Selfish Peers Research Articles

Gender and cultural differences in the development of reciprocity in young children.

A foundational mechanism underlying human cooperation is reciprocity. In the context of repeated interactions with others, it is not always clear the degree to which in-kind responses reflect responsiveness to partners' prior behaviors ("reactive" responses), an interest unrelated to the partner ("nonreactive" responses), or any combination of the two. To disentangle these two types of responses, we presented children with sequential, one-shot, and costly interactions between themselves and either egalitarian or selfish peers. Study 1 tested direct, generalized, and normative reciprocal scenarios (N = 144 seven-year-old German children; 50% girls and 50% boys), finding that "nonreactive" responses were dominant for boys and manifested in the form of "selfish" resource distribution. Among girls, "reactive" responses were dominant and manifested in the form of in-kind resource distribution. Study 2 addressed even younger German children (N = 144; 4- to 8-year-old German children; 50% girls and 50% boys), exposing the same phenomenon among 4-year-olds, but not among 5.5-year-olds. Study 3 addressed 7-year-old Israeli children (N = 95; 49% girls and 51% boys), and replicated the basic phenomenon, with an additional cultural variation. The early emergence of gender differences in reciprocity and implications are discussed in cultural, socio-developmental, and evolutionary accounts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Identification of vulnerable selfish peer in P2P network using nature-inspired optimization techniques

Microservice is considered a distributed cloud native approach where a standalone application is composed of multiple loosely coupled and free-spirited deployable components in the form of peers. These peers are heterogeneous and autonomous by nature and behave according to their good interests. In a distributed environment, some peers subsidize others and could behave vulnerably or selfishly. These selfish peers severely affect the performance of the distributed P2P network if not addressed adequately. In distributed microservices applications, these selfish peers are addressed in two ways, i.e. Identification and Mitigation of the selfish peers. This study presents the AI-based approach towards identifying selfish peers in P2P microservices distributed networks with nature-inspired algorithms for feature selection. Efforts are also made to generate a real dataset SAMPARK followed by the standard procedure for the distributed P2P network. The developed hybrid feature selection (FS) technique composed of Grey-Wolf-Optimization (GWO) and Particle-Swarm-Optimization (PSO) is used for selecting the important feature subset. The feature selection technique combines AI-enabled techniques to develop six hybrid models for the identification of selfish peers in microservice networks. In experimentation, the effectiveness of all the proposed algorithms and models is found satisfactory, with an achieved maximum accuracy of 99.60% and outperforming other proposed approaches.

Social influence in adolescence as a double-edged sword.

Social learning is fundamental to human development, helping individuals adapt to changing circumstances and cooperate in groups. During the formative years of adolescence, the social environment shapes people's socio-cognitive skills needed in adulthood. Although peer influence among adolescents is traditionally associated with risky and unruly conduct, with long-term negative effects on educational, economic and health outcomes, recent findings suggest that peers may also have a positive impact. Here, we present a series of experiments with 10–20-year-olds (n = 146) showing that positive and negative peer effects reflect a domain-general factor of social information use which declines during adolescence. Exposure to disobedient peers provoked rule breaking, and selfish peers reduced prosocial behaviour, particularly in early adolescence. However, compliant peers also promoted rule compliance and fair peers increased prosociality. A belief formation task further revealed that younger adolescents tend to assimilate social information, while older adolescents prioritize personal views. Our results highlight early adolescence as a key window for peer-based interventions to improve developmental trajectories.

Data Broker: Dynamic Multi-Hop Routing Protocol in Blockchain Radio Access Network

Mobile ad-hoc network (MANET) is an infrastructure-less extended scenario of blockchain radio access network (B-RAN); meanwhile, the decentralization of B-RAN befits MANETs. In the framework of B-RAN, we propose a blockchain-based multi-hop routing protocol, namely Data Broker, for non-cooperative MANETs. To incentivize the collaboration among selfish peers, we record the identity information of participating brokers on a ledger list. However, blockchain cannot guarantee the integrity of such a routing ledger directly. Therefore, we design a ledger safeguard mechanism, along with reward policies, to avoid the ledger list being tampered with or modified by dishonest brokers. The experimental results show that the proposed Data Broker can significantly improve the network performance of non-cooperative MANETs regarding latency and loss rate.

