Mobile Opportunistic Networks Research Articles

Destination-aware metric based social routing for mobile opportunistic networks

Although centrality is widely used to differentiate the importance of nodes for social-aware routing in mobile opportunistic networks (MONs), it is destination-agnostic since such metrics are usually measured without destination information. To this end, we propose a destination-aware social routing scheme for MONs, namely DAS, which utilizes the destination-aware betweenness centrality (DBC) to choose the right nodes as relays given the specific destination node. During the process of message dissemination, the number of replicas for a message is calculated by the source and each relay independently in respond to the network condition in a dynamic manner. Therefore, DAS disseminates only a few message copies to ensure data delivery as well as reducing routing cost. We conduct extensive simulations using real trace data sets to show improved performance with low overhead in comparison with existing social-aware routing approaches in various scenarios.

Performance Comparison of Deterministic and Stochastic Utility Ascent Routing Algorithms in Opportunistic Mobile Networks

Opportunistic mobile networks (OMNs) are a class of MANETs where complete endto-end paths rarely exist between sources and destinations; consequently, the end-to-end delays in these networks are much greater than typical MANETs. In this harsh environment networking algorithm actions and decisions are inherently local; the algorithms are mostly greedy, choosing the best solution among the locally ones. In this paper, we study the delivery performance of two classes of gradient utility ascent routing algorithms, deterministic (greedy) and stochastic utility ascent ones, in OMNs. We furthermore discuss the impact of these algorithms on traffic load distribution fairness among the network nodes, since this issue is critical in mobile networking whose nodes typically possess limited resources, e.g. power and storage capacity. Using simulation, driven by real human mobility traces, we investigate the performance of greedy ascent algorithms, Hill Climbing (HC) and Delegation Forwarding (DF), compared with that of stochastic ascent algorithms, Simulated Annealing (SA). The results under the scenario show an advantage of SA over HC and DF in terms of total delivered messages, average delivery delay, and traffic distribution fairness; however, SA negatively impacts the delivery cost performance, i.e. increasing the total message copies beyond those of HC and DF.

ARTIFICIAL INTELLIGENCE BASED CONGESTION CONTROL MECHANISM VIA BAYESIAN NETWORKS UNDER OPPORTUNISTIC

Nodes in Mobile Opportunistic Network (MON) must cache packets to deal with the key challenging issue of intermittent connection. Management strategy of handling buffer therefore plays a vital impact on the performance of MON, and it attracts more attention recently. Intermittent connection and lengthy postpone are the key challenges in such networks for this reason the internet congestion control mechanisms aren’t suitable for DTNs. An inefficient buffer management strategy would badly affect the performance of such networks. In recent years, to handle congestion buffer manipulate strategies in MON are considered an active vicinity for research studies.

Reducing the Overhead of Multicast Using Social Features in Mobile Opportunistic Networks

Through utilizing user mobility and short-range device-to-device communication techniques, mobile opportunistic networks (MONs) enable end-to-end message delivery without the dependence on reliable network infrastructures. Examples of these networks include mobile social networks and vehicular networks. Multicast in MONs is used to disseminate information to a group of mobile nodes, which have attracted considerable attention due to high resource utilization. In this paper, we focus on reducing the overhead of multicast in MONs without compromising the delivery performance, through utilizing static social features of nodes and time-varying social behaviors. We first conduct a trace data analysis using the information entropy theory to identify the most important and representative social features in a popular trace, the Infocom06 trace. Based on these static social features, we propose a Social Profile-based Multicast (SPM) routing scheme, that supports efficient multicast message dissemination with a small maintenance overhead, i.e., little cost on maintaining the historical records. Furthermore, by exploring the time-varying social behaviors during daytime and nighttime, we verify that a small number of forwardings during that daytime is sufficient to achieve a desirable delivery ratio. We thus propose an improved overhead-reducing scheme Social Profile-based Multicast-Overhead Reducing (SPMOR) that restricts the number of forwardings during the daytime. The extensive trace-driven simulations show that SPM achieves desirable delivery performance with small maintenance and transmission cost, and SPMOR can further reduce the transmission overhead under diurnal user behaviors. At last, we conduct a similar study on a campus-based MON trace, SocialBlueConn. We find that the main conclusions and performance results on SocialBlueConn are consistent with the Infocom06 trace, which verifies our methodology is scalable.

