Wireless sensor and actuator networks (WSANs) are composed of sensors and actuators to perform distributed sensing and actuating tasks. Most WSAN applications (e.g., fire detection) demand that actuators rapidly respond to observed events. Therefore, real-time (i.e., fast) and fault-tolerant transmission is a critical requirement in WSANs to enable sensed data to reach actuators reliably and quickly. Due to limited power resources, energy-efficiency is another crucial requirement. Such requirements become formidably challenging in large-scale WSANs. However, existing WSANs fall short in meeting these requirements. To this end, we first theoretically study the Kautz graph for its applicability in WSANs to meet these requirements. We then propose a Kautz-based REal-time, Fault-tolerant and EneRgy-efficient WSAN (REFER). REFER embeds Kautz graphs into the physical topology of a WSAN for real-time communication and connects the Kautz graphs using distributed hash table (DHT) for high scalability. We also theoretically study routing paths in the Kautz graph, based on which we develop an efficient fault-tolerant routing protocol. It enables a relay node to quickly and efficiently identify the next shortest path from itself to the destination based only on node IDs upon routing failure, rather than relying on retransmission from the source. REFER is advantageous over previous Kautz graph based works in that it does not need an energy-consuming protocol to find the next shortest path and it preserves the consistency between the overlay and physical topology. We further improve routing in REFER by multi-path based routing and energy-efficient multicasting within and between Kautz graph cells, respectively. Extensive experimental results demonstrate the superior performance of REFER in comparison with existing WSAN systems in terms of real-time communication, energy-efficiency, fault-tolerance and scalability.

Real-world applications, such as peer-to-peer (P2P) networks, e-commerce and social networks, usually employ reputation systems to provide guidance in selecting trustworthy node for high system reliability and security. A reputation system computes and publishes reputation score for each node based on a collection of opinions from others about the node. However, collusion behaviors impair the effectiveness of reputation systems in trustworthy node selection. Though many reputation calculation methods have been proposed to mitigate collusion's influence, little effort has been devoted to specifically tackling collusion. Based on the important collusion behavior characteristics in reputation evaluation and influence on reputation values, we propose a basic collusion detection method to specifically detect suspicious collusion behaviors in pairs. We further optimize the method by reducing the computing overhead. We also propose two pre-processing methods to firstly identify partial reputation raters of a node that are more likely to be colluders before applying the collusion detection method on them, thus reducing the collusion detection overhead. Extensive experimental results show that our proposed methods can significantly enhance the capability of existing reputation systems to detect collusion with low overhead. Also, the pre-processing methods are effective in reducing the collusion detection overhead without affecting the collusion detection accuracy.

Question and Answer (Q&A) websites such as Yahoo! Answers provide a platform where users can post questions and receive answers. These systems take advantage of the collective intelligence of users to find information. In this paper, we analyze the online social network (OSN) in Yahoo! Answers. Based on a large amount of our collected data, we studied the OSN's structural properties, which reveals strikingly distinct properties such as low link symmetry and weak correlation between indegree and outdegree. After studying the knowledge base and behaviors of the users, we find that a small number of top contributors answer most of the questions in the system. Also, each top contributor focuses only on a few knowledge categories. In addition, the knowledge categories of the users are highly clustered. We also study the knowledge base in a user's social network, which reveals that the members in a user's social network share only a few knowledge categories. Based on the findings, we provide guidance in the design of spammer detection algorithms and distributed Q&A systems. We also propose a friendship-knowledge oriented Q&A framework that synergistically combines current OSN-based Q&A and web Q&A. We believe that the results presented in this paper are crucial in understanding the collective intelligence in the web Q&A OSNs and lay a cornerstone for the evolution of next-generation Q&A systems.

Hybrid wireless networks combining the advantages of both mobile ad-hoc networks and infrastructure wireless networks have been receiving increased attention due to their ultra-high performance. An efficient data routing protocol is important in such networks for high network capacity and scalability. However, most routing protocols for these networks simply combine the ad-hoc transmission mode with the cellular transmission mode, which inherits the drawbacks of ad-hoc transmission. This paper presents a Distributed Three-hop Routing protocol (DTR) for hybrid wireless networks. To take full advantage of the widespread base stations, DTR divides a message data stream into segments and transmits the segments in a distributed manner. It makes full spatial reuse of a system via its high speed ad-hoc interface and alleviates mobile gateway congestion via its cellular interface. Furthermore, sending segments to a number of base stations simultaneously increases throughput and makes full use of widespread base stations. In addition, DTR significantly reduces overhead due to short path lengths and the elimination of route discovery and maintenance. DTR also has a congestion control algorithm to avoid overloading base stations. Theoretical analysis and simulation results show the superiority of DTR in comparison with other routing protocols in terms of throughput capacity, scalability, and mobility resilience. The results also show the effectiveness of the congestion control algorithm in balancing the load between base stations.

In a hybrid wireless network that combines a mobile ad-hoc network and an infrastructure network, efficient and reliable data routing is important for high throughput. Existing routing schemes that simply combine ad-hoc and infrastructure routings inherit the drawbacks of ad-hoc routing including congestion and high overhead for route discovery and maintenance. Although current reputation systems help increase routing reliability, they rely on local information exchanges between nodes to evaluate node reputations, so they are not sufficiently effective and efficient. A challenge here is if we can coordinately develop an efficient routing algorithm and effective cooperation incentives for reliable routing. To handle this challenge, this paper presents a peer-to-peer (P2P)-based Market-guided Distributed Routing mechanism (MDR). MDR takes advantage of widespread base stations to coordinately realize highly efficient data routing, and effective reputation management and trading market management for reliable data routing. The packets from a source node are distributively transmitted to base stations directly or indirectly, and then they are transmitted to the destination. The base stations form a P2P structure for reputation collection and querying to avoid local information exchanges, and for managing the service transactions between nodes in the trading market. By leveraging the single-relay transmission feature, base stations can monitor the actual transmitted packets of relay nodes to more accurately and efficiently evaluate their reputations and execute trading market management, as well as detect falsely reported reputation information. We further propose market-based policies to strengthen cooperation incentives. Simulation results show that MDR outperforms the traditional hybrid routing schemes and reputation systems in achieving high throughput.

