A Centrality-Based ACK Forwarding Mechanism for Efficient Routing in Infrastructureless Opportunistic Networks

A game theoretic context-based routing protocol for opportunistic networks in an IoT scenario

In opportunistic networks(oppnets),because of low density,nodal mobility,and limited transmission range,network topology will disrupt and nodes will detach from the networks.Thus,an end-to-end path from source to destination may not exist,which is a challenge for the design of routing protocols.This issue has attracted more and more attention recently.Firstly,the concept,architecture,and characteristics of opportunistic networks were introduced.Then,the progress and the new mechanisms of routing protocols for opportunistic networks were described.The present routing protocols were also classified,introduced,and compared.Finally,the future directions were given,which aimed to benefit the future research and applications of opportunistic networks.

Intermittently connected opportunistic networks experience frequent disconnections and shorter contact durations. Therefore routing of messages towards their destinations needs to be handled from various points of view. Predictability and connectedness are two information which can be determined by participating mobile nodes of an opportunistic content distribution network using their past contacts. Epidemic or probabilistic routing protocols do not fully utilize these information to route messages towards their destinations. In this paper we describe the routing algorithm, implementation details, experiment design and the performance validation of a new, adaptive routing protocol which utilizes the predictability and connectedness information to route messages efficiently. Simulation based comparative studies show that the proposed routing protocol outperforms existing Epidemic and probabilistic routing protocols in delivering messages.

Opportunistic networks are a kind of Ad Hoc networks that make use of meeting opportunities from mobile nodes to realize the network communication which doesn't need a full path between the source nodes and destination nodes. It’s tolerant in latency and network division. It’s a great challenge to design routing protocols in opportunistic networks in that the movements of nodes are uncertain leading to intermittent connection which make it hard to transmit message to its destination. However, the problems have also attracted more and more attention from researchers. Firstly, in this paper, we introduced the basic concept of opportunistic networks. Secondly we classified and interpreted routing protocols according to the infrastructure, scenario, forwarding mechanism etc. Thirdly, we carried out the computer simulation and analysis for opportunistic network routing protocols in aspect of message delivery ratio, network latency, routing overhead and the number of lost packets. Then we summarized the advantages and disadvantages of routing protocol and discussed its applicability. Finally we looked ahead the hotspot of recent routing protocols under opportunistic networks.

Opportunistic Networks (OppNets) is gaining popularity day-by-day due to their various applications in the real-life world. The two major reasons for its popularity are its suitability to be established without any requirement of additional infrastructure and the ability to tolerate long delays during data communication. Opportunistic Network is also considered as a descendant of Mobile Ad hoc Networks (Manets) and Wireless Sensor Networks (WSNs), therefore, it inherits most of the traits from both mentioned networking techniques. Apart from its popularity, Opportunistic Networks are also starting to face challenges nowadays to comply with the emerging issues of the large size of data to be communicated and blind forwarding of data among participating nodes in the network. These issues lower the overall performance of the network. Keeping this thing in mind, ML-Fresh-a novel framework has been proposed in this paper which focuses to overcome the issue of blind forwarding of data by maintaining an optimum path between any pair of participating nodes available in the OppNet using machine learning techniques viz. pattern prediction, decision tree prediction, adamic-adar method for complex networks. Apart from this, ML-Fresh also uses the history of successful encounters between a pair of communicating nodes for route prediction in the background. Simulation results prove that the ML-Fresh outperforms the existing framework of Opportunistic Networks on the grounds of standard Quality-of-Service (QoS) parameters.

Opportunistic networks are a new evolution of mobile ad hoc networks composed of intermittently connected nodes, in which the routing for the dynamic topology is a challenging issue. In opportunistic networks, the mobile nodes with common interest or close relationship may form into groups and move together, which brings us a good feature to employ when designing the routing protocol for opportunistic networks under resource constraints. Our main idea in this paper is to maximize the message delivery probability in a group-aware opportunistic network under the constraints of bandwidth and buffer space. Embedded this idea, we propose a cooperative routing protocol using the group feature, which includes the cooperative message transfer scheme and the buffer management strategy. In the cooperative message transfer scheme, the limited bandwidth is considered and the message transfer priorities are designed to maximize the delivery probability. In the buffer management strategy, by considering the constraint of buffer space, we propose the cooperative message caching scheme and the dropping order of the messages is designed to minimize the reduced delivery probability. Finally, we conduct the simulations to demonstrate the effectiveness of our proposed routing protocol.

