Selfish Nodes Research Articles

An evolutionary bargaining‐based approach for incentivized cooperation in opportunistic networks

SummaryOpportunistic networking enables users to communicate in an environment where connectivity is intermittent or unstable. However, such networking scheme assumes that mobile nodes voluntary cooperate, which cannot be guaranteed. Some nodes can simply exhibit selfish behavior and thus diminish the effectiveness of the approach. In this paper, a game scenario is formulated in which the nodes try to convince each other to participate in packets forwarding. Each node is considered as a player in this game. When a node comes in the communication range of another, a bargaining game starts between them as part of the message forwarding process. Both players try to have a mutual agreement on a price for message forwarding. We present a new incentive mechanism called evolutionary bargaining‐based incentive scheme (EBIS) to motivate selfish nodes to cooperate in data forwarding. In EBIS, a node negotiates with other nodes to obtain an agreeable amount of credit for its forwarding service. Nodes apply a sequential bargaining game and then adapt their strategies using an evolutionary model to maximize the probability of reaching an agreement. Unlike classical bargaining games, nodes in our model are boundedly rational. In addition, we use the evolutionary stable strategy (ESS) concept to determine the adaptive strategies for the nodes. The comparison of EBIS with a benchmarked model demonstrates that EBIS performs better in terms of packet delivery ratio and average latency.

A Bio-Inspired Reward-Based Message Forwarding For Vehicular Social Network

The Ants capability of working together and their way of message transmission makes us inspired especially their cooperation for their common goal and dropping down pheromones which used as a trail for themselves and for others. We would use Ants broadcasting method for drivers‟ cooperation and message forwarding. Therefore if there were any driver who couldn‟t broadcast any emergency message related to car accident like Ants done to locate the food source, could be considered as a selfish node and couldn‟t be rewarded as cooperatives do. The previous vehicle to vehicle communication or message forwarding schemes that was tried to motivate cooperative drivers used incentive methods in which it is not motivating all participant nodes and some of them used punishment methods. These types of methods are not effective to stimulate selfish drivers. We conducted the research using embedded traffic simulation in which it includes mobility and network simulation capabilities merged together and served as multi-functional software. From the tools falling into the embedded category is National Chiao Tung University network simulator (NCTUns) which provides a full range of network stack simulation tools. Watchdog was our selfishness detection method, in which the RSU controls whether nodes are cooperative or not because the (Road Side Unit) RSU knows where the nodes were located and in which path they are drive and what messages they broadcasted. The rewarding model we used is paying per packet. The payment principle is for each successfully transmitted unit-sized packet, each of N intermediate nodes should receive λ credits, whereas the service provider (SP) would pay λ ∗ N in total. The proposed solution was evaluated by the three performance metrics which are dropped packets, selfishness detection method and delivery ratio. Based on our experiments result the number of accident increased as the number of total nodes increased in the same simulation area. As the number of nodes participated on the area increases the probability of having an accident increases and the probability of stimulating selfish nodes increased using the same incentive method and amount because there is a probability of accident commit increases and the rewarding and acknowledgement messages also increases in which it can stimulate the selfish nodes.

A scheme for stimulating message relaying cooperation

It is expected that all the nodes try their best to store and forward the messages in the intermittent connection network. However, due to the limited resources, such as memory, power, and transient communication chance, selfish nodes may relay the messages without responsibility. Stimulating selfish to behave normally could improve network performance. However, usually, it is difficult to distinguish the selfish behavior from the inability to relaying the message. By monitoring the behavior of relay nodes that messages are forwarding through, this article proposes a scheme that relays messages according to the reputation of intermediate nodes. The proposed reputation estimation scheme combines selfish behavior and inability behavior, and reveals very little private information for the protocol practice. There is a trade-off between the threshold and the performance. Too low value may not distinguish the selfish nodes. On the contrary, some normal nodes may excluded and cannot join the relaying and processing. Simulation effectively demonstrates that the reputation affects the node’s performance, and an appropriate reputation threshold value is useful for improving the network performance.