Encouraging cooperation and deterring selfish behaviors are important for proper operations of mobile ad hoc networks (MANETs). For this purpose, most previous efforts rely on either reputation systems or price systems. However, these systems are neither sufficiently effective in providing cooperation incentives nor sufficiently efficient in resource consumption. Nodes in both systems can be uncooperative while still being considered trustworthy. Also, information exchange between mobile nodes in reputation systems and credit circulation in price systems consumes significant resources. This paper presents a hierarchical Account-aided Reputation Management system (ARM) to efficiently and effectively provide cooperation incentives. ARM builds a hierarchical locality-aware distributed hash table (DHT) infrastructure for efficient and integrated operation of both reputation and price systems. The infrastructure helps to globally collect all node reputation information in the system, which can be used to calculate more accurate reputation and detect abnormal reputation information. Also, ARM integrates reputation and price systems by enabling higher-reputed nodes to pay less for their received services. Theoretical analysis demonstrates the properties of ARM. Simulation results show that ARM outperforms the individual reputation system and price system in terms of effectiveness and efficiency of providing cooperation incentives and deterring selfish behaviors.

A peer-to-peer (P2P) file sharing system provides a platform that enables a tremendous number of nodes to share their files. Retrieving desired files efficiently and trustworthily is critical in such a large and jumbled system. However, the issues of efficient searching and trustworthy searching have only been studied separately. Simply combining the methods to achieve the two goals doubles system overhead. In this paper, we first study trace data from Facebook and BitTorrent. Guided by the observations, we propose a system that integrates a social network into a P2P network, named Social-P2P, for simultaneous efficient and trustworthy file sharing. It incorporates three mechanisms: (1) interest/trust-based structure, (2) interest/trust-based file searching, and (3) trust relationship adjustment. By exploiting the social interests and relationships in the social network, the interest/trust-based structure groups common-multi-interest nodes into a cluster and further connects socially close nodes within a cluster. The comparably stable nodes in each cluster form a Distributed Hash Table (DHT) for inter-cluster file searching. In the interest/trust-based file searching mechanism, a file query is forwarded to the cluster of the file by the DHT routing first. Then, it is forwarded along constructed connections within a cluster, which achieves high hit rate and reliable routing. Moreover, sharing files among socially close friends discourages nodes from providing faulty files because people are unlikely to risk their reputation in the real-world. In the trust relationship adjustment mechanism, each node in a routing path adaptively decreases its trust on the node that has forwarded a faulty file in order to avoid routing queries towards misbehaving nodes later on. We conducted extensive trace-driven simulations and implemented a prototype on PlanetLab. Experimental results show that Social-P2P achieves highly efficient and trustworthy file sharing compared to current file sharing systems and trust management systems.

The explosive growth of unsolicited e-mails has prompted the development of numerous spam filter techniques. Bayesian spam filters are superior to static keyword-based spam filters in that they can continuously evolve to tackle new spam by learning keywords in new spam emails. However, Bayesian spam filters are easily poisoned by clever spammers who avoid spam keywords and add many innocuous words in their emails. Also, Bayesian spam filters need a significant amount of time to adapt to a new spam based on user feedback. Moreover, few current spam filters exploit social networks to assist in spam detection. In order to develop an accurate and user-friendly spam filter, we propose a SOcial network Aided Personalized and effective spam filter (SOAP) in this paper. In SOAP, each node connects to its social friends; i.e., nodes form a distributed overlay by directly using social network links as overlay links. Each node uses SOAP to collect information and check spam autonomously in a distributed manner. Unlike previous spam filters that focus on parsing keywords (e.g., Bayesian filters) or building blacklists, SOAP exploits the social relationships among email correspondents and their (dis)interests to detect spam adaptively and automatically. In each node, SOAP integrates four components into the basic Bayesian filter: social closeness-based spam filtering, social interest-based spam filtering, adaptive trust management, and friend notification. We have evaluated the performance of SOAP using simulation based on trace data from Facebook. We also have implemented a SOAP prototype for real-world experiments. Experimental results show that SOAP can greatly improve the performance of Bayesian spam filters in terms of accuracy, attack-resilience, and efficiency of spam detection. The performance of the Bayesian spam filter is SOAP’s lower bound.

As wireless communication gains popularity, significant research has been devoted to supporting real-time transmission with stringent Quality of Service (QoS) requirements for wireless applications. At the same time, a wireless hybrid network that integrates a mobile wireless ad hoc network (MANET) and a wireless infrastructure network has been proven to be a better alternative for the next generation wireless networks. By directly adopting resource reservation-based QoS routing for MANETs, hybrids networks inherit invalid reservation and race condition problems in MANETs. How to guarantee the QoS in hybrid networks remains an open problem. In this paper, we propose a QoS-Oriented Distributed routing protocol (QOD) to enhance the QoS support capability of hybrid networks. Taking advantage of fewer transmission hops and anycast transmission features of the hybrid networks, QOD transforms the packet routing problem to a resource scheduling problem. QOD incorporates five algorithms: 1) a QoS-guaranteed neighbor selection algorithm to meet the transmission delay requirement, 2) a distributed packet scheduling algorithm to further reduce transmission delay, 3) a mobility-based segment resizing algorithm that adaptively adjusts segment size according to node mobility in order to reduce transmission time, 4) a traffic redundant elimination algorithm to increase the transmission throughput, and 5) a data redundancy elimination-based transmission algorithm to eliminate the redundant data to further improve the transmission QoS. Analytical and simulation results based on the random way-point model and the real human mobility model show that QOD can provide high QoS performance in terms of overhead, transmission delay, mobility-resilience, and scalability.

