APPOW:An Advanced Routing Protocol Based on Parameters Optimization in the Weighted Mobile Social Network

Due to the increasing number of wireless mobile devices,the possibility of mobile communications without infrastructure becomes a reality.The Decentralized Mobile Social Network(DMSN) is a paradigm where nodes can move freely and organize themselves arbitrarily.Routing in these environments is difficult for the reason of the rapid changes of the social relationship graph's topology.Meanwhile,the social ties among nodes change overtime.Therefore,an efficient data forwarding mechanism should be considered over the temporal weighted relationship graph.In this paper,an Advanced routing Protocol based on Parameters Optimization in the Weighted mobile social network(APPOW) is proposed to improve the delivery success ratio and reduce the cost of exchanging information.APPOW combines the normalized relative weights of three local social metrics,i.e.,LinkRank,similarity and contact strength,to select the next relay node.The weights of the three metrics are derived by pair-wise learning algorithm.The result shows that APPOW outperforms the state-ofthe-art SimBet Routing in delivering message and significantly reduces the average hops.Additionally,the delivery performance of APPOW is close to Epidemic Routing but without message duplications.

A Survey on Routing Algorithms for Opportunistic Mobile Social Networks

Opportunistic Mobile Social Networks(OMSNs)are kind of Delay Tolerant Networks(DTNs)that leverage characteristics of Mobile Ad Hoc Networks(MANETs)and Social Networks,particularly the social features,to boost performance of routing algorithms.Users in OMSNs communicate to share and disseminate data to meet needs for variety of applications.Such networks have attracted tremendous attention lately due to the data transmission requirement from emerging applications such as IoT and smart city initiatives.Devices carried by human is the carrier of message transmission,so the social features of human can be used to improve the ability of data transmission.In this paper,we conduct a comparative survey on routing algorithms in OMSNs.We first analyze routing algorithms based on three social features.Since node selfishness is not really considered previously in aforementioned routing algorithms,but has significant impact on network performance,we treat node selfishness as another social feature,classify and elaborate routing algorithms based on incentive mechanism.To assess the impact of social features on routing algorithms,we conducted simulation for six routing algorithms and analyzed the simulation result.Finally,we conclude the paper with challenges on design of routing in OMSNs and point out some future research directions.

CRSG:A congestion control routing algorithm for security defense based on social psychology and game theory in DTN

The inherent selfishness of each node for the enhancement of message successful delivery ratio and the network overall performance improvement are reflected in the contradiction relationship of competition and cooperation in delay/disruption tolerant networks (DTN). In particular, the existence of malicious node aggravates this contradiction. To resolve this contradiction, social relationship theory and group theory of social psychology were adopted to do an in-depth analysis. The concrete balancing approach which leveraged Nash equilibrium theory of game theory was proposed to resolve this contradiction in reality. Thus, a new congestion control routing algorithm for security defense based on social psychology and game theory (CRSG) was put forward. Through the experiment, this algorithm proves that it can enhance the message successful delivery ratio by more than 15% and reduce the congestion ratio over 15% as well. This algorithm balances the contradiction relationship between the two key performance targets and made all nodes exhibit strong cooperation relationship in DTN.

Trusted Routing Based on Identity-Based Threshold Signature for Opportunistic Networks

In opportunistic Networks,compromised nodes can attack social context-based routing protocols by publishing false social attributes information.To solve this problem,we propose a security scheme based on the identity-based threshold signature which allows mobile nodes to jointly generate and distribute the secrets for social attributes in a totally self-organized way without the need of any centralized authority.New joining nodes can reconstruct their own social attribute signatures by getting enough partial signature services from encounter opportunities with the initial nodes.Mobile nodes need to testify whether the neighbors can provide valid attribute signatures for their routing advertisements in order to resist potential routing attacks.Simulation results show that:by implementing our security scheme,the network delivery probability of the social context-based routing protocol can be effectively improved when there are large numbers of compromised nodes in opportunistic networks.

Energy-Aware Multi.Replica Routing in Delay Tolerant Mobile Sensor Network

The two-phase replication-based routing has great prospects for Delay Tolerant Mobile Sensor Network (DTMSN) with its advantage of high message delivery ratio, but the blind spraying and the low efficiency forwarding algorithm directly influences the overall network performance. Considering the characteristic of the constrained energy and storage resources of sensors, we propose a novel two-phase multi-replica routing for DTMSN, called Energy-Aware Sociality-Based Spray and Search Routing (ESR), which implements the quota-style message replication mechanism by utilizing the energy and speed information of sensors. In addition, based on the difference of history encounters, a sociality metric is defined to improve the forwarding efficiency in search phase. Simulation experiments show that ESR can reduce the message delay and improve the resource utilization while maximizing the message delivery ratio compared with the exiting popular two-phase routing protocols.

