Utilization of resources effectively by using an economic based model in opportunistic networks

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.

Opportunistic networking is a new communication paradigm which explores the potential of inter-device contacts due to human mobility [3, 5]. Intermittent connectivity, non existence of an end-to-end path between nodes and extreme dynamism in topological structure are inherent characteristics of opportunistic networking [2, 5]. In opportunistic networking devices make control and management decisions by themselves with locally available information. The unique differences that draw the line between opportunistic networking and other legacy networking environments call for newer approaches for systems development.

In this paper, we present a forwarding algorithm for mobile social opportunistic networks which is both centrality-based and energy-aware. In opportunistic networks, the nodes usually exploit a contact opportunity to perform hop-by-hop routing, since an end-to-end path between the source node and destination node may not exist. Centrality-based forwarding schemes use the structurally important nodes (i.e. nodes having many contacts with neighbors) as message relays and have been shown to be good candidates for social forwarding in opportunistic network environments. However, the most central nodes which are structurally more important than other network nodes, have the disadvantage to be used very frequently as message relays, thus consuming their energy very quickly. Here, we exploit the advantage of using a centrality-based scheme which is distributed and needs only the history of contacts as social information. In addition, we modulate the computed node centrality value using the node energy level in order to avoid transmissions in nodes more central but with a limited energy budget. Simulating three real-world mobility traces, we show that our scheme is able to achieve better delivery ratio and overhead ratio while sensibly reducing the energy consumption of nodes.

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.

An increasing number of mobile users (MUs) share common interests in general information, such as traffic information, weather forecast, and domestic/international news. However, the centralized infrastructure-based system has not been designed for efficiently disseminating the information of common interest (IoCI) to numerous requesters. Thanks to the rapid development of mobile devices equipped with large storage and multiple communication modes, opportunistic communication between a pair of MUs can be readily realized. With the aid of opportunistic networks formed by MUs, we can improve the connectivity of cellular networks in the rural areas, we can offload the tele-traffic from the overloaded cellular networks, and we can efficiently disseminate the IoCI in the densely populated areas. We commence with a detailed survey on the cross-disciplinary research area of social network analysis aided telecommunication networking. We continue by focusing our attention on the family of integrated cellular and large-scale opportunistic networks, whose performance is dominated by the inter-contact duration as well as the contact duration between any pairs of MUs. A continuous-time-pure-birth Markov chain is invoked for analyzing the relevant performance. We demonstrate that the delivery ratio of the IoCI before it expires becomes higher than 90% with the aid of opportunistic networks consisting of 20 MUs. Moreover, our experiments based on the InfoCom 2006 mobility trace show that the opportunistic networks are capable of offloading 58% of the tele-traffic from the cellular networks. Thereafter, we propose a hybrid information dissemination scheme for the integrated cellular and small-scale opportunistic networks, which is comprised of two main stages: 1) the base station-aided single-hop multicast (BSSHM) stage and 2) the cooperative multicast aided spontaneous dissemination stage. In contrast to their large-scale counterparts, in small-scale opportunistic networks, the information dissemination is mainly affected by the mobility of the MUs, by the wireless channel attenuation and by the resource scheduling, where a discrete-time-pure-birth Markov chain is utilized for characterizing the relevant performance. We demonstrate that our hybrid information dissemination scheme outperforms the traditional BSSHM in terms of various delay metrics, despite consuming less energy.

In opportunistic networks the communication opportunities (contacts) are intermittent and there is no need to establish an end-to-end link between the communication nodes. The enormous growth of devices having access to the Internet, along the vast evolution of the Internet and the connectivity of objects and devices, has evolved as Internet of Things (IoT). There are different issues for these networks. One of them is the selection of IoT devices in order to carry out a task in opportunistic networks. In this work, we implement a Fuzzy-Based System for IoT device selection in opportunistic networks. For our system, we use four input parameters: IoT Device’s Number of Past Encounters (IDNPE), IoT Contact Duration (IDCD), IoT Device Storage (IDST) and IoT Device Remaining Energy (IDRE). The output parameter is IoT Device Selection Decision (IDSD). The simulation results show that the proposed system makes a proper selection decision of IoT devices in opportunistic networks. The IoT device selection is increased up to 15% and 27% by increasing IDNPE and IDRE, respectively.

Clusters, Grids, and peer‐to‐peer (P2P) networks have emerged as popular paradigms for next generation parallel and distributed computing. They enable aggregation of distributed resources for solving large‐scale problems in science, engineering, and commerce. In Grid and P2P computing environments, the resources are usually geographically distributed in multiple administrative domains, managed and owned by different organizations with different policies, and interconnected by wide‐area networks or the Internet. This introduces a number of resource management and application scheduling challenges in the domain of security, resource and policy heterogeneity, fault tolerance, continuously changing resource conditions, and politics. The resource management and scheduling systems for Grid computing need to manage resources and application execution depending on either resource consumers' or owners' requirements, and continuously adapt to changes in resource availability.The management of resources and scheduling of applications in such large‐scale distributed systems is a complex undertaking. In order to prove the effectiveness of resource brokers and associated scheduling algorithms, their performance needs to be evaluated under different scenarios such as varying number of resources and users with different requirements. In a Grid environment, it is hard and even impossible to perform scheduler performance evaluation in a repeatable and controllable manner as resources and users are distributed across multiple organizations with their own policies. To overcome this limitation, we have developed a Java‐based discrete‐event Grid simulation toolkit called GridSim. The toolkit supports modeling and simulation of heterogeneous Grid resources (both time‐ and space‐shared), users and application models. It provides primitives for creation of application tasks, mapping of tasks to resources, and their management. To demonstrate suitability of the GridSim toolkit, we have simulated a Nimrod‐G like Grid resource broker and evaluated the performance of deadline and budget constrained cost‐ and time‐minimization scheduling algorithms. Copyright © 2002 John Wiley & Sons, Ltd.

