Search

In distributed key-value stores, multiple replica servers are always available for each key-value access operation when the eventual consistency model is employed. Accordingly, the completion times of the key-value access operations generated by an end-user request at different servers may be of great difference, especially when the replica servers are heterogeneous and have time-varying performance. Accordingly, the replica selection algorithm is crucial to cut the response time of end-user requests. The main challenge of making replica selection for each light-weighted key-value access operation is to timely know the status of replica serves. Recently, the adaptive replica selection algorithm C3 suggests guiding the replica selection with the piggybacked information of replica server in the returned “value”. Although C3 has good performance, the poor timeliness of feedback information makes a large performance gap between C3 and the ideal replica selection algorithm. To narrow this gap, the Accelerated Information Dissemination (AID) mechanism is proposed in this paper. Specifically, AID removes the bottleneck of information dissemination at the “slow” servers by letting both “client” and “replica server” store the records about the status of replica servers and both “key” and “value” piggyback multiple records. AID is implemented in Cassandra and evaluated by experiments and large scale simulations. The results show AID can significantly improve the timeliness of feedback information, especially when the number of nodes is large. Accordingly, AID helps C3 to greatly reduce the latency. [ABSTRACT FROM AUTHOR]

Floating Content (FC) is a paradigm for localized infrastructure-less content dissemination, that aims at sharing information among nodes within a restricted geographical area by relying only on opportunistic content exchanges. FC provides the basis for the probabilistic spatial storage of shared information in a completely decentralized fashion, usually without support from dedicated infrastructure. One of the key open issues in FC is the characterization of its performance limits as functions of the system parameters, accounting for its reliance on volatile wireless exchanges and on limited user resources. This paper takes a first step towards tackling this issue, by elaborating a model for the storage capacity of FC, i.e., for the maximum amount of information that can be stored through the FC paradigm. The storage capacity of FC, and of similar probabilistic content dissemination systems, is evaluated with a powerful information theoretical approach, based on a mean field model of opportunistic information exchange. In addition, an extremely simple explicit approximate expression for storage capacity is derived. The numerical results generated by our analytical models are compared to the predictions of realistic simulations under different setups, proving the accuracy of our analytical approaches, and characterizing the properties of the FC storage capacity. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) with advanced communication, computing, and control capabilities will provide not only a convenient means of transportation but also an emerging platform for real-time social communications and networking. Thereby, it is crucial to enable timely exchange of information over the dynamic cyber-physical-social system enabled by the AVs. In this paper, we consider age of information (AoI)-centric real-time information dissemination over vehicular social networks (VSNs), where the AoI captures the freshness of received data packets. We first propose a mathematical framework based on the mean-field theory (MFT) to analyze the network AoI (NAoI) of VSNs, namely AoI for the AV that lastly receives the information update in the network. The proposed analytical framework considers both the social features of vehicular networks, which are characterized using the scale-free network theory, and the underlying wireless communication process to evaluate the NAoI. Then, we further consider joint optimization of the information update rate at the source node and the transmit probabilities at the AVs for minimization of the average peak NAoI (PNAoI), i.e., the time average of peak values occurred in the evolution of the NAoI, which is solved via a novel parametric optimization scheme. Both analytical and simulation results show that compared with several baseline schemes, the proposed scheme can exploit the scale-free feature of the VSNs to significantly lower the average PNAoI by up to 96 $\%$. [ABSTRACT FROM AUTHOR]

Recently, many new applications were developed on smartphones to facilitate our daily life. One of the potential approaches for information gathering and dissemination on smartphones is through cooperatively relaying among user devices based on short range wireless communications. To facilitate information dissemination and to improve user experiences, data transfer that neither requires connection setup nor interferes users’ current operations is highly desirable. In this work, we study the feasibility of automatic information dissemination based on Bluetooth Low Energy (BLE) legacy advertising. To improve applicability, we additionally propose an automatic connectionless undirected information dissemination (ACLUID) scheme to effectively exchange information among users without knowing the BLE device addresses in advance. We analyze the performance of the proposed ACLUID scheme to investigate the effectiveness of message exchange. By applying the ACLUID scheme, users can opportunistically exchange some messages of interest when they are occasionally located within the effective communication range of BLE with BLE activities running in the background. In addition, we implement our scheme in Android-based smartphones and perform some experiments to examine the feasibility of ACLUID under various scenarios. According to the experimental results, a large message can be completely transferred from an advertiser to a scanner within a short period of time with a high probability.

