Transmission Failure Tolerance and Node Punishment Mechanism in Opportunistic Network Based on Repeated-Game

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.

In non-cooperative environments such as public hotspots, selfish nodes can take advantage of the mechanisms in place to obtain unfair shares of the network resources. Unfortunately, such a selfish behavior may impact the performance of the other nodes. In this paper, we address the problem of fairly sharing the IEEE 802.11 network resources in the presence of selfish nodes. Our objective is to propose a novel medium access control protocol which can fairly share the available medium among cooperative nodes but can retaliate if some selfish ones are present. We present a novel game theoretical strategy that is specifically designed to cope with the short-term unfairness of resource sharing. The proposed reputation-based Tit-for-Tat mechanism is immune to infrequent misclassification errors and motivates rational selfish nodes to cooperate under the threat of retaliation. The mechanism depends on the observation of the nodes' behavior. If any node misbehaves, the monitoring nodes should adapt and behave selfishly. Thus, rational nodes should not have any other choice than to follow the legitimate behavior defined in the IEEE 802.11 standard in order to maximize their potential payoff.

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.

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.

Barter trade improves message delivery in opportunistic 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.

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.

Existing routing schemes in opportunistic networks can be classified two categories: multi-copy based schemes and single-copy based schemes. Multi-copy based schemes can increase the robustness of networks and reduce average delay. However, they consume a large amount of network resources and induce large overhead. Single-copy based schemes can save network resources, but it is difficult to guarantee the transmission delay and packet delivery ratio for these schemes. Hence, how to design an efficient routing scheme which can make a tradeoff between multi-copy schemes and single-copy schemes in opportunistic networks with limited resources is a challenging issue. Inspired by this issue, in this paper, we propose a novel routing scheme based on game theory in opportunistic networks under limited resources, named GTR. In GTR, we map the message transmission to a multi-player bargaining model. By constructing proper utility function, GTR can save the network resources, and achieve better performance in dense opportunistic networks. Our extensive simulation results also demonstrate this point, and GTR performs better compared to some classical routing schemes in terms of packet delivery ratio, average delay, overhead ratio and average hops.

Opportunistic networks have recently seen increasing interest in the networking community. They can serve a range of application scenarios, most of them being destination-less, i.e., without a-priori knowledge of who is the final destination of a message. In this paper, we explore the usage of data popularity for improving the efficiency of data forwarding in opportunistic networks. Whether a message will become popular or not is not known before disseminating it to users. Thus, popularity needs to be estimated in a distributed manner considering a local context. We propose Keetchi, a data forwarding protocol based on Q-Learning to give more preference to popular data rather than less popular data. Our extensive simulation comparison between Keetchi and the well known Epidemic protocol shows that the network overhead of data forwarding can be significantly reduced while keeping the delivery rate the same.

Consider the opportunistic network application scenario in which mobile node users socialize with nearby users of common interest, but there exists no pre-established session by which users register or de-register their presence. As nodes are dynamically joining and leaving the network, how a node discovers any newly joined nodes or knows when to release conversations from nodes who have left the network. In this paper, we propose to build a complete topology of end-to-end paths and periodically refresh it, to thereby erase all the connectivity and node position history, to adapt to the dynamic changing nature of opportunistic networks. We call it Topology Refresh Protocol (TRP). TRP integrates the solution to the questions with data forwarding. It is lightweight and efficient that requires no path break detection and repair as required by on-demand routing, no complex route calculations as in probabilistic routing, and no duplication of messages as used in the class of epidemic routing. We studied the TRP performance in terms of overhead, delay, and acceptance rate by computer simulations and found that TRP outperforms the AODV and ERP protocols.

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.

A route between the sensors and the sink in opportunistic sensor networks may have never existed. Traditional routing protocols cannot be directly applied to deal with the disconnection problem. This paper develops a mobile relay infrastructure approach to supporting data delivery in opportunistic sensor networks. Mobile relays cooperate to establish a routing infrastructure by modifying their trajectories. Unlike previous mobile infrastructure schemes, mobile relays move to specific positions and forward data instead of buffering and carrying data. The approach has been successfully implemented in the network simulator ns-2. The simulation results show that our infrastructure significantly improves the packet delivery rate.

Mobile Ad Hoc Networks (MANETs) use many different routing protocols to route data packets between nodes. These routing protocols are designed taking for granted that all nodes are cooperative and willing to forward control and data packets from and to other nodes. However, when selfish nodes exist in the network, the network performance may degrade significantly. In previous research, selfish nodes are predetermined, meaning that a node is either selfish or not from the beginning (i.e., when deployment takes place) and retains that behavior over time. However, in reality selfishness is not static, but an outcome of restricted energy. In this paper, we investigate the operation of Dynamic Source Routing (DSR) protocol, as selfish behavior emerges in a MANET due to energy depletion. We define different levels of selfishness, and we investigate the protocol performance when the nodes inside the network exhibit different levels of selfishness.

In order to overcome the phenomenon that the existence of end-to-end path is no longer guaranteed because of intermittent connection in the opportunistic network, it is desirable to employ a special node called ferry, which moves in a specific trace and forwards data for disconnected nodes, to provide communication opportunities. In this paper, Global Ferry Scheme (GFS), which exploits multiple local ferries and a global ferry to deliver messages, is proposed to minimize the average message delivery delay. The performance of GFS is evaluated and compared to the existing ferry-based routing method by analysis and simulation. The results show that GFS can improve the performance of opportunistic networks in terms of average delay and delivery ratio.

Link Prediction Techniques for Opportunistic Networks Using Machine Learning