The growth of the opportunistic network (OppNet) in recent years has created a variety of opportunities and concerns. Machine-to-machine (M2M) communications, which are a critical component of OppNet, provide a novel means for connecting and communicating among machine-type communication devices (MTCDs) without the need for human interaction. OppNet data play a significant role in M2M communications and it emphasizes more powerful data storage, computation, processing, as well as the security and stability of data transfer. This paper proposes a joint optimization framework for dueling Deep Q-network (DQN)-based opportunistic data transmission in M2M communication using blockchain technology. The best choice and decision of caching servers, blockchain systems, and computing nodes, can be made in accordance with the dynamic decision-making process by DQN, a decision to increase the system incentives, including high data processing efficiency, lower cost, and improved data interaction security. Simulation results using various parameters demonstrate that the proposed model has more benefits and is more efficient than the existing random, greedy, and Conventional DQN benchmark schemes.

Industrial Internet of Things (IoT) and Industry 4.0 enable interconnection among various devices. An opportunistic IoT network is an ad hoc network that is formed by the nodes (e.g., smart vehicles and mobile phones) by utilizing various short radio range techniques. In this kind of network, information forwarding and dissemination among other smart devices is based upon the opportunistic contact nature mainly due to network dynamics and user mobility. Routing plays an important part in these kinds of networks since there does not exist a pre-established route (Tyagi and Kumar, 2013). Nodes are often selected dynamically based upon many parameters such that messages can be delivered successfully to the destination devices or sinks. However, these intermediate nodes are often selfish because routing these packets costs energy. In the case of incomplete cooperation and asymmetric information, message delivery can be severely degraded, which increases the network delay affecting the overall network performance. Therefore, this work proposes an incentive design mechanism based on the contract theory to reward intermediate nodes appropriately to forward the messages. The contract theory is used to model the forwarding-forwarder node interaction as a labor market with private information. First, the users are classified into a finite number of types according to their ability of forwarding the message, and the service trading between the forwarding and forwarder nodes is properly modeled. Furthermore, the necessary and sufficient conditions are derived to provide the incentives to the nodes involved in the message forwarding. Extensive simulations show that the proposed mechanism is effective in providing incentives and outperforms other benchmark schemes in terms of delivery probability, average latency, and overhead ratio.

In this paper, soft clustering on network nodes using Multivariate Gaussian Mixture Models (MGMM) is applied to design a machine learning-based routing protocol for Opportunistic Networks (OppNets). The proposed protocol, called Multivariate Gaussian Mixture-based Prediction routing (MGMP), involves sending messages in concentrated bursts to a group of comparable devices detected using a clustering technique. The network features are utilized for training the MGMM-based clustering model to help identify the relay nodes as the best hop for message transmission. The performance of the proposed MGMP protocol is evaluated using the Haggle Infocom-2006 real dataset and compared against two benchmark protocols KNNR and MLPROPH. The considered performance metrics are average latency, delivery probability and, messages dropped. It has been found that when the TTL is increased, the delivery probability increases and then decreases. Indeed, MGMP outperforms MLPROPH and KNNR in terms of delivery probability by 18.10% and 21.30%, respectively.

This paper proposes a novel routing protocol for Opportunistic networks called Energy Efficient MultiObjectives Optimized Routing (E2MOOR), which uses a multi-objectives weight function for efficient routing. The proposed protocol is energy efficient and predicts the next optimal forwarder based on four objectives, which consists of hop encounter, distance between the source/intermediate and destination, delivery probability, and node’s energy consumption, as context information. The pareto optimal solutions set is extracted through the Naive and Slow algorithm. The nodes in the set are further used for forwarding the data packets to the destination node. The proposed protocol is evaluated considering the double, triple, and quadruple objective functions, when the predefined threshold values are varied. Simulations results show that the proposed E2MOOR scheme outperforms the E-Epidemic, E-PRoPHET, and E-EDR chosen as benchmarks routing protocols.

This paper proposes a novel routing protocol for opportunistic networks called Fuzzy logic-based Q-Learning Routing Protocol (FQLRP), which uses fuzzy based Qlearning for efficient routing. The proposed protocol predicts the next optimal forwarder of a message based on a reward mechanism that considers the node's energy, movement, and buffer space as parameters. Throughout the routing process, the residual energy of each node and the energy distribution of a group of nodes, are both considered in determining a reward function, which in turn helps in deciding the most suitable forwarders of the message towards its destination. Simulation results show that the proposed FQLRP scheme outperforms the Q-Learning based routing and the Epidemic routing protocols, chosen as benchmarks, in terms of delivery rate, average delay and overhead ratio.

Opportunistic Networks are composed of wireless nodes opportunistically communicating with each other following the store, carry and forward mechanism. These networks are designed to operate in an environment characterized by high delay, intermittent connectivity and non-guarantee of the end-to-end path between the sender and the destination. The messages are transmitted on the basis of best-effort procedure. If the nodes are not able to forward the message for reasons like missing connectivity, insufficient buffer space or low-confidence among nodes, the messages are temporarily buffered according to the waiting-list policy and it is resumed when the connection is established again. The nodes drop the message on the basis of delete policy in a congested network environment. While there are multiple policies for effective buffer utilization in Opportunistic Networks such as FIFO, LIFO, and Random, none allow message transmission on the basis of message- type. In this paper, a Priority based Buffer Management Technique (PBMT) has been introduced that considers the priority of a message to address the aforementioned problems. This policy allows solving the underlying problem of transmitting messages in a random fashion, by transmitting them in a systematic and orderly method. The proposed PBMT shows considerable difference in routing processes. Simulation results that are provided, confirm that the proposed PBMT is more secure and efficient than traditional buffer management policies for opportunistic networks by using the Haggle INFOCOM 2006 real mobility data trace.