High-efficiency and Low-overhead Selfish Node Detection Algorithm in Opportunistic Networks

Barter trade improves message delivery in opportunistic networks

During the data forwarding in opportunistic network, the selfishness of rational nodes leads to a serious decline in network performance. To solve this problem, this paper proposes a mechanism referred as TFT-NP (transmission failure tolerance and node punishment). TFT-NP takes transmission failure into account, and judges whether the node is selfish or not. It introduces repeated-game theory and sets the penalty cycle of selfish node reasonably. It forces rational nodes to cooperate for a greater profit. Experimental results show that, in opportunistic network with selfish nodes, TFT-NP can improve message delivery rate and reduce message delay significantly.

Analyzing the effect of nodes' selfishness in the opportunistic network is a hot research. In previous studies, people only perform simulation scenarios under simple movement models. In practice, selfish nodes with different movement models will have different effects in the network. So it's necessary to study the effect of selfishness in the opportunistic network under different movement models. In real-life scenarios, the characteristics of the mobile node's movement is random and clustered. So, in this paper, we discuss how the selfishness of nodes affects performance of the opportunistic network with two movement models, the random walk model (RW) and the clustered movement model. With the experiments, we get the conclusion that effects of selfishness in the opportunistic network is related to the movement model.

In Vehicular Ad Hoc Networks (VANETs), vehicles act as independent nodes that can freely establish connections and exchange messages. However, during data forwarding, vehicle nodes may exhibit selfish behavior due to limited resources such as buffer space and bandwidth, or because of social bias, which leads to a decrease in message delivery rate and an increase in communication overhead. To address this issue, this paper proposes a Dual-Trust Selfish Node Detection Algorithm (DTSDA) based on behavioral and social characteristics. The algorithm first employs a node forwarding behavior evaluation mechanism to detect selfish behaviors caused by resource constraints. Then, it introduces behavioral and social features to construct a dual-trust computation model, which further identifies nodes that are difficult to classify. Finally, a message acknowledgment feedback mechanism is incorporated to detect potential false negatives. Experiments are conducted on the ONE simulation platform, and the proposed DTSDA is compared with STCDA, CCSDA, and DSNDA algorithms. The results demonstrate that DTSDA significantly improves the message delivery rate while reducing the end-to-end delay. This study shows that the proposed algorithm can accurately detect selfish nodes in highly dynamic VANET environments, providing a new approach to enhancing communication reliability in vehicular networks.

In opportunistic networks, selfish nodes will refuse to forward messages for others to save their precious resources such as bandwidth, buffer and energy, and the system performance will degrade significantly. Therefore, it is necessary to motivate the selfish nodes to cooperatively forward messages. However, current cooperation mechanisms for opportunistic networks mainly focus on encouraging nodes to participate in message forwarding, but fail to consider the node congestion problem. When many messages are forwarded to the nodes with high connection degree, these nodes will become congested and discard most messages, which will seriously degrade the routing performance. To stimulate the selfish nodes with limited buffer to cooperatively forward messages, this paper proposes a congestion-aware node cooperation mechanism based on double auction, called CANCMDA. In CANCMDA, nodes first determine the self congestion degree according to current free buffer and message receiving speed. Then, combining message delivery probability and congestion degree, nodes trade messages based on double auction model. The double auction trade process is a bayesian game, and nodes get the optimal bid by solving the bayesian equilibrium to trade messages. The experimental results show that CANCMDA can effectively stimulate selfish nodes to cooperatively forward messages when congested, and achieves higher message delivery ratio with lower overhead ratio, compared with other mechanisms.

The lack of pre-existing infrastructure or dynamic topology makes it impossible to establish end-to-end connections in opportunistic networks (OppNets). Instead, a store-and-forward strategy can be employed. However, such loosely knit routing paths depend heavily on the cooperation among participating nodes. Selfish or malicious behaviors of nodes impact greatly on the network performance. In this paper, we design and validate a dynamic trust management model for secure routing optimization. We propose the concept of incorporating social trust into the routing decision process and design a trust routing based on social similarity (TRSS) scheme. TRSS is based on the observation that nodes move around and contact each other according to their common interests or social similarities. A node sharing more social features in social history record with the destination is more likely to travel close to the latter in the near future and should be chosen as the next-hop forwarder. Furthermore, social trust can be established based on an observed node's trustworthiness and its encounter history. Based on direct and recommended trust, those untrustworthy nodes will be detected and purged from the trusted list. Since only trusted nodes' packets will be forwarded, the selfish nodes have the incentives to behave well again. Simulation evaluation demonstrates that TRSS is very effective in detecting selfish or even malicious nodes and achieving better performance.