Public opinion inversion and other nonlinear phenomena often occur during online social network information dissemination (OSNID) in modern society. To explore the influence of overconfidence on OSNID, we develop a multi-agent simulation model integrating overconfidence and evolutionary games, as previous research has scarcely paid any attention to irrational behavior in information dissemination. This integrated paradigm provides an effective tool for managers to control the spread of OSNID and reduce the harm caused by rumors. The theoretical model is constructed from the perspective of an evolutionary game and, combined with overconfidence theory, three overconfidence scenarios are designed: benefit overconfidence, cost overconfidence, and both benefit and cost overconfidence. Then, a multi-agent simulation model of OSNID under different overconfidence scenarios is realized. The proposed simulation model exhibited better performance (e.g., faster diffusion speed) than the traditional Bass model; its performance was also validated by comparison with real-world cases. The results demonstrate that (1) with increasing benefit overconfidence, the convergence speed of OSNID will be accelerated, and the user group will reach stability at a faster speed. (2) With increasing cost overconfidence, the user’s decision will change from adopting dominant to tit-for-tat, and the adoption ratio will gradually decrease. (3) Compared with overconfidence in earnings, overconfidence in costs is more conducive to improving stability. This study attempts to provide new ideas for OSNID and attempts to propose an integration framework for behavioral theory and simulation methods.

In fact, information is not transmitted in a single message, but rather multiple messages are transmitted simultaneously and interact with each other as they are transmitted. Based on this phenomenon, a multi-information overlay network model that differs from the traditional network structure is first constructed on the basis of two classical network models, BA scale-free and WS Small World. When multiple messages are transmitted simultaneously, the competitive nature of the message affects the transmission process to some extent, and therefore a sense of competition for the message is introduced into the classical SIS model of infectious disease. Simulation experiments have shown that a sense of competition enhances or inhibits the spread of information to varying degrees, and that information with a high sense of competition has a greater scale of spread, in line with the real-life “Matthew effect”. In addition, the sense of competition also accelerates the spread of information. Therefore, it is possible to inhibit the spread of negative information by controlling the sense of competition, so as to achieve timely warning and control of negative information.

Information dissemination in vehicular ad hoc networks (VANETs) is inseparable from the interaction between vehicles and infrastructure. The trust authority (TA) often plays a pivotal role in VANETs and requires interaction with multiple vehicles. However, when the TA sends the same message to multiple vehicles, there are many redundancies, as it needs to negotiate with each vehicle and send them different ciphertexts. This greatly reduces the work efficiency of the TA. To the best of our knowledge, there is no research on the problem of redundancy that occurs when the same message is sent to multiple vehicles in VANETs. The proposed scheme adopts identity-based broadcast encryption (IBBE) technology, which is a secure data-sharing scheme suitable for the vehicle-to-infrastructure communication mode, in VANETs for the first time. Thus, with only one encryption, the TA can generate a fixed-length ciphertext for a group of vehicles. When there are new vehicles that subsequently request a service, the TA can assign encryption tasks to the proxy server. In terms of security, our scheme meets the particular requirements of VANETs. The encryption overhead of the sender and the length of the ciphertext were comparatively analyzed. The results demonstrated that the performance of the scheme improved significantly. Thus, our scheme can prevent redundancies and effectively improve the work efficiency of TA. [ABSTRACT FROM AUTHOR]

Predicting emerging hot events in an early stage is essential for various applications, including information dissemination mining, ads recommendation and etc. Existing techniques either require a long-term observation over the event or features that are expensive to extract. However, given limited data at the early stage of an emerging event, the temporal features of hot events and non-hot events are not distinctive enough yet. In this work, we introduce BEEP, a Bayesian perspective Early stage Event Prediction model, that tackles this dilemma. We formulate the hot event prediction problem by two Semi-Naive Bayes Classifiers, where we consider both the temporal features and structural features and perform distribution test for the selected features. Theoretical analysis and extensive empirical evaluations on two real datasets demonstrate the effectiveness of our methods. [ABSTRACT FROM AUTHOR]

Securing information dissemination is extremely important for various vehicular ad hoc networks (VANETs) applications. However, most of the applications require disseminating the critical information only to the authorized vehicles through V2I (Vehicle to Infrastructure) communications. Moreover, V2I communications usually suffer from incomplete information to vehicles in one RSU’s transmission range. Therefore, it is an even challenging task that how to ensure reliable dissemination of encrypted data to the recipient vehicles in multi-RSU settings. We propose a security-aware information dissemination scheme with fine-grained access control in cooperative multi-RSU of VANETs. Our proposed scheme uses ciphertext-policy attribute-based encryption (CP-ABE) to ensure confidential communication in a broadcasting way, which ensures that only the vehicles that satisfy the access control policy can have the ability to access the information; and we employ proxy re-encryption in the communication protocol to make sure the moving vehicles in high-speed can get the whole encrypted information. Performance analysis shows that our scheme can enable fine-grained access control for the broadcasted information, and make sure the vehicles receive reliable information in the whole disseminating process. And our scheme is applicable and efficient in various scenarios of information dissemination, especially in cooperative multi-RSU of VANETs. [ABSTRACT FROM AUTHOR]