Score-based Incentive Mechanism (SIM) for live multimedia streaming in peer-to-peer network

A decentralized model is primarily used for communication and file transfer, the peer-to-peer network is based on this model. The field of real-time communication and media streaming has witnessed enormous growth in recent times owing to their use of peer-to-peer network. A significant part of Internet traffic is being created by the peer-to-peer network resulting in an increase in its demand. The rise in prominence of peer-to-peer network can be attributed to its properties like resource utilization and distributed nature. Resource utilization of peer-to-peer network is one foremost motive for selecting this network over the traditional client-server architecture. The availability of selfish peer or free riders affects the total resource utilization and degrades the performance of the network. So, there is a requirement of an incentive-based mechanism to motivate the selfish peer or free riders in the network to improve the performance of the network. In this paper, we will discuss a new score based incentive mechanism to improve the overall resource utilization. This approach is based on a reward-punishment based method, so the peers are encouraged or motivated to share more resources and chastise selfish peers. For calculating the score value of a peer, parameters like upload capacity, video quality, control packets, a time period for which peers stay in the network can be used. Different score values are assigned for different parameters. The simulation results presented in this paper verify the approach and illustrate that the quality of video and performance of network improves using our new score based incentive mechanism. The parameters used for calculating the performances are; end-to-end delay, playback delay, start-up delay, and frame redundancy etc.

FLHyO: fuzzy logic based hybrid overlay for P2P live video streaming

In recent times, enormous growth has been observed in the field of media streaming and real-time communication using peer-to-peer (P2P) network. The popularity of P2P network for live streaming can be credited to its fundamental characteristics such as a decentralized model, low complexity and scalability. Some of the key factors that affect the performance of P2P live streaming are overlay, scheduling schemes and selfish peer. Many structured and unstructured P2P overlay construction techniques have been introduced till now. But both type of overlays have proven to be inadequate in fulfilling the requirements of today’s streaming systems. Generally, the peers in a P2P network are heterogeneous, isolated and mobile in nature. It is difficult to construct an overlay in such a way that resource utilization along with other QoS parameters in the network is maximal. So in this paper, we propose a novel Fuzzy Logic based Hybrid Overlay (FLHyO). FLHyO uses the fuzzy system for determining the priority of each peer during overlay creation. Some of the other factors considered by FLHyO for overlay creation are geographical location, upload bandwidth, age and utilization of a peer. A comparative analysis of FLHyO with some of the landmark overlay approaches verifies that our proposed approach provides better quality of service (QoS) in the network and also improves the video quality at receiver side.

Topological influence on optimality of Tit-for-Tat based P2P content distribution

Peer-to-Peer (P2P) content distribution is a powerful scheme to distribute content on the Internet. Since the P2P content distribution relies on the cooperation among peers, one of the most famous P2P file distribution systems, BitTorrent, has applied a game theoretical approach called the Tit-for-Tat (TFT) strategy to encourage selfish peers to cooperatively exchange the fragments of a file, i.e., pieces, with others. In recent years, the basic characteristics of such TFT-based P2P content distribution under a full-mesh network has been investigated by modeling the determination of the optimal piece flow as Integer Linear Programming (ILP). However, the topological influence on optimal piece flow has not been revealed yet. In this paper, we propose an approach to analyze the topological influence by extending the previous model. Through numerical results, we reveal that the optimal piece flow can be achieved on a hierarchical and circular topology with O(NP) links where NP is the number of peers. We also show the whole network can be divided into multiple sub-networks while keeping the system performance.

Token-based incentive mechanism for peer-to-peer video streaming networks

We consider the problem of live video streaming in peer-to-peer networks consisting of selfish peers aiming at maximizing peers’ individual utilities. We use token as an internal currency to encourage peers to cooperate in video distribution. Peers gain tokens by accepting requests from other peers for uploading video chunks to them, and they spend tokens for sending requests to the other peers and downloading video chunks from them. To handle the heterogeneity in the bandwidth of peers, we assume the video is coded as multilayered and each layer has a different type of token. Peers can request for a different number of layers to have their best quality based on their available token pools. To address this problem, we first study the peers’ optimal strategies using the formalism of repeated games, and then, we show that peers’ optimal strategy for each layer is a threshold strategy based on the number of tokens held by the peer for that layer. Then, we show that the repeated game can be converted to a simple one-shot game. In this new game, we study the impact of other peers’ strategy on peers’ utilities and strategies. We show that the game has a Nash equilibrium.

An Artificial Immune System-Based Strategy to Enhance Reputation in MANETs

In Mobile Ad hoc Networks (MANETs) the nodes act as a host as well as a router, thereby forming a self-organizing network that does not rely upon fixed infrastructure, other than gateways to other networks. Security is important for MANETs and trust computation is used to improve collaboration between nodes. This paper proposes an Artificial Immune System-based reputation (AISREP) algorithm to compute trust and thereby provide a resilient reputation mechanism. In this paper, the presence of selfish nodes are considered. Selfish nodes are known to enhance the reputation of their selfish peers which in turn causes packet loss. In the event of the packet being routed using the AISREP algorithm, even though the number of selfish nodes increases, this algorithm identifies the selfish nodes and avoids using the selfish nodes from the routing path thereby improving the overall performance of the network.