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.

Under the opportunistic network environments, normal network protocols can hardly transfer data successfully and efficiently. In order to overcome this problem, many new routing algorithms have been raised pertinently. Thereinto, the Epidemic routing algorithm is a typical approach, which maintains least delay with a highest delivery ratio. However, it is a defective method for routing data as it requires an infinite node cache and results in infinite circulate even one of the data duplicates arrived at the destination, which is proved to be an extremely limitation on its routing performance. Confronted with this drawback, this paper puts forward a novel Epidemic Routing based on Mobility and Probability of Occurrence (MPOER), which endows message with a Hop Life dynamically. And, we formulates with a simulation platform called The ONE, and the experiment shows that the approach proposed can increase the routing performance in several aspects just sacrifice some delay performance.

Roadside Units (RSUs), which enable the vehicles-to-infrastructure communication, are deployed along the roadside to handle the growing communication demands as the number of vehicles increases. How to optimally deploy the RSUs and efficiently use them to enhance the vehicular network performance are important problems in the RSU-aided opportunistic networks. In order to solving these problems, we need to understand fundamental laws and properties of the opportunistic contacts. Due to the prohibitive costs to collect enough realistic contact records, to the best of our knowledge, little experiment work has been conducted to study the communication contact patterns in RSU-aided opportunistic networks. In this work, we carry out an extensive experiment involving tens of thousands of operational taxis in Beijing city. Based on this newly collected Beijing trace and the existing Shanghai trace, we find some invariant characters for the communication contacts for large scale RSU-aided vehicular opportunistic networks. In terms of inter-contact time between the RSUs and vehicles, we find it obeys exponential distribution, while in terms of contact rate, it follows Poisson distribution. Our observations reveal fundamental patterns for large scale vehicular mobility, and further provide useful guidelines for the design of new routing protocols and their performance evaluation for RSU-aided vehicular opportunistic networks.

Opportunistic Networking allows wireless nodes to exchange data and information of interest with peers in communication range. These nodes form a large, dynamic, multi-hop network on the fly. Challenging optimization problems arise, such as end-to-end routing, resource allocation (e.g., for buffer space and bandwidth), content placement etc., exacerbated by the lack of end-to-end connectivity. While globally optimal solutions are normally sought in network optimization, node actions and decisions in this context are inherently local. As a result, most solutions proposed rely on local heuristics without any guarantees about their convergence properties towards a desired global outcome. In this paper, we argue that the framework of Markov Chain Monte Carlo (MCMC) optimization can be applied to many problems in Opportunistic Networking, providing efficient local algorithms that provably converge to a globally optimal solution. As a case study, we use the problem of optimal relay selection for group communication (e.g., multicast), based on node contact patterns.

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.

Opportunistic networks are mobile networks that rely on the store-carry-and-forward paradigm, using contacts between nodes to opportunistically transfer data. For this reason, traditional routing mechanisms are no longer suitable. To increase the probability of successfull message delivery, we propose SPRINT, an opportunistic routing algorithm that introduces an additional routing criterion: online social information about nodes. Furthermore, previous results show that, for particular environments, contacts between devices in opportunistic networks are highly predictable. When users follow rare events-based mobility patterns, we show that human mobility can be approximated as a Poisson distribution. Based on this result, we add an additional prediction component into our routing algorithm. Our solution delivers better results compared to traditional social-based routing approaches, for different real-world and synthetic mobility scenarios.

Opportunistic networks are special multi-hop ad hoc networks in which nodes exploit any opportunity of pairwise contact to forward information, without requiring any fixed infrastructures. Routing is one of the most challenging problems in opportunistic networks, because there is not an end-to-end path in most situations. To deal with this issue, researchers have investigated various routing protocols based on redundancy messages or the prediction of future contacts according to the nodes' mobility history. However, most of the previous work ignored the social attributes of nodes, which can be fully utilized to improve routing efficiency. In this paper, we proposes an efficient routing algorithm, community-based routing (CR), where nodes determine which community it belongs to and choose a proper next hop node in order to reduce the number of forwarding and improve the delivery probability. A unique feature of this algorithm is that it records the contact history and calculates the tie strength with other nodes while moving around and exploits the recorded contact history to optimize the forwarding path. We show through simulation that this algorithm is effective and it achieves better performance than Epidemic, Bubble Rap and PROPHET.

This paper presents a publish/subscribe-based multicast distribution infrastructure for DTN-based opportunistic networking environments. The distribution approach is designed to combine an effective distribution of content to interested nodes in the presence of resource constraints, mobility and unstable connectivity. By considering local resource constraints such as limited storage space and limited available bandwidth at opportunistic contacts as well as knowledge about interest for content in the network environment, nodes make local decisions about resource utilization and DTN bundle prioritization. Without further coordination, this approach uses the overall available network resources more effectively compared to other approaches such as epidemic forwarding. We have implemented this approach and have performed a series of measurements in mobile opportunistic networking scenarios under different configurations.