A SURVEY ON REPUTATION-BASED COOPERATION

ENFORCEMENT SCHEMES IN WIRELESS

AD HOC NETWORKS

Malamati Louta

Department of Informatics and Telematics, Harokopio University, 89 Harokopou str., Athens, Greece

Stylianos Kraounakis

Department of Informatics and Telecommunications Engineering, University of Western Macedonia, Kozani, Greece

Angelos Michalas

Department of Informatics and Computer Technology, Technological Educational Institute of Western Macedonia

Kozani, Greece

Keywords: Wireless mobile ad hoc networks, Cooperation enforcement, Trust, Misbehaviour, Reputation mechanism.

Abstract: Mobile ad hoc networks rely on node cooperation to perform and support basic functions like packet

forwarding, routing and network management. In general, nodes’ misbehaviour can significantly degrade

the performance of the network. Cooperation enforcement schemes are seen as a lightweight alternative to

conventional security techniques, providing a “softer” security layer to protect basic networking operations.

The aim of this paper is to survey representative cooperation enforcement schemes exploiting a reputation

system proposed in related research literature. Their distinct features are analyzed and their relative merits

and weaknesses are discussed.

1 INTRODUCTION

Mobile Ad Hoc Networks (MANETs) may be

defined as distributed wireless communication

systems, which comprise potentially a large number

of heterogeneous nodes (e.g., PDAs, laptops)

belonging to the same or different administrative

authorities (depending on the specific application

domain considered), operating over a large

geographical area without existence and support

from fixed infrastructure (e.g. base station, access

point), under diverse and rapidly changing

conditions with respect to connectivity and resource

limitations (e.g., bandwidth, energy, memory,

computation). These systems are inherently self-

organizing and self-configuring so as to cope with

dynamic operation conditions.

Mobile ad hoc networks rely on node

cooperation to perform and support basic functions

like packet forwarding, routing and network

management, which increases network performance

sensitivity to nodes’ misbehaviour. Misbehaviour, in

general, may be defined as deviation from regular

functionality, which may be unintentional due to

e.g., faults, transmission errors and node mobility or

intentional in order for selfish / malicious parties to

take advantage of certain situations. Intentional

misbehaviour may be attributed to nodes’

selfishness, wishing to save their own resources

(e.g., CPU, memory, battery) by not forwarding

packets that are not directly of interest to them (even

though they expect other nodes to forward their own

generated traffic) and to nodes’ maliciousness that

wish to harm and disrupt the normal operation of the

network.

In MANETs, cooperation enforcement schemes

are seen as a viable, lightweight alternative to

conventional security techniques involving

cryptographically signed certificates exchange,

providing a “softer” security layer to protect basic

Louta M., Kraounakis S. and Michalas A. (2010).

A SURVEY ON REPUTATION-BASED COOPERATION ENFORCEMENT SCHEMES IN WIRELESS AD HOC NETWORKS.

In Proceedings of the International Conference on Wireless Information Networks and Systems, pages 90-93

DOI: 10.5220/0002986400900093

 SciTePress

networking operations. Cooperation enforcement

schemes fall within two broad categories: trust

establishment by means of reputation systems and

pricing and credit-based schemes. The first category

is based on building reputation of nodes, while the

second provides for economic incentives. The aim of

this paper is to survey representative cooperation

enforcement schemes exploiting a reputation system

proposed in related research literature. Their distinct

features will be analyzed and the authors will

discuss on their relative merits and weaknesses.

The rest of the paper is structured as follows.

Section 2 presents six representative approaches

proposed in the related research literature. Section 3

discusses on our findings, while section 4 concludes

the paper and highlights our future plans.

2 REPUTATION SCHEMES

2.1 Watchdog - Pathrater

In (Marti, 2000) two extensions to the Dynamic

Source Routing (DSR) protocol are introduced,

namely the watchdog and the pathrater, so as to

mitigate the effects of routing misbehaviour. The

watchdog identifies misbehaving nodes by listening

to the next node’s transmission, exploiting

promiscuous mode of operation. Each node

maintains a buffer of recently sent packets; in case

the packet is not forwarded on within a certain

timeout or the overheard packet is different than the

one stored in the buffer, the watchdog increments a

failure counter for the node responsible for

forwarding the packet. If the counter exceeds a

certain threshold, the node is considered as

misbehaving and the source is notified. The

pathrater combines knowledge of misbehaving

nodes with link reliability data to select the route

most – likely to be reliable. Specifically, each node

maintains a rating for every other node it knows

about in the network and calculates a path metric by

averaging the node ratings in the path, enabling thus

the selection of the shortest path in case reliability

information is unavailable. Negative path values

indicate the existence of one or more misbehaving

nodes in the path. If a node is marked as

misbehaving due to temporary malfunction or

incorrect accusation, a second-chance mechanism is

considered, by slowly increasing the ratings of nodes

that have negative values or by setting them to a

non-negative value after a long-timeout.