This paper investigates the optimization of Automatic Identification System (AIS) message transmission for Virtual Aids to Navigation (VATONs) in maritime communication networks under challenging environmental conditions. The study addresses critical network bottlenecks by analysing the impact of path loss (α = 2 to 4.0) on communication reliability and evaluating opportunistic networking solutions, specifically Epidemic routing protocols, for enhanced message delivery. Using OMNeT++ simulations with 40 vessels across a 100km × 100km maritime area, we demonstrate that traditional shore-to-ship communication suffers significant degradation when path loss exponents exceed 2.8, with packet reception rates dropping below 20% at α > 3.2. Our research reveals that Epidemic routing can effectively address these connectivity gaps through ship-to-ship relay mechanisms, maintaining critical VATON position updates even when direct shore-based communication fails. The findings indicate that opportunistic networking achieves substantial improvements in message delivery rates under adverse conditions. This work presents a proof-of-concept demonstration of opportunistic networking principles in maritime environments, establishing a foundation for future field validation studies.

The Mobile Ad Hoc Network (MANET) depending on dynamic distributed topology presents several challenges such as decentralized infrastructure. In MANET, each node act as a host as well as a router to exchange packet. From Source to destination, the MANET nodes offer the ability of transmission to send, receive, and route traffic. Moreover, the presence of selfish nodes or malicious nodes in MANETs significantly degrades network performance. Identifying and isolating such nodes poses a formidable challenge. Consequently, this research introduces a security model founded on the principles of an Artificial Immune System (AIS) to detect and defend against Selfish Node (SN) attacks. This research work employs the principles of AIS to categorize a node's behavioral state based on the Mature Context Immune Antigen Rate (MCIAR). Subsequently, a hybrid protocol named Reliable History‐dependent Resource Conscious Clustered and Defensive AODV (RRCC‐DAODV) is introduced to detect Selfish Nodes (SNs) and counteract their attacks. Within the RRCC algorithm, the identification of selfish nodes relies on both Trust Value (TV) and Residual Energy (RE). Additionally, the proposed DAODV protocol incorporates the V‐detector algorithm to enhance defense mechanisms against potential attacks. The simulation of the proposed model is carried out in NS3. The proposed mechanism achieves a detection ratio of 94.05%. The simulation outcomes demonstrate the excellence of the implemented system. This superiority is observed when compared to other standard protocols, as indicated by the parameters of throughput, residual energy, packet delivery ratio, and delay.

Now a days, selfish nodes is a huge problem in WSNs. In Wireless Sensor Network (WSN) communication, every node forwards the data packets to neighboring nodes and use up its battery power, bandwidth and memory space. All the nodes transmits packets to other nodes according to their necessities in the ideal situation. Presence of selfish node in the network creates data communication failure and nodes don't transmits packets and utilize the network resources for its own profit but it refuses to share their personal resources for other nodes. Due to presence of selfish node in the network, the network is defective and the data communication between nodes is disrupted. It is an important issue to timely detect a selfish node and its behavior in wireless sensor networks. In this paper we have proposed an algorithm for detection of a selfish node in a given network topology.

In a Mobile P2P (M-P2P) network, selfish nodes can drop packets to affect the efficiency of the whole network. Therefore, we propose a simple and efficient mechanism using virtual currency to identify selfish nodes in the network. Each node issues a receipt to its broker to prove that it has provided a forwarding service. Based on receipts received each broker provides well-behaved nodes some virtual currency whereas selfish nodes are punished on dropping packets to make the network more efficient. When compared to other known approaches, our scheme provides real-time detection of selfish nodes and does not require/assume the presence of any tamper proof hardware at each node and can work with any routing algorithm. Simulation results show that our scheme is efficient in terms of (i) time to detect selfish nodes, (ii) the number of false positives and (iii) the number of packets exchanged when compared with others.