This paper addresses the data search problem in large-scale highly mobile and dense wireless networks. Current wireless network data search systems are not suitable for large-scale highly mobile and dense wireless networks. This paper presents a scalable and mobility-resilient LOcality-based distRibuted Data search system (LORD) for large-scale wireless networks with high mobility and density. Taking advantage of the high density, rather than mapping data to a location point, LORD maps file metadata to a geographical region and stores it in multiple nodes in the region, thus enhancing mobility-resilience. LORD has a novel region-based geographic data routing protocol that does not rely on flooding or GPSs for data publishing and querying, and a coloring-based partial replication algorithm to reduce data replicas in a region while maintaining the querying efficiency. LORD also works for unbalanced wireless networks with sparse regions. Simulation results show the superior performance of LORD compared to representative data search systems in terms of scalability, overhead, and mobility resilience in a highly dense and mobile network. The results also show the high scalability and mobility-resilience of LORD in an unbalanced wireless network with sparse regions, and the effectiveness of its coloring-based partial replication algorithm.

Recently, emerging research efforts have been focused on question and answer (Q&A) systems based on social networks. The social-based Q&A systems can answer non-factual questions, which cannot be easily resolved by web search engines. These systems either rely on a centralized server for identifying friends based on social information or broadcast a user's questions to all of its friends. Mobile Q&A systems, where mobile nodes access the Q&A systems through Internet, are very promising considering the rapid increase of mobile users and the convenience of practical use. However, such systems cannot directly use the previous centralized methods or broadcasting methods, which generate high cost of mobile Internet access, node overload, and high server bandwidth cost with the tremendous number of mobile users. We propose a distributed Social-based mObile Q&A System (SOS) with low overhead and system cost as well as quick response to question askers. SOS enables mobile users to forward questions to potential answerers in their friend lists in a decentralized manner for a number of hops before resorting to the server. It leverages lightweight knowledge engineering techniques to accurately identify friends who are able to and willing to answer questions, thus reducing the search and computation costs of mobile nodes. The trace-driven simulation results show that SOS can achieve a high query precision and recall rate, a short response latency and low overhead. We have also deployed a pilot version of SOS for use in a small group in Clemson University. The feedback from the users shows that SOS can provide high-quality answers.

Video sharing has been an increasingly popular application in online social networks (OSNs). However, its sustainable development is severely hindered by the intrinsic limit of the client/server architecture deployed in current OSN video systems, which is not only costly in terms of server bandwidth and storage but also not scalable with the soaring amount of users and video content. The peer-assisted Video-on-Demand (VoD) technique, in which participating peers assist the server in delivering video content, has been proposed recently. Unfortunately, videos can only be disseminated through friends in OSNs. Therefore, current VoD works that explore clustering nodes with similar interests or close location for high performance are suboptimal, if not entirely inapplicable, in OSNs. Based on our long-term real-world measurement of over 1,000,000 users and 2,500 videos on Facebook, we propose SocialTube, a novel peer-assisted video sharing system that explores social relationship, interest similarity, and physical location between peers in OSNs. Specifically, SocialTube incorporates four algorithms: a social network (SN)-based P2P overlay construction algorithm, an SN-based chunk prefetching algorithm, chunk delivery, and scheduling algorithm, and a buffer management algorithm. Experimental results from a prototype on PlanetLab and an event-driven simulator show that SocialTube can improve the quality of user experience and system scalability over current P2P VoD techniques.

Radio frequency identification (RFID) and wireless sensor networks (WSNs) have been popular in the industrial field, and both have undergone dramatic development. RFID and WSNs are well known for their abilities in identity identification and data transmission, respectively, and hence widely used in applications for environmental and health monitoring. Though the integration of a sensor and an RFID tag was proposed to gather both RFID tag and sensed information, few previous research efforts explore the integration of data transmission modes in the RFID and WSN systems to enhance the performance of the applications. In this paper, we propose a hybrid RFID and WSN system (HRW) that synergistically integrates the traditional RFID system and WSN system for efficient data collection. HRW has hybrid smart nodes that combine the function of RFID tags, the reduced function of RFID readers, and wireless sensors. Therefore, nodes can read each other's sensed data in tags, and all data can be quickly transmitted to an RFID reader through the node that first reaches it. The RFID readers transmit the collected data to the back-end servers for data processing and management. We also propose methods to improve data transmission efficiency and to protect data privacy and avoid malicious data selective forwarding in data transmission. Comprehensive simulation and trace-driven experimental results show the high performance of HRW in terms of the cost of deployment, transmission delay and capability, and tag capacity requirement.