Graph-based namespaces are being increasingly used to represent the organization of complex and ever-growing information eco-systems and individual user roles. Timely and accurate information dissemination requires an architecture with appropriate naming frameworks, adaptable to changing roles, focused on content rather than network addresses. Today’s complex information organization structures make such dissemination very challenging. To address this, we propose POISE, a name-based publish/subscribe architecture for efficient topic-based and recipient-based content dissemination. POISE proposes an information layer, improving on state-of-the-art Information-Centric Networking solutions in two major ways: 1) support for complex graph-based namespaces, and 2) automatic name-based load-splitting. POISE supports in-network graph-based naming, leveraged in a dissemination protocol which exploits information layer rendezvous points (RPs) that perform name expansions. For improved robustness and scalability, POISE supports adaptive load-sharing via multiple RPs, each managing a dynamically chosen subset of the namespace graph. Excessive workload may cause one RP to turn into a “hot spot”, impeding performance and reliability. To eliminate such traffic concentration, we propose an automated load-splitting mechanism, consisting of an enhanced, namespace graph partitioning complemented by a seamless, loss-less core migration procedure. Due to the nature of our graph partitioning and its complex objectives, off-the-shelf graph partitioning, e.g., METIS, is inadequate. We propose a hybrid, iterative bi-partitioning solution, consisting of an initial and a refinement phase. We also implemented POISE on a DPDK-based platform. Using the important application of emergency response, our experimental results show that POISE outperforms state-of-the-art solutions, demonstrating its effectiveness in timely delivery and load-sharing. [ABSTRACT FROM AUTHOR]

Internet-of-Vehicles (IoV) is a novel technology to ensure safe and secure transportation by enabling smart vehicles to communicate and share sensitive information with each other. However, the realization of IoV in real-life depends on several factors, including the assurance of security from attackers and propagation of authentic, accurate and trusted information within the network. Further, the dissemination of compromised information must be detected and vehicle disseminating such malicious messages must be revoked from the network. To this end, trust can be integrated within the network to detect the trustworthiness of the received information. However, most of the trust models in the literature relies on evaluating node or data at the application layer. In this study, we propose a novel hybrid trust management scheme, namely, NOTRINO, which evaluates trustworthiness on the received information in two steps. First step evaluates trust on the node itself at transport layer, while second step computes trustworthiness of the data at application layer. This mechanism enables the vehicles to efficiently model and evaluate the trustworthiness on the received information. The performance and accuracy of NOTRINO is rigorously evaluated under various realistic trust evaluation criteria (including precision, recall, F-measure and trust). Furthermore, the efficiency of NOTRINO is evaluated in presence of malicious nodes and its performance is benchmarked against three hybrid trust models. Extensive simulations indicate that NOTRINO achieve over 75% trust level as compared to benchmarked trust models where trust level falls below 60% for a network with 35% malicious nodes. Similarly, 92% precision and 87% recall are achieved simultaneously with NOTRINO for the same network, comparing to benchmark trust models where precision and recall falls below 87% and 85% respectively. [ABSTRACT FROM AUTHOR]

When an emergency occurs, as a factor in online emergency information dissemination, the peak represents the maximum strength and turning point. However, the role of these peaks in emergencies is still unclear. In this article, we try to disentangle this problem and investigate the effect of these peaks on the lifecycle and influence of online emergency information dissemination. We first propose an Online Emergency Information Dissemination Process (OEIDP) model drawing from the ideas of system cybernetics and system identification to present the emergency lifecycle with a decay parameter, time constant, and delay parameter. Based on this model, we then analyze the effect of peaks using empirical data from the Weibo, WeChat, and 20000 media platforms of 169 emergencies. The results reveal the positive significant impacts of the peak time, peak volume, and spikes on emergency information dissemination. The results enable us to make accurate judgements when facing peaks and remind us of the need to balance the strongest manifestation and final influence. Our work complements the gaps on the peak effect in the existing communication literature and introduces the concept of system cybernetics to solve information dissemination which provides a new direction for future studies to solve related problems using system control techniques.

