Continuous Influence-based Community Partition for Social Networks

Community partition is of great importance in social networks because of the rapid increasing network scale, data and applications. We consider the community partition problem under LT model in social networks, which is a combinatorial optimization problem that divides the social network to disjoint $m$ communities. Our goal is to maximize the sum of influence propagation through maximizing it within each community. As the influence propagation function of community partition problem is supermodular under LT model, we use the method of Lov{$\acute{a}$}sz Extension to relax the target influence function and transfer our goal to maximize the relaxed function over a matroid polytope. Next, we propose a continuous greedy algorithm using the properties of the relaxed function to solve our problem, which needs to be discretized in concrete implementation. Then, random rounding technique is used to convert the fractional solution to integer solution. We present a theoretical analysis with $1-1/e$ approximation ratio for the proposed algorithms. Extensive experiments are conducted to evaluate the performance of the proposed continuous greedy algorithms on real-world online social networks datasets and the results demonstrate that continuous community partition method can improve influence spread and accuracy of the community partition effectively.Comment: arXiv admin note: text overlap with arXiv:2003.1043

EDDA: An Efficient Distributed Data Replication Algorithm in VANETs

Efficient data dissemination in vehicular ad hoc networks (VANETs) is a challenging issue due to the dynamic nature of the network. To improve the performance of data dissemination, we study distributed data replication algorithms in VANETs for exchanging information and computing in an arbitrarily-connected network of vehicle nodes. To achieve low dissemination delay and improve the network performance, we control the number of message copies that can be disseminated in the network and then propose an efficient distributed data replication algorithm (EDDA). The key idea is to let the data carrier distribute the data dissemination tasks to multiple nodes to speed up the dissemination process. We calculate the number of communication stages for the network to enter into a balanced status and show that the proposed distributed algorithm can converge to a consensus in a small number of communication stages. Most of the theoretical results described in this paper are to study the complexity of network convergence. The lower bound and upper bound are also provided in the analysis of the algorithm. Simulation results show that the proposed EDDA can efficiently disseminate messages to vehicles in a specific area with low dissemination delay and system overhead

UAV-assisted data dissemination based on network coding in vehicular networks

Efficient and emergency data dissemination service in vehicular networks (VN) is very important in some situations, such as earthquakes, maritime rescue, and serious traffic accidents. Data loss frequently occurs in the data transition due to the unreliability of the wireless channel and there are no enough available UAVs providing data dissemination service for the large disaster areas. UAV with an adjustable active antenna can be used in light of the situation. However, data dissemination assisted by UAV with the adjustable active antenna needs corresponding effective data dissemination framework. A UAV-assisted data dissemination method based on network coding is proposed. First, the graph theory to model the state of the data loss of the vehicles is used; the data dissemination problem is transformed as the maximum clique problem of the graph. With the coverage of the directional antenna being limited, a parallel method to find the maximum clique based on the region division is proposed. Lastly, the method\u27s effectiveness is demonstrated by the simulation; the results show that the solution proposed can accelerate the solving process of finding the maximum clique and reduce the number of UAV broadcasts. This manuscript designs a novel scheme for the UAV-assisted data dissemination in vehicular networks based on network coding. The graph theory is used to model the state of the data loss of the vehicles. With the coverage of the directional antenna being limited, then a parallel method is proposed to find the maximum clique of the graph based on the region division. The effectiveness of the method is demonstrated by the simulation

Efficient Message Dissemination on Curve Road in Vehicular Networks

Effective emergency message dissemination is a great importance on a specific road in vehicular networks (VN). The existing methods are not most efficient solutions for message dissemination on the curve road, which primarily focus on highway and urban road. In order to improve the efficiency of message dissemination on the curved road, the paper proposed a message dissemination method based on bidirectional relay nodes. The message can be disseminated in two directions simultaneously. The paper designed a relay node selection method based on the neighbor nodes’ coverage length of the road. Different waiting delays are assigned to the neighbor nodes according to the cover capability of the road in which the message has not arrived. Simulation results demonstrated that the efficiency of the proposed method is superior to the common solutions in terms of the contention delay and the propagation velocity

UAV-Assisted Sensor Data Dissemination in mmWave Vehicular Networks Based on Network Coding

Due to good maneuverability, UAVs and vehicles are often used for environment perception in smart cities. In order to improve the efficiency of sensor data sharing in UAV-assisted mmWave vehicular network (VN), this paper proposes a sensor data sharing method based on blockage effect identification and network coding. The concurrent sending vehicles selection method is proposed based on the availability of mmWave link, the number of target vehicles of sensor data packet, the distance between a sensor data packet and target vehicle, the number of concurrent sending vehicles, and the waiting time of sensor data packet. The construction method of the coded packet is put forward based on the status information about the existing packets of vehicles. Simulation results demonstrated that efficiency of the proposed method is superior to baseline solutions in terms of the packet loss ratio, transmission time, and packet dissemination ratio

Different Approximation Algorithms for Channel Scheduling in Wireless Networks

We introduce a new two-side approximation method for the channel scheduling problem, which controls the accuracy of approximation in two sides by a pair of parameters . We present a series of simple and practical-for-implementation greedy algorithms which give constant factor approximation in both sides. First, we propose four approximation algorithms for the weighted channel allocation problem: 1. a greedy algorithm for the multichannel with fixed interference radius scheduling problem is proposed and an one side -IS-approximation is obtained; 2. a greedy -approximation algorithm for single channel with fixed interference radius scheduling problem is presented; 3. we improve the existing algorithm for the multichannel scheduling and show an time -approximation algorithm; 4. we speed up the polynomial time approximation scheme for single-channel scheduling through merging two algorithms and show a -approximation algorithm. Next, we study two polynomial time constant factor greedy approximation algorithms for the unweighted channel allocation with variate interference radius. A greedy -approximation algorithm for the multichannel scheduling problem and an -approximation algorithm for single-channel scheduling problem are developed. At last, we do some experiments to verify the effectiveness of our proposed methods

Delay-Constrained Routing Based on Stochastic Model for Flying Ad Hoc Networks

This paper aims at solving the end-to-end delay-constrained routing problem in a local way for flying ad hoc networks (FANETs). Due to the high mobility, it is difficult for each node in FANETs to obtain the global information. To solve this issue, we propose an adaptive delay-constrained routing with the aid of a stochastic model, which allows the senders to deliver the packets with only local information. We represent the problem in a mathematical form, where the effective transmission rate is viewed as the optimization objective and the link quality and end-to-end delay as the constraints. And, some mathematical tools are used to obtain the approximate solutions for the optimization problem. Before designing the routing scheme, the senders calculate the transition probability for its relay node by jointly considering local delay estimation and expected one-hop delay. Then, the sender transmits the packets to their relay node with transition probability. Finally, we prove the convergence of the proposed routing algorithm and analyse its performances. The simulation results show that the proposed routing policy can improve the network performance effectively in terms of throughput, loss rate, and end-to-end delay