In an ad hoc network, the transmission range of nodes is limited; hence nodes mutually cooperate with its neighbouring nodes in order to extend the overall communication. However, along with the cooperative nodes, there may be some reluctant nodes like selfish nodes and malicious nodes present in the network. Such nodes degrade the performance of the network. This paper, gives a survey of reputation based mechanism and credit based mechanism. These include different strategies by which non cooperative nodes are detected, isolated and/or prevented, their advantages and limitations. Also, a global reputation based scheme is proposed in this paper for the detection and isolation of selfish node. A cluster head is used which is responsible for reputation management of each node in the network. Detection of selfish nodes is accomplished which are created due to nodes conserving their energy using NS2. After their detection, performance analysis of network with selfish node and the network after isolation of selfish node is carried out.

With smartphones gaining relevance in everyone's regular day more than ever, the application of opportunistic networks on those devices has become a popular field of research, as it enables smartphones to connect locally with high bandwidth and to provide networked applications for regional use. Conventional Delay Tolerant Networks and opportunistic network methods and techniques however cannot be directly applied on smartphones. Challenges are brought up by missing ad-hoc communication possibilities and system architecture constraints. Through tethering hotspot-based communication it is possible to create opportunistic networks for smartphones, although with specific network characteristics. We conduct a survey on the impact of device characteristics and network transmission properties on the overall network performance. Using the ONE simulator, we deploy a tethering hotspot-based connection strategy and, as the main contribution of this paper, asses the influence of single device properties onto the global opportunistic network performance. By applying a Linear Regression, we conclude a device's radio transmission range as the most significant factor one should consider when designing algorithms for smartphone-based opportunistic networks.

In Mobile Ad-hoc Networks (MANETs), each node is free to move and connect with one another over a wireless connection, without the requirement for a centralized controller or base station. These features make MANET useful and functional in a variety of areas, including tactical situations, sensor networks, rescue operations, and students on campus. However, this type of network also has a range of issues such as security, Quality of Service, dynamic topology, scalability, the absence of central management, and energy consumption. Since MANET nodes have little battery life, the power usage is among the most important design considerations in wireless ad-hoc networks. Each node is responsible for forwarding the packets to its neighboring nodes. Since the MANET does not contain a hierarchy, some nodes may not participate in forwarding the packets and prefer to keep their remaining energy for themselves; these nodes are called selfish nodes. The presence of selfish nodes in the MANET greatly affects the efficiency and activity of the network. The process of detecting and isolating the selfish node does not constitute an easy task. It still requires further investigation and study. Therefore, this survey is intended to determine, study and analyze some new detection mechanisms used in detecting selfish nodes in MANETs. It also classifies them into three categories which are credit-based, reputation-based, and acknowledgment-based techniques. In addition, it highlights some critical issues in MANET according to selfish behavior, and points out the necessary requirements for better improvement as well as fields for future research.

A mobile ad hoc network (MANET) is a group of nodes constituting a network of mobile nodes without predefined and pre-established architecture where mobile nodes can communicate without any dedicated access points or base stations. In MANETs, a node may act as a host as well as a router. Nodes in the network can send and receive packets through intermediate nodes. However, the existence of malicious and selfish nodes in MANETs severely degrades network performance. The identification of such nodes in the network and their isolation from the network is a challenging problem. Therefore, in this paper, a simple reputation-based scheme is proposed which uses the consumption and contribution information for selfish node detection and cooperation enforcement. Nodes failing to cooperate are detached from the network to save resources of other nodes with good reputation. The simulation results show that our proposed scheme outperforms the benchmark scheme in terms of NRL (normalized routing load), PDF (packet delivery fraction), and packet drop in the presence of malicious and selfish attacks. Furthermore, our scheme identifies the selfish nodes quickly and accurately as compared to the benchmark scheme.

This paper presents the friendship and selfishness forwarding (FSF) algorithm for Delay Tolerant Networks. This novel solution is based on two social characteristics of nodes: friendship and selfishness. When a contact opportunity arises, FSF analyzes two aspects to make message forwarding decisions: first, FSF assesses the friendship strength among the pair of nodes, then it determines the individual selfishness of the relay node. Unlike other algorithms proposed in the DTN literature, we use a machine learning algorithm to quantify the friendship strength among pairs of nodes in the network. The individual selfishness of the relay node is determined by using a model based on the current level of its resources. The primary goal is to take into account the case where, despite a strong friendship with the message destination, the relay node does not accept processing the message to save its resources. By using trace-driven simulations we show that the FSF algorithm achieves better results in terms of delivery rate, average cost and efficiency.