This work focuses on Delay Tolerant Networks (DTNs) in a social network environment. DTNs do not have a complete path from a source to a destination most of the time. Previous data routing approaches in DTNs are primarily based on either flooding or single-copy routing. However, these methods incur either high overhead due to excessive transmissions or long delays due to suboptimal choices for relay nodes. Probabilistic forwarding that forwards a message to a node with a higher delivery utility enhances single-copy routing. However, current probabilistic forwarding methods only consider node contact frequency in calculating the utility while neglecting the influence of contact duration on the throughput, though both contact frequency and contact duration reflect the node movement pattern in a social network. In this paper, we theoretically prove that considering both factors leads to higher throughput than considering only contact frequency. To fully exploit a social network for high throughput and low routing delay, we propose a Social network oriented and duration utility-based distributed multicopy routing protocol (SEDUM) for DTNs. SEDUM is distinguished by three features. First, it considers both contact frequency and duration in node movement patterns of social networks. Second, it uses multicopy routing and can discover the minimum number of copies of a message to achieve a desired routing delay. Third, it has an effective buffer management mechanism to increase throughput and decrease routing delay. Theoretical analysis and simulation results show that SEDUM provides high throughput and low routing delay compared to existing routing approaches. The results conform to our expectation that considering both contact frequency and duration for delivery utility in routing can achieve higher throughput than considering only contact frequency, especially in a highly dynamic environment with large routing messages.

In peer-to-peer networks (P2Ps), many autonomous peers without preexisting trust relationships share resources with each other. Due to their open environment, the P2Ps usually employ reputation systems to provide guidance in selecting trustworthy resource providers for high reliability and security; however, node collusion impairs the effectiveness of reputation systems in trustworthy node selection. Although some reputation systems have certain mechanisms to counter collusion, the effectiveness of the mechanisms is not sufficiently high. In this paper, we leverage social networks to enhance the capability of reputation systems in combating collusion. We first analyzed real trace of the reputation system in the Overstock online auction platform, which incorporates a social network. The analysis reveals the impact of the social network on user purchasing and reputation rating patterns. We, thus, identified suspicious collusion behavior patterns and propose a social network-based mechanism, SocialTrust, to counter collusion. SocialTrust adaptively adjusts the weight of ratings based on the social distance and interest relationship between peers. Experiment results show that SocialTrust can significantly strengthen the capability of current reputation systems in combating collusion.

Internet-based video streaming applications are becoming more and more popular, attracting millions of online viewers every day. The incredible growth of viewers, dynamics of participants, and high video quality of service (QoS) requirement pose scalability, availability, and low-latency challenges to peer-to-peer (P2P) live video streaming systems. Tree-based systems have low-delay but are vulnerable to churn, while mesh-based systems are churn-resilient but suffer from high delay and overhead. Also, both structures cannot make full utilization of the bandwidth in the system. To tackle the challenges, we propose a DHT-aided chunk-driven overlay (DCO). It introduces a scalable DHT ring structure into a mesh-based overlay to efficiently manage video stream sharing. DCO includes a two-layer hierarchical DHT-based infrastructure, a chunk sharing algorithm, and a video provider selection algorithm. It selects stable nodes to form a scalable DHT-based infrastructure. The nodes in the DHT serve as distributed matchmakers between video providers and requesters. To motivate stable nodes to serve as the DHT nodes, we introduce an incentive mechanism based on the game theory. Aided by DHT, DCO guarantees stream chunk availability and assigns to a chunk requester a provider among all available providers in the system so that stream chunks are transmitted along a dynamic tree with top-down decreasing node bandwidth. In this way, DCO takes full advantage of available bandwidth in the system and, at the same time, provides high scalability and low latency. Experimental results show the superior performance of DCO compared with mesh-based and tree-based systems, and the effectiveness of the incentive mechanism and provider selection algorithm.

Peer-to-peer networks (P2Ps) use reputation systems to provide incentives for nodes to offer high quality of service (QoS) and thwart the intentions of dishonest or selfish nodes. Existing reputation systems have two problems. First, they directly regard node reputation as trust. Rather, reputation represents the opinions formed by others about a node's QoS behavior, while trust represents a node's honesty and willingness to cooperate. In addition to trust, factors such as node capacity and lifetime also influence reputation. Due to these factors' heterogeneity and variance over time, reputation cannot directly reflect a node's trust or current QoS. Second, existing reputation systems guide a node to select the server with the highest reputation, which may not actually select the highest QoS server and would overload the highest reputed nodes. This work aims to accurately reflect node trust and provide guidance for high-QoS server selection. Through experimental study, we find that node trust, available capacity, and lifetime positively affect node reputation. Based on this observation, we first propose a manual trust model and an automatic trust model that remove the influence of additional factors on reputation to truly reflect node trust. We then propose a high-QoS server selection algorithm that separately considers node trust, current available capacity, and lifetime. Extensive simulation results demonstrate the effectiveness of the trust models in accurate node trust reflection compared with an existing reputation system. Moreover, the server selection algorithm dramatically increases the success rate of service requests and avoids overloading nodes.

Question and Answer (Q&A) websites such as Yahoo!Answers provide a platform where users can post questions and receive answers. These systems take advantage of the collective intelligence of users to find information. In this paper, we analyze the online social network (OSN) in Yahoo!Answers. Based on a large amount of our collected data, we studied the OSN's structural properties, which reveals strikingly distinct properties such as low link symmetry and weak correlation between indegree and outdegree. After studying the knowledge base and behaviors of the users, we find that a small number of top contributors answer most of the questions in the system. Also, each top contributor focuses on only a few knowledge categories. In addition, the knowledge categories of the users are highly clustered. We also study the knowledge base in a user's social network, which reveals that the members in a user's social network share only a few knowledge categories. Based on the findings, we provide guidance in the design of spammer detection algorithms and distributed Q&A systems. We also propose a friendship-knowledge oriented Q&A framework that synergically combines current OSN-based Q&A and web Q&A. We believe that the results presented in this paper are crucial in understanding the collective intelligence in the web Q&A OSNs and lay a cornerstone for the evolution of next-generation Q&A systems.

