using MRHOF-ETX is likely to

choose the path with fewer hops where the cumulative

ETX value is relatively smaller. In order to avoid this

situation, authors calculated the ETX value per hop

by dividing ETX by the number of hops. This yield

a higher PDR, shorter latency, and lower energy con-

sumption in comparison to OF0 and MRHOF-ETX.

The results suggest that network shows better perfor-

mance when multiple RPL metrics are used together

to choose paths. These metrics could be combined

in various ways according to requirements of differ-

ent applications. In (Karkazis et al., 2012) two differ-

ent strategies for combining four different RPL met-

rics that are hop count, ETX, packet forwarding in-

dication, and remaining energy are proposed. These

are additive and lexical combinations. While these

metrics values are averaged with relative weights in

the additive combination, it is the prioritization of the

SENSORNETS 2022 - 11th International Conference on Sensor Networks

metrics that plays a key role in the lexical combina-

tion. For example, in the lexical combination of hop

count and packet forwarding indication, the hop count

is checked ﬁrst and only if two paths have an equal

hop count metric, then the packet forwarding indica-

tion metric value is taken into consideration.

The performance of an RPL-based IoT network is

not only affected by the OFs, but also by malicious

attempts; since attackers could dramatically harm the

resource, trafﬁc, and topology of a network. There-

fore, there are also studies in the literature that an-

alyze the effects of RPL attacks. The version num-

ber attack is studied in (Aris et al., 2016). The effect

of this attack on network performance is analyzed by

two parameters: the attacker location (with respect to

the root node) and attacking probability. They found

that the attacker location has a clear effect on PDR

and overhead, but not the key factor for power con-

sumption and latency. As expected, all performance

metrics dramatically decrease as the attacking proba-

bility increases. This study is extended in (Arıs¸ and

Oktu

g, 2020) by integrating multiple attackers into

the network.

The rank value plays an important role in RPL op-

erations such as including creation of optimal topol-

ogy, prevention of loop formation in DODAG. How-

ever, it could be exploited by malicious nodes in order

to dramatically affect the network’s resources, topol-

ogy, and trafﬁc. This illegitimate attempt is known as

rank attack. The rank attack is analyzed with differ-

ent attacker locations in (Le et al., 2013). The anal-

ysis here shows that the bigger the forwarding load

area, which is the sum of the forwarding load of all

nodes in the area, is, the more impact attack leads

to on the network performance. In addition, the co-

operation of multiple attackers gives severe damage

to the network performance. The DIO packets are

very crucial in constructing the DODAG, and ﬁnd-

ing upward routing paths where the majority of ap-

plication trafﬁc follows. In order to harm a network,

the malicious nodes could either interrupt or at least

slow down the propagation of DIO packets, which is

known as DIO suppression attack. This attack is an-

alyzed in (Perazzo et al., 2017) with ﬁve cooperative

malicious nodes. It is shown that DIO suppression at-

tack considerably affects the packet delivery ratio and

network path stretch which is a metric used in order

to show the difference between the cost of a current

route cost and the cost of the shortest path.

As shown in the literature, the performance of

an RPL-based IoT network is very sensitive to rout-

ing attacks. In addition, how a network is affected

might vary according to the OFs as RPL attacks target

network- or node-related properties. That’s why, it is

worth analyzing OFs against various RPL speciﬁc at-

tacks. This is the main motivation of this study. To the

best of our knowledge, there is only a study (Semedo

et al., 2018) in the literature that addresses this prob-

lem. However, in that study the authors assess only

OF0 and MRHOF-ETX objective functions against

rank attack. In their experiements, only one network

with a single attacker node is simulated. That’s why,

the impact of the number of attackers on different OFs

is not studied. Moreover, the simulation is run on a

very small network consisting of only 19 nodes, lead-

ing a very low trafﬁc. Hence, how OFs perform in

dense networks is disregarded in that study. More im-

portantly, it is suggested in (Kim et al., 2017) to use

at least 25 nodes in network simulations in order to

see multi-hop characteristics of RPL. In short, a lot

of research questions are left unresolved in (Semedo

et al., 2018). In this current study, we include mul-

tiple RPL speciﬁc attacks with varying number of

attackers. Instead of a single network, we consider

ten networks with 50 nodes for each attack scenario.

The main contribution of this current study is to ana-

lyze OF0, MRHOF-ETX and MRHOF-ENERGY ob-

jective functions with security perspective thoroughly

and discuss the effects of attacks on these OFs with

comprehensive simulations.

4 ANALYSIS OF RPL OBJECTIVE

FUNCTIONS

This study analyzes the impact of routing attacks on

RPL-based IoT networks based on different objective

functions. In order to that, different attack scenarios

are implemented on simulated networks. The subse-

quent chapters introduces the details of these simu-

lated networks and the performance metrics that are

used to compare different objective functions. Finally,

the experimental results are discussed thoroughly.

4.1 Simulation Settings

RPL attacks, objective functions, and the number of

attackers are used as parameters that are to be inves-

tigated in the experiments. The combination of these

input parameters deﬁnes a network scenario. In order

to simulate each scenario, Cooja simulator (Osterlind

et al., 2006), a Java-based network simulator of sen-

sor nodes running the Contiki (version 2.7) operating

system (Contiki-Ng, 2004), is used. Each scenario is

simulated with a parameter set given in Table 1.

