Paper AwardsWe are pleased to announce that the following papers have won best paper awards.
Best Paper AwardInfrastructure Optimization of Flight-Formation Inspired Self-organization for Address Configuration in Sensor Networks
Rui Teng, Bing Zhang, and Jianqin Liu (National Institute of Information and Communications Technology, Japan)
Abstract. Self-organized sensor networks are expected to automatically configure sensor nodes into networks. Node addresses are essential for network communications, and the address size has a substantial impact on the amount of energy consumption during the long-term report of small sensing data. This paper introduces a Scalable and Dynamic Infrastructure based Configuration scheme (SDIC), which addresses the problem of how to organize a large number of sensor nodes to configure small addresses with scalability and energy efficiency. The special features that make our approach unique are the exploitation of an optimized autoconfiguration infrastructure that is inspired by the flight formation of migration birds. SDIC enables sequential address assignment in a deterministic manner without address conflicts. SDIC utilizes mechanisms of the optimized server term control to achieve the scalability of infrastructure and configuration operation. The evaluation results of SDIC show that it achieves small-size address with few conflicts and with energy efficiency.
Best Student Paper AwardsEnhancing Sampling of the Conformational Space Near the Protein Native State
Brian Olson, Kevin Molloy, Amarda Shehu (George Mason University)
Abstract. A protein molecule assumes specific conformations under native conditions to fit and interact with other molecules. Due to the role that three-dimensional structure plays in protein function, significant efforts are devoted to elucidating native conformations. Many search algorithms are proposed to navigate the high-dimensional protein conformational space and its underlying energy surface in search of low-energy conformations that comprise the native state. In this work, we identify two strategies to enhance the sampling of native conformations. We show that employing an enhanced fragment library with greater structural diversity to assemble low-energy conformations allows sampling more native conformations. To efficiently handle the ensuing vast conformational space, only a representative subset of the sampled conformations are maintained and employed to further guide the search for native con- formations. Our results show that these two strategies greatly enhance the sampling of the conformational space near the native state.
An Evolutionary Game Theoretic Framework for Adaptive, Cooperative and Stable Network Applications
Chonho Lee (University of Massachusetts, Boston), Junichi Suzuki (University of Massachusetts Boston), and Athanasios Vasilakos (University of Western Macedonia)
Abstract. This paper investigates a bio-inspired framework, iNet-EGT/C, to build adaptive, cooperative and stable network applications. In this framework, each application is designed as a decentralized set of agents, each of which provides a functional service and possesses biological behaviors such as migration, replication and death. iNet-EGT/C implements an adaptive behavior selection mechanism for agents. Its selection process is modeled as a series of evolutionary games among behaviors. iNet-EGT/C allows agents to seek to operate at evolutionarily stable equilibria and adapt to dynamic networks by invoking evolutionarily stable behaviors. It is theoretically proved that each behavior selection process retains stability (i.e., reachability to at least one evolutionarily stable equilibrium). iNet-EGT/C leverages the notion of coalitions for agents to select behaviors as coalitional decisions in a cooperative manner rather than individual decisions in a selfish manner. This cooperative behavior selection accelerates agents' adaptation speed by up to 78%.
Best Workshop Paper AwardsSelf-Organized Data Aggregation among Selfish Nodes in an Isolated Cluster
K. Habibul Kabir, Masahiro Sasabe and Tetsuya Takine (Osaka University)
Abstract. This paper considers a delay tolerant network, where a message ferry travels multiple isolated clusters, collects data from nodes in the clusters, and finally delivers the data to a sink node. In our previous work, we proposed a self-organized data aggregation technique for collecting data from nodes efficiently, which can automatically accumulate data from cluster members to a limited number of cluster members called aggregators. The proposed scheme was developed based on the evolutionary game theoretic approach, in order to take account of the inherent selfishness of the nodes for saving their own battery life. The number of aggregators can be controlled to a desired value by adjusting the energy that the message ferry supplies to the aggregators. In this paper, we further examine the proposed system in terms of success of data transmission and system survivability. We first introduce a new type of game model with retransmissions. Through both theoretic and simulation approaches, we then reveal feasible parameter settings which can achieve a system with desirable characteristics: stability, survival, and successful data transfer.
Bio-inspired Self-organized Public Key Authentication Mechanism for Mobile Ad-hoc Networks
Parisa Memarmoshrefi, Roman Seibel, and Dieter Hogrefe (University of Göttingen)
Abstract. In mobile ad-hoc networks (MANETs), where there is no centralized authority to provide security, trust and reputation mechanisms are applied to maintain security by identifying trustworthy and untrustworthy nodes. However, traditional authentication mechanisms are infeasible for MANETs due to the lack of infrastructure and frequent topology changes. In this paper, we propose a self-organized and localized public key authentication mechanism based on ant colony systems. Every node generates its own public-private key pair, issues certificates to neighboring nodes and provides on-demand authentication services by means of gathering certificate chains towards a target node. Pheromone concentration left by ants along the path of the certificate chains represents the trust level of a node towards other nodes. This model is able to authenticate public keys by selecting the most trustworthy path in certificate chains gathered by ants and can identify and prevent certificate chains with individual or colluding malicious nodes.