Li Liu

Bio: Li Liu is an academic researcher from Dalian University of Technology. The author has contributed to research in topics: Routing protocol & Mobile computing. The author has an hindex of 8, co-authored 13 publications receiving 457 citations.

Socially Aware Networking: A Survey

Abstract: The widespread proliferation of handheld devices enables mobile carriers to be connected at anytime and anywhere. Meanwhile, the mobility patterns of mobile devices strongly depend on the users' movements, which are closely related to their social relationships and behaviors. Consequently, today's mobile networks are becoming increasingly human centric. This leads to the emergence of a new field which we call socially aware networking (SAN). One of the major features of SAN is that social awareness becomes indispensable information for the design of networking solutions. This emerging paradigm is applicable to various types of networks (e.g., opportunistic networks, mobile social networks, delay-tolerant networks, ad hoc networks, etc.) where the users have social relationships and interactions. By exploiting social properties of nodes, SAN can provide better networking support to innovative applications and services. In addition, it facilitates the convergence of human society and cyber-physical systems. In this paper, for the first time, to the best of our knowledge, we present a survey of this emerging field. Basic concepts of SAN are introduced. We intend to generalize the widely used social properties in this regard. The state-of-the-art research on SAN is reviewed with focus on three aspects: routing and forwarding, incentive mechanisms, and data dissemination. Some important open issues with respect to mobile social sensing and learning, privacy, node selfishness, and scalability are discussed.

CAIS: A Copy Adjustable Incentive Scheme in Community-Based Socially Aware Networking

Abstract: Socially aware networking (SAN) is a new communication paradigm, in which the social characteristics of mobile nodes are exploited to improve the performance of data distribution. In SAN, mobile carriers may exhibit selfish behaviors and refuse to relay messages for others for various reasons, such as limited resources (e.g., buffer, energy, and bandwidth) or social relationships. Several incentive schemes have recently been investigated to stimulate selfish users for cooperation in data forwarding. However, a majority of the existing methods have not fully studied nodes' social relationships in their selfish behaviors. In this paper, we propose a copy adjustable incentive scheme (CAIS), which adopts the virtual credit concept to stimulate selfish nodes to cooperate in data forwarding. In CAIS, we consider a network in which the nodes are divided into certain communities based on their social relationships. Then, we apply two types of credits, i.e., social credit and nonsocial credit, to reward the nodes when they relay data for other nodes inside their community or outsiders, respectively. Based on our mechanism, the number of messages a node can replicate to other nodes is adjusted according to its cooperation level and earned credits. To further improve the performance of CAIS, a single-copy data replication policy is employed, which manages the credit distribution of each node according to its available resources. The results of our extensive experiments using both synthetic and trace-driven simulations illustrate that CAIS copes well with node selfishness in community-based networks and outperforms other benchmark protocols with high data delivery ratio, low communication overhead, and short data delivery latency.

BEEINFO: Interest-Based Forwarding Using Artificial Bee Colony for Socially Aware Networking

Abstract: Socially aware networking (SAN) provides a promising paradigm for routing and forwarding data packets by exploiting social properties of involved entities, for example, in vehicular social networks (VSNs). The mobility of individuals often features some regularity in location and time, particularly in vehicular environments. However, individuals' learning capability and awareness to the dynamic environments have not been well explored in the literature. Inspired by the artificial bee colony, we present BEEINFO, which is a set of interest-based forwarding schemes for SAN, which consists of BEEINFO-D, BEEINFO-S, and BEEINFO-D&S. BEEINFO adopts the food foraging behavior of bees to detect the environment information and to optimize the forwarding procedure. BEEINFO takes advantage of individuals' perceiving and learning capability to gather information of density and social ties. BEEINFO-D, BEEINFO-S, and BEEINFO-D&S are distinct from each other according to different utilization of density and social ties. This enhances the adaptability to dynamic environments. Additionally, BEEINFO performs message scheduling and buffer management to improve the forwarding performance. Extensive simulations have been conducted to compare BEEINFO with two representative protocols, i.e., PRoPHET and Epidemic. The results illustrate that BEEINFO outperforms PRoPHET and Epidemic with higher message delivery ratio, less overhead, and fewer hop counts.

