Trust Framework for Data Forwarding in Opportunistic Networks Using Mobile Traces

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.

The model of indirect trust value is done further research in statistics principle and a new model of trust value is proposed. On this basis, the electoral method of cluster node is studied, and then a routing algorithm is given based on considering the trust value, the density, the distance between nodes and the forward capability. Experiment and simulation results show that the established trust value model is considerably reliable, and with the increase of cycles, the trust value gradually tends to 1. In a certain range about the number of nodes, in comparison with references 10 and 11, this algorithm improves convergence speed largely and is stable during the information transfer process. When the network topology changes, this algorithm can do fast rerouting, which shows this optimization algorithm has widely application.

Vehicular ad hoc network (VANET) applications become increasingly popular. Particular characteristics of VANETs profoundly impede the deployment of the VANET applications. Therefore, the merits of routing significantly affect the accomplishments of the VANETs. Though myriads of efforts have been made, the problems are partially solved. Quality of Service (QoS) requirements should be satisfied for various applications. Existing routing protocols are incapable of well satisfying distinct QoS needs. To remedy this problem, a next-hop selection scheme is proposed in this paper; which is able to select the next-hop node for various applications with diverse QoS requirements. Aspects including QoS type classification, link lifetime prediction, bandwidth evaluation, and next-hop node selection; are elaborately addressed in the proposed scheme. The simulation results show that the proposed next-hop selection scheme promotes packet delivery rate, shortens end-to-end delay, and decreases signaling overhead in VANETs.

Past research on opportunistic communications has demonstrated the power of mobile phones as information mediators and content providers to co-located devices. As an extension of opportunistic communication paradigm, opportunistic computing and services research leverages the powerful capabilities and pervasive nature of modern smartphones to provision services to nearby nodes. When an opportunistic network is viewed as a provider of services rather than as a simple message routing system, node and service discovery come to play a critical role. This paper discusses several factors that affect the discover ability of pervasive services with current existing devices in light of their practical limitations. Moreover, the impact of these limitations to pervasive service discovery is studied in evaluation results.

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 nodes having access to the Internet, along the vast evolution of the Internet and the connectivity of objects and nodes, has evolved as Internet of Things (IoT). There are different issues for these networks. One of them is the selection of IoT nodes in order to carry out a task in opportunistic networks. In this work, we implement a Fuzzy-based System for IoT node selection in opportunistic networks. For our proposed system, we use four input parameters: Node’s Free Buffer Space (NFBS), Node’s Successful Delivery Ratio (NSDR), Node’s Battery Level (NBL), Node Contact Duration (NCD). The output parameter is Node Selection Decision (NSD). The results show that the proposed system makes a proper selection decision of IoT nodes in opportunistic networks. The IoT node selection is increased up to 17% and 40% by increasing NBL and NSDR, respectively.

SummaryVehicular delay‐tolerant networks (VDTNs) play prominent part in smart transportation and smart city paradigm to diminish the drivers' risk to meet accidents and offer many infotainment and entertainment services. All the operations are carried out by exchanging data among nodes. However, due to open communication nature of VDTN, the data broadcasted by mobile nodes are quiet susceptible to security attacks. Recently, a number of protocols are proposed for open communication. However, securing such communications and establishing trust value among vehicles are major addressable issues. This is because the existing fraudulent peers can lead to catastrophic circumstances on roads. Along with securing the data carriers, the authenticity and reliability of data is also equally important. In order to identify reliable nodes and maintaining the authenticity of data being communicated, a new artificial intelligence (AI)‐based hybrid trust management framework is proposed in this article. The study particularly exploits the proximity‐based k‐nearest neighbour (kNN) classification model for selecting controlling nodes. Further, decision stump is employed to accomplish trust estimation rules, and backpropagation is used to self‐train the mobile nodes, whenever anticipated trust value is not attained. The proposed scheme maintains the integrity of vehicular nodes and efficiently handles the specified. The trust framework uses a multifaceted direct and recommended trust estimation approach to calculate the global trust values. For comparison, the performance of the proposed method was compared with other trust management approaches.

A geo-temporal mobility prediction method for cooperative time-validity-constrained content delivery in opportunistic networks

Data dissemination in opportunistic networks poses a series of challenges, since there is no central entity aware of all the nodes' subscriptions. Each individual node is only aware of its own interests and those of a node that it is contact with, if any. Thus, dissemination is generally performed using epidemic algorithms that flood the network, but they have the disadvantage that the network overhead and congestion are very high. In this paper, we propose ONSIDE, an algorithm that leverages a node's online social connections (i.e. friends on social networks such as Facebook or Google+), its interests and the history of contacts, in order to decrease congestion and required bandwidth, while not affecting the overall network's hit rate and the delivery latency. We present the results of testing our algorithm using an opportunistic network emulator and three mobility traces taken in different environments.