Deep learning at scale is dominated by communication time. Distributing samples across nodes usually yields the best performance, but poses scaling challenges due to global information dissemination and load imbalance across uneven sample lengths. State-of-the-art decentralized optimizers mitigate the problem, but require more iterations to achieve the same accuracy as their globally-communicating counterparts. We present Wait-Avoiding Group Model Averaging (WAGMA) SGD, a wait-avoiding stochastic optimizer that reduces global communication via subgroup weight exchange. The key insight is a combination of algorithmic changes to the averaging scheme and the use of a group allreduce operation. We prove the convergence of WAGMA-SGD, and empirically show that it retains convergence rates similar to Allreduce-SGD. For evaluation, we train ResNet-50 on ImageNet; Transformer for machine translation; and deep reinforcement learning for navigation at scale. Compared with state-of-the-art decentralized SGD variants, WAGMA-SGD significantly improves training throughput (e.g., 2.1× on 1,024 GPUs for reinforcement learning), and achieves the fastest time-to-solution (e.g., the highest score using the shortest training time for Transformer). [ABSTRACT FROM AUTHOR]

Recently, vehicle platooning has attracted a lot of attention as one of the promising solutions to the explosion of vehicle numbers. By exchanging information among vehicles through communication networks, vehicle platooning can significantly improve traffic safety and efficiency. In this paper, we develop a joint systematic design of platoon communication and control to reduce position errors of consecutive vehicles and to improve platoon safety. Through separate information dissemination of the platoon leader and followers, we improve the success probability of the platoon leader's information dissemination. To extend the communication range of the platoon leader and also the platoon scale, we propose to use relays to forward the platoon leader's messages. Further, an adaptive distributed model predictive control (DMPC) based controller is presented, which can adjust its control parameters according to platoon state and dynamic information sources. Simulation results not only verify the proposed systematic design in terms of position errors, but also show that our scheme performs well in vehicle failure cases where collisions can be avoided and platoon safety improved. [ABSTRACT FROM AUTHOR]

The study of the laws and influencing factors of information dissemination in social networks is critical to analyzing the spread of public opinion, preventing the spread of rumors, and guiding information transmission. This paper addresses the shortcomings of the traditional SEIR model. We establish the SETQR model and use the probability theorem to derive the law of information propagation. Furthermore, the equilibrium point and the basic regeneration number of the SETQR model are obtained using differential dynamics and the regenerative matrix method. The stability of the SETQR model at the equilibrium point is derived theoretically. Finally, experimental verification is conducted. The simulation results indicate that the SETQR model achieves local stability at the equilibrium point, which is consistent with the results of the theoretical analysis. Through further simulation, the effects of time lag, containment, and forgetting mechanisms on the speed of information dissemination and the time required for the network to reach equilibrium are analyzed.

Electric vehicles (EVs) have been regarded as a promising solution to address the pressure to reduce greenhouse gas (GHG) emissions in the transportation sector. Prior research has primarily focused on their adoption behavior (intention) per se. However, gaining and processing EV-related information as the foundation for adoption have been ignored in previous research. Social media such as WeChat are influential communication channels that play a significant role in promoting information dissemination and innovation diffusion. Data from a sample of 334 respondents were collected in China to investigate EV-related information credibility on WeChat and its impact on information dissemination through a moderated dual-process model. The empirical results show that information source-based trust and the habit of believing have a significantly positive impact on information content credibility, while recipient expertise negatively affects content credibility. Information content credibility, in turn, has a direct and positive impact on information dissemination on WeChat. Personal relevance negatively moderates the links from information source-based trust and the habit of believing in information content credibility while positively moderating the relationships between recipient expertise and content credibility and between content credibility and information dissemination. Considering the empirical results, we discuss the implications for promoting EV-related information dissemination and provide suggestions for future study.

In the last fifteen years, the network-based new media has greatly promoted the development of social networks, therefore it becomes the main form of social composition. Research on social networks commenced a long time ago and revealed meaningful features of the old social network. However, in the new social networks, group members are almost 24-h online and can exchange various information that cannot be dynamically handled or described by the most of the existing studies. In this paper, we construct a formula to calculate the amount of the data dissemination of a node. Thereafter, we present participation equilibrium index formula and maximum equilibrium index formula that can measure the dynamic equilibrium of social networks even for different topics. With these formulas, we define λ-set and core node set of a social group and also present algorithms to work out the two sets. To evaluate our theory and method, we have developed SN-Python-I that can filter and calculate the amount of data exchanged by any node in any group. Based on a contract signed in 2013 with Ali and Tencent, we are permitted to track 300 actual groups for the past three years. Real data of those group reveals that equilibrium index as well as λ-set and core node set of social network change with the time, ages, topics, and classification of the groups. What's more, monthly equilibrium index can also indicate life stages of a group. Those significant properties and rules have not yet been revealed by traditional social network models.

