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1 Background A thorough understanding of the influence of the foot skeletal structure on hallux valgus (HV) is required for HV prevention. We developed a system using a 3D foot scanner on a smartphone to clarify the relationships between foot features and HV risk. Methods Two-dimensional video images were recorded on a smartphone, sent to a computer or cloud server, and used to construct a 3D foot-feature model, considering 10 foot features associated with HV. The participants (419 individuals, aged 40–89 years) stood with their toes 12 cm apart and heels 8 cm apart during video recording. The height and weight were measured for body-mass index calculation. Results Age-dependent foot-feature variations were observed slightly for males and distinctively for females. For females, the great toe–first metatarsal head–heel (GFH) angle associated with HV increased with age, i.e., the GFH angle increased with age, suggesting that HV increased with age. Multiple regression analysis revealed that the features determining the GFH angle are the second toe–heel–navicular angle, bone distance axis, and transverse arch length and height. The adjusted coefficients of determination were 0.54 and 0.52 for males and females, respectively. Conclusion This approach enables simple foot structure assessment for HV risk evaluation.

Background Flat feet increase the risk of knee osteoarthritis and contribute to frailty, which may lead to worse life prognoses. The influence of the foot skeletal structure on flat feet is not yet entirely understood. Footprints are often used to evaluate feet. However, footprint-based measurements do not reflect the underlying structures of feet and are easily confounded by soft tissue. Three-dimensional evaluation of the foot shape can reveal the characteristics of flat feet. Therefore, foot shape evaluations have garnered increasing research interest. This study aimed to determine the correlation between the three-dimensional (3D) features of the foot and the measurement results of footprint and to predict the evaluation results of flat feet from the footprint based on the 3D features. Finally, the three-dimensional characteristics of flat feet, which cannot be revealed by footprint, were determined. Methods A total of 403 individuals (40–89 years) participated in this study. The proposed system was developed to identify seven skeletal features that were expected to be associated with flat feet. The loads on the soles of the feet were measured in a static standing position and with a digital footprint device. Specifically, two footprint indices were calculated: the Chippaux–Smirak index (CSI) and the Staheli index (SI). In the analysis, comparisons between male and female measurement variables were performed using the Student’s t test. The relationships between the 3D foot features and footprint index parameters were determined by employing the Pearson correlation coefficient. Multiple linear regression was utilized to identify 3D foot features that were strongly associated with the CSI and SI. Foot features identified as significant in the multivariate regression analysis were compared based on a one-way analysis of variance (ANOVA) with Tukey’s post hoc test. Results The CSI and SI were highly correlated with the instep height (IH) and navicular height (NH) of the 3D foot scanning system and were also derived from multiple regression analysis. In addition to the NH and IH, the indicators of the forefoot, transverse arch width, and transverse arch height were considered. In the flat foot group with CSI values above 62.7%, NH was 13.5% (p p = 0.01) for females, and the axis of the bone distance was 5.3% (p = 0.05) for males and 4.9% (p = 0.10) for females. In particular, for CSI values above 62.7% and NH values below 13%, the axis of the bone distance was large and the foot skeleton was deformed. Conclusions Decreased navicular bone height could be evaluated with the 3D foot scanning system even when flat feet were not detected from the footprint. The results indicate that the use of quantitative indices for 3D foot measurements is important when evaluating the flattening of the foot. Trial registration number UMIN000037694. Name of the registry: University Hospital Medical Information Network Registry. Date of registration: August 15, 2019.

Recently, wet-bulb globe temperature (WBGT) has attracted a lot of attention as a useful index for measuring heat strokes even when core body temperature cannot be available for the prevention. However, because the WBGT is only valid in the vicinity of the WBGT meter, the actual ambient heat could be different even in the same room owing to ventilation, clothes, and body size, especially in hot specific occupational environments. To realize reliable heat stroke prevention in hot working places, we proposed a new personalized vital sign index, which is combined with several types of vital data, including the personalized heat strain temperature (pHST) index based on the temperature/humidity measurement to adjust the WBGT at the individual level. In this study, a wearable device was equipped with the proposed pHST meter, a heart rate monitor, and an accelerometer. Additionally, supervised machine learning based on the proposed personalized vital index was introduced to improve the prevention accuracy. Our developed system with the proposed vital sign index achieved a prevention accuracy of 85.2% in a hot occupational experiment in the summer season, where the true positive rate and true negative rate were 96.3% and 83.7%, respectively.

