Information Dissemination In Wireless Networks Research Articles

Media Information Dissemination Model of Wireless Networks Using Deep Residual Network

Information dissemination and its prediction in wireless networks is a challenging task. Researchers have studied the prediction process of media information dissemination in wireless networks using various methods. In this paper, we analyze information dissemination in wireless networks using a deep residual network model. In the proposed model, the relative weight of nodes and the dissemination probability of media information in wireless networks are obtained. The obtained information is the inputs into the deep residual network as features. The convolution feature extractor is used to obtain the details of the input features. Finally, the propagation information is classified according to the extracted features through the full connection layer. We have used the SELU activation function to optimize the deep residual network. In this way, a complete media information dissemination prediction of wireless networks is obtained. The simulation results show that the proposed model has fast convergence and a low bit error rate of information dissemination. It reflects the characteristics of media information dissemination in a wireless network in real-time applications. The results show accurate prediction of media information dissemination in wireless networks.

Analysis of the Differential Update Method for Control Information Dissemination in Wireless Networks

Abstract—Various wireless networks scenarios involve dissemination of large amounts of control information. This limits the channel resources available for transmission of the user data. This problem is especially crucial in dense networks, sensor networks, and high-mobility networks (e.g., networks of vehicles and drones). Several methods for dissemination of control information are known. This study is focused on the promising method called differential update. The idea behind it is that control messages sent by stations should contain only the information modified since the transmission of the previous message. An analytical model of this method has been developed. It allows one to estimate the average amount of transmitted control information and the reliability and promptness of its delivery to neighboring stations. An algorithm for adaptive tuning of parameters of the differential update method has been developed on the basis of this model. Simulation results obtained with NS-3 simulator demonstrate that the proposed algorithm minimizes the amount of sent control information while satisfying the set of requirements on reliability and promptness of its delivery.

Probabilistic flooding coverage analysis for efficient information dissemination in wireless networks

Information dissemination under Probabilistic flooding is revisited here in an attempt to realize full coverage in modern wireless networks and at the same time avoid unnecessary transmissions that waste valuable resources. Approaches like traditional flooding, are not suitable in these typically large scale and inherently dynamic environments due to the large number of transmitted information messages. Probabilistic flooding is capable of pruning unnecessary transmissions, while maintaining a large proportion of the network nodes covered. In this paper, an algebraic-based approach is employed to derive an estimation regarding the particular threshold probability value that would allow for high network coverage and reduced number of sent information messages. For the analysis’ purposes, coverage is studied here and eventually modeled as a polynomial; its higher root being related to the threshold probability. By studying this polynomial’s roots, the paper’s contribution is twofold: (i) results existing in the open literature are confirmed; and (ii) an algorithm is introduced here to estimate the threshold probability. Simulation results confirm the analytical findings and in addition, they demonstrate that the estimated value derived here regarding threshold probability, is suitable for achieving high coverage and reducing the number of transmitted information messages.

Unraveling the Impact of Users’ Interest on Information Dissemination in Wireless Networks

In this paper, we analyze the throughput of data dissemination at the level of users’ interests. We show that users’ interests have the ability to drastically improve upon existing throughput scaling’s established under the assumption that users show the same preference in any type of data they encounter. More precisely, we consider the scenario where each data source estimates the recipients that will be interested in its data based on user interest probability, which is described by a Zipf-distributed data popularity that decays of exponent $\alpha $ with data ranking. For such a user-centric model, we divide our analysis into different cases depending on data catalogue size $K$ and study their respective throughput performance. With totally $n$ users assumed, we present closed-form expressions of user-centric throughput versus $n$ , $\alpha $ , and $K$ . In particular, our results reveal that when $\alpha =1$ where users’ interests exhibit a moderate level of heterogeneity, the maximum throughput of $\Theta (\sqrt {n})$ (except for a poly-logarithmic factor) can be achieved in all the situations, with appropriate choice of $K$ . The results augment the existing scaling laws derived in network-centric situation, in that given the same throughput data can be disseminated efficiently to more recipients in a user-centric network.

TurfCast: A Service for Controlling Information Dissemination in Wireless Networks

Recent years have witnessed mass proliferation of mobile devices with rich wireless communication capabilities as well as emerging mobile device-based information dissemination applications that leverage these capabilities. This paper proposes TurfCast, a novel information dissemination service that selectively broadcasts information in particular "turfs,â abstract logical spaces in which receivers are situated. Such turfs can be temporal or spatial based on receivers' lingering time or physical areas, respectively. TurfCast has many applications such as electronic proximity advertising and mobile social networking. To enable TurfCast, we propose two supporting technologies: TurfCode and TurfBurst. TurfCode is a nested 0-1 fountain code that enables the broadcaster to transmit either all information or none at all to receivers. TurfBurst exploits the Shannon bound to differentiate among receivers: those who cannot receive information fast enough receive none at all, even if they linger near the broadcaster. We implement TurfCast on real-world devices and conduct experiments in both indoor and outdoor environments. Our experimental results illustrate TurfCast's potential for controlling information dissemination in wireless networks.

Randomized Information Dissemination in Dynamic Environments

We consider randomized broadcast or information dissemination in wireless networks with switching network topologies. We show that an upper bound for the e-dissemination time consists of the conductance bound for a network without switching, and an adjustment that accounts for the number of informed nodes in each period between topology changes. Through numerical simulations, we show that our bound is asymptotically tight. We apply our results to the case of mobile wireless networks with unreliable communication links and establish an upper bound for the dissemination time when the network undergoes topology changes and periods of communication link erasures.

Fundamental limits of information dissemination in wireless ad hoc networks-part I: single-packet reception

We present the first unified modeling framework for the computation of the capacity-delay tradeoff of random wireless ad hoc networks. This framework considers information dissemination by means of unicast routing, multicast routing, broadcasting, or different forms of anycasting. We introduce (n, m, k) -casting as a generalization of all forms of one-toone, one-to-many, and many-to-many information dissemination in wireless networks. In this context, n, m, and k denote the total number of nodes in the network, the number of destinations for each communication group, and the actual number of communication-group members that receive information (k ¿ m), respectively. We describe the capacity-delay tradeoff for (n, m, k) -casting in wireless ad hoc networks in which receivers perform single-packet reception (SPR). Our results are consistent with prior results in wireless networks and extend them to the general (n,m,k) -cast case.