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Virtualization of wireless sensor networks (WSN) is widely considered as a foundational block of edge/fog computing, which is a key technology that can help realize next-generation Internet of things (IoT) networks. In such scenarios, multiple IoT devices and service modules will be virtually deployed and interconnected over the Internet. Moreover, application services are expected to be more sophisticated and complex, thereby increasing the number of modifications required for the construction of network topologies. Therefore, it is imperative to establish a method for constructing a virtualized WSN (VWSN) topology that achieves low latency on information transmission and high resilience against network failures, while keeping the topological construction cost low. In this study, we draw inspiration from inter-modular connectivity in human brain networks, which achieves high performance when dealing with large-scale networks composed of a large number of modules (i.e., regions) and nodes (i.e., neurons). We propose a method for assigning inter-modular links based on a connectivity model observed in the cerebral cortex of the brain, known as the exponential distance rule (EDR) model. We then choose endpoint nodes of these links by controlling inter-modular assortativity, which characterizes the topological connectivity of brain networks. We test our proposed methods using simulation experiments. The results show that the proposed method based on the EDR model can construct a VWSN topology with an optimal combination of communication efficiency, robustness, and construction cost. Regarding the selection of endpoint nodes for the inter-modular links, the results also show that high assortativity enhances the robustness and communication efficiency because of the existence of inter-modular links of two high-degree nodes.

This paper presents a nonlinear dynamical model for predicting activities of voxels in a human primary visual cortex (V1); the activities are represented by blood-oxygen-level-dependent (BOLD) signals. The prediction performance is shown to be better than those of other traditional major models, e.g., general linear model and multivariable autoregressive model.

An essential application of wireless sensor networks is to successfully respond to user queries. Query packet losses occur in the query dissemination due to wireless communication problems such as interference, multipath fading, packet collisions, etc. The losses of query messages at sensor nodes result in the failure of sensor nodes reporting the requested data. Hence, the reliable and successful dissemination of query messages to sensor nodes is a non-trivial problem. The target of this paper is to enable highly successful query delivery to sensor nodes by localized and energy-efficient discovery, and recovery of query losses. We adopt local and collective cooperation among sensor nodes to increase the success rate of distributed discoveries and recoveries. To enable the scalability in the operations of discoveries and recoveries, we employ a distributed name resolution mechanism at each sensor node to allow sensor nodes to self-detect the correlated queries and query losses, and then efficiently locally respond to the query losses. We prove that the collective discovery of query losses has a high impact on the success of query dissemination and reveal that scalability can be achieved by using the proposed approach. We further study the novel features of the cooperation and competition in the collective recovery at PHY and MAC layers, and show that the appropriate number of detectors can achieve optimal successful recovery rate. We evaluate the proposed approach with both mathematical analyses and computer simulations. The proposed approach enables a high rate of successful delivery of query messages and it results in short route lengths to recover from query losses. The proposed approach is scalable and operates in a fully distributed manner.

SUMMARY Several task forces have been working on how to designthe future Internet in a clean slate manner and mobility management isone of the key issues to be considered. However, mobility managementin the future Internet is still being designed in an “all-in-one” way whereall management functions are tightly kept at a single location and this re-sults in cost inefficiency that can be an obstruction to constructing flexiblesystems. In this paper, we propose a new function-distributed mobilitymanagement architecture that can enable more flexible future Internet con-struction. Furthermore, we show the effectiveness of our proposed systemvia a cost analysis and computer simulation with a random walk mobilitymodel. key words: distributed mobility, future Internet, random walk model, costanalysis 1. Introduction Recently, societal requirements have become so compli-cated that the current Internet is struggling to meet themcompletely and it is now facing many challenging issues.In order to accomplish not only an incremental, but dras-tic innovation, many research works for new generationnetworks, in other words the future Internet, are being at-tempted from scratchall overthe world. High capacity, hugenumbers of devices, high reliability, as well as ecologicaland sustainable society support are among these challeng-ing issues. We can see high activities in future Internet re-search efforts [1] in order to support the requirements aboveand make our future life more prosperous and it is easy toimagine that supporting user mobility is one of those criticalfunctions.In the current IP network era

Nature is Earth's most amazing invention machine for solving problems and adapting to significant environmental changes. Its ability to address complex, large-scale problems with robust, adaptable, and efficient solutions results from many years of selection, genetic drift, and mutations. Thus, it is not surprising that inventors and researchers often look to natural systems for inspiration and methods to solve problems in human-created artificial environments. This has resulted in the development of evolutionary algorithms including genetic algorithms and swarm algorithms, and of classifier and pattern detection algorithms, such as neural networks, for addressing hard computational problems.