Pervasive sensing devices make an unprecedented increase in data sensing, collection, and processing in edge network and they form the edge content server system. The edge content server system combines the content-centric network (CCN) to form a huge content propagation system which makes it challenging to achieve efficient content propagation. Different from IP-based, host-oriented Internet architecture, the CCN systems focus on the information that is contained in network and directly accessible, providing more secure and flexible Internet services. This emerging network architecture supports a number of novel applications, such as common interests sharing, mobile data offloading, and information dissemination without Internet access. In this paper, an analytical framework is proposed to address the problem of content propagation among users with the same interests in leveraging location-based social networks, where the check-in patterns of users are recorded. Particularly, we propose a content propagation effectiveness quantitative model that considers the distance between users, users’ interests, and contact rates to formulate the propagation effect and latency. We also apply our framework to two real-world datasets for the evaluation of its effectiveness. Compared with previous studies, our simulated annealing-based algorithm can greatly improve the effects by as much as 25.4%–65.6%, and the contents can be disseminated faster by about 24.6%–57.8%. [ABSTRACT FROM AUTHOR]

This paper studies the fundamental communication properties of urban vehicle networks by exploiting the self-similarity and hierarchical organization of modern cities. We use an innovative model called “hyperfractal” that captures the self-similarities of both the traffic and vehicle locations but avoids the extremes of regularity and randomness. We use analytical tools to derive theoretical upper and lower bounds for the information propagation speed in an urban delay tolerant network (i.e., a network that is disconnected at all time, and thus uses a store-carry-and-forward routing model). We prove that the average broadcast time behaves as $n^{1-\delta }$ times a slowly varying function, where $\delta $ depends on the precise fractal dimension. Furthermore, we show that the broadcast speedup is due in part to an interesting self-similar phenomenon, that we denote as information teleportation. This phenomenon arises as a consequence of the topology of the vehicle traffic, and triggers an acceleration of the broadcast time. We show that our model fits real cities where open traffic data sets are available. We present simulations confirming the validity of the bounds in multiple realistic settings, including scenarios with variable speed, using both QualNet and a discrete-event simulator in Matlab. [ABSTRACT FROM AUTHOR]

Consider the classical problem of information dissemination: one (or more) nodes in a network have some information that they want to distribute to the remainder of the network. In this paper, we study the cost of information dissemination in networks where edges have latencies, i.e., sending a message from one node to another takes some amount of time. We first generalize the idea of conductance to weighted graphs by defining $\phi _*$φ* to be the "critical weighted conductance" and $\ell _*$ℓ* to be the "critical latency". One goal of this paper is to argue that $\phi _*$φ* characterizes the connectivity of a weighted graph with latencies in much the same way that conductance characterizes the connectivity of unweighted graphs. We give near tight lower and upper bounds on the problem of information dissemination, up to polylogarithmic factors. Specifically, we show that in a graph with (weighted) diameter $D$D (with latencies as weights) and maximum degree $\Delta$Δ, any information dissemination algorithm requires at least $\Omega (\min (D+\Delta, \ell _*/\phi _*))$Ω(min(D+Δ,ℓ*/φ*)) time in the worst case. We show several variants of the lower bound (e.g., for graphs with small diameter, graphs with small max-degree, etc.) by reduction to a simple combinatorial game. We then give nearly matching algorithms, showing that information dissemination can be solved in $O(\min ((D+\Delta)\log ^3{n}, (\ell _*/\phi _*)\log n)$O(min((D+Δ)log3n,(ℓ*/φ*)logn) time. This is achieved by combining two cases. We show that the classical push-pull algorithm is (near) optimal when the diameter or the maximum degree is large. For the case where the diameter and the maximum degree are small, we give an alternative strategy in which we first discover the latencies and then use an algorithm for known latencies based on a weighted spanner construction. (Our algorithms are within polylogarithmic factors of being tight both for known and unknown latencies.) While it is easiest to express our bounds in terms of $\phi _*$φ* and $\ell _*$ℓ*, in some cases they do not provide the most convenient definition of conductance in weighted graphs. Therefore we give a second (nearly) equivalent characterization, namely the average weighted conductance $\phi _{avg}$φavg. [ABSTRACT FROM AUTHOR]