2.2 CONFIDANT

In (Buchegger, 2002), the authors propose

CONFIDANT, a routing protocol for MANET based

on Dynamic Source Routing (DSR) protocol. Upon

detection of the node’s malice, its packets are not

forwarded by normally behaving nodes, while it is

avoided in case of a routing decision and deleted

from a path cache. CONFIDANT architecture

comprises 4 components residing on each node: the

Monitor, the Reputation System, the Path Manager

and the Trust Manager components. The Monitor

component enables nodes to detect deviations of the

next node on the source route by either listening to

the transmission of the next node (“passive

acknowledgement”) or by observing route protocol

behaviour.

In order to convey warning information in case

of identification of a bad behaviour, an ALARM

message is sent to the Trust Manager component,

where the source of the message is evaluated. The

rating is updated only if there is sufficient evidence

of malicious behaviour that is significant for a node

and that has occurred a number of times, exceeding

a threshold to rule out coincidences (e.g., collisions).

Evidence could come either from a node’s own

experiences through the Monitor system or from the

Trust Manager in the form of Alarm messages.

Second-hand information is attributed with low

significance (by means of a constant weighting

factor w) with respect to the first-hand information,

irrespective of its source node.

Local rating lists and/or black lists are

maintained at each node and potentially exchanged

with friends. Black lists may be used in a route

request, so as to avoid bad nodes along the way to

the destination or to not handle a request originating

from a malicious node and in forward packet

requests, so as to avoid forwarding packets for nodes

that have bad rating.

2.3 CORE

In (Michialdi, 2002), considering node’s

misbehaviour, the authors discern between selfish

nodes that use the network, while not cooperating,

saving, thus, battery for their own communications

and malicious nodes that aim at damaging other

nodes by causing network outage, while saving

battery life is not a priority. They propose CORE, a

collaborative reputation mechanism so as to enforce

node cooperation in MANETs. CORE defines three

different types of reputation: (i) Subjective

Reputation, (ii) Indirect Reputation and (iii)

A SURVEY ON REPUTATION-BASED COOPERATION ENFORCEMENT SCHEMES IN WIRELESS AD HOC

NETWORKS

Functional Reputation. The former is the reputation

observed locally by a node with regards to other

nodes. The Indirect Reputation is reputation

provided by nodes to other nodes. Subjective

Reputation and Indirect Reputation are merged by

means of a weighted combining formula in order to

compute a final value of reputation concerning a

specific evaluation criterion (e.g. packet forwarding)

forming Functional Reputation, the last type of

reputation considered. By combining different

functional reputation values concerning different

evaluation criteria, a global reputation value may be

estimated. The subjective reputation is computed by

giving more relevance to past observations than to

recent ones. Subjective Reputation values are

updated on the basis of a Watchdog mechanism, if

misbehaviour is identified. Indirect Reputation

values are updated by means of a reply message that

contains a list of all entries that correctly behaved in

the context of each function. In this study

distribution of positive ratings is allowed so as to

avoid potential denial of service attacks. In case

reputation of an entity is negative, the execution of

any requested operation will be denied by all other

entities in the system. CORE does not provide for a

second-chance mechanism.

2.4 SORI

SORI (Secure and Objective Reputation-based

Incentive) scheme is proposed in (He, 2004) so as to

encourage packet forwarding. SORI consists of three

components, namely, neighbour monitoring (used to

collect information about packet forwarding

behaviour of neighbours), reputation propagation

(employed so as to share information of other nodes

with neighbours) and punishment (involved in the

decision process of dropping packet action, taking

into account the overall evaluation record of a node

and a threshold so as to consider collision events).

Reputation rating formation considers first-hand

information weighted by a confidence value used to

describe how confident a node is for its judgement

on the reputation of another node and second-hand

information weighted by the credibility of nodes

which contribute to the calculation of reputation.

Credibility of a node is defined on the basis of a

node’s behaviour as forwarder and not as a witness.

Reputation rating itself is based on packet

forwarding ratio of a node. SORI does not

discriminate between selfish and misbehaving node

terms. Both terms are used interchangeably

throughout the paper. Additionally, SORI does not

comprise a second-chance / redemption mechanism.

Finally, SORI, in order to tackle with impersonation

threats, constructs an authentication mechanism

based on a one-way-hash chain.

2.5 OCEAN

OCEAN (Observation-based Cooperation

Enforcement in Ad Hoc Networks) approach to

selfishness in ad-hoc networks is to disallow any

second-hand information exchanges (Bansal, 2003).