OF0, MRHOF-ETX, and MRHOF-ENERGY are

taken into consideration as objective functions in the

simulation environment; where version number, DIS

Analysis of RPL Objective Functions with Security Perspective

Table 1: Simulation Parameters.

Simulation Parameters Values

Radio Environment UDGM: Distance Loss

Objective Functions OF0, MRHOF-ETX, MRHOF-ENERGY

TX Range 50m

INT Range 100m

Simulation Time 1 hour

Area of Deployment 200x200

Number of Sink Node 1 node

Number of Sensor Node 50 nodes

Platform Sky mote

Trafﬁc Pattern UDP packets, every 60 sec. by sensor nodes

ﬂooding, and worst parent attacks are considered to

perform malicious activities in the network. The ef-

fects of these attacks on the objective functions are

also investigated with respect to the number of attack-

ers. To do that, the simulations are ﬁrst run without

any attacker node to create the baseline performance

of the objective functions. Then, the same simulation

scenarios are run with one (2%), three (6%), and ﬁve

(10%) attacker nodes.

For each scenario, the simulation is run with 10

different network topologies where the nodes are ran-

domly localized. The simulation is run for one hour

for every scenario considered. Unit Disk Graph

Medium (UDGM) is used to simulate the real lossy

environment in LLN.

4.2 Performance Evaluation Metrics

In order to reveal how the network is affected by the

objective function employed, four performance met-

rics are used in this study: packet delivery ratio, traf-

ﬁc overhead, average latency, and average power. In

the following, these metrics are explained:

• Overhead (OVR): It represents the total number

of control packets propagated in DODAG to build

network. Therefore, it is expected for a network

to have less OVR value when it is operated under

ideal conditions. The calculation of OVR is given

in (5).

OV R =

∑

CP (5)

where CP ∈ {DIO,DIS, DAO} stands for control

packets propagated in the network.

• Packet delivery ratio (PDR): It is deﬁned as the

ratio of the total number of packets received by

the root node to the the total number of packets

sent to the root node. PDR shows how reliable the

network is, and the greater the PDR value is, the

more reliable the network is. The calculation of

PDR is given in (6).

PDR =



RT P

ST P



× 100 (6)

where RT P and ST P represent the total number of

packets that are, respectively, received and sent by

the root node.

• Average Latency (ALT): Packet latency is the time

between sending a packet and reaching its destina-

tion. ALT is calculated by the total latency (T L)

of each packet divided by the total received pack-

ets (T RP), which is given in (7).

ALT =

T L

T RP

(7)

• Power Consumption (PC): One of the most impor-

tant performance metrics in LLNs is power con-

sumption, since power is one of the main con-

straints in sensor devices. PC stands for the power

measured from nodes during the lifetime of the

network. The average power consumption of the

network is calculated by the ratio of the total PC

of the network to the total number of nodes. Pow-

ertrace (Dunkels et al., 2011) is integrated into the

Contiki OS in order to calculate PC of nodes by

using the (8).

Energy(mJ) = (Transmit × 19.5mA+

Listen ×21.5mA +CPU × 1.8mA

+LPM ×0.0545mA) × 3V /32768

(8)

PC(mW ) = Energy(m j)/Time(sec) (9)

4.3 Simulation Results

Ten random networks are simulated for each attack

type with different percentage of attackers (2%, 6%,

10%), and the average results of these ten runs are

shown in the results. Please note that while each

topology is randomly created, the same ten network

topologies in which attackers are located in the same

SENSORNETS 2022 - 11th International Conference on Sensor Networks

positions are run for each attack type/attacker percent-

age for a fair comparison.

Firstly, the average overhead introduced by at-

tacks is shown in Figure 1. As expected, version num-

ber attack results in a big increase in the number of

control packets, since it triggers the reconstruction of

DODAG. DIS ﬂooding attack by its nature also in-

creases the number of DIS packets and the number

of DIO packets as a response to these DIS requests

considerably. The maximum number of control pack-

ets is produced by MRHOF-ENERGY in all attack

types. Moreover, MRHOF-ENERGY shows dramatic

increase in the number of control packets, particularly

at DIS ﬂooding attacks. As more attackers take part

in the network; MRHOF-ETX results in lower over-

head than OF0; while OF0 still shows an incline to

increase in the number of control packets, MRHOF-

ETX is less affected by the number of attackers.

The average PDR of simulated networks with dif-

ferent objective functions under version number, DIS

ﬂooding, and worst parent attacks is shown in Fig-

ure 2. As shown in the ﬁgure, OF0 and MRHOF-

ETX reach to nearly the optimal PDR when there

is no attack in the network. MRHOF-ENERGY not

only shows the lowest PDR, but also shows the fastest

decrease in PDR when the network is under attack.

However, all objective functions are considerably af-

fected by the version number attack even when there

is only one attacker in the network. As shown in the

results, while OF0 and MRHOF-ETX show compara-

ble performance against attacks, OF0 is slightly better

than MRHOF-ETX in each scenario.