An algorithm for determining data forwarding strategy based on recommended trust value in MANET

In mobile ad hoc networks (MANET) with selfish nodes and malicious nodes, the network performance is seriously affected. We propose an algorithm based on the recommended trust value, i.e., collaborative computing trust model (CCTM) algorithm, to decide the data forwarding strategy. In the algorithm, the carrier node carrying the message collects recommended data of neighbour nodes adopts K-nearest neighbour (KNN) algorithm principle to filter the false recommended data and select K neighbour nodes as collaborative computing nodes to calculate the recommended trust value of neighbour nodes respectively, and then selects the neighbour node with the highest recommended trust value as the next hop node. The simulation experiments show that when the selfish and malicious nodes number is 10, CCTM is higher than Epidemic algorithm and MDT about 3% and 8% respectively in terms of transmission success rate; CCTM is higher than Epidemic about 14% and lower than MDT about 15% in terms of average transmission delay; CCTM is lower than MDT about 3% in terms of routing overhead. Overall, CCTM algorithm not only has better performance in terms of transmission success rate, delay and routing overhead, but also improves the security of data transmission.

Learning for Multiple-Relay Selection in a Vehicular Delay Tolerant Network

In a vehicular delay tolerant network (VDTN), there is no static connection, and the network behavior is highly temporal. This makes determining the routing protocol critical for the performance of a network. However, traditional routing technology rarely considers the influence of a VDTN’s selfish nodes. Under ideal conditions, all nodes will try to store and transfer as many messages as possible. However, selfish nodes may not transfer messages to other nodes due to limited resources. Taking selfish nodes into account is necessary for improving the performance of a VDTN. In this paper, we calculate each node’s credit value by recording each node’s behavior when messages are transferring to avoid selecting selfish nodes as the relay. We present an efficient VDTN specific multi-copy routing algorithm that combines Q-learning and the credit value to determine whether a candidate node is suitable for delivering a message. Because multi-copy routing protocols require a high buffer to store messages, we also optimize the node buffer to reduce network congestion. The proposed algorithm is evaluated by a number of different performance metrics, such as delivery probability, network overhead, and message latency. The proposed algorithm achieves better results in different configurations and provides improved delivery probability, low message latency, and network overhead.

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

To address the problems of large overhead and inaccurate judgment for selfish node of the existing selfish node detecting algorithm in opportunistic networks, a high-efficiency and low-overhead algorithm to detect the selfish node HLSND algorithm is proposed. The algorithm combines the SV list interactive information and attributes of the message forwarded by encounter node to judge its selfishness. According to the message attributes forwarded by the node, it can be judged whether it has the selfish behavior of forged the message in SV list. The RSSI technique is used to measure the distance of the nodes to improve the judgment accuracy of self – interest behavior. At the same time, information of selfish node is carried when forward other message to reduce the system overhead. The simulation results show that the HLSND detection algorithm can effectively improve the throughput and message delivery rate in the network and reduce the energy consumption and time delay of the system.

An Efficient and Secure Data Transmission Mechanism for Internet of Vehicles Considering Privacy Protection in Fog Computing Environment

Aiming at the problems for existing data transmission mechanisms in Internet of vehicles, such as real-time performance, high efficiency of computing tasks, vehicle data privacy, etc., this paper proposes an efficient and secure data transmission mechanism for Internet of vehicles considering privacy protection in fog computing environment. Firstly, this paper proposes a system model and main message attack model for Internet of vehicles in fog computing environment, and designs a data transmission system based on privacy protection to improve data transmission efficiency in Internet of vehicles and protect the privacy information of vehicle users. Then, the task allocation and data aggregation mechanism of privacy protection is proposed in the crowd-sensing model based on the assistance of fog nodes, and the location prediction method based on social infection theory is used to predict the vehicle location for better allocating network resources. Finally, for the selfishness of end users in Internet of vehicles, selfish node incentive mechanism based on Robin Steiner bargaining game model is proposed to encourage selfish nodes to perform data transmission and reduce time delay. Operational network environment simulation software is used to carry out experiments. The experimental results show that when malicious nodes and selfish nodes exist in the Internet of Vehicles, the proposed method has stronger competitiveness in resisting attacks and improving the efficiency of message transmission compared with other methods, which can achieve efficient and secure data transmission.

Trust Research on Behavior Evaluation Based on Fuzzy Similarity

As the society and technology are developing, people tend to spend more time online in daily life frequently, such as shopping, reading novels, sharing files and so on. However, how to guarantee the safety of their online behaviors has been a topic of public concern, and the trust of node behaviors in the network has become one of the significant guarantees. A trust model based on fuzzy similarity is proposed in this article in accordance with the features of node behaviors. Firstly, the evaluation message is given to various nodes, and the theory of fuzzy similarity is applied to process the evaluation message. Through integrating these evaluation message, the rules of node behaviors are obtained, Besides, for the malicious and selfish nodes, the trust update algorithm is proposed. Simulation results show the effectiveness and scientific of the mode proposed.