PIS: A Multi-Dimensional Routing Protocol for Socially-Aware Networking

Abstract: Socially-aware networking is an emerging paradigm for intermittently connected networks consisting of mobile users with social relationships and characteristics. In this setting, humans are the main carriers of mobile devices. Hence, their connections, social features, and behaviors can be exploited to improve the performance of data forwarding protocols. In this paper, we first explore the impact of three social features, namely physical proximity, user interests, and social relationship on users’ daily routines. Then, we propose a multi-dimensional routing protocol called Proximity-Interest-Social (PIS) protocol in which the three different social dimensions are integrated into a unified distance function in order to select optimal intermediate data carriers. PIS protocol utilizes a time slot management mechanism to discover users’ movement similarities in different time periods during a day. We compare the performance of PIS to Epidemic, PROPHET, and SimBet routing protocols using SIGCOMM09 and INFOCOM06 data sets. The experiment results show that PIS outperforms other benchmark routing protocols with the highest data delivery ratio with a low communication overhead.

PIS: A Multi-dimensional Routing Protocol for Socially-aware Networking

Abstract: Socially-aware networking is an emerging paradigm for intermittently connected networks consisting of mobile users with social relationships and characteristics. In this setting, humans are the main carriers of mobile devices. Hence, their connections, social features, and behaviors can be exploited to improve the performance of data forwarding protocols. In this paper, we first explore the impact of three social features, namely physical proximity, user interests, and social relationship on users' daily routines. Then, we propose a multi-dimensional routing protocol called Proximity-Interest-Social (PIS) protocol in which the three different social dimensions are integrated into a unified distance function in order to select optimal intermediate data carriers. PIS protocol utilizes a time slot management mechanism to discover users' movement similarities in different time periods during a day. We compare the performance of PIS to Epidemic, PROPHET, and SimBet routing protocols using SIGCOMM09 and INFOCOM06 data sets. The experiment results show that PIS outperforms other benchmark routing protocols with the highest data delivery ratio with a low communication overhead.

CreditCoin: A Privacy-Preserving Blockchain-Based Incentive Announcement Network for Communications of Smart Vehicles

Abstract: The vehicular announcement network is one of the most promising utilities in the communications of smart vehicles and in the smart transportation systems. In general, there are two major issues in building an effective vehicular announcement network. First, it is difficult to forward reliable announcements without revealing users’ identities. Second, users usually lack the motivation to forward announcements. In this paper, we endeavor to resolve these two issues through proposing an effective announcement network called CreditCoin , a novel privacy-preserving incentive announcement network based on Blockchain via an efficient anonymous vehicular announcement aggregation protocol. On the one hand, CreditCoin allows nondeterministic different signers (i.e., users) to generate the signatures and to send announcements anonymously in the nonfully trusted environment. On the other hand, with Blockchain, CreditCoin motivates users with incentives to share traffic information. In addition, transactions and account information in CreditCoin are tamper-resistant. CreditCoin also achieves conditional privacy since Trace manager in CreditCoin traces malicious users’ identities in anonymous announcements with related transactions. CreditCoin thus is able to motivate users to forward announcements anonymously and reliably. Extensive experimental results show that CreditCoin is efficient and practical in simulations of smart transportation.

A self-adaptive artificial bee colony algorithm based on global best for global optimization

Abstract: Intelligent optimization algorithms based on evolutionary and swarm principles have been widely researched in recent years. The artificial bee colony (ABC) algorithm is an intelligent swarm algorithm for global optimization problems. Previous studies have shown that the ABC algorithm is an efficient, effective, and robust optimization method. However, the solution search equation used in ABC is insufficient, and the strategy for generating candidate solutions results in good exploration ability but poor exploitation performance. Although some complex strategies for generating candidate solutions have recently been developed, the universality and robustness of these new algorithms are still insufficient. This is mainly because only one strategy is adopted in the modified ABC algorithm. In this paper, we propose a self-adaptive ABC algorithm based on the global best candidate (SABC-GB) for global optimization. Experiments are conducted on a set of 25 benchmark functions. To ensure a fair comparison with other algorithms, we employ the same initial population for all algorithms on each benchmark function. Besides, to validate the feasibility of SABC-GB in real-world application, we demonstrate its application to a real clustering problem based on the K-means technique. The results demonstrate that SABC-GB is superior to the other algorithms for solving complex optimization problems. It means that it is a new technique to improve the ABC by introducing self-adaptive mechanism.

Vehicular Social Networks: Enabling Smart Mobility

Abstract: Vehicular transportation is an essential part of modern cities. However, the ever increasing number of road accidents, traffic congestion, and other such issues become obstacles for the realization of smart cities. As the integration of the Internet of Vehicles and social networks, vehicular social networks (VSNs) are promising to solve the above-mentioned problems by enabling smart mobility in modern cities, which are likely to pave the way for sustainable development by promoting transportation efficiency. In this article, the definition of and a brief introduction to VSNs are presented first. Existing supporting communication technologies are then summarized. Furthermore, we introduce an application scenario on trajectory data-analysis-based traffic anomaly detection for VSNs. Finally, several research challenges and open issues are highlighted and discussed.