Wireless Sensor and Actuator Networks (WSANs) are composed of sensors and actuators to perform distributed sensing and actuating tasks. Most WSAN applications (e.g., fire detection) demand that actuators rapidly respond to events under observation. Therefore, real-time and fault-tolerant transmission is a critical requirement in WSANs to enable sensed data to reach actuators reliably and quickly. Due to limited power resources, energy-efficiency is another crucial requirement. Such requirements become formidably challenging in large-scale WSANs. However, existing WSANs fall short in meeting these requirements. To this end, we first theoretically study the Kautz graph for its applicability in WSANs to meet these requirements. We then propose a Kautz-based Real-time, Fault-tolerant and Energy-efficient WSAN (REFER). REFER has a protocol that embeds Kautz graphs into the physical topology of a WSAN for real-time communication and connects the graphs using Distributed Hash Table (DHT) for high scalability. We also theoretically study routing paths in the Kautz graph, based on which we develop an efficient fault-tolerant routing protocol. It enables a relay node to quickly and efficiently identify the next shortest path from itself to the destination only based on node IDs upon routing failure. REFER is advantageous over previous Kautz graph based works in that it does not need an energy-consuming protocol to find the next shortest path and it can maintain the consistency between the overlay and physical topology. Experimental results demonstrate the superior performance of REFER in comparison with existing systems in terms of real-time communication, energy-efficiency, fault-tolerance and scalability.

A peer-to-peer (P2P) file sharing system provides a platform that enables users to share their files. Retrieving files efficiently and trustworthily in such a large and jumbled system is critically important. However, the issues of efficient searching and trustworthy searching have only been studied separately. Simply combining two separate strategies dealing with each issue doubles system overhead. In this paper, we first study trace data from Facebook and BitTorrent. Guided by the study observations, we propose a P2P system based on social networks for simultaneous efficient and trustworthy file sharing, namely Social-P2P. Social-P2P groups common-multi-interest nodes into a cluster and further connects socially close nodes within a cluster. The comparably stable nodes in each cluster form a DHT for inter-cluster file searching. A file query is forwarded to the cluster of the file by the DHT routing and then is forwarded along constructed connections within a cluster, which achieves high hit rate and reliable routing. Sharing files among socially close friends discourages nodes from providing faulty files since people are unlikely to risk their reputation in the real world. Experimental results show that by leveraging a social network, Social-P2P achieves highly efficient and trustworthy file sharing.

In peer-to-peer networks (P2Ps), many autonomous nodes without preexisting trust relationships share resources (e.g., files) between each other. Due to their open environment, P2Ps usually employ reputation systems to provide guidance in selecting trustworthy resource providers for high system reliability and security. A reputation system computes and publishes reputation score for each node based on a collection of opinions from others about the node. However, collusion behaviors impair the effectiveness of reputation systems in trustworthy node selection. Though many reputation calculation methods have been proposed to mitigate collusion's influence, little effort has been devoted to specifically tackling collusion. In this paper, we analyze transaction ratings in the Amazon and Overstock online transaction platforms during one year. The analysis of real trace confirms the existence of collusion as well as its important behavior characteristics and influence on reputation values in real reputation systems. Accordingly, we propose a collusion detection method to specifically thwart collusion behaviors. We further optimize the method by reducing the computing cost. Experimental results show that the proposed method can significantly enhance the capability of existing reputation systems to deter collusion with low cost.

Video sharing has been an increasingly popular application in online social networks (OSNs). However, its sustainable development is severely hindered by the intrinsic limit of the client/server architecture deployed in current OSN video systems, which is not only costly in terms of server bandwidth and storage but also not scalable. The peer-assisted Video-on-Demand (VOD) technique, in which participating peers assist the server in delivering video content has been proposed recently. Unfortunately, videos can only be disseminated through friends in OSNs. Therefore, current VOD works that explore clustering nodes with similar interests or close location for high performance are suboptimal, if not entirely inapplicable, in OSNs. Based on our long-term real-world measurement of over 1,000,000 users and 2,500 videos in Facebook, we propose SocialTube, a novel peer-assisted video sharing system that explores social relationship, interest similarity, and physical location between peers in OSNs. Specifically, SocialTube incorporates three algorithms: a social network (SN)-based P2P overlay construction algorithm, a SN-based chunk prefetch algorithm, and a buffer management algorithm. The trace driven based simulation results show that SocialTube can improve the quality of user experience and system scalability over current P2P VOD techniques.

In current peer-to-peer (P2P) live streaming systems, nodes in a channel form a P2P overlay for video sharing. To watch a new channel, a node depends on the centralized server to join in the overlay of the channel. The increase in the number of channels in today's live streaming applications triggers users' desire of watching multiple channels successively or simultaneously. However, the support of such watching modes in current applications is no better than joining in different channel overlays successively or simultaneously, which if widely used, poses heavy burden on the centralized server. In order to achieve higher efficiency and scalability, we propose a Social network-Aided efficient liVe strEaming system (SAVE). SAVE regards users' channel switching or multi-channel watching as interactions between channels. By collecting the information of channel interactions and nodes' interests and watching times, SAVE forms nodes in multiple channels with frequent interactions into an overlay, constructs bridges between overlays of channels with less frequent interactions, and enables nodes to identify friends sharing similar interests and watching times. Thus, a node can connect to a new channel while staying in its current overlay, using bridges or relying on its friends, reducing the need to contact the centralized server. Extensive experimental results from the PeerSim simulator and PlanetLab verify that SAVE outperforms other popular protocols in system efficiency and server load reduction.