An Incentive Mechanism Based on Bertrand Game for Opportunistic Edge Computing

In a real edge network, many nodes may be selfish and unwilling to forward messages for other nodes. In this case, an incentive mechanism is needed to encourage the selfish nodes to participate in message forwarding. In this paper, we analyze the interaction between the source node and the relay node in edge opportunistic networks, and propose an incentive mechanism based on game theory to encourage the cooperation between nodes. Firstly, we define the interaction steps between the source and the relay node, which include that the source node decides the price of forwarding a message, the relay node responses the forwarding plan to the source node, and all nodes can get reward for their participant in message forwarding. We provide two-stage incentives to nodes, that is, the nodes can get reward from both receiving and forwarding messages. Since the nodes may be selfish, both the source node and the relay node want to maximize their utilities. Then, we model the cooperation between the source and the relay node as Bertrand Game, and the utility functions of the source and the relay node are defined. Furthermore, we find that the Nash Equilibrium is existed and unique, and present the best pricing scheme for the source node and the best forwarding plan for the relay node. The simulation results show that the proposed incentive mechanism can encourage the cooperation between selfish nodes, and improve the performance of routing algorithm in terms of delivery ratio and delay.

Game Theoretical Defense Mechanism Against Reputation Based Sybil Attacks

Reputation mechanisms of a network or p2p applications can be compromised by a large number of fake or stolen identities recommending higher trust values of a malicious node. A high trust value classify such malicious or selfish nodes as legitimate nodes and help them to gain more access to network resource. It is detrimental to network security and makes a network vulnerable to Sybil attacks. In this paper, a game theoretical defense strategy based on zero sum and imperfect information game is proposed to discourage sybil attacks which exploit underlying reputation mechanism of a network. An attacker tries to maintain an optimum level of trusts of its sybil identities in the network in an intention to launch a successful sybil attack and obtain an attack benefit. Simultaneously, the attacker has to incur some cost to maintain trustworthiness of its identities. A rational attacker analyses associated costs and benefits prior to launch an attack. The proposed mechanism uses this idea and makes the attack costly by setting a global trust threshold for nodes or identities to remain trustworthy and active in network. In this work, utility functions of attacker and defender are defined. Nash Equilibrium strategies of the game are discussed and experiments are carried out which suggest the effectiveness of proposed mechanism to enhance network security against sybil attacks.

A Review on Cooperative Bait Based Intrusion Detection in MANET

Mobile Ad Hoc Network (MANET) is a kind of wireless network where a number of mobile nodes are wirelessly connected without any fixed infrastructure. Several attacks or intrusions may occur because of the presence of selfish or malicious nodes in MANETs while performing data transmission from one mobile node to another. Vulnerability occurs in the mobile nodes of the MANETs, when an intruder node easily captures the traffic pattern of the network. A Cooperative Bait Detection Scheme is introduced for detecting the selfish node and malicious node in MANET. CBDS contains the more benefit for proactive and reactive protection constructions using routing scheme to identify the black hole attacks in MANETs. With the design of CBDS, the source node selects the adjacent node address which is taken as bait destination address for transmitting bait malicious node to reply message. Then the malicious nodes are detected from participating the routing operation based on reverse tracing technique for providing better security.

A comprehensive approach to avoid node selfishness and data redundancy

A comprehensive approach to avoid node selfishness and data redundancy

Thwarting Sybil Attackers in Reputation-based Scheme in Mobile Ad hoc Networks

Routing in mobile ad hoc networks is performed in a distributed fashion where each node acts as host and router, such that it forwards incoming packets for others without relying on a dedicated router. Nodes are mostly resource constraint and the users are usually inclined to conserve their resources and exhibit selfish behaviour by not contributing in the routing process. The trust and reputation models have been proposed to motivate selfish nodes for cooperation in the packet forwarding process. Nodes having bad trust or reputation are detected and secluded from the network, eventually. However, due to the lack of proper identity management and use of non-persistent identities in ad hoc networks, malicious nodes can pose various threats to these methods. For example, a malicious node can discard the bad reputed identity and enter into the system with another identity afresh, called whitewashing. Similarly, a malicious node may create more than one identity, called Sybil attack, for self-promotion, defame other nodes, and broadcast fake recommendations in the network. These identity-based attacks disrupt the overall detection of the reputation systems. In this paper, we propose a reputation-based scheme that detects selfish nodes and deters identity attacks. We address the issue in such a way that, for normal selfish nodes, it will become no longer advantageous to carry out a whitewash. Sybil attackers are also discouraged (i.e., on a single battery, they may create fewer identities). We design and analyse our rationale via game theory and evaluate our proposed reputation system using NS-2 simulator. The results obtained from the simulation demonstrate that our proposed technique considerably diminishes the throughput and utility of selfish nodes with a single identity and selfish nodes with multiple identities when compared to the benchmark scheme.