One of the major shortcomings of Centralized Radio Access Networks (C-RAN) is that the large capacity is required for fronthaul network between Remote Radio Heads (RRHs) and central office with baseband unit (BBU) pool. Possible solutions are to introduce lower-cost networking technology for fronthaul network, such as Time and Wavelength Division Multiplexing Passive Optical Network (TWDM-PON), and to introduce functional split, that moves some baseband processing functions to cell site to decrease the utilization of the fronthaul network. In this paper, we construct the mathematical model for selecting function split options of baseband processing to minimize the power consumption of TWDM-PON based fronthaul network. In detail, we formulate the optimization problem for minimizing the total power consumption of fronthaul network in terms of the capacity of TWDM-PON, the number of RRHs in each cell site, server resources, latency constraints, the amount of traffic from each RRH, physical/virtual server power consumption characteristics. Numerical examples are shown for confirming the correctness of the proposed model and for presenting the effect of resource enhancement methods on the capacity and energy efficiency of the system.

The condition of arteriovenous fistula (AVF) blood flow is typically checked by using auscultation; however, auscultation should require a qualitative judgment dependent on the skills of doctors, and further attention to contact infection is required. For these reasons, this study developed a non-contact and non-invasive medical device to measure the pulse wave of AVFs by applying optical imaging technology. As a first step toward realization of the quantification judgment based on non-contact AVF measurement, we experimentally validated the developed system, whereby the hemodynamics of 168 subjects were visually and quantitatively evaluated based on clinical tests. Based on the evaluation results, the fundamental statistical characteristics of the non-contact measurement, including the average and median values, and distribution of measured signal-to-noise power ratio, were demonstrated. The clinical test results contributed to the future construction of quantified criteria for the AVF condition with the non-contact measurement.

Recently, security issues have become more serious with the spread of IoT devices. To reduce the security risks when using communication devices, network administrators should know detailed information of all devices connected in the network and control them accordingly. Although there are many studies about identifying devices based on information from the network, there is a limit to improving the accuracy of device identification by using a single identification method.In this paper, we propose a new method to improve the accuracy of device identification by using a combination of results from multiple identification methods. We define a new index, called similarity, between multiple devices by using the results from multiple identification methods and identify devices based on the similarity. The results of evaluations for communication devices connected to a real network show that our method can improve the accuracy of device identification by 23.0 percent.

As the number of IP-based telephony-like voice over IP (VoIP) users increases, interoperability of devices will become much more important to achieve smooth communications between arbitrary pairs of devices. To this end, detection and analysis of communication failures are key issues for validating interoperability. However, current failure (error) detections must be manually confirmed, which requires significant effort. Therefore, an automatic error-identification method is necessary. A straightforward approach can be considered that involves using a learning-based algorithm (e.g., k-nearest-neighbor-based classification) to identify errors; however, it is difficult to collect useful training data to achieve high identification accuracy. Since the number of conditions that we can prepare for in advance to collect training data is quite a bit less than that in a real environment, many errors may be misidentified when such cases are not included in the training data. To solve this problem, we propose a method which considers the characteristics of message sequences in a VoIP session control protocol (i.e., Session Initiation Protocol) by applying a similar approach of editing the distance between strings. Experiments using real environment data show that our method can improve the accuracy of identifying errors that are not included in the training data and enable administrators to collect new training data for unknown errors.

Constructing a large-scale network in outdoor fields with wired or wireless antennas involves a huge deployment cost. New devices, called movable routers, are able to realize the networks at a low cost. Recently, a large number of internet of things nodes have been deployed into the networks in outdoor fields. In the current communication network, users intend to communicate with devices without knowing where they are, but the current network still focuses on device locations, complicating configuration and implementation of the network. In this study, we design an architecture called router-movable information-centric networking (RMICN) to realize content-based communication instead of host-based communication, for facilitating communications between disjointed networks using movable routers. We incorporate information-centric networking technologies in the design of the system for the naming scheme and the strategy layer, enabling device control over the movable routers, called flying routers. In the simulation results, we show that RMICN can further reduce the response time of content requests compared with delay-tolerant networking. Furthermore, we present a proof-of-concept implementation of the architecture to confirm the feasibility of the design.