SocialRank:DTN环境中具有社会特性的转发策略

随着移动设备与无线网络技术发展,延迟容忍网络(DTN)被研究者广泛关注.由于端到端网络无法持续连通,因此传统无线路由已经不适用于DTN环境.很多研究表明,无线网络手持设备由人类携带,因此移动时具有社会学特性,也被称之为社会网络.社会网络路由被许多学者提出、改进并证明其具有良好特性及实用性.提出一种SocialRank算法,使其适用于DTN环境并具有社会学特性,提高社会网络中信息交付率以及减少信息转发延时.仿真实验表明SocialRank具有优异的交付率以及最低的延时,并且可以使用较少的节点进行中介转发,减少了网络负载.

学院制学生社区管理模式下大学生诚信价值观培育路径研究

文章首先介绍了学院制学生社区管理模式的特点,包括学生主体性、综合性、自我管理和社区文化等,然后分析了当前大学生诚信教育存在的问题及其成因,最后提出了培育诚信教育的路径,包括完善诚信教育体系、创新教育方法、丰富实践活动、强化社区文化引领、建立诚信评价机制等。旨在优化大学生对诚信价值观的认知,增强其对诚信价值观的认同,并提高诚信价值观培育的实效性。

A Novel Routing Method for Social Delay-Tolerant Networks

The lack of continuous connectivity and a complete path from source to destination makes node communication quite difficult in Delay-Tolerant Networks(DTNs). Most studies focus on routing problems in idealized network environments without considering social properties. Communication devices are carried by individuals in many DTNs; therefore, DTNs are unique social networks to some extent. To design efficient routing protocols for DTNs, it is important to analyze their social properties. In this paper, a more accurate and comprehensive metric for detecting the quality of the relationships between nodes is proposed, by considering the contact time, contact frequency, and contact regularity. An overlapping hierarchical community detection method is designed based on this new metric, and a tree structure is built. Furthermore, we exploit the overlapping community structure and the tree structure to provide message-forwarding paths from the source node to the destination node.The simulation results show that our Routing method based on Overlapping hierarchical Community Detection(ROCD) achieves better delivery rate than SimBet and Bubble Rap, the classic routing protocols, without affecting the average delay.

晚年马克思社会形态理论革新的再探析

马克思的社会形态理论革新呈现为对社会历史的持续探索而不断地调整唯物史观整体致思方向的开放性过程。以人类史前史和东方社会史研究成果为理论前提并借助于地质学中用以描述地质层系构成的“原生、次生和再次生类型”概念,晚年马克思尝试性地构建具有历史普遍性与“历史具体”内在张力的社会形态演进图式。新的社会形态理论研究完善了关于人类原初社会形态的认识,论证了东西方社会形态演进的差异化路径,重新唤醒了东方社会作为“实际地改变和变革现存社会”推动力量的自觉意识,从而在尊重人类历史发展规律的同时,指明了从具体民族的生产方式与历史前提来探索人之解放这一根本指向的具体化路径。

Detour for the inexperienced? Migration count data suggest mostly juvenile Greater Spotted Eagles appear in coastal peninsulas in China

Soaring bird migration often relies on suitable terrain and airflow;therefore,route selection is vital for successful migration.While age and experience have been identified as key factor influencing migration route selection among soaring raptors in the African-Eurasian Flyway,how they shape the migration route of soaring raptors in East Asia is still largely unknown.In this study,we investigated potential variations in the routes and timing in autumn migration of juvenile and older soaring birds,using count data of Greater Spotted Eagles(Clanga clanga)from two coastal sites and two inland sites in China.From 2020 to 2023,we recorded a total of 340 individuals,with the highest site averaging over 90 individuals per autumn,making it one of the world’s top single-season counts and thus a globally important site for this species.We found that 82% and 61% records from coastal sites were juveniles,significantly higher than inland sites(15% and 24%).Juveniles at all four sites exhibited markedly earlier median passage time than non-juveniles,with brief overlapping in their main migration periods.Both coastal sites are located on the tip of peninsulas stretching southwest,requiring long overwater flights if crossing the Bohai Bay or Beibu Gulf,which would be energetically demanding and increase mortality risk.Experienced individuals may have learned to avoid such terrain and subsequent detour,while juveniles are more prone to enter these peninsulas due to lack of experience and opportunities for social learning,or following other raptor species that are more capable of powered flight.Our findings highlight the importance of age and experience in migration route selection of large soaring birds.