Design of an Agent Based Analysis Approach for Secure Communication Against the Existing of Selfish Node Attack

The internal participating nodes in mobile network can be affects fully or partially by selfish nature to improve its own communication rate. This nature can degrade the forwarding rate and affects the overall performance of the network. The detection of such selfish nodes is required to improve the communication throughput and network performance. In this paper, an Agent based analysis approach is defined to detect the selfish node in the network. The fuzzy based zonal analysis method is suggested to perform the zonal evaluation on the mobile network. The distributed Agents performed the analysis on the fuzzy based communication parameters. The parameter specific evaluation method is able to separate the good and selfish nodes. Once the selfish nodes are identified, the preventive route formation is done for safe communication. In this paper, the Agent adaptive model is presented for detection and prevention of selfish nodes. The proposed Agent adaptive model is simulated in NS2 environment. The comparative results are generated against token based, agent based and watchdog methods. The analysis results show that the proposed protocol has achieve the 17% improvement in packet communication, 37% reduction in communication loss and 53ms reduction in packet delay.

Making Knowledge Tradable in Edge-AI Enabled IoT: A Consortium Blockchain-Based Efficient and Incentive Approach

Nowadays, benefit from more powerful edge computing devices and edge artificial intelligence (edge-AI) could be introduced into Internet of Things (IoT) to find the knowledge derived from massive sensory data, such as cyber results or models of classification, and detection and prediction from physical environments. Heterogeneous edge-AI devices in IoT will generate isolated and distributed knowledge slices, thus knowledge collaboration and exchange are required to complete complex tasks in IoT intelligent applications with numerous selfish nodes. Therefore, knowledge trading is needed for paid sharing in edge-AI enabled IoT. Most existing works only focus on knowledge generation rather than trading in IoT. To address this issue, in this paper, we propose a peer-to-peer (P2P) knowledge market to make knowledge tradable in edge-AI enabled IoT. We first propose an implementation architecture of the knowledge market. Moreover, we develop a knowledge consortium blockchain for secure and efficient knowledge management and trading for the market, which includes a new cryptographic currency knowledge coin, smart contracts, and a new consensus mechanism proof of trading. Besides, a noncooperative game based knowledge pricing strategy with incentives for the market is also proposed. The security analysis and performance simulation show the security and efficiency of our knowledge market and incentive effects of knowledge pricing strategy. To the best of our knowledge, it is the first time to propose an efficient and incentive P2P knowledge market in edge-AI enabled IoT.

Penalty Based Reliable Cooperative Intrusion Detection System for Mobile Adhoc Network Environment

Intrusion detection is the process of finding the malicious behaviour happening in the network to enhance the network performance. This is the most focused research by various researchers to ensure the secured network to the users for increasing their satisfaction level. This is achieved in the previous work by introducing the method namely Cooperative intrusion detection system (CIDS). However this research method doesn’t focus on selfish node presence which might affect the overall intrusion detection process. There is a chance of that monitoring node also can act as selfish node. This is resolved in the proposed work by introducing the method namely Penalty based Reliable Cooperative Intrusion Detection System (PR-CIDS). In this work, initially monitoring nodes are selected optimally by using modified artificial bee colony algorithm through which intrusion detection will be performed. Here intrusion detection is performed based on variation in the forwarded traffic. In order to avoid the selfish node presence, in this work unique secret keys are generated for each monitoring node by using which intrusion decision will be encrypted and then send to the third party node. Here third party node will authenticate the node and then will utilize the intrusion information to make the final decision. Third party node plays more important role in making the intrusion decision. Thus the most suitable third party node is selected by using Hybrid Ant colony with Genetic method. Based on this decision, penalty will be assigned to the selfish monitoring nodes. The overall evaluation of the research work is done in the NS2 simulation environment to prove their performance improvement.