Probabilistic flooding is commonly considered in vehicular ad-hoc networks (VANETs) to alleviate the celebrated broadcast storm problem. Heuristics and simulations have so far proved to be an efficient way with which to design probabilistic information dissemination solutions with demonstrated effectiveness. Supplementing with mathematical analysis for both design and evaluation is expected to generalize the results, and increase the confidence in the proposed solutions. So far, this has been limited due to the difficulties associated with the underlying stochastic dependencies. In this paper, we introduce simple models of single and multiple lane roads to design effective probabilistic flooding schemes for VANETs. The proposed analytical framework offers a novel and generic tool which can cover any number of lanes, vehicle transmission range, and density. We derive difference equations whose solutions yield the probability of all vehicles receiving the emergency warning message as a function of the rebroadcast probability, the number of neighbors of each vehicle, and the dissemination distance. The models are validated using simulations, realistically representing both the traffic and networking aspects, and are used as a baseline to design two schemes for information dissemination which employ probabilistic flooding. The one attempts direct calculation of the required parameters whereas the other adaptively regulates the rebroadcast probability based on the vehicle speed. The schemes are evaluated using simulations and are found to achieve significant performance improvements compared to blind flooding with respect to the achieved reachability, end-to-end delay, and number of rebroadcasts, comparable to the performance achieved by optimal flooding, obtained via brute force. [ABSTRACT FROM AUTHOR]

This paper describes a new type of communication network called data and energy integrated communication networks (DEINs), which integrates the traditionally separate two processes, i.e., wireless information transfer (WIT) and wireless energy transfer (WET), fulfilling co-transmission of data and energy. In particular, the energy transmission using radio frequency is for the purpose of energy harvesting (EH) rather than information decoding. One driving force of the advent of DEINs is wireless big data, which comes from wireless sensors that produce a large amount of small piece of data. These sensors are typically powered by battery that drains sooner or later and will have to be taken out and then replaced or recharged. EH has emerged as a technology to wirelessly charge batteries in a contactless way. Recent research work has attempted to combine WET with WIT, typically under the label of simultaneous wireless information and power transfer. Such work in the literature largely focuses on the communication side of the whole wireless networks with particular emphasis on power allocation. The DEIN communication network proposed in this paper regards the convergence of WIT and WET as a full system that considers not only the physical layer but also the higher layers, such as media access control and information routing. After describing the DEIN concept and its high-level architecture/protocol stack, this paper presents two use cases focusing on the lower layer and the higher layer of a DEIN network, respectively. The lower layer use case is about a fair resource allocation algorithm, whereas the high-layer section introduces an efficient data forwarding scheme in combination with EH. The two case studies aim to give a better explanation of the DEIN concept. Some future research directions and challenges are also pointed out.

The most important application of vehicular networks is the dissemination of safety information via broadcasting protocols. How to reduce the redundant retransmission of the same information to avoid excessive channel collisions while ensure low latency and high reliability of the dissemination over the network has become the most challenging problem for multi-hop broadcasting protocols. The existing solutions use either probability-based multi-hop broadcasting for low latency or time-based deterministic approaches for high reliability. In this work, we propose a new probability-based multi-hop broadcast protocol to overcome the reliability and latency issues. First, we propose to assign a higher forwarding probability to the nodes that are farther from the source node by using a node's index number and other parameters, which is different from the existing probability-based approaches. The parameters can be adjusted for a higher forwarding success probability and maintain the required reliability of the message delivery. Second, we propose a clustering architecture so that redundancy and latency issues can be significantly reduced. Third, we analyze the protocol performance, such as the forwarding success probability, the average number of copies, and the average one-hop delay. The effectiveness of the proposed protocol has been demonstrated by extensive simulation results with significantly improved network performance. [ABSTRACT FROM AUTHOR]