In this paper, a channel selection metric is presented for WLAN. It is based on SNR and the number of users. A delay time is employed to define metric of channel selection. Nth-order polynomials are introduced to obtain a polynomial metric in a tractable form. The advantage of formulation is that more parameters other than SNR or the number of users can be added to metric without taking up complex delay analysis. Numerical results are presented to demonstrate metric as a function of SNR and the number of users. The effective range of metric is discussed with a difference between the polynomial and delay time metrics.

Recently, in wireless communications a transmission rate degradation due to the increase of traffic becomes a serious problem. To deal with this problem, the number of users is an important parameter. To the estimation of the number of users, a probability distribution of backoff is applicable with the maximum likelihood approach. In this paper, an analysis method is proposed to derive the probability distribution of backoff considering frozen backoff in time axis. The analysis and simulation results are compared to show the validity the proposed method.

Information-centric networking (ICN) has gained attention from network research communities due to its capability of efficient content dissemination. In-network caching function in ICN plays an important role to achieve the design motivation. However, many researchers on in-network caching have focused on where to cache rather than how to cache: the former is known as contents deployment in the network and the latter is known as cache replacement in an ICN element. Although, the cache replacement has been intensively researched in the context of web-caching and content delivery network previously, the conventional approaches cannot be directly applied to ICN due to the fine granularity of cacheable items in ICN, which eventually changes the access patterns. In this paper, we argue that ICN requires a novel cache replacement algorithm to fulfill the requirements in the design of high performance ICN element. Then, we propose a novel cache replacement algorithm to satisfy the requirements named Compact CLOCK with Adaptive Replacement (Compact CAR), which can reduce the consumption of cache memory to one-tenth compared to conventional approaches., Comment: 15 pages, 29 figures, submitted to Computer Communications

International audience

Recently, Information-Centric Networking (ICN) attracts attention as the communication infrastructure for the Internet of Things (IoT) environment that requires various device cooperation and the mobility environment where an address frequently changes. By using “control”, “management”, and “data processing” in the network, ICN is expected not only as a simple data transfer but also an information distribution infrastructure with service provision function. However, many ICN researches focus on accessing static content with a name, and an ICN architecture that provides dynamic services has not been sufficiently researched. In order to develop network functions that realize dynamic services in ICN, we design and implement the tracking system for moving objects with ICN. We implement the designed system and conduct operation experiments using actual equipment, and confirm that ICN can realize a dynamic video distribution service that tracks a moving object based on the designed system and ICN has the possibility as an information distribution infrastructure with dynamic service provision function.

An exact analysis is presented for bit error probability of Max-Log-MAP decoding in sequence dependent channels. In the channels, an average bit error probability is derived by averaging the bit error probability of fixed sequence over all sequences, however, it causes the exponential complexity increase with a sequence length.In this paper, a new method is proposed for the averaging operation in the exact error probability analysis of Max-Log-MAP decoding. It is based on the integration of the probability density function (pdf’s) of merging path metrics at every trellis transition. An illustrative example of bit error probability is shown for 2-state intersymbol interference channels.

Recently, Information-Centric Networking (ICN), is attracting attention as a network capable of flexible communication especially in IoT mobility environments. As a research on flexible controls in ICN, we have been involved in a movable router using Unmanned Aerial Vehicles (UAVs) or drones (called Flying Routers (FRs)) to realize communication in disjoint networks. In previous studies, control of Flying Routers was targeted reactively where FRs move according to the events on receiving Interest or Data. However, in order to realize a collaborative movement strategy such as content retrieval in consideration of cooperation by multiple FRs, more strategic ways which directly control FRs according to decision of path optimization. For this purpose, in this paper, we design a communication control system which strategically controls FRs by sending explicit Interests. We incorporates ICN technologies in the design of the system: naming scheme and strategy layer, which enable flexible control over FRs. Furthermore, we present a Proof-of-Concept (PoC) implementation of the system to confirm the feasibility of the design.

Recently, network management becomes therefore more important to save a safe and secure ICT infrastructure. Since it is difficult to take appropriate actions against rapid, complicated, and diversed variation of behaviors in networking and service systems, conventional network management heavily relies on highly expertised operators. Automation of the management of ICT infrastructure is now a big challenging for future networks due to shortage of experts in network operations and management. In this paper, we propose a new framework for automating operations and management of ICT infrastructure, by combination of both networking/system incidents and records of operations. Our framework mainly focuses on the integration of various types of log data (e.g., alerts, flow analysis, access logs, command execution logs, etc.). As a proof of concept (PoC), we demonstrate an implementation of our framework by using honeypots and ssh-based agents.