A secure routing scheme based on social network analysis in wireless mesh networks

As an extension of wireless ad hoc and sensor networks, wireless mesh networks(WMNs) are employed as an emerging key solution for wireless broadband connectivity improvement. Due to the lack of physical security guarantees, WMNs are susceptible to various kinds of attack. In this paper, we focus on node social selfish attack, which decreases network performance significantly. Since this type of attack is not obvious to detect, we propose a security routing scheme based on social network and reputation evaluation to solve this attack issue. First, we present a dynamic reputation model to evaluate a node's routing behavior, from which we can identify selfish attacks and selfish nodes. Furthermore, a social characteristic evaluation model is studied to evaluate the social relationship among nodes. Groups are built based on the similarity of node social status and we can get a secure routing based on these social groups of nodes. In addition, in our scheme, nodes are encouraged to enter into multiple groups and friend nodes are recommended to join into groups to reduce the possibility of isolated nodes. Simulation results demonstrate that our scheme is able to reflect node security status, and routings are chosen and adjusted according to security status timely and accurately so that the safety and reliability of routing are improved.

An Expert System to Detect Political Arabic Articles Orientation Using CatBoost Classifier Boosted by Multi-Level Features

The number of blogs and other forms of opinionated online content has increased dramatically in recent years.Many fields,including academia and national security,place an emphasis on automated political article orientation detection.Political articles(especially in the Arab world)are different from other articles due to their subjectivity,in which the author’s beliefs and political affiliation might have a significant influence on a political article.With categories representing the main political ideologies,this problem may be thought of as a subset of the text categorization(classification).In general,the performance of machine learning models for text classification is sensitive to hyperparameter settings.Furthermore,the feature vector used to represent a document must capture,to some extent,the complex semantics of natural language.To this end,this paper presents an intelligent system to detect political Arabic article orientation that adapts the categorical boosting(CatBoost)method combined with a multi-level feature concept.Extracting features at multiple levels can enhance the model’s ability to discriminate between different classes or patterns.Each level may capture different aspects of the input data,contributing to a more comprehensive representation.CatBoost,a robust and efficient gradient-boosting algorithm,is utilized to effectively learn and predict the complex relationships between these features and the political orientation labels associated with the articles.A dataset of political Arabic texts collected from diverse sources,including postings and articles,is used to assess the suggested technique.Conservative,reform,and revolutionary are the three subcategories of these opinions.The results of this study demonstrate that compared to other frequently used machine learning models for text classification,the CatBoost method using multi-level features performs better with an accuracy of 98.14%.

Understanding taxi drivers＇ routing choices from spatial and social traces

Most of our learning comes from other people or from our own experience. For instance, when a taxi driver is seeking passengers on an unknown road in a large city, what should the driver do？ Alternatives include cruising around the road or waiting for a time period at the roadside in the hopes of finding a passenger or just leaving for another road enroute to a destination he knows （e.g., hotel taxi rank）？ This is an interesting problem that arises everyday in cities all over the world. There could be different answers to the question poised above, but one fundamental problem is how the driver learns about the likelihood of finding passengers on a road that is new to him （as he has not picked up or dropped off passengers there before）. Our observation from large scale taxi driver trace data is that a driver not only learns from his own experience but through interactions with other drivers. In this paper, we first formally define this problem as socialized information learning （SIL）, second we propose a framework including a series of models to study how a taxi driver gathers and learns information in an uncertain environment through the use of his social network. Finally, the large scale real life data and empirical experiments confirm that our models are much more effective, efficient and scalable that prior work on this problem.

Energy Efficient Social Routing Framework for Mobile Social Sensing Networks

Mobile social sensing network is one kind of emerging networks in which sensing tasks are performed by mobile users and sensing data are shared and collected by leveraging the intermittent inter-contacts among mobile users. Traditional ad hoc routing protocols are inapplicable or perform poorly for data collection or data sharing in such mobile social networks because nodes are seldom fully connected. In recent years, many routing protocols （especially social-based routing） are proposed to improve the delivery ratio in mobile social networks, but most of them do not consider the load of nodes thus may lead to unbalanced energy consumption among nodes. In this paper, we propose a simple Energy Efficient framework for Social-based Routing （EE-SR） in mobile social sensing networks to balance the load of nodes while maintaining the delivery ratio within an acceptable range by limiting the chances of forwarding in traditional social-based routing. Furthermore, we also propose an improved version of EE-SR to dynamically adjust the controlling parameter. Simulation results on real-life mobile traces demonstrate the efficiency of our proposed framework.