A hybrid wireless network combines a mobile ad-hoc network and an infrastructure network. Efficient and reliable data routing is important for high throughput in such networks. Existing routing schemes that simply combine ad-hoc and infrastructure routings inherit the drawbacks of ad-hoc routing and fail to take advantage of the infrastructure for high efficiency. Current reputation systems relying on local information exchange are not sufficiently effective and efficient in guiding reliable routing. This paper presents a peer-to-peer (P2P)-based Market-guided Distributed Routing mechanism (MDR) to increase the throughput of hybrid networks by achieving a high efficiency and reliability. Taking advantage of the high density of base stations, the packets from a source node are distributively transmitted to base stations directly or indirectly. The packet transmission in MDR is modeled as a market trading behaviors, in which source nodes pay credits to relay nodes. The service price is determined by the supply and demand equilibrium of the nodes in the system. MDR organizes base stations into a P2P structure to facilitate high efficient data operation for service price determination. An erasure coding-based distributed routing algorithm is also proposed to facilitate an efficient and reliable market trading. Theoretical analysis demonstrates the distinguishing features of MDR and simulation results show that MDR outperforms the traditional hybrid routing schemes and reputation systems.

In mobile ad hoc networks (MANETs), tasks are conducted based on the cooperation of nodes in the networks. However, since the nodes are usually constrained by limited computation resources, selfish nodes may refuse to be cooperative. Reputation systems and price-based systems are two main solutions to the node noncooperation problem. A reputation system evaluates node behaviors by reputation values and uses a reputation threshold to distinguish trustworthy nodes and untrustworthy nodes. A price-based system uses virtual cash to control the transactions of a packet forwarding service. Although these two kinds of systems have been widely used, very little research has been devoted to investigating the effectiveness of the node cooperation incentives provided by the systems. In this paper, we use game theory to analyze the cooperation incentives provided by these two systems and by a system with no cooperation incentive strategy. We find that the strategies of using a threshold to determine the trustworthiness of a node in the reputation system and of rewarding cooperative nodes in the price-based system may be manipulated by clever or wealthy but selfish nodes. Illumined by the investigation results, we propose and study an integrated system. Theoretical and simulation results show the superiority of the integrated system over an individual reputation system and a price-based system in terms of the effectiveness of cooperation incentives and selfish node detection.

One critical function of cyber-physical systems (CPS) is object search in the physical world through the cyber sphere that enables interaction between the cyber and physical spheres. Some of the previously proposed physical object search engines use RFID tracking, and others collect the information of object locations into a hierarchical centralized server. The difficulty of widely deploying RFID devices, the centralized search, and the need for periodical location information collection prevent CPS from achieving higher scalability and efficiency. To deal with this problem, we propose a Social-aware distributed Cyber-Physical human-centric Search engine (SCPS) that leverages the social network formed by wireless device users for object search. Without requiring periodical location information collection, SCPS locates objects held by users based on the routine user movement pattern. Moreover, using a social-aware Bayesian network, it can accurately predict the users' locations when exceptional events (e.g., inclement weather) occur, which breaks user movement pattern. Thus, SCPS is more advantageous than all previous social network based works which assume that user behaviors always follow a certain pattern. Further, SCPS conducts the search in a fully distributed manner by relying on a DHT structure. As a result, SCPS achieves high scalability, efficiency and location accuracy. Extensive real-trace driven simulation results show the superior performance of SCPS compared to other representative search methods.

Encouraging cooperative and deterring selfish behaviors are important for proper operations of MANETs. For this purpose, most previous efforts either rely on reputation systems or price systems. However, both systems are neither sufficiently effective in providing cooperation incentives nor efficient in resource consumption. Nodes in both systems can be uncooperative while still being considered trustworthy. Also, information exchange between mobile nodes in reputation systems and credit circulation in price systems consume significant resources. This paper presents a hierarchical Account-aided Reputation Management system (ARM) to efficiently and effectively provide cooperation incentives. ARM builds a hierarchical locality-aware DHT infrastructure for efficient and integrated operations of both reputation and price systems. The infrastructure helps to globally collect all reputation information in the system, which helps to calculate more accurate reputation and detect abnormal reputation information. Also, ARM coordinately integrates resource and price systems by enabling higher-reputed nodes to pay less for their received services. Theoretical analysis demonstrates the properties of ARM. Simulation results show that ARM outperforms both a reputation system and price system in terms of effectiveness and efficiency.

The explosive growth of unsolicited emails has prompted the development of numerous spam filtering techniques. A Bayesian spam filter is superior to a static keywordbased spam filter because it can continuously evolve to tackle new spam by learning keywords in new spam emails. However, Bayesian spam filters can be easily poisoned by avoiding spam keywords and adding many innocuous keywords in the emails. In addition, they need a significant amount of time to adapt to a new spam based on user feedback. Moreover, few current spam filters exploit social networks to assist spam detection. In order to develop an accurate and user-friendly spam filter, in this paper, we propose a SOcial network Aided Personalized and effective spam filter (SOAP). Unlike previous filters that focus on parsing keywords (e.g, Bayesian filter) or building blacklists, SOAP exploits the social relationship among email correspondents to detect the spam adaptively and automatically. SOAP integrates three components into the basic Bayesian filter: social closeness-based spam filtering, social interest-based spam filtering, and adaptive trust management. We evaluate performance of SOAP based on the trace data from Facebook. Experimental results show that SOAP can greatly improve the performance of Bayesian spam filters in terms of the accuracy, attack-resilience and efficiency of spam detection. We also find that the performance of Bayesian spam filters is the lower bound of SOAP.