Online Social Networks (OSNs) have become an efficient media to disseminate content like news and advertisements. In this paper, we are interested in investigating the open problem of how does the content popularity influence the information coverage exactly. Specifically, we assume that each user has $q$ friends and forwards the received content to each of his friends with the forwarding probability $p$ , where $p$ is only determined by the content popularity. Based on a geography-aware social model leveraging the forwarding behavior of OSNs users, we explore the exact relationship between the content popularity and the information coverage by the method of network partitions. Then, we mainly have the following observations: 1) The information coverage is only related to $p$ and $q$ ; 2) In order to disseminate the content to a constant portion of all users within the network, the forwarding probability $p$ should be no less than $\Theta \left(\frac{1}{q}\right)$. Extensive evaluations on both the generated data and the real OSN topology validate our observations. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of local content dissemination among mobile users via decentralized device-to-device (D2D) communication, where the concerned contents have relevance only within a geographic span centered at their respective location of creation. This has several potential applications, for example, advertising and sharing contents specific to an event. To this end, we propose a scheme for locality-bounded content and information dissemination (LUCID) leveraging opportunistic D2D communications. Unlike other existing works, LUCID innovates by not requiring users to share locations of the centers of localities and, thereby, provides a natural defense against possible location privacy attacks. In this paper, we theoretically characterize the behavior of LUCID for different locality sizes. Moreover, we investigate the extent to which an individual user can attempt to infer the location of a center of a locality, and discuss why that is difficult to achieve. Finally, we also consider an alternative version of LUCID where locations are partially shared. Results of simulation-based performance evaluation indicate that, when compared to an optimal scheme, LUCID can deliver contents to about two-third of the relevant users without requiring any location sharing. [ABSTRACT FROM AUTHOR]

In this letter, we investigate the performance of algebraic gossip algorithms for information dissemination in mobile ad hoc networks (MANETs). In the gossip algorithms, in each time slot, each node communicates with a neighboring node, which is randomly selected by an Exchange algorithm, to exchange their information. The goal of the algorithm is to efficiently disseminate all the messages to all the nodes as fast as possible. We analyze the convergence time of network coding (NC) based gossip algorithm by taking into account the node mobility in MANETs, and derived a lower bound on the convergence time. It showed that the convergence time of the NC gossip algorithm with node mobility can be reduced by the order of O(n^1/2/\log ^3/2n) over that without considering node mobility. But it is only $O(\log n)$ quicker than the gossip algorithm with node mobility but no NC. [ABSTRACT FROM PUBLISHER]

A major building block of Vehicular Ad Hoc Networks (VANETs) is broadcasting: the use of wireless communication for sharing information among vehicles, or between the vehicles and infrastructure. Dozens of broadcast protocols have been developed in recent years, including protocols for 1-hop broadcasting of vehicle status information (beaconing) and for geocasting-based applications. However, most of these protocols were designed for one application and cannot co-exist, nor can one broadcast solution meet the demands of all applications. These observations motivated our effort to develop a holistic network layer for VANETs. We identify the need for making VANET broadcast context-aware, and for supporting four different classes of broadcast protocols, each with its own properties. These classes are not only able to co-exist on the same network layer, but also to complement one another’s functionality. Thus, large applications as well as more holistic Transport protocols can be designed by combining two or more broadcast classes. We discuss the specific characteristics of these classes and design candidate protocols for each class. [ABSTRACT FROM PUBLISHER]

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.

We model information dissemination as a susceptible-infected epidemic process and formulate a problem to jointly optimize seeds for the epidemic and time varying resource allocation over the period of a fixed duration campaign running on a social network with a given adjacency matrix. Individuals in the network are grouped according to their centrality measure and each group is influenced by an external control function—implemented through advertisements—during the campaign duration. The aim is to maximize an objective function which is a linear combination of the reward due to the fraction of informed individuals at the deadline, and the aggregated cost of applying controls (advertising) over the campaign duration. We also study a problem variant with a fixed budget constraint. We set up the optimality system using Pontryagin’s maximum principle from optimal control theory and solve it numerically using the forward–backward sweep technique. Our formulation allows us to compare the performance of various centrality measures (pagerank, degree, closeness, and betweenness) in maximizing the spread of a message in the optimal control framework. We find that degree—a simple and local measure—performs well on the three social networks used to demonstrate results: 1) scientific collaboration; 2) Slashdot; and 3) Facebook. The optimal strategy targets central nodes when the resource is scarce, but noncentral nodes are targeted when the resource is in abundance. Our framework is general and can be used in similar studies for other disease or information spread models—that can be modeled using a system of ordinary differential equations—for a network with a known adjacency matrix. [ABSTRACT FROM AUTHOR]

In wireless ad hoc networks, each node participates in routing by forwarding data to other nodes without a pre-existing infrastructure. Particularly, with the wide adoption of smart devices, the concept of smartphone ad hoc networks (SPANs) has evolved to enable alternate means for information sharing. Using unlicensed frequency spectrum and short-range wireless technologies, a SPAN enables a new paradigm of applications and thus is seen as an attractive component in future wireless networks. In a SPAN, smartphones form local peer-to-peer networks to cooperate and share information efficiently. Recent studies have shown that if the users? social relations are considered while designing cooperation schemes and protocols in SPANs, the cooperation initialization and content dissemination can be notably improved to increase the overall network efficiency and communications reliability. In this article, we present a social-aware framework for optimizing SPANs by exploiting two layers: users? relationships in the online social network layer and users? offline connections and interactions in the physical wireless network layer. The online content popularity distribution is also studied as a result of the users? online interaction profiles. In the end, we integrate both online and offline layers, and discuss possible applications to further enhance the network performance. [ABSTRACT FROM PUBLISHER]

