This work focuses on the incorporation of incentive stimulations into data dissemination in autonomous mobile social networks with selfish nodes. The key challenge of enabling incentives is to effectively track the value of a message under such a unique network setting with intermittent connectivity and multiple interest data types. We propose two data dissemination models: the data pulling model where mobile users pull data from data providers, and the data pushing model where data providers generate personalized data and push them to the intended users. For data pulling, we present effective mechanisms to estimate the expected credit reward of a message that helps intermediate nodes to evaluate the potential reward of it. Nodal message communication is formulated as a two-person cooperative game, whose solution is found by a heuristic approach which achieves Pareto optimality. Under the data pushing model, “virtual checks” are introduced to eliminate the needs of accurate knowledge about whom and how many credits data providers should pay. The check buying process is formulated as an online auction model to further accelerate the circulation of credits. Extensive simulations carried out based on real-world traces show the proposed schemes achieve better performance than fully cooperative scheme, but significantly reduce communication cost.

In this paper, we propose a Self-Interest-Driven (SID) incentive scheme to stimulate cooperation among selfish nodes for ad dissemination in autonomous mobile social networks. As a key innovation of SID, we introduce “virtual checks” to eliminate the needs of accurate knowledge about whom and how many credits ad provider should pay. A virtual check is included in each ad packet. When an intended receiver receives the packet for the first time from an intermediate node, the former authorizes the latter a digitally signed check, which serves as a proof of successful ad delivery. Multiple copies of a virtual check can be created and signed by different receivers. When a node that owns a signed check meets the ad provider, it requests the provider to cash the check. Both ad packets and signed checks can be traded among mobile nodes. We propose the effective mechanisms to define virtual rewards for ad packets and virtual checks, and formulate the nodal interaction as a two-player cooperative game, whose solution is obtained by the Nash Bargaining Theorem. Extensive simulations are carried out to compare SID with other existing incentive algorithms under real world mobility traces.

This paper focuses on the problem of rostering in intermittently connected passive RFID networks. It aims to report a list of tagged mobile nodes that appear in given interested area(s) and time interval(s). Such rostering faces several unique challenges. First, the network consists of two dramatically different types of nodes: powerful static readers and extremely resource-constrained mobile tags. Communication can be established from a reader to a tag only, but not tags to tags or readers to readers. Therefore, the connectivity is very low and intermittent. Besides connectivity, the tag's computation power is also intermittent. It is available only for a short interval when the tag is powered up by a nearby reader, rendering any continuous functions impossible. Moreover, the capacity of tags is so limited that it becomes the critical network resource and communication bottleneck. To address the above challenges, we propose a rostering algorithm that employs a dynamic space-efficient coding scheme to construct hypothetic packet candidates, appraises their values according to information redundancy and tag mobility, and establishes a 0-1 Knapsack model to choose the best set of packets, which together maximize their total (redundancy-excluded) value, but do not exceed the capacity of a tag. We carry out experiments that involve 38 volunteers for nine days and perform large-scale simulations to evaluate the proposed rostering scheme.

This paper focuses on distributed data query in intermittently connected passive RFID networks, which are characterized by extraordinarily limited communication capacity and asynchronous and opportunistic communication links. To address such unique challenges, we propose a distributed data query framework that clusters RFID readers and establishes a 0-1 Knapsack model based on dynamic packet appraisal to enable highly efficient data transmission. We implement a prototype by using Alien RFID gears and carry out experiments that involve 52 volunteers for 14 days to evaluate the proposed data query framework.

This work centers on data dissemination in delay-tolerant mobile networks, where data fall into a range of interest types and each node may have one or multiple interests. The goal is to deliver data messages from sources to nodes with corresponding interests. We consider selfish nodes with rational behavior, and propose a credit-based incentive scheme to promote nodal collaboration. The key challenge is to effectively track the value of a message under such a unique network setting with intermittent connectivity and multiple interest types. Given poor end-to-end connections, credits are rewarded to the final deliverer only. Thus the value of a message for an intermediate node highly depends on its probability to deliver the message. Such probability itself is nontrivial to estimate. Moreover, a message is usually desired by multiple mobile users. Therefore, it can be potentially “sold” multiple times to different receivers. On the other hand, while more than one copies can be created during the transmissions of a message, a particular receiver “pays” for the first received copy only. These characteristics together make the development of incentive mechanism a unique, interesting, and challenging problem. In this paper, we present effective schemes to estimate the expected credit reward, and formulate nodal communication as a two-person cooperative game, whose solution is found by using the Nash Theorem. Extensive simulations are carried out based on real-world traces to evaluate the proposed scheme in terms of data delivery rate, delay and overhead. To our best knowledge, this is the first work that incorporates incentive stimulation into data dissemination in delay-tolerant mobile networks with selfish nodes and multiple interest types.

This research addresses the problem of counting in Delay Tolerant Networks (DTNs). The goal is to estimate the total number of nodes in the network with short delay time, high accuracy and small storage overhead. DTNs are occasionally connected networks that may suffer from frequent partitions. While counting in conventional (well-connected) networks has been extensively studied, it remains challenging in DTNs, due to the intermittent network connectivity and heterogeneous nodal mobility. In this paper, we propose a novel scheme that exploits effective nodal contact probability to guide the counting process. Extensive simulations based on real mobility traces are carried out to evaluate the performance of our proposed scheme. The results demonstrate that it is highly efficient and outperforms all existing solutions.