In peer-to-peer networks (P2Ps), many autonomous peers without preexisting trust relationships share resources with each other. Due to their open environment, the P2Ps usually employ reputation systems to provide guidance in selecting trustworthy resource providers for high reliability and security. However, node collusion impairs the effectiveness of reputation systems in trustworthy node selection. Although some reputation systems have certain mechanisms to counter collusion, the effectiveness of the mechanisms is not sufficiently high. In this paper, we leverage social networks to enhance the capability of reputation systems in combating collusion. We first analyzed real trace of the reputation system in the Overstock online auction platform which incorporates a social network. The analysis reveals the important impact of the social network on user purchasing and reputation rating patterns. We thus identified suspicious collusion behavior patterns and propose a social network based mechanism, namely Social Trust, to counter collusion. Social Trust adaptively adjusts the weight of ratings based on the social distance and interest relationship between peers. Experimental results show that Social Trust can significantly strengthen the capability of current reputation systems in combating collusion.

Since centralized data storage and search schemes often lead to high overhead and latency, distributed data-centric storage becomes a preferable approach in large-scale wireless sensor networks (WSNs). However, most of existing distributed methods lack optimization for spatial-temporal search to query events occurred in a certain geographical area and a certain time period. Furthermore, for data search routing, most methods rely on locating systems (e.g., GPS), which consume high energy. This paper proposes a distributed spatial-temporal Similarity Data Storage (SDS) scheme. SDS provides efficient spatial-temporal and similarity data searching service, and is applicable for both static and dynamic WSNs. It disseminates event data in such a way that the distance between WSN neighborhoods represents the similarity of data stored in them. In addition, SDS carpooling routing algorithm efficiently routes messages without the aid of GPS. Theoretical and experimental results show that SDS yields significant improvements on the efficiency of data querying compared with existing approaches, and obtains stable performance in dynamic environments.

Internet-based video streaming applications is becoming more and more popular and attract millions of online viewers every day. The incredible growth of viewers, dynamics of participants, and high video quality-of-service (QoS) requirement pose scalability, availability and low-latency challenges to peer-to-peer (P2P) live video streaming systems. Tree-based systems have low-delay but are vulnerable to churn, while mesh-based systems are churn-resilient but suffers high delay and overhead. Both systems cannot make full utilization of the bandwidth in the system. To tackle the challenges, we propose a DHT-aided Chunk-driven Overlay (DCO). It introduces a scalable DHT ring structure into a mesh-based overlay to efficiently manage video stream sharing. DCO includes a hierarchical DHT-based infrastructure and a chunk sharing algorithm. Aided by DHT, DCO guarantees stream chunk availability. In this way, DCO flexibly takes full advantage of available bandwidth in the system and at the same time provides high scalability and low-latency. Simulation results show the superiority of DCO compared with mesh-based and tree-based systems.

As wireless communication gains popularity, significant research has been devoted to supporting realtime transmission with stringent Quality of Service (QoS) requirements for wireless applications. At the same time, a wireless hybrid network that integrates a mobile wireless ad hoc network (MANET) and a wireless infrastructure network has been proven to be a better alternative for the next generation wireless networks. By directly adopting resource reservation-based QoS routing for MANETs, hybrids networks inherit invalid reservation and race condition problems in MANETs. How to guarantee the QoS in hybrid networks remains as an open problem. In this paper, we propose a QoS-oriented Distributed routing protocol (QOD) to enhance the QoS support capability of hybrid networks. Taking advantage of fewer transmission hops and anycast transmission features of the networks, QOD transforms the packet routing problem to a resource scheduling problem. QOD includes a QoS-guaranteed neighbor selection algorithm to meet the transmission delay requirement, a distributed packet scheduling algorithm to further reduce transmission delay, and mobility-based segment resizing algorithm that adaptively adjusts segment size according to node mobility in order to reduce transmission time. Analytical results show the QOD's properties of lower transmission delay and dynamism-resilience. Simulation results show that QOD can provide high QoS performance in terms of overhead, transmission delay, dynamism-resilience and scalability compared to a resource reservation-based mechanism.

In cooperative systems such as wireless ad hoc networks, tasks are conducted based on the cooperation of nodes in the system. However, selfish nodes may refuse to be cooperative. Reputation system and price-based system are two main solutions to this problem. In this paper, we use game theory to investigate the underlying cooperation incentives of both systems. We found that the strategies of using a threshold to determine the trustworthiness of a node in the reputation system and of rewarding cooperative nodes in the price-based system may be manipulated by clever selfish nodes to gain benefits while still being selfish. Illumined by the investigation results, an integrated system is proposed and studied. Theoretical and simulation results show the superiority of the integrated system over individual reputation system and price-based system in terms of the effectiveness of cooperation incentives.

Since centralized data storage and search schemes often lead to high overhead and latency, distributed data centric storage becomes a preferable approach in large-scale WSNs. However, most of existing methods lack optimization for spatial- temporal search and similarity search for multi-attribute data. Some methods are optimized under circumstances where nodes are equipped with locating systems (e.g., GPS) which consumes high energy. This paper proposes a distributed spatial-temporal similarity data storage scheme (SDS). It disseminates event data in such a way that the distance between WSN neighborhoods represents the similarity of data stored in them. In addition, SDS carpooling routing algorithm efficiently routes messages without the aid of a locating system. SDS provides efficient spatial- temporal and similarity data searching service. Experimental results show that SDS yields significant improvements on the efficiency of data querying compared with existing approaches.