Designed as a specialized ad hoc network suitable for applications such as emergency responses, OppNets are considered as a sub-class of DTN where communication opportunities are intermittent, so an end-to-end path between the source and the destination may never exist. Existing networks have already brought connectivity to a broad range of devices, such as hand held devices, laptops, tablets, PC, etc. The Internet of Things (IoT) will extend the connectivity to devices beyond just mobile phones and laptops, but to buildings, wearable devices, cars, different things and objects. There are different issues for these networks. One of them is the selection of IoT nodes in order to carry out a task in opportunistic networks. In this work, we implement a Fuzzy-Based System for IoT node selection in opportunistic networks. For our proposed system, we use four input parameters: IoT Node’s Unique Encounters (NUE), IoT Node’s Free Buffer Space (NFBS), IoT Node’s Batter Level (NBL) and IoT Node Contact Duration (NCD). The output parameter is IoT Node Selection Decision (NSD). The results show that the proposed system makes a proper selection decision of IoT nodes in opportunistic networks. The IoT node selection is increased up to 25% and 11% by increasing NCD and NUE, respectively.

How to shorten time delay and enhance delivery ratio is still an open problem in the study of opportunistic networks. Most proposals are trying to deal with this issue by introducing infrastructures. Although related research has been proven to be useful in improving the routing performance of the network, there is still room for further improvement. In this article, inspired by the powerful message synchronization capability of wireless sensor networks (WSNs), we propose a new opportunistic network framework called WON that introduces WSNs into opportunistic network. With the support of WSNs, fast message delivery and high success ratio can be achieved. We specifically present the layered architecture of WON and compare WON to existing architectures in opportunistic networking. The simulation results concerning delivery delay and success ratio are highly encouraging. Finally, open issues are outlined.

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.

Link prediction for opportunistic networks faces the challenges of frequent changes in topology and complex and variable spatial-temporal information. Most existing studies focus on temporal or spatial features, ignoring ample potential information. In order to better capture the spatial-temporal correlations in the evolution of networks and explore their potential information, a link prediction method based on spatial-temporal attention and temporal convolution network (STA-TCN) is proposed. It slices opportunistic networks into discrete network snapshots. A state matrix based on topology information and attribute information is constructed to represent snapshots. Time convolutional networks and spatial-temporal attention mechanisms are employed to learn spatial-temporal information. Furthermore, to better improve link prediction performance, the proposed method converts the auto-correlation error into non-correlation error. On three real opportunistic network datasets, ITC, MIT, and Infocom06, experimental results demonstrate the superior predictive performance of the proposed method compared to baseline models, as shown by improved AUC and F1-score metrics.

MANETs are highly dynamic and suffer from frequent and unpredictable changes in the network topology due to the fact that devices are mobile and operate with low battery power. Due to limited computing power, high mobility and the lack of a central coordinating entity, service discovery in MANETs has been an attractive area of research. However, proposed solutions are broadcast-based and result in overloading the constrained network. The issue addressed in this paper is therefore to provide a cross-layer Service Discovery in order to reduce energy consumption. In this work we propose an efficient Energy-Aware Service And Route Discovery scheme called EASARD. We extended the Zone Routing Protocol in order to encapsulate service information in its routing messages. We present a performance comparison of EASARD versus another integrated protocol in terms of energy consumption. We evaluate how the different approaches affect the energy usage of mobile devices. Extensive simulation demonstrate the superiority of our proposed approach over other cross-layer schemes.

Mobile devices have changed from being terminal equipment for making voice calls, video calls, playing games and sending messages to including differing functionalities such as acting as a server and client for sharing information. Information sharing directly between modern mobile phones can be done using Bluetooth or WiFi functionalities. Such a network does not need a fixed infrastructure, thereby allowing people to create Opportunistic mobile phone networks when they need the service, thereby avoiding charges from established service providers. Node movement out of or into the communication range of other nodes changes not only its close node relationship with other nodes, but also changes all routes based on those relationships. Movement and mobility of connecting nodes plays a crucial role in the performance of such a network. In this paper, movement of cluster mobile phones of moving students at a university campus is described and mobility model(s) are identified. The main objective is to understand the pattern of node cluster movement and assist to evaluate Ad-Hoc or Opportunistic network performance. This initial study will help understand geographical paths of interest for a further study on optimal routing schemes for practical opportunistic network that can emerge at a high education institution campus.

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.