SSR: Using the Social Similarity to Improve the Data Forwarding Performance in Mobile Opportunistic Networks

SSR: Using the Social Similarity to Improve the Data Forwarding Performance in Mobile Opportunistic Networks

A novel segmented routing algorithm based on the markov decision process in mobile opportunistic networks

The purposes of research of routing algorithms in the mobile opportunistic networks are to improve the message delivery ratio, control overhead, and reduce the end‐to‐end delay. Now the performance of routing algorithms is very low because the opportunistic network structure is sparse and the network topology is variable. In this article, we use the Markov random process to propose a new segmented routing algorithm with a higher efficiency in opportunistic networks. In the stage of multicopy, we propose a forwarding strategy with the Markov decision process (MPD) to forward messages to multiple relay nodes quickly. In the stage of singe copy, we establish a precise delivery model to forward messages to the destination node with less relay nodes by combining the MPD with historical encountering information of nodes. The two‐stage segmented routing algorithm not only balances the relationship between the delivery ratio and the network overhead, but also saves network resources. Compared with other routing algorithms, our proposed segmented routing algorithm is superior to the existing routing algorithms in the message delivery ratio, message delay, and network overhead. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A novel energy-efficient probabilistic routing method for mobile opportunistic networks

In mobile opportunistic networks (MONs), a large number of intelligent devices carried by moving people can conduct message transmission without the support of communication infrastructures. Routing is one of the concerning and challenging research hotspots in MONs because of nodes’ mobility, intermittent connectivity, limited nodes’ energy, and the dynamic changing quality of the wireless channel. However, only one or several of the above factors are considered in most current routing methods. Moreover, owing to the high mobility of nodes, it is particularly important to decide the time when to make the forwarding decision and which nodes can be selected as the relay nodes. Additionally, due to the dynamic changing quality of the wireless channel, the predetermined relay nodes for the next hop may not receive the message. In this paper, we propose a novel energy-efficient probabilistic routing method for MONs, in which energy and mobility of nodes and the quality of wireless channel are all taken into account. Firstly, we explore the regularity of nodes’ mobility and the encounter probability among nodes to decide the time when to forward messages to other nodes. Secondly, by maintaining the energy fairness among nodes, the network lifetime can be prolonged. Thirdly, by fully taking the dynamic changing quality of the wireless channel into consideration, the retransmission number of messages can be effectively reduced. Additionally, we adopt a forwarding authority transfer policy for each message, which can effectively control the number of replicas for each message. The extensive simulation results show that the proposed method outperforms the existing routing algorithms in terms of the delivery ratio and the overhead ratio.

A SIR-based model for contact-based messaging applications supported by permanent infrastructure

In this paper we focus on the study of coupled systems of ordinary differential equations (ODE's) describing the diffusion of messages between mobile devices. Communications in mobile opportunistic networks take place upon the establishment of ephemeral contacts among mobile nodes using direct communication. SIR (Sane, Infected, Recovered) models permit to represent the diffusion of messages using an epidemiological based approach. The question we analyse in this work is whether the coexistence of a fixed infrastructure can improve the diffusion of messages and thus justify the additional costs. We analyse this case from the point of view of dynamical systems, finding and characterising the admissible equilibrium of this scenario. We show that a centralised diffusion is not efficient when people density reaches a sufficient value. This result supports the interest in developing opportunistic networks for occasionally crowded places to avoid the cost of additional infrastructure.

