A Congestion-Aware Node Cooperation Mechanism based on Double Auction for Opportunistic Networks

In Delay-Tolerant Networks (DTNs), nodes may be selfish and reluctant to expend their precious resources on forwarding messages for others. Therefore, an incentive scheme is necessary to motivate selfish nodes to cooperatively forward messages. However, the current incentive schemes mainly focus on encouraging nodes to participate in message forwarding, without considering the node congestion problem. When many messages are forwarded to the nodes with high connection degree, these nodes will become congested and deliberately discard messages, which will seriously degrade the routing performance and reduce the benefits of other nodes. To address this problem, we propose a credit-based congestion-aware incentive scheme (CBCAIS) for DTNs. In CBCAIS, a check and punishment mechanism is proposed to prevent forwarding nodes from deliberately discarding message. In addition, a message acceptance selection mechanism is proposed to allow the nodes to decide whether to accept other messages, according to self congestion degree. The experimental results show that CBCAIS can effectively stimulate selfish nodes to cooperatively forward messages, and achieve a higher message delivery ratio with lower overhead ratio, compared with other schemes.

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.

Barter trade improves message delivery in opportunistic networks

In socially aware networking, nodes typically behave selfishly due to resource constraints and social correlations, resulting in low network performance. To incentivize selfish nodes to actively participate in message forwarding, this paper proposes a resource-based dynamic pricing and forced forwarding incentive algorithm (DFIA). Firstly, the algorithm introduces virtual currency as a transaction medium and then designs a pricing function based on factors such as the node’s resource status, participation contribution, location relevance, and social connectivity. It ensures that the forwarding service is transacted at a reasonable price through bargaining rules. Secondly, a forced forwarding strategy is implemented to compel selfish nodes, which are unwilling to participate in other nodes’ message forwarding, to forward a certain number of non-local messages. Meanwhile, in order to prevent nodes from discarding messages and to ensure successful forwarding to the destination, specific rules are used to allocate contribution values to nodes that successfully participate in message forwarding. Lastly, to avoid false quotation behavior, blockchain technology is employed. Transaction information is packaged into blocks and added to the blockchain after consensus validation by other nodes in the network, ensuring the transparency and immutability of transaction data. Simulation results indicate that compared with the existing incentive algorithms, this algorithm not only enhances message delivery probability but also effectively reduces average latency.

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.

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.

This paper examines an extended double auction model where market clearing is restricted by temporal constraints. It is found that the allocation problem in this model can be effectively transformed into a weighted bipartite matching in graph theory. By using the augmentation technique, we propose a Vickrey-Clarke-Groves (VCG) mechanism in this model and demonstrate the advantages of the payment compared with the classical VCG payment (the Clarke pivot payment). We also show that the algorithms for both allocation and payment calculation run in polynomial time. It is expected that the method and results provided in this paper can be applied to the design and analysis of dynamic double auctions and futures markets.

Optimal pricing-based computation offloading and resource allocation for blockchain-enabled beyond 5G networks

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.

Opportunistic networks enable users to communicate in the absence of network infrastructure.But forwarding messages in such a network incurs costs for nodes in terms of energy and storage.This may lead to nodes being selfish and not forwarding messages for other nodes, resulting in degraded network performance.This paper presents a novel incentive mechanism for opportunistic networks that uses pre-existing social-network information to detect and punish selfish nodes, incentivising them to participate in the network.Trace-driven simulations demonstrate that our mechanism performs better than existing mechanisms, and that social-network information can also be used to improve existing incentive mechanisms.

Opportunistic networks are type of challenged mobile Ad-Hoc Networks (MANETs). In Opportunistic Networks (OppNets), mobile devices send out the messages by exploiting the unswerving contacts, without the requisite of an end-to-end communications. In such a system, nodes are sporadic and there does not subsist an ample path from source to destination customarily. The message forwarding in such a network is a strenuous problem and is based on the hypothesis that users are willing to forward messages to each other but this assumption does not hold true always due to privacy as user's location may be leaked. To boot, the path can also be exceedingly unstable and may vary or shatter swiftly. So in line to craft communication doable in an opportunistic network, the intermediate nodes may take incarceration of data during the collapse and redirect it when the connectivity resumes.

SummaryOpportunistic network applications are usually assumed to work only with unordered immutable messages, like photos, videos, or music files, while applications that depend on ordered or mutable messages, like chat or shared contents editing applications, are ignored. In this paper, we examine how total ordering can be achieved in an opportunistic network. By leveraging on existing dissemination and causal order algorithms, we propose a commutative replicated data type algorithm on the basis of Logoot for achieving total order without using tombstones in opportunistic networks where message delivery is not guaranteed by the routing layer. Our algorithm is designed to use the nature of the opportunistic network to reduce the metadata size compared to the original Logoot, and even to achieve in some cases higher hit rates compared to the dissemination algorithms when no order is enforced. Finally, we present the results of the experiments for the new algorithm by using an opportunistic network emulator, mobility traces, and Wikipedia pages.

In opportunistic networks, mobile nodes are enabled to communicate with each other even if a route connecting them never exists. Because nodes are not supposed to possess any knowledge about the network topology, each single node acts as a gateway and uses its local knowledge to decide next hop when forwarding messages. It is necessary to find those nodes who can spread messages more efficiently to be the next hop. We proposed a moving scope aware routing protocol (MSAR). MSAR can detect similarity of moving scope between two nodes by comparing their common encounters without knowing geographical position by hardware. It forwards messages according to the comparison threshold. The simulation results show that MSAR can acquire a good packet delivery ratio and a relatively low delay with resources constraints. It can also improve routing efficiency and decrease network overhead.

Integrating social networks properties such as centrality, tie strength, etc into message forwarding protocols in opportunistic networks has grown into a vital major benchmark. The opportunistic network is a demanding network with no set route to travel a message from the source to be able to the destination. During these networks, nodes use possibilities gained based on store-carry-forward patterns to forward communications. Every node that obtains a message when it activities another node makes selection concerning the forwarding or not necessarily delivering the node came across. Most of these message forwarding protocols use the benefit of social properties information like contact information and social relationship enclosed by the nodes in the social opportunistic network. In this paper, a Bonding based forwarding technique is proposed which is finding direct and indirect bonding among nodes by exploiting contact information and social pattern. In the proposed protocol, we also focus on indirect bonding by finding weakest direct bonded nodes and then replace it with strong indirect bonded nodes of the network. In this work, the balance between transmission delay and network traffic is considered by using shortest path map based mobility model. ONE simulator is used for simulation and performance of the proposed protocol is compared contrary popular approaches for instance Epidemic, PRoPHET, and BubbleRap, and Interaction based when using the shortest path map based mobility model. The Bonding based forwarding technique performs adequately well concerning the number of messages delivered, overhead ratio, message dropping and average latency.

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