An Opportunistic Network is an intermittently connected Mobile Ad-hoc Networks that exploits the communication opportunity between the nodes for data transmission whenever they are within the communication range of each other even for a short time. In contrast to the Mobile Ad-hoc Networks, Opportunistic Networks follow store-carry-forward approach for the data transmission. Routing in this type of network depends on many factors, like the direction of the node's movement, the supported interface bandwidth (Bluetooth, high-speed Internet, etc.), the node's speed, and the node's buffer size. In this research work, a context-aware routing protocol is proposed that uses the frequency of contacts among nodes as context information. The frequency of meetings between any two nodes is found to be a good heuristic to identify the message's best forwarder. The proposed routing protocol is simulated on opportunistic network environment (ONE) simulator and the results are compared with the most prominent routing protocols of context-oblivious and context-aware classes, and it was found that the proposed routing protocol performs better than other protocols in terms of delivery probability and buffer average time.

Assessments on routing protocols for delay tolerant network (DTN) or opportunistic network considered various metrics such as delivery ratio, overhead and delay, but energy consumption as a metric is not well analyzed. Since the energy aspect is now an issue, routing protocols should be considered from an energy consumption point of view. In this article, some basic routing protocols of the opportunistic network analyzed from the energy aspect alongside other standard metrics. Afterward, the performance of each routing protocol for different situations assessed through network effective factors. Finally, the simulation results show that Spray and Wait algorithm has better energy performance compared to Epidemic and PRoPHET. Analyzing a protocol with a single-relay selection that minimizes the transmission energy and increase the energy efficiency of all devices in a wireless network. This technique is analyze the opportunistic relaying in the paradigm of D2D communication and study the opportunistic schemes with a single relay selection to minimize energy consumption of the devices in a wireless network. The fact is that The D2D communication ensures that the energy consumed for the data relaying is negligible and The D2D communication ensures that the selection of the relaying device exploits the diversity from neighboring devices in order to minimize the energy consumed by the devices for forwarding the data to the BS.

An opportunistic network (OppNet) is a kind of mobile ad hoc network which does not require any predefined end-to-end path to deliver a message to a pair of nodes. OppNets are an infrastructure-less network, i.e. it does not require any repeaters or routers to be placed in the network. OppNets have a degrading factor due to lossy links and varying channel conditions. The two main parameters to improve route optimization are mobility models and routing protocols. Mobility models are those which depict the path or motion of mobile nodes while routing protocols help in forwarding messages as per the directions. The taxonomy of routing protocols is characterized into six categories: geographic-based, link-state-aware, context-aware, probabilistic, optimization-based and cross-layer. Opportunistic routing protocols use the broadcast nature of the wireless medium to allow hop-by-hop transmission. Opportunistic routing outperforms traditional routing in many ways, such as efficient utilization of broadcasting and better usage of spatial diversity of the wireless medium.

Opportunistic networks (OppNets) provide an infrastructure-less environment for delivering messages from source to destination. However, random nature of network topologies, unstable node connections and many other factors make routing of messages difficult. Transfer of a message from source to destination depends on the opportunity available to the carrier node for its encounter with the destination. Hence, an intelligent store-carry-forward paradigm needs to be defined which judicially transfers the message from source to the intermediate node till it reaches its destination. In this paper, a new routing protocol called Cognitive Routing Protocol for Opportunistic networks (CRPO) is proposed which makes intelligent decisions based on its past experiences and sensory inputs. It defines a neural network at each node and learns about the simulating environment with time for the successful delivery of messages. From simulation results, the authors observed that the proposed routing protocol outperforms traditional routing protocols such as Epidemic and PRoPHET, and an advanced routing protocol HBPR in terms of message delivery ratio, overhead ratio and number of messages getting dropped.