PGTrust: a decentralized free-riding prevention model for DG systems

Desktop grids (DG) offer large amounts of computing power coming from internet-based volunteer networks. They suffer from the free-riding phenomenon. It may be possible for users to free ride, consuming resources donated by others but not donating any of their own. In this paper, we present PGTrust: our decentralized free-riding prevention model designed for PastryGrid. PastryGrid is a decentralized DG system which manages resources over a decentralized P2P network. PGTrust relies on the notion of score which is a metric of reputation used to evaluate the level of QoS of a peer. We have conducted out experimentations on Grid'5000 testbed. Obtained results prove the benefits of our free-riding prevention model. PGTrust is able to improve application running time by discouraging free-riders and motivating selfish peers to contribute. It offers a considerable speedup over distributed applications.

Impacts of Selfish Behaviors on the Scalability of Hybrid Client–Server and Peer-to-Peer Caching Systems

This paper considers a hybrid peer-to-peer (p2p) system, a dynamic distributed caching system with an authoritative server dispensing contents only if the contents fail to be found by searching an unstructured p2p system. We study the case when some peers may not be fully cooperative in the search process and examine the impact of various noncooperative behaviors in the aspect of scalability, more specifically average server load and average peer load as the peer population size increases. We categorize selfish peers into three classes: impatient peers that directly query the server without searching the p2p system, non-forwarders that refuse to forward query requests, and non-resolvers that refuse to share contents. It is shown that in the hybrid p2p system, impatient and/or non-forwarding behaviors prevent the system from scaling well because of the high server load, while the system scales well under the non-resolving selfish peers. Our study implies that the hybrid p2p system does not mandate an incentive mechanism for content sharing, which is in stark contrast to unstructured p2p systems, where incentivizing peers to share contents is known to be a key factor for the system's scalability.

A New Reputation Model for P2P Network Based on Set Pair Analysis

P2P reputation systems are useful to evaluate the trustworthiness of peers and to combat malicious and selfish peer behaviors. The reputation system assigns each peer a global reputation scores through collecting locally generated peer feedbacks and aggregation. In traditional reputation system, a peer will rate the other by a score, after finished a transaction, No more detail of transaction are considered in the process. In order to solve this problem, we propose a novel reputation aggregation scheme, detail of transaction could be considered, based on set pair analysis (SPA). The empirical evaluation results reveal that the proposed approach is scalable, accurate, robust, and fault-tolerant.

Frontier technologies of trust computing and network security

The increasing complexity of computer systems and communication networks induces tremendous requirements for trust and security. This special issue includes topics on trusted computing, risk and reputation management, network security and survivable computer systems/networks. These issues have evolved into an active and important area of research and development. The past decade has witnessed a proliferation of concurrency and computation systems for practice of highly trust, security and privacy, which has become a key subject in determining future research and development activities in many academic and industrial branches. This special issue aims to present and discuss advances of current research and development in all aspects of trusted computing and network security. In addition, this special issue provides snapshots of contemporary academia work in the field of network trusted computing. We prepared and organized this special issue to record state-of-the-art research, novel development and trends for future insight in this domain. In this special issue, 14 papers have been accepted for publication, which demonstrate novel and original work in this field. A detailed overview of the selected works is given below.

Variegated competing peer-to-peer systems with selfish peers

Thanks to years of research and development, current peer-to-peer (P2P) networks are anything but a homogeneous system from a protocol perspective. Specifically, even for the same P2P system (e.g., BitTorrent), a large number of protocol variants have been designed based on game theoretic considerations with the objective to gain performance advantages. We envision that such variants could be deployed by selfish participants and interact with the original prescribed protocol as well as among them. Consequently, a meta-strategic situation—judicious selection of different protocol variants—will emerge. In this work, we propose the usage of population games, evolutionary and learning dynamics in the study of node coevolution for selfish protocol selection, and, most importantly, its impact on system performance. We apply our models and algorithms to P2P systems and draw on extensive simulations to characterize the dynamics of selfish protocol selection. In particular, our proposed distributed algorithms outperform others in terms of download rate. We believe that evolution patterns identified in our study will shed light on both further theoretical study and the design of next generation distributed systems.

Bandwidth and Latency Aware Contribution Estimation in P2P Streaming System

In this article, a bandwidth and latency aware contribution estimation (BLACE) mechanism is proposed to enhance the efficiency of content distribution in a mesh peer-to-peer (P2P) streaming system. This is because the mesh P2P streaming system outperforms others in many aspects, but most content distribution mechanisms use only one of upload bandwidth and link latency as a factor, several contribution-aware schemes (CASs) just punish selfish peers, and some CASs only rearrange the content receiving order. Hence, the BLACE proposes a prevention way considering every peer's upload bandwidth, link latency, and the maximum playing delay of a stream at the same time to prioritize the content distribution. Moreover, BLACE is implemented on Coolstreaming with NS2 and experimental results show that BLACE could at the most shorten the receiving time about 15.44%, 13.94%, and 8.26%, and improve the whole performance about 7.05%, 2.81%, and 2.69% at the most, when it compares with the Coolstreaming, the Coolstreaming with bandwidth preference, and the Coolstreaming with latency preference, respectively.