In vehicular networks, information dissemination/sharing among vehicles is of salient importance. Although diverse mechanisms have been proposed in the existing literature, the related information credibility issues have not been investigated. Against this background, in this paper, we propose a credible information-sharing mechanism capable of ensuring that the vehicles do share genuine road traffic information (RTI). We commence with the outage-probability analysis of information sharing in vehicular networks under both a general scenario and a specific highway scenario. Closed-form expressions are derived for both scenarios, given the specific channel settings. Based on the outage-probability expressions, we formulate the utility of RTI sharing and design an algorithm for promoting the sharing of genuine RTI. To verify our theoretical analysis and the proposed mechanism, we invoke a real-world dataset containing the locations of Beijing taxis to conduct our simulations. Explicitly, our simulation results show that the spatial distribution of the vehicles obeys a Poisson point process (PPP), and our proposed credible RTI sharing mechanism is capable of ensuring that all vehicles indeed do share genuine RTI with each other. [ABSTRACT FROM PUBLISHER]

Mobile opportunistic networks make use of node mobility to provide occasional contact opportunities for mobile devices to deliver data. In this paper, we investigate the inherent properties of data disseminations in mobile opportunistic networks by answering the question regarding how far and how fast a packet can be disseminated in mobile opportunistic networks. This problem has been overlooked by recent research, but it is very important because it reveals the potential for mobile opportunistic networks to support emerging applications such as mobile commerce and emergency services that may involve time and location sensitive information dissemination. Our investigations are taken from the perspectives of small and large scales. From the perspective of small scale, the probability distributions of the minimum time needed by the data to spread to a given region, namely, \mathcalT, are deduced for both the one-copy case and the multiple-copy case. From the perspective of large scale, by using the large deviation theory, the probability distributions of \mathcalT are deduced for both the one-copy case and the multiple-copy case when the destination region is far enough from the data origin. [ABSTRACT FROM PUBLISHER]

As mobile devices become more ubiquitous, the amount of cellular traffic for multimedia content grows explosively. Therefore, data dissemination through proximity-based opportunistic communications attracts the attention of service providers who are eager for solutions of traffic offloading. In this paper, we propose PrefCast, a preference-aware opportunistic content dissemination protocol that uses as few cellular bandwidth as possible to maximally satisfy user preferences for content objects. The efficiency of PrefCast depends on 1) how does the base-station select initial sources, and 2) how does each user forward objects within a limited contact duration. Since mobile users typically form communities and have heterogeneous preferences, PrefCast ’s base-station selects sources that efficiently produce the maximal utility to their communities. We then derive a model to predict how much utility a forwarder can contribute to future contacts. PrefCast’s users can hence use such prediction to find their optimal forwarding schedule, which maximizes the utility contribution, in a distributed way. Our trace-based evaluation shows that, without explicit source selection, PrefCast produces a 15.7 and 22.6 percent higher average utility than the protocols that only consider contact frequency or preference of local contacts, respectively. Enabling source selection in PrefCast further improves the utility by 49.3 percent. [ABSTRACT FROM PUBLISHER]

Improving the safety of roads and passengers is the main goal of intelligent transportation systems (ITS). However, reducing traffic and car accidents can only be achieved by disseminating safety information in a timely manner with high reliability. Although mathematical modeling of this process is extremely beneficial, analyzing the information dissemination is considerably complex due to the dynamics of vehicles and varying travel behavior. We present a mathematical model to study the delay–reliability features in a vehicular network. The proposed model not only captures both physical and medium access control (MAC) layers' characteristics of this network but addresses the partitioning problem, i.e., the tendency of cars to form disconnected islands, as well. To the best of the authors' knowledge, this is the first delay model that does all of these, and it can be used to understand the message propagation behavior in vehicular networks. The common assumption in the literature is that the intervehicle distances (IVDs) both inside the island and between islands have the same exponential distribution. We show that this assumption is flawed, and subsequently, a new and more accurate model is presented. Simulation studies validate the accuracy and effectiveness of the proposed model for both highway and urban scenarios. [ABSTRACT FROM PUBLISHER]