The adaptive routing protocol recently proposed by researchers for opportunistic networks often makes an assumption that when each node makes an intelligent decision of choosing a best forwarder node to route its messages, the chosen forwarder will often have a large buffer storage and will always altruistically devote its resources for forwarding those messages towards their destination nodes. However, copies of messages fill up buffers on receiving intermediate nodes very quickly when few nodes become more central than the other nodes and when the connectivity becomes more and more intermittent. This paper investigates about a more realistic behavior of mobile opportunistic nodes where the storage buffer in nodes gets full very quickly, especially at the more centric nodes, when such nodes store, carry and forward messages for other nodes. Because of these practical issues, messages passed to most popular nodes just get dropped when the buffers get filled up which ultimately results in the performance degradation of the network. We investigate the cases of buffer overflow and analyze how the performance of a considered opportunistic networking environment can get degraded when buffers get overflowed. We then propose an algorithmic approach where nodes choose the potential nodes based not only on potential nodes' predicted connectivity to other nodes but also based on the other factors such as available buffer space in the potential intermediate nodes and the intermediate nodes' willingness to store, carry and forward messages for other nodes. Our initial simulation based experiment results are encouraging and indicate that a congestion aware adaptive routing protocol performs well and the overall network performance increases by means of the number of messages delivered when the message forwarding is constrained by the buffer size.

Opportunistic Networks develops rapidly in recent years. With the popularity of GPS (Global Positioning System), velocity or position prediction plays an important role in opportunistic network routing. In this paper, a new opportunistic routing protocol named LSMPSF (Least Squares Method Prediction-based Spray and Focus) is proposed. With least squares method, LSMPSF predicts velocity by curve fitting. According to the predicted velocities, it estimates the neighbor nodes' delivery ratio and makes routing decision. Simulation results reveal that LSMPSF effectively increases the prediction accuracy and reduces resource consumption. Compared with SF and PROPHET, LSMPSF achieves better performance on delivery ratio and overhead ratio.

To improve the energy efficient of n-Epidemic routing protocol in the opportunistic networks, where lack end to end forwarding path, a novel adjustments strategy of parameter n is proposed. Firstly, nodes dynamically update their energy level of current environment while moving around. Secondly, nodes with low energy level take the large n avoiding energy consumption during the messages exchange and vice versa. Thirdly, nodes will only replicate message to their neighbors when the number of neighbors reach the threshold n. So the number of messages transmitted is reduced and energy is saved. The simulation results show that, n-Epidemic routing protocol with the proposed adjustment method can efficiently reduce energy consumption and balance the energy consumption among nodes compared with the prime n-Epidemic routing protocol. And it obviously prolongs the life time of network.

The opportunistic networks are the variants of Delay Tolerant Networks (DTNs). These networks can be useful for routing in places where there are few base stations and connected routes for long distances. In an opportunistic network, when nodes move away or turn off their power to conserve energy, links may be disrupted or shut down periodically. These events result in intermittent connectivity. When there is no path existing between the source and the destination, the network partition occurs. Therefore, nodes need to communicate with each other via opportunistic contacts through store-carry-forward operation. In this work, we consider the IoT device selection problem in opportunistic networks. We propose a delay-aware fuzzy-based system consisting of three input parameters. The output parameter is IoT device selection decision. We evaluate the performance of the proposed system by simulations. The simulation results show that the proposed system makes a proper selection decision of IoT-devices in opportunistic networks.

The Internet of Things (IoT) enhances the succeeding generation of web facilities by supporting pleasant relations among people and linked devices. A common way to connect IoT devices is to create an environment based on global infrastructure to create connections between objects, tasks, people, sensors, places, and machinery. Suitable organization of IoT-based structures in smart cities as well as societies based on channeling, treating, processing, sensing data, and faster communication systems for IoT devices. The crucial need for universal access and real-time performance induces the expedition for a network-based IoT substructure. Opportunistic networks and IoT are originally distributed computing standards. Aside from the circumstance that IoT and opportunistic networks are unrelated and measured separate independent communication sites, furthermore the paper proposes a novel opportunistic-IoT network (opportunistic network based on IoT’s qualities). In addition, it is stated that how we can create a well-defined opportunistic network through discovering essential connections among individuals and smart devices is one key issue between opportunistic societies. Because of these realities, the paper analyses a few basic applications, forthcoming challenges in the formation of opportunistic networks based on the IoT background, and the organization of opportunistic network’s forwarding procedures. The paper also implements, links, and evaluates the functionality of three forwarding protocols named PRoPHET, MaxProp, and Epidemic with a realistic city model by using THE ONE simulator.