基于节点运动预测的社会性DTN高效路由

延迟容忍网络是一种缺乏持续连接的新型网络体系结构,采用存储-携带-转发的路由方式进行消息的转发.考虑到节点的特性及运动模式,基于分簇、社会网络的路由协议已成为近年来的一个研究热点.该文分析现有基于分簇、社会网络的路由算法的缺陷及社会网络的特点,提出了一种新型的混合路由算法CS-DTN(Clustering&Social Delay Tolerant Network):预测节点间的相遇概率并据此对节点进行分簇,簇内根据节点间相遇概率限定消息的副本数量,将消息以二分喷射的方式转发到更可能到达目的节点的中继节点;簇间对节点进行运动预测并结合节点的特性将消息不断地转发到更接近目的节点的中继节点.不同于已有的基于分簇、社会网络的路由算法,CS-DTN从多个角度结合了节点自身及节点间的特性,更考虑了对节点的运动预测,降低了由不必要的消息转发带来的资源浪费,同时也能更高效地将消息投递到目的节点.仿真结果表明CS-DTN较为明显地提升了消息的成功投递率并降低了消息的平均端到端延迟,在缓存足够的前提下,CS-DTN可以实现高达95%的投递率,而在资源有限的情况下也能获得80%以上的投递率.

全国性单项体育协会的社会合法性挑战和发展途径

依据合法性理论,在我国社团组织社会合法性的相关研究的基础上,提出我国全国性单项体育协会的社会合法性的概念和分析框架。通过对全国性单项体育协会所面临的组织定位模糊、“规则虚置”与“监督弱化”、权力的垄断与利益矛盾、共同利益的代表性弱化等社会合法性挑战的分析,提出整合组织双重权力关系、建立利益协调与资源整合机制、完善相关法规与制度的发展对策。提出社会合法性是动态的,在新的社会环境和条件下,单项体育协会组织应从权力和利益的整合出发重建新时期的社会合法性。

基于社会上下文认知的机会路由算法

无线智能设备的普遍使用促进了机会网络的发展.这类网络处于间歇性连接状态,以自组织方式转发数据.路由协议设计时考虑节点携带者的社会特征和日常行为能够提高机会网络的性能.提出了一种基于社会上下文认知的机会路由算法SCOR,该算法利用网络中的社会上下文信息,通过BP神经网络模型预测节点的移动行为.路由决策过程充分考虑移动节点活动的时间和空间属性,当接收节点与发送节点同时处于网络中的同一连通域时,数据转发采用同步方式,否则采用异步方式.仿真分析和实验结果表明,与其它经典算法相比,SCOR算法提高了数据成功转发的比率,减少了网络的开销.

基于路网的LBSN用户移动轨迹聚类挖掘方法

基于LBSN(基于位置的社交网络)中数据的地理和社交属性,结合用户轨迹和好友关系,有助于提高不确定轨迹聚类挖掘的效率。根据LBSN用户的好友关系特征,引入评分函数,对用户影响力进行排序,找出其中的活跃用户;在传统路网子轨迹匹配和对签到数据清理的基础上,加入子轨迹匹配准确性监测,并存储活跃用户匹配成功的路段,进而减少路网匹配时间。最后综合R*树的空间索引机制和DBSCAN聚类算法对城市内的热点路径进行挖掘。理论分析和实验表明,相比于已有方法,改进的的聚类挖掘方法在LBSN环境中的时间效率和准确性都有较大的提高,且有较好的可伸缩性。

机会社会网络中基于社区的消息传输算法

针对机会网络中的主流转发协议未考虑到节点移动社会性的问题,提出一种基于副本限制和人类社会特性的路由算法。该算法根据节点间的社会关系强度,动态自适应地将节点划分为不同的社区,通过限制消息副本数来减少网络中消息的冗余,并利用活跃性高的节点带动消息的转发和传递。仿真结果表明,该协议能够保证较高的消息传输成功率和相对较低的传输时延,降低网络的资源消耗。

基于系统动力学的云南生态文明建设有效路径研究

云南要努力成为生态文明建设的排头兵,必须探寻一条生态文明建设的有效路径。本文构建了包含社会、经济、资源和环境四个子系统的生态文明建设指标体系。通过分析四个子系统之间的相互作用和相互影响关系,运用系统动力学原理和方法,构建生态文明建设系统模型,并对模型设置的合理性和科学性进行检验。根据云南实际,选择8个能够全面并重点反映生态文明建设状况的指标作为控制变量,8个显示生态文明建设效果的变量,设计维持现状发展路径、社会发展优先路径、经济发展优先路径、节约资源和保护环境优先路径、协调发展路径等生态文明建设的5条路径,通过模拟仿真,得到2025年和2035年生态文明建设效果。并从生产总值、总人口、就业人数、资源效率和环境绩效等五方面进一步分析云南生态文明建设路径的有效性,得到:(1)经济发展优先路径和维持现状发展路径不利于生态文明建设;(2)社会发展优先路径促进了云南就业人数增加,节约资源和保护环境优先路径提升了资源效率,但都不能全面推动生态文明建设;(3)协调发展路径对云南生态文明建设的经济、社会和环境子系统具有较强的促进作用,是优先选择发展路径。针对选择路径提出三方面建议:(1)社会、经济、资源与环境必须协调发展;(2)提高资源利用效率和加大可再生能源的应用;(3)充分发挥公众的环境监督作用。