A Social-Based Watchdog System to Detect Selfish Nodes in Opportunistic Mobile Networks


Detecting selfish nodes in opportunistic mobile networks can reduce the loss of network resources, thus improve the data delivery performance. Most of existing detection schemes primarily rely on the nodes' contact records and do not consider their individual and social preferences in their data relaying behavior, which result in long detection time and high communication overhead. In addition, they cannot distinguish the nodes' selfishness type and degree, which is important because the charge and rewarding mechanisms applied to stimulate different nodes may not be the same. In this paper, we propose a Social-based Watchdog system (SoWatch) in which watchdog nodes analyze messages received from their encountered nodes with respect to their social tie information to identify the nodes' selfish behavior in message relaying. Meanwhile, the watchdog nodes apply the second-hand watchdog information received from other nodes to improve the detection time and accuracy. Next, we design a reputation system in which watchdog nodes identify selfish nodes based on their direct and indirect watchdog information and distinguish individually and socially selfish nodes. Furthermore, we design a watchdog evaluation module to protect SoWatch against wrong watchdogs disseminated by malicious nodes in which a watchdog node investigates the truthfulness of the indirect watchdogs before applying them. Our experiments using real-world datasets illustrate that SoWatch outperforms a benchmark contact-based watchdog system in terms of detection time by 45% and detection ratio by 10% with less communication overhead.


Computer Science

Research Center/Lab(s)

Intelligent Systems Center

Second Research Center/Lab

Center for High Performance Computing Research


The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group No. (RGP-1438-27). This work was partially supported by the National Natural Science Foundation of China (61572106 , 61772551), and the US National Science Foundation grants CNS-1355505, CCF-1533918, and CCF-1725755. We are grateful to the anonymous reviewers for their constructive suggestions to help us improve the quality of the manuscript.

Keywords and Phrases

Game theory; Wireless networks; Communication overheads; Cooperative routing; Data delivery performance; Evaluation modules; Incentive schemes; Real-world datasets; Reputation systems; Selfish behaviors; Mobile telecommunication systems; Opportunistic mobile networks

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Article - Journal

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© 2019 Elsevier, All rights reserved.

Publication Date

01 Mar 2019