Internet of Vehicles (IoV) is a new emerging concept and is an extended notion of Vehicular Ad-hoc networks (VANETs). In IoV the vehicles (nodes) are connected to the internet and able to transmit information. However, due to resources constraint nature of vehicles, they may not want to cooperate in order to save its own resources such as memory, energy, and buffer, etc. This behavior may lead to poor system performance. IoV needs an efficient solution to motivate the nodes in terms of cooperation to avoid selfish behavior. A novel mechanism Incentive and Punishment Scheme (IPS) has been proposed in this article where vehicles with higher weight and cooperation are elected as Heads during the election process. Vickrey, Clarke, and Groves (VCG) model has been used to scrutinize the weight of these heads. Vehicle participating in the election process can increase its incentives (reputation) by active participation (forwarding data). Vehicles with repeated selfish behavior are punished. The monitoring nodes monitor the performance of their neighbor nodes after the election process. A mathematical model and algorithms has been developed for the election, monitoring and incentive processes. The proposed approach has been simulated through VDTNSim environment to analyze the performance of the proposed IPS. The performance results demonstrate that the proposed schemes outperform the existing schemes in terms of packet delivery ratio, average delivery delay, average cost, and overhead. INDEX TERMS Internet of vehicles, smart objects, VCG model, selfish behavior, incentive techniques.

Various operational communication models are using Delay-Tolerant Network as a communication tool in recent times. In such a communication paradigm, sometimes there are disconnections and interferences as well as high delays like vehicle Ad hoc networks (VANETs). A new research mechanism, namely, the vehicle Delay-tolerant network (VDTN), is introduced due to several similar characteristics. The store-carry-forward mechanism in VDTNs is beneficial in forwarding the messages to the destination without end-to-end connectivity. To accomplish this task, the cooperation of nodes is needed to forward messages to the destination. However, we cannot be sure that all the nodes in the network will cooperate and contribute their computing resources for message forwarding without any reward. Furthermore, there are some selfish nodes in the network which may not cooperate to forward the messages, and are inclined to increase their own resources. This is one of the major challenges in VDTNs and incentive mechanisms are used as a major solution. This paper presents a detailed study of the recently proposed incentive schemes for VDTNs. This paper also gives some open challenges and future directions for interested researchers in the future.

Many Internet of Things (IoT) applications have been developed and implemented on unreliable wireless networks like the delay tolerant network (DTN); however, efficient data transfer in DTN is still an important issue for the IoT applications. One of the application areas of DTN is vehicular delay tolerant network (VDTN) where the network faces communication disruption due to lack of end‐to‐end relay route. It is challenging as some of the nodes show selfish behavior to preserve their resources like memory and energy and become noncooperative. In this article, an honesty‐based democratic scheme (HBDS) is introduced where vehicles with higher honesty level are elected as heads—during the process. Vehicles involved in the process would maximize their rewards (reputation) through active participation in the network activities, whereas vehicles with noncooperative selfish behavior are punished. The honesty level of the heads is analyzed using Vickrey, Clarke, and Groves (VCG) model. The mathematical model and algorithms developed in the proposed HBDS technique are simulated using the VDTNSim framework to evaluate their efficiency. The performance results show that the proposed scheme dominates current schemes in terms of packet delivery probability, packet delivery delay, number of packets drop, and overhead ratio.

The Social Internet of Things (SIoT) can be seen as integrating the social networking concept into the Internet of Things (IoT). Such networks enable different devices to form social relationships among themselves depending on pre-programmed rules and the preferences of their owners. When SIoT devices encounter one another on the spur of the moment, they seek out each other’s assistance. The connectivity of such smart objects reveals new horizons for innovative applications empowering objects with cognizance. This enables smart objects to socialize with each other based on mutual interests and social aspects. Trust building in social networks has provided a new perspective for providing services to providers based on relationships like human ones. However, the connected IoT nodes in the community may show a lack of interest in forwarding packets in the network communication to save their resources, such as battery, energy, bandwidth, and memory. This act of selfishness can highly degrade the performance of the network. To enhance the cooperation among nodes in the network a novel technique is needed to improve the performance of the network. In this article, we address the issue of the selfishness of the nodes through the formation of a credible community based on honesty. A social process is used to form communities and select heads in these communities. The selected community heads having social attributes prove effective in determining the social behavior of the nodes as honest or selfish. Unlike other schemes, the dishonest nodes are isolated in a separate domain, and they are given several chances to rejoin the community after increasing their honesty levels. The proposed social technique was simulated using MATLAB and compared with existing schemes to show its effectiveness. Our proposed technique outperforms the existing techniques in terms of throughput, overhead, packet delivery ratio (PDR), and packet-delivery latency.