Functional magnetic resonance imaging (fMRI) is a technique to measure brain activity dynamics as the time series of spatial measurement units (voxels). Each voxel aggregates the neural activity in its spatial region, which changes over time depending on an external stimulus and the activities of other voxels. This paper uses fMRI data from a visual stimulus experiment to extract transition rules in a cellular automaton-like formulation. We identify states in the brain through k-means clustering and determine basic transition rules from the sequences of states. The relevant voxels in each rule are selected based on their statistical characteristics, resulting in a compact formulation of rewriting rules.

Recently societal requirements have become so complicated that the current Internet is facing many challenging issues to overcome. Several task forces have been working on research on the future Internet in a clean slate manner and mobility management is one of the key issues to be considered. Mobility management in the future Internet is still designed in an “all-in-one” way where all management functions are tightly kept at a single location and this results in cost inefficiency and can be an obstruction to constructing flexible systems. In this paper, we propose a function-distributed mobility management system that can enable more flexible future Internet construction. Furthermore, we show the effectiveness of our proposed system via the cost analysis with a random walk mobility model.

In this paper we analytically derive the sojourn time of a user accessing an online IPTV recording service. Basically, the system consists of a server (or server farm) and the bandwidth at which a user can download recorded files depends on the number of other concurrently downloading users. In particular, we consider both cases, one in which the user's download speed is limited by his own access bandwidth, whereas in the other case the server is the bottleneck and all downloading users share the server's upload capacity. Our model includes user impatience which depends on the actual download speed. A user may abort his download attempt if a threshold duration is exceeded. The file size distribution is obtained by measurements of offered files at an existing server.

Television over the Internet (IPTV) is regarded as the killer application for next generation networks (NGN). While the ITU is currently moving toward the standardized of IPTV, several existing applications like Joost and Zattoo are currently already providing a large amount of video content to Internet users. In this paper we study some popular video content delivery mechanisms and characterize them by measurements to study the user-perceived quality of experience (QoE).

In this paper we evaluate the performance of a content distribution service with respect to reliability and efficiency. The considered technology for realizing such a service can either be a traditional client/server (CS) architecture or a peer-to-peer (P2P) network. In CS, the capacity of the server is the bottleneck and has to be dimensioned in such a way that all requests can be accommodated at any time, while a P2P system does not burden a single server since the content is distributed in the network among sharing peers. However, corrupted or fake files may diminish the reliability of the P2P service due to downloading of useless contents. We compare a CS system to P2P and evaluate the downloading time, success ratio, and fairness while considering flash crowd arrivals and corrupted contents.

In recent years, interesting phenomena were revealed where the inherent noise enhances the adaptability of a system to its changing environment in a resilient manner. The application of such methods can be found in a wide variety of fields, from biology to engineering. In this paper we discuss the application of noise-assisted mechanisms to control and manage information networks. Specifically, we focus on two mechanisms, stochastic resonance and adaptive response by attractor selection, and show how they can be applied in self-adaptive network control to improve the robustness and reliability of the system.

In this paper we propose an analytical model for file diffusion in a peer-to-peer (P2P) file-sharing network based on biological epidemics. During the downloading process, the peer shares the downloaded parts of the file and, thus, contributes to distributing it in the network. This behavior is similar to the spreading of epidemic diseases which is a well researched subject in mathematical biology. Unlike other P2P models based on epidemics, we show that steady state assumptions are not sufficient and that the granularity of the diffusion model may be appropriately selected.

Recent measurement studies [8] have shown that peer-to-peer (P2P) file sharing applications are the major traffic source in the Internet. P2P applications, such as eDonkey, Kazaa, or BitTorrent, form overlay networks on the application layer and offer its peers to download and share their files with other peers in a highly distributed way. As a consequence, peers act simultaneously as both clients and servers. For a comprehensive survey of P2P technology, we refer to [7].