Energy-Aware Temporal Reachability Graphs for Time-Varying Mobile Opportunistic Networks

With the rapid emergence of applications in Mobile Opportunistic Networks (MONs), understanding and characterizing their properties becomes extremely important. A fundamental model for MONs is time-varying graph, which currently remains poorly understood since many well-recognized properties of static graphs have no obvious counterpart in dynamic ones. In MONs, the dynamical links change opportunistically and usually the system energy is very limited, which results in unique and unknown properties about the network connectivity and reachability. In this paper, taking the communication energy into account, we introduce the concept of Energy-aware Temporal Reachability Graphs (ETRG), which characterizes the connectivity of MONs with the consideration of communication consumed energy, and consequently reveals the communication capabilities of MONs with the given of real-world system parameters of data size, tolerable delay, and energy budget. We come up with efficient algorithm to calculate ETRG from the corresponding time-varying graphs. By applying ETRG to several mobile networks recorded by real-life human and vehicular mobility traces, we characterize their network connectivity properties in terms of average reachability, communication asymmetry, and stability. Moreover, utilizing ETRG that places upper bounds of the communications capabilities, we reveal the fundamental relations and tradeoffs among large-scale variability of the system metrics of energy budget, tolerable delay, and data size on the system performance.

Network Resource Constrained Traffic Allocation for Delay Sensitive Mobile Crowdsourcing

In mobile opportunistic networks, a common assumption is that nodes are able to complete as much data exchange as needed during a communication opportunity, which is, however, not the case in practice due to limited wireless link bandwidth. Beyond that, the storage capacity at a node is also limited, further impacting the communication efficiency. In this paper, we explore delay sensitive and network resource constrained traffic allocation for mobile device-to-device crowdsourcing. With the requirement of restricted node storage and link bandwidth, we first formulate a non-linear traffic allocation optimization problem that would be at least as hard as NP-hard. In order to practically solve it, based on the submodular property, we propose an approximation algorithm and a distributed heuristic in the same design principle. We implement the latter on Dell Streak tablets and deploy an experiment with 21 nodes for a period of three weeks. Moreover, we extract the implementation codes from the prototype and run simulations using the Haggle trace to study its performance trend. The experiment and simulation outcomes verify that the proposed mechanisms achieve the close-to-optimal performance with affordable computation complexity, which can be easily implanted in practical network environment.

Context-adaptive forwarding in mobile opportunistic networks

When two opportunistic network peers encounter, utility functions are generally employed to select the messages that have to be exchanged, with the purpose of maximizing message delivery probability and reduce congestion. These functions compute weighted sums of various parameters, like centrality, similarity, and trust. Most of the existing solutions statically compute the weights based on offline observations and apply the same values regardless of a node’s context. However, mobile networks are not necessarily constant in terms of behavior and characteristics, so the classic approach might not be suitable. The network might be split into sub-networks, which behave differently from each other. Thus, in this paper, we show that, by dynamically adjusting the behavior of a node based on its context, through the adjustment of the utility function on the fly, the opportunistic forwarding process can be improved. We show that nodes behave differently from each other and have different views of the network. Through real-life trace-based simulations, we prove that our solution is feasible and is able to improve an opportunistic network’s performance from the standpoint of hit rate, latency, and delivery cost.

FARS: A Fairness-aware Routing Strategy for Mobile Opportunistic Networks

Mobile opportunistic network is a kind of ad hoc networks, which implements the multi-hop routing communication with the help of contact opportunity brought about by the mobility of the nodes. It always uses opportunistic data transmission mode based on store-carry-forward to solve intermittent connect problem of link. Although many routing schemes have been proposed, most of them adopt the greedy transmission mode to pursue a higher delivery efficient, which result in unfairness extremely among nodes. While, this issue has not been paid enough attention up to now. In this paper, we analyzed the main factors that reflect fairness among nodes, modeled routing selection as a multiple attribute decision making problem, and proposed our Fairness-aware Routing Strategy, named FARS. To evaluate the performance of our FARS, extensive simulations and analysis have been done based on a real-life dataset and a synthetic dataset, respectively. The results show that, compared with other existing protocols, our FARS can greatly improve the fairness of the nodes when ensuring the overall delivery performance of the network.