The comparative average latency values (in sec) of

simulated networks under version number, DIS ﬂood-

ing, and worst parent attacks are given in Figure 3.

There is no considerable performance difference of

OFs in terms of latency when they operate completely

in a benign network. However, they behave differ-

ently on networks under attacks. Version number and

DIS ﬂooding attacks result in much more latency on

networks than worst parent attack, because of the ex-

tra overhead introduced by these attacks. Further-

more, in these attack scenarios, packet arrival times

are often delayed as the number of attackers increases.

Again here, OF0 shows much better performance than

the MRHOF objective functions. An interesting re-

sult here is that, MRHOF-ENERGY shows a slight

decrease in overhead and a decrease in latency in

the version attack scenarios, when the number of at-

tackers gets bigger than 6% of all nodes in the net-

work. Since it shows a big reaction to even a single

attacker (2%), this attack is less affected by the fur-

ther increase in the number of attackers. MRHOF-

ENERGY also performs a smaller rate of increase in

latency for DIS ﬂooding attack, when the number of

attackers are increased up to 10% of all nodes. In the

future, this can be further explored by simulating net-

works with more attackers.

Average power consumption of simulated net-

works is demonstrated in Figure 4. Even when there

is no attacker in the network, MRHOF-ENERGY re-

sults in more power consumption than other objec-

tive functions. Again, MRHOF-ENERGY shows a

big jump in power consumption even when a single

attacker is introduced into the network. While the

MRHOF objective functions behave similarly under

attacks, MRHOF-ENERGY always consumes more

energy than MRHOF-ETX. Again, version number

and DIS ﬂooding attacks consume much more en-

ergy than worst parent attack due to the higher num-

ber of control packets they introduced. Please note

that MRHOF-ENERGY prefer routes that consist of

nodes with higher remaining energy among alterna-

tive routes between the same end-points, it does not

aim to decrease energy consumption but to guarantee

the delivery of packets. Hence, it might compute a

longer path for some trafﬁc in order to increase the

network life duration (Barthel et al., 2012).

To sum up, the presence of attackers adversely af-

fects the performance of networks as expected, espe-

cially, the effects of version number and DIS ﬂood-

ing attacks on simulated networks are more obvious

as they inherently bring more control packets to the

network. On one hand, the worst parent attack which

aims to change the network topology does not cause

a dramatic effect on networks using different objec-

tive functions. On the other hand, version and DIS

ﬂooding attacks that target consuming of network re-

sources clearly affect networks in all performance

metrics. However, networks using different OFs are

affected differently from such attacks. Networks us-

ing OF0 are more robust to attacks. Since attacks of-

ten negatively affect the quality of the links and the

energy of the nodes, thus affect the ETX and EN-

ERGY metrics used in the MRHOF objective func-

tions, attacks can cause more changes such as fre-

quent parent changes in networks using such objec-

tive functions.

5 CONCLUSION

This is the ﬁrst study that comprehensively analyzes

the standardized RPL objective functions from the se-

curity point of view. Since objective functions play a

key role in determining optimal routes between end-

points, they might be affected differently from differ-

ent type of attackers. The comprehensive simulation

Analysis of RPL Objective Functions with Security Perspective

0 2 6 10

Total Overhead

a) Version Number Attack

0 2 6 10

Attacker Percentage

b) DIS Flooding Attack

0 2 6 10

c) Worst Parent Attack

MRHOF-ETX

MRHOF-ENERGY

OF0

Figure 1: Average overhead of simulated networks.

0 2 6 10

100

PDR (%)

a) Version Number Attack

0 2 6 10

Attacker percentage

100

b) DIS Flooding Attack

0 2 6 10

100

c) Worst Parent Attack

MRHOF-ETX

MRHOF-ENERGY

OF0

Figure 2: Average PDR of simulated networks.

0 2 6 10

Latency (sec)

a) Version Number Attack

0 2 6 10

Attacker Percentage

b) DIS Flooding Attack

0 2 6 10

c) Worst Parent Attack

MRHOF-ETX

MRHOF-ENERGY

OF0

Figure 3: Average latency of simulated networks.

0 2 6 10

1.5

2.5

3.5

4.5

Average Power Consumption (mW)

a) Version Number Attack

0 2 6 10

Attacker Percentage

1.5

2.5

3.5

4.5

b) DIS Flooding Attack

0 2 6 10

1.5

2.5

3.5

4.5

c) Worst Parent Attack

MRHOF-ETX

MRHOF-ENERGY

OF0

Figure 4: Average power consumption of simulated networks.

SENSORNETS 2022 - 11th International Conference on Sensor Networks

results conﬁrm this hypotheses and show that OF0 is

more robust to attacks than MRHOF and, MRHOF-

ENERGY obtains the worst packet delivery ratio. The

results also show that attacks targeting network re-

sources (version number and DIS ﬂooding attacks)

have a more clear effect on all network performance

metrics. We believe researchers developing security

solutions for RPL will beneﬁt from the results pre-

sented in this current study. The study could be ex-

tended with more attack types and more parameters

such as mobility in the future.

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