Hybrid wireless networks combining the advantages of both ad-hoc networks and infrastructure wireless networks have been receiving increasingly attentions because of their ultra-high performance. An efficient data routing protocol is an important component in such networks for high capacity and scalability. However, most routing protocols for the networks simply combine an ad-hoc transmission mode and a cellular transmission mode, which fail to take advantage of the dual-feature architecture. This paper presents a distributed Three-hop Routing (DTR) protocol for hybrid wireless networks. DTR divides a message data stream into segments and transmits the segments in a distributed manner. It makes full spatial reuse of system via high speed ad-hoc interface and alleviate mobile gateway congestion via cellular interface. Furthermore, sending segments to a number of base stations simultaneously increases the throughput, and makes full use of wide-spread base stations. In addition, DTR significantly reduces overhead due to short path length and eliminates route discovery and maintenance overhead. Theoretical analysis and simulation results show the superiority of DTR in comparison with other routing protocols in terms of throughput capacity, scalability and mobility resilience.

Data sharing is one of the most popular applications that dominate 70% of data traffic on the Internet. This application has been penetrating wireless mobile networks with dramatic speed, which allows the sharing of data whenever and wherever. However, traditional client-server data sharing model suffers from single point failure and low-scalable transmission in a highly dense and dynamic scenario. MANET is a promising alternative structure for flexible and distributed data sharing. However, the topology-based flooding employed in MANETs for data routing and querying prevents the data sharing system from achieving high scalability and robustness. Building a DHT on a MANET reduces data query overhead, but the inconsistency between the overlay and the underlying topology degrade system's performance. Geographic routing based data sharing in MANETs reduces the data routing overhead. However, it has more requirements (e.g. GPS) on the nodes. This paper presents a locality-based distributed data sharing system (LORD) for mobility and congestion resilient data management. LORD consists of a DHT-based data index and retrieval protocol and a locality-aware region-based data routing protocol. It provides highly efficient, scalable and dynamism-resilient data sharing with low overhead. Moreover, it offers similarity data searching function. Theoretical analysis and simulation results show the superiority of LORD compared with other data sharing systems in terms of scalability, overhead and dynamism-resilience in a highly dense and dynamic MANET.

In delay tolerant network, a complete routing path from a source to a destination can not be guaranteed at most of the time. Therefore, traditional routing method for ad hoc network is not applicable in these situations. Current approaches for such networks are primarily based on redundant transmissions and single copy direct routing. However, they incur either high overhead due to excessive transmissions or long delays due to the incorrect path choices during forwarding. In this paper, we propose a direction based geographic routing scheme (DIG) for the intermittently connected network. Relying on the geographic location information, the packets are routed in a approximate ideal path to the destination, which significantly reduces the resource required in flooding-based algorithm and lead to decreased delay compared to the direct routing. Theoretical analyzes and simulations show that compared to the epidemic routing and direct routing, DIG provides nearly optimal delay with very low overhead.

Internet-based distributed systems enable globally scattered resources to be collectively pooled and used in a cooperative manner to achieve unprecedented petascale super computing capabilities. Numerous resource discovery approaches have been proposed to help achieve this goal. To report or discover a multi-attribute resource, most approaches use multiple messages with each message for an attribute, leading to high overhead. Anther approach can reduce multi-attribute to one index, but it is not practically effective in an environment with a large number of different resource attributes. Furthermore, few approaches are able to locate resources geographically close to the requesters, which is critical to system performance. This paper presents a P2P-based intelligent resource discovery (PIRD) mechanism that weaves all attributes into a set of indices using locality sensitive hashing, and then maps the indices to a structured P2P. It further incorporates Lempel-Ziv-Welch algorithm to compress attribute information for higher efficiency. In addition, it helps to search resources geographically close to requesters by relying on a hierarchical P2P structure. PIRD significantly reduces overhead and improves the efficiency and effectiveness of resource discovery. Theoretical analysis and simulation results demonstrate the efficiency of PIRD in comparison with other approaches. It dramatically reduces overhead and yields significant improvements on the efficiency of resource discovery.

Radio frequency identification (RFID) and wireless sensor networks (WSN) have been popular in industrial and academic field due to their ability in identity identification and data transmission respectively. Both RFID and wireless sensor networks have undergone huge development separately. In this paper, it has been discussed how helpful and effective it will be when RFID and WSN have been combined together. It also discusses about some applications of this integrated technology in the field of precious animal and patient health monitoring where real time information is of utmost importance. A system structure of the integrated RFID and WSN has been introduced and the simulation results show that the new system outperforms traditional RFID monitory system in terms of the cost of deployment, updating delay and tag capacity requirement.

Intermittently connected mobile networks don't have a complete path from a source to a destination at most of the time. Such an environment can be found in very sparse mobile networks where nodes meet only occasionally or in wireless sensor networks where nodes always sleep to conserve energy. Current transmission approaches in such networks are primarily based on: multi-copy flooding scheme and single-copy forwarding scheme. However, they incur either high overheads due to excessive transmissions or long delay due to possible incorrect choices during forwarding. In this paper, we propose a A utility-based distributed routing algorithm with multi-copies called UDM, in which a packet is initially replicated to a certain number of its neighbor nodes, which sequentially forward those packets to the destination node based on a probabilistic routing scheme. Some buffer management methods are also proposed to further improve its performance. Theoretical analyze and simulations show that compared to epidemic routing, spray and wait routing, UDM routing scheme provides a nearly optimal delay performance with a stable packet arrive rate in the community mobility model.

In mobile ad hoc network, all the packets are forwarded in a multi-hop fashion relying on the contribution of each participants. In order to encourage the cooperation between the nodes in the system, many incentive mechanisms have been proposed. Although these incentive schemes can improve the cooperation to some certain extend, they are still suffering from some drawbacks. In this paper, the efficiency of these incentive mechanisms has been analyzed based on game theory modules. Their performances have also been compared with a proposed hybrid cooperation enforcement mechanism ARM. The simulation and theoretical results show the superiority of the ARM over traditional reputation based scheme and price-based scheme.