Rethink data dissemination in opportunistic mobile networks with mutually exclusive requirement

With the increase of mobile devices, opportunistic mobile networks become a promising technique for disseminating data in a local area. However, existing works focus on the single data dissemination and fail to consider the practical applications where there are multiple data under different topics. Multiple data dissemination shows the potential applications in many scenarios, e.g., product coupon distribution. In this paper, we focus on budget-constrained multiple data dissemination services. A mobile user may be interested in data under different topics, but receiving data for any topic is enough due to user experiences and participation constraints. This is the mutually exclusive delivery requirement in many scenarios. In light of the different amounts of data and the different popularity levels of data in each topic, deciding which data should be forwarded to mobile users becomes an important problem. This paper aims to design an efficient data dissemination scheme that minimizes the maximum dissemination delay while incurring a small communication overhead for the aforementioned scenario. In this paper, we discuss three different scenarios according to different knowledge. We start with the data dissemination with network topology, and a corresponding optimal solution is proposed. Later, we consider the probability estimation with k-hop information, and lastly propose a distributed data forwarding algorithm, which considers the amount of data in different topics, the mobile users’ interest, and their data forwarding abilities, respectively. The real trace-driven experiments show that the proposed scheme achieves a good performance.

Fine-Grained Control for Neighbour Node Anonymity in Opportunistic Mobile Networks

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Delay and cost performance analysis of the diffie-hellman key exchange protocol in opportunistic mobile networks

Diffie-Hellman (DH) provides an efficient key exchange system by reducing the number of cryptographic keys distributed in the network. In this method, a node broadcasts a single public key to all nodes in the network, and in turn each peer uses this key to establish a shared secret key which then can be utilized to encrypt and decrypt traffic between the peer and the given node. In this paper, we evaluate the key transfer delay and cost performance of DH in opportunistic mobile networks, a specific scenario of MANETs where complete end-to-end paths rarely exist between sources and destinations; consequently, the end-to-end delays in these networks are much greater than typical MANETs. Simulation results, driven by a random node movement model and real human mobility traces, showed that DH outperforms a typical key distribution scheme based on the RSA algorithm in terms of key transfer delay, measured by average key convergence time; however, DH performs as well as the benchmark in terms of key transfer cost, evaluated by total key (copies) forwards.

Forwarding in Heterogeneous Mobile Opportunistic Networks

Due to intermittent connectivity among nodes in mobile opportunistic networks, the direct packet delivery strategy often experiences large delays. To alleviate this problem, nodes often pass a copy of their message to relay nodes, which in turn could deliver it to the destination. In this setting, we consider the problem of relay selection that minimizes the mean delivery delay. Applying ideas from a renewal theory , we derive a closed-form expression for the mean delivery delay in a heterogeneous network under any probabilistic two-hop relay selection policy. Utilizing this expression, a policy that assures the least mean delivery delay can be obtained as a solution of a linear program (the time complexity of $\mathcal {O}(n^{3})$ ). However, exploiting the structure of the mean delay minimization problem, we compute an optimal solution using an algorithm with linearithmic ( $\mathcal {O}(n \log n)$ ) time complexity.

Quality of Video Oriented and Multi-Meeting Based Routing Algorithm for Video Data Offloading