Selfish node detection based on hierarchical game theory in IoT

Cooperation between nodes is an effective technology for network throughput in the Internet of Things. The nodes that do not cooperate with other nodes in the network are called selfish and malicious nodes. Selfish nodes use the facilities of other nodes of the network for raising their interests. But malicious nodes tend to damage the facilities of the network and abuse it. According to reviews of the previous studies, in this paper, a mechanism is proposed for detecting the selfish and malicious nodes based on reputation and game theory. The proposed method includes three phases of setup and clustering, sending data and playing the multi-person game, and update and detecting the selfish and malicious nodes. The process of setup and clustering algorithm are run in the first phase. In the second phase, the nodes of each cluster cooperate with each other in order to execute an infinite repeated game while forwarding their own or neighbor nodes’ data packets. In the third phase, each node monitors the operation of its neighbor nodes for sending the data packets, and the process of cooperation is analyzed for determining the selfish or malicious nodes which forwarded the data packets with delay or even not sent them. The other nodes reduce the reputation of the nodes which does not cooperate with them, and they do not cooperate with the selfish and malicious nodes, as punishment. So, selfish and malicious nodes are stimulated to cooperate. The results of simulation suggest that the detection accuracy of the selfish and malicious nodes has been increased by an average of 12% compared with the existing methods, and the false-positive rate has been decreased by 8%.

Selfish Caching and Facsimile Allocation over a Mobile Ad Hoc Network

Mobile adhoc networks (MANETs) have drawn attention to multitudinous consideration because of the univerality of mobile devices as well as the developments in wireless era. MANET is a peer-to-peer multi hop cellular wireless era community which does not have both difficult and speedy infrastructure and a relevant server. Every vertex of a MANET performs like a router and communicates with every unique. There exist numerous information duplication strategies which were presented to reduce the execution squalor. All are concluded that everyone cell vertices cooperate completely from the perspective of sharing their memory vicinity. But, via a few methods few vertices might additionally behave selfishly and determine simplest to cooperate in part or never with different vertices. The selfish vertices ought to then lessen the overall information approachability within the network. From this work, we try to take a look at the influence of selfish vertices in a mobile ad hoc community in terms of reproduction issuance i.e Selfish nodes are dealt with in replica allocation.

Intrusion Detection for Mobile Ad Hoc Networks Based on Node Reputation

The mobile ad hoc network (MANET) is more vulnerable to attacks than traditional networks, due to the high mobility of nodes, the weakness of transmission media and the absence of central node. To overcome the vulnerability, this paper mainly studies the way to detect selfish nodes in the MANET, and thus prevent network intrusion. Specifically, a data-driven reputation evaluation model was proposed to detect selfish nodes using a new reputation mechanism. The mechanism consists of a monitoring module, a reputation evaluation module, penalty module and a response module. The MANET integrated with our reputation mechanism was compared with the traditional MANET through simulation. The results show that the addition of reputation mechanism can suppress the selfish behavior of network nodes and enhance network security.

Social‐based reputation‐aware data forwarding for improved multicast delivery in the presence of selfish nodes in DTNs

SummaryDelay/disruption tolerant networks (DTNs) are potentially applicable in the challenged scenarios like post‐disaster environments. In such networks, data forwarding generally relies on the mutual cooperation of the nodes. However, in reality, despite the availability of necessary resources for data forwarding, a node could misbehave by dropping messages received from other nodes with whom it has no strong social ties. Such a node is called a socially selfish node, which would cause a poor delivery ratio in the network. In this paper, we aim to address the problem of multicast data forwarding in the presence of such selfish nodes, by means of efficient relay selection in DTNs. First, we define a realistic reputation model, in contrast to existing models, to define the socially selfish/misbehaving nodes in the network. Further, a game‐theoretic analysis is carried out that implies data forwarding cost is also an influential parameter in handling selfishness/misbehavior. Subsequently, the problem is formulated as a constrained optimization problem, which is NP hard. Therefore, a heuristic is proposed by combining the reputation of a node and the cost of message forwarding to appropriately identify relay nodes, thus improve the performance of the multicast message delivery in the network. We utilize a social metric, centrality to minimize the message forwarding cost in terms of the number of relay nodes. Finally, the comparative performance evaluation in ONE simulator with practical scenarios shows the superiority of the proposed scheme over the other prominent schemes.