Instead, a node makes routing decisions based solely

on direct observations of its neighbouring nodes’

interactions with it. OCEAN is designed on top of

DSR protocol, may reside on each node in the

network and hosts five components: Neighbour

Watch (in order to observe the behaviour of the

neighbours of a node), Route Ranker (estimating and

maintaining ratings for each of the neighbouring

nodes), Rank-based Routing (so as to avoid routes

containing nodes in the faulty list), Malicious Traffic

Rejection (rejecting all traffic from nodes it

considers misleading so that a node is not able to

relay its own traffic under the guise of forwarding it

on somebody else’s behalf) and Second Chance

Mechanism (using a time-out based approach for

removing a node from a faulty list after a fixed

period of observed inactivity and assigning to it a

neutral value). Once the rating of a node falls below

a certain threshold, the node is added to the faulty

list comprising all misbehaving nodes. In order to

tackle selfish behaviour, the authors introduce a

simple packet forwarding economy scheme, relying

again only on direct observations of interactions

with neighbours. Due to the usage of only first-hand

information, OCEAN is more resilient to rumour

spreading. Finally, the authors rely on recent work

on proof-of-effort mechanisms and mandate that a

new identity will be accepted only if the owner

shows reasonable effort in generating that identity.

2.6 LARS

In (Hu, 2006), the authors propose LARS (Locally

Aware Reputation System) to mitigate misbehaviour

and enforce cooperation. Each node only keeps the

reputation values of all its one-hop neighbours. The

reputation values are updated on the basis of direct

observations of the node’s neighbours. If the

reputation value of a node drops below an

untrustworthy threshold, then it is considered

misbehaving by the specific evaluator node. In such

a case, the evaluator node will notify its neighbours

about misbehaviour, by initiating a WARNING

message. An uncooperative node is identified in the

WINSYS 2010 - International Conference on Wireless Information Networks and Systems

neighbourhood region, in case a WARNING

message issued by a node is co-signed by m different

one hop-neighbours, where m-1 is an upper bound

on the number of nodes considered in the one-hop

neighbourhood, in order to prevent false accusations

and problems caused with inconsistent reputation

values. Additionally, a fade factor has been

introduced to give less weight to evidence received

in the past. The misbehaving node is not excluded

from the network for ever. After a time-out period, it

is accepted, but with the reputation value unchanged

so it would have to built its reputation by good

cooperation.

3 DISCUSSION

After surveying the schemes proposed in related

research literature, it is found that the different

approaches lack unity. Each scheme is based on

quite different assumptions, while the

trust/reputation framework considered varies

significantly in many aspects. Without being

exhaustive, we could refer to information gathering

for reputation computation exploiting only first hand

information or both first-hand and second-hand

information, propagation of second-hand

information considering only positive, negative or

both types of recommendation, degree of

propagation, adopted model for reputation value

computation, dishonest second-hand information

provisioning, identification of misbehaving nodes,

actions taken, node re-integration in the system,

etc.). The presented schemes address in a quite

different manner some of the aforementioned issues,

while, to the best of our knowledge, a

comprehensive list identifying all critical aspects

and their implications to the design of a reputation-

based cooperation enforcement scheme in MANETs

is missing from related research literature.

Additionally, even though simulation results are

provided in most of the works surveyed, we could

not reach to safe conclusions, as the simulation

configurations, the parameters examined and

measured and the assumptions that are made

significantly vary. The authors believe that it would

be quite interesting to analyze the performance of

the examined cooperation enforcement with respect

to network throughput realized, communication

overhead introduced, time required for obtaining

accurate reputation ratings/detecting misbehaving

nodes, robustness against spurious ratings under a

common reference scenario, which however entails a

significant degree of difficulty.

4 CONCLUSIONS

In this paper, a representative set of reputation-based

cooperation enforcement methods proposed in

related research literature are surveyed, while their

distinct features and relative merits and weaknesses

are discussed. The authors conclude that the

proposed schemes lack unity, while a comprehensive

list of critical aspects and their implications to the

design of a reputation-based cooperation

enforcement scheme in MANETs is missing from

related research literature. We plan to continue our

work towards that direction, which could hopefully

form the basis for defining a unified framework in

the future.

REFERENCES

Bansal, S., Baker, M., 2003. “Observation-based

Cooperation Enforcement in Ad hoc Networks”,

arxiv:cs/0307012v2.

Buchegger, S. Le Boudec J.-Y.,, 2002. “Performance

analysis of the confidant protocol (cooperation of

nodes: fairness in dynamic ad hoc networks)”, in

MobiHoc’02, IEEE/ACM Symposium on Mobile Ad

Hoc Networking and Computing.

He, Q., Wu, D., Khosla, P., 2004. “SORI: A Secure and

Objective Reputation-based Incentive Scheme for Ad-

Hoc Networks” in WCNC’04 IEEE Wireless

Communications and Networking Conference.

Hu, J., Burmester, M., 2006. “LARS: a locally aware

reputation system for mobile ad-hoc networks”, in 44

annual ACM Southeast Regional Conference.

Marti, S., Giuli, T. J., Lai, K., Baker, M., 2000.

“Mitigating Routing Misbehavior in Mobile Ad Hoc

Networks”, in ACM MobiCom 2000 Conference.

Michiardi, P., Molva, R., 2002. “Core: a collaborative

reputation mechanism to enforce node cooperation in

mobile ad-hoc networks”, in CMS’02,

Communications and Multimedia Security Conference.

A SURVEY ON REPUTATION-BASED COOPERATION ENFORCEMENT SCHEMES IN WIRELESS AD HOC

NETWORKS