The increasing popularity of video delivery among mobile users makes the problem of explosive traffic growth becoming more and more serious for traditional wireless networks, and video transmission-based on device-to-device communication through mobile opportunistic networks (MONs) is regarded as an ideal way to resolve this issue. However, data transmission in the MONs is mainly through the two ways: data replication and data forwarding. Thus, to achieve high delivery ratio and low delivery delay, data replication is usually excessively exploited, and the large number of redundant replicas will not only consume large amount of nodal resource but also greatly increase the overload of networks. For video transmission, this issue becomes more severe for its volume and continuity. Thus, in this paper, we propose a novel routing scheme for video data transmission in the MONs, namely, video routing-based multi-player cooperative game which can maximize the quality of reconstructed video data while minimizing the overhead of nodal and network resources. Specifically, we first construct the marginal gain model for video delivery quality, and then model the video data transmission among multiple encounters as a multi-player cooperative game. Under the guidance of the Nash equilibrium theory, the video data carried by these encountering nodes is adaptively and optimally re-assigned among them. Extensive simulations based on real-life mobility traces and synthetic traces have validated the effectiveness of the proposed routing algorithm.

An Analytical Model Based on Population Processes to Characterize Data Dissemination in 5G Opportunistic Networks

The scarcity of bandwidth due to the explosive growth of mobile devices in 5G makes the offloading messaging workload to Wi-Fi devices that use opportunistic connections, a very promising solution. Communications in mobile opportunistic networks take place upon the establishment of ephemeral contacts among mobile nodes using direct communication. In this paper, we propose an analytical model based on population processes to evaluate data dissemination considering several parameters, such as user density, contact rate, and the number of fixed nodes. From this model, we obtain closed-form expressions for determining the diffusion time, the network coverage, and the waiting time. Newer 5G wireless technologies, such as WiGig, can offer multi-gigabit speeds, low latency, and security-protected connectivity between nearby devices. We therefore focus our work on the impact of high-speed and short-range wireless communications technologies for data dissemination in mobile opportunistic networks. Moreover, we test whether the coexistence with a fixed infrastructure can improve content dissemination, and thus justify its additional cost. Our results show that, when user density is high, the diffusion is mainly performed through the opportunistic contacts between mobile nodes, and that the diffusion coverage is close to 100%. Moreover, the diffusion is fast enough to dynamically update the information among all the participating members, so users do not need to get closer to fixed spots for receiving updated information.

A Data Dissemination Method Based on Region Type Correlations for Mobile Opportunistic Networks

In Mobile Opportunistic Networks (MONs), due to the node movements and the uncontrollable on/off switches of the carried communication devices, the contacts between nodes may be scarce and momentary, and thus a data packet should be transferred through some discrete hops. To avoid the costly flooding of data packets, the data packets are typically disseminated to some relay nodes selected by data holders. However, the mobility patterns of nodes will become different in different types of regions (such as residential regions, commercial regions, scenery regions, or industrial regions); i.e., the movement directions and movement ranges of nodes are frequently varied when the nodes move among various regions. At present, the issues regarding the region types and region type correlations have not been investigated for the data dissemination in existing works. To this end, we propose a Region Type based Data Dissemination Method (RTDDM) for MONs, which exploits the region type correlations and selects the proper relay nodes through a Markov decision model. To verify the performance of RTDDM, we give some theoretical analysis as well as an elaborated simulation study, the results of which show that RTDDM can improve the delivery ratio and reduce the delivery delay, especially in the applications with various region types.

A Survey on Mobile Data Offloading Technologies

Recently, due to the increasing popularity of enjoying various multimedia services on mobile devices (e.g., smartphones, ipads, and electronic tablets), the generated mobile data traffic has been explosively growing and has become a serve burden on mobile network operators. To address such a serious challenge in mobile networks, an effective approach is to manage data traffic by using complementary technologies (e.g., small cell network, WiFi network, and so on) to achieve mobile data offloading. In this paper, we discuss the recent advances in the techniques of mobile data offloading. Particularly, based on the initiator diversity of data offloading, we classify the existing mobile data offloading technologies into four categories, i.e., data offloading through small cell networks, data offloading through WiFi networks, data offloading through opportunistic mobile networks, and data offloading through heterogeneous networks. Besides, we show a detailed taxonomy of the related mobile data offloading technologies by discussing the pros and cons for various offloading technologies for different problems in mobile networks. Finally, we outline some opening research issues and challenges, which can provide guidelines for future research work.