Give-and-take based peer-to-peer content distribution networks

Content Distribution Networks (CDNs) are widely used to distribute data to large number of users. Traditionally, content is being replicated among a number of surrogate servers, leading to high operational costs. In this context, Peer-to-Peer (P2P) CDNs have emerged as a viable alternative. An issue of concern in P2P networks is that of free riders, i.e., selfish peers who download files and leave without uploading anything in return. Free riding must be discouraged. In this paper, we propose a criterion, the Give-and-Take (G&T) criterion, that disallows free riders. Incorporating the G&T criterion in our model, we study a problem that arises naturally when a new peer enters the system: viz., the problem of downloading a ‘universe’ of segments, scattered among other peers, at low cost. We analyse this \(\mathcal {N}\mathcal {P}-\)hard problem, and characterize the optimal download cost under the G&T criterion. We propose an optimal algorithm, and provide a sub-optimal algorithm that is nearly optimal, but runs much more quickly; this provides an attractive balance between running time and performance. Finally, we compare the performance of our algorithms with that of a few existing P2P downloading strategies in use. We also study the computation time for prescribing the strategy for initial segment and peer selection for the newly arrived peer for various existing and proposed algorithms, and quantify cost–computation time trade-offs.

Evolution of Cooperation in P2P Systems Based on Reputation

The emergence of cooperation has attracted scholars in many relevant fields. Evolution of Cooperation in P2P systems composed of selfish peers only can be stimulated by some additional mechanisms. In this paper, we propose a mechanism based on reputation to investigate the evolution of cooperation in P2P file-sharing system. The mechanism lets the peers update their strategies and adjust their relationship with others merely according to the opponents’ reputation. We design a P2P file-sharing evolutionary game based on classic prisoners dilemma game to show the effectiveness of our mechanism.

Analysis and evaluation of incentive mechanisms in P2P networks: a spatial evolutionary game theory perspective

SummaryIn peer‐to‐peer (P2P) networks, contributions are made by peers voluntarily for the autonomous character of peers. However, selfish peers may refuse to be cooperative when considering their limited transmission resources. Incentive mechanisms are always used to guarantee successful cooperations among peers. Although the inventive mechanisms have been widely investigated on the basis of game theory, most researches assume that peers are well mixed in the network, regardless of the influence of peers' transaction relationships. In this paper, a novel analysis framework based on spatial evolutionary game theory is proposed to verify the effectiveness of incentive mechanisms. In the framework, a transaction overlay network is used to model the transaction relationships of peers. The transactions between clients and servers are modeled as the donor‐recipient game to satisfy their asymmetric characters. Influences of the learning noise and some common behaviors of peers on incentive mechanisms are also considered. Moreover, in order to demonstrate the utility of the framework, a reciprocation‐based incentive mechanism, which considers the requestors' behaviors of providing and consuming services, is thoroughly investigated under the framework in scenarios with homogeneous and heterogeneous benefits of services. By using the framework, besides the effectiveness of incentive mechanisms, the detailed spatiotemporal evolutions of peers' strategies driven by incentive mechanisms can also be obtained. Copyright © 2014 John Wiley & Sons, Ltd.

An Evolutionary Game-Based Mechanism for Routing P2P Network Flow among Selfish Peers

Aimed to improving the efficiency of self-interested P2P node’s routing traffic through a congested network and overcoming confusion condition caused by selfish routing in P2P networks, we introduce an evolution game-based routing model to study the selfish routing behaviors of nodes in P2P networks. In the paper, we model the routing behaviors of nodes as a noncooperative routing game, in which self-interested player’s route traffic through a congestion-sensitive network. We extend the model of selfish routing to study the dynamical behaviors of nodes, by adopting a generalized approach of imitative dynamics. We not only analyze the model’s stability and convergence, but reveal that the efficiency of P2P node’s routing can be improved when the model reach an equilibrium state. Finally we also give an algorithm and experiments on how to improve P2P traffic efficiency based on our evolutionary game model.

A Cooperative Peer Clustering Scheme for Unstructured Peer-to-Peer Systems

This paper proposes a peer clustering scheme for unstructured Peer-to-Peer (P2P) systems. The proposed scheme consists of an identification of critical links, local reconfiguration of incident links, and a retaliation rule. The simulation result indicates that the proposed scheme improves the performance of previous schemes and that a peer taking a cooperative action will receive a higher profit than selfish peers.