A Game Theoretic Approach to Modelling Jamming Attacks in Delay Tolerant Networks

Cyberspace plays a prominent role in our social, economic and civic welfare and cyber security issues are of paramount importance today. Growing reliance of the intertwined military and civilian applications on wireless computer networks makes these networks highly vulnerable to attacks of which jamming attacks are a vital and exigent problem. In this paper, we study defence against jamming attacks as game in a delay tolerant network, with two adversarial players: the jammer playing against the transmitter. The transmitters seek to choose an optimal time to schedule his transmission securely, so as to maximize the probability of successful delivery before his session expires, while these transmissions are subject to inference from the jammer, who attempts to minimize this probability . We design strategies for the transmitters that offset transmission period based inference of network traffic by the jammer. We model these interactions and decisions as a game and use simulation as a tool to evaluate the games. Probability distribution functions over finite set of strategies are proposed to compute the expected payoff of both the players. Simulation results are used to evaluate the expected payoff along with the resulting equilibrium in cases where players are biased and unbiased. These results are used to strategically decide on the optimal time for both the players, and evaluate the efficiency of the strategies used by the transmitters against jammer attacks.

Cross-layer Balanced and Reliable Opportunistic Routing Algorithm for Mobile Ad Hoc Networks

For improving the efficiency and the reliability of the opportunistic routing algorithm, in this paper, we propose the cross-layer and reliable opportunistic routing algorithm (CBRT) for Mobile Ad Hoc Networks, which introduces the improved efficiency fuzzy logic and humoral regulation inspired topology control into the opportunistic routing algorithm. In CBRT, the inputs of the fuzzy logic system are the relative variance (rv) of the metrics rather than the values of the metrics, which reduces the number of fuzzy rules dramatically. Moreover, the number of fuzzy rules does not increase when the number of inputs increases. For reducing the control cost, in CBRT, the node degree in the candidate relays set is a range rather than a constant number. The nodes are divided into different categories based on their node degree in the candidate relays set. The nodes adjust their transmission range based on which categories that they belong to. Additionally, for investigating the effection of the node mobility on routing performance, we propose a link lifetime prediction algorithm which takes both the moving speed and moving direction into account. In CBRT, the source node determines the relaying priorities of the relaying nodes based on their utilities. The relaying node which the utility is large will have high priority to relay the data packet. By these innovations, the network performance in CBRT is much better than that in ExOR, however, the computation complexity is not increased in CBRT.Comment: 14 pages, 17 figures, 31 formulas, IEEE Sensors Journal, 201

Community-based Message Opportunistic Transmission

Mobile Social Networks (MSNs) is a kind of opportunistic networks, which is composed of a large number of mobile nodes with social characteristic. Up to now, the prevalent communitybased routing algorithms mostly select the most optimal social characteristic node to forward messages. But they almost don\u27t consider the effect of community distribution on mobile nodes and the time-varying characteristic of network. These algorithms usually result in high consumption of network resources and low successful delivery ratio if they are used directly in mobile social networks. We build a time-varying community-based network model, and propose a community-aware message opportunistic transmission algorithm (CMOT) in this paper. For inter-community messages transmission, the CMOT chooses an optimal community path by comparing the community transmission probability. For intra-community in local community, messages are forwarded according to the encounter probability between nodes. The simulation results show that the CMOT improves the message successful delivery ratio and reduces network overhead obviously, compared with classical routing algorithms, such as PRoPHET, MaxProp, Spray and Wait, and CMTS

Joint Head Selection and Airtime Allocation for Data Dissemination in Mobile Social Networks

Mobile social networks (MSNs) enable people with similar interests to interact without Internet access. By forming a temporary group, users can disseminate their data to other interested users in proximity with short-range communication technologies. However, due to user mobility, airtime available for users in the same group to disseminate data is limited. In addition, for practical consideration, a star network topology among users in the group is expected. For the former, unfair airtime allocation among the users will undermine their willingness to participate in MSNs. For the latter, a group head is required to connect other users. These two problems have to be properly addressed to enable real implementation and adoption of MSNs. To this aim, we propose a Nash bargaining-based joint head selection and airtime allocation scheme for data dissemination within the group. Specifically, the bargaining game of joint head selection and airtime allocation is first formulated. Then, Nash bargaining solution (NBS) based optimization problems are proposed for a homogeneous case and a more general heterogeneous case. For both cases, the existence of solution to the optimization problem is proved, which guarantees Pareto optimality and proportional fairness. Next, an algorithm, allowing distributed implementation, for join head selection and airtime allocation is introduced. Finally, numerical results are presented to evaluate the performance, validate intuitions and derive insights of the proposed scheme

CALAR: Community Aware Location Assisted Routing Framework for Delay Tolerant Networks

Infrastructure less communication strategies havegreatly evolved and found its way to most of our real lifeapplications like sensor networks, terrestrial communications,military communications etc. The communication pattern for allthese scenarios being identical i.e. encounter basedcommunication,characteristics of each communication domainare distinct. Hence the protocols applied for each environmentshould be defined carefully by considering its owncommunication patterns. While designing a routing protocol themain aspects under consideration include delay, connectivity,cost etc. In case of applications having limited connectivity,concept of Delay tolerant network (DTN) is deployed, whichassists delivering messages even in partitioned networks withlimited connectivity by using store and forward architecture.Node properties like contact duration, inter contact duration,location, community, direction of movement, angle of contact etc.were used for designing different classes of routing protocols forDTN. This paper introduces a new protocol that exploits thefeatures of both community based as well as location basedrouting protocols to achieve higher data delivery ratio invehicular scenarios. Results obtained show that proposedalgorithms have much improved delivery ratio comparedtoexisting routing algorithms which use any one of the aboveproperty individually

A novel mathematical framework for similarity-based opportunistic social networks

In this paper we study social networks as an enabling technology for new applications and services leveraging, largely unutilized, opportunistic mobile encounters. More specifically, we quantify mobile user similarity and introduce a novel mathematical framework, grounded in information theory, to characterize fundamental limits and quantify the performance of sample knowledge sharing strategies. First, we introduce generalized, non-temporal and temporal profile structures, beyond geographic location, as a probability mass function. Second, we examine classic and information-theoretic similarity metrics using data in the public domain. A noticeable finding is that temporal metrics give lower similarity indices on the average (i.e., conservative) compared to non-temporal metrics, due to leveraging the wealth of information in the temporal dimension. Third, we introduce a novel mathematical framework that establishes fundamental limits for knowledge sharing among similar opportunistic users. Finally, we show numerical results quantifying the cumulative knowledge gain over time and its upper bound, the knowledge gain limit, using public smartphone data for the user behavior and mobility traces, in the case of fixed as well as mobile scenarios. The presented results provide valuable insights highlighting the key role of the introduced information-theoretic framework in motivating future research along this ripe research direction, studying diverse scenarios as well as novel knowledge sharing strategies