Recently, variety of communication devices such as printers, IP telephones, network cameras are used widely, with the support of networking in consumer electronics. As a spread of IoT (Internet of Things), the number of embed devices are significantly increasing, however, such devices have lack of capability on security. It is therefore desirable that a network identifies these devices to take appropriate operations. In this paper, we propose an identification method of communication devices from monitoring patterns of traffic, here we use statistical metrics such as packet inter-arrival time or packet size, and we apply a machine learning for the identification. Through evaluations using real traffic, we show that our method can achieve over 90% of identification to 9 commiunication devices.

A flexible, scalable, and robust framework that enables fine-grained flow control under fixed or dynamic policies while addressing trustworthiness as a built-in network level functionality is a desirable goal of the future Internet. Furthermore, the level of trustworthiness may possibly be different from one network to another. It is also desirable to provide user-centric or service-centric routing capabilities to achieve service-oriented traffic controls as well as trust and policy management for security. Addressing these aspects, we present the SeRViTR (Secure and Resilient Virtual Trust Routing) framework. In particular, we discuss the goal and scope of SeRViTR, its implementation details, and a testbed that enables us to demonstrate SeRViTR. We have designed protocols and mechanisms for policy and trust management for SeRViTR and show a validation on the functional implementation of several SeRViTR components to illustrate virtual domains and trust level changes between virtual domains that are achieved under SeRViTR protocols. Going from implementation to testbed, we demonstrate SeRViTR in a virtual network provisioning infrastructure called the Geo-distributed Programmable Layer-2 Networking Environment (G-PLaNE) that connects three institutions spanning the US and Japan.

In recent years, social multimedia sharing services such as YouTube, which share User Generated Content (UGC) have become much attracted. An efficient control of UGC is one of important roles to achieve, e.g., an optimized placement of advertisements for end users, or content-aware caching control for improving the utilization of network resources. For this reason, it is effective to forecast the future popularity of the content as early as possible, so that we can take a proactive action to highly popular contents. In this paper, we propose a method to classify the popularity of UGCs in real time using K-means clustering, and analyze tendencies led by popularity patterns.We then propose a method to identify UGCs which are expected to be popular in future, by taking both the initial part of popularity patterns and actual counts of content retrieves into consideration. Our experimental results show that the accuracy of identification of popular UGCs can be increased around 10% by considering the initial part of popularity patterns.

For green networking, Sliced Router Architecture was proposed, which controls the power consumption of routers by adjusting the routers' performance on the basis of the volume of traffic. In this architecture, traffic prediction is used for appropriate power control of router. For obtaining the efficient gain of power reduction, we need to consider the impact of overestimation or underestimation. In this paper, we propose a traffic prediction method by considering the impact of overestimate and underestimate on power efficiency and processing performance of Sliced Router Architecture. We evaluate our method by trace-driven simulations with real traffic, we show that our approach can control the power consumption of Sliced Router without significant performance degradation.

International audience

New network architectures and routing technologies are being proposed to reflect the changes in the current trend of Internet communication that are becoming more and more resource-centric; the demand for the hierarchically structured resource in the network is emphasized, not the location of the resource. Therefore, routing by the resource rather than routing by the conventional Internet protocol address is more suitable for the future Internet with some attractive advantages such as reducing the burden of resolving identifier to location and achieving higher scalability by using a provider-independent addressing structure. In this paper, we propose resource name-based routing within the routers in the network layer since te ardware architecture should also be able to support the paradigm shift from host-centric to resource-centric communication. Through evaluating the required network architecture and memory size in routers, we show the feasibility of resource name-based routing by using fully qualified domain names (FQDN) as an example of describing structured information. Using approximately 700 million existing FQDNs, the evaluation result shows that resource name-based routing is feasible even when considering the limitations of ternary content addressable memory size in routers.

Information-Centric Networking (ICN) enables retrieval of content with various controls using flexibility of a content name, which is treated as a routing information. In existing works, however, data processings on end nodes or routers are mainly considered. Therefore, controls of physical behaviors, especially actual movement of devices, are not considered so much. we consider to incorporate physical movement of routers into routing control in ICN, and we propose a novel architecture of ICN that makes it possible to retrieve unretrievable contents between physically disjoint networks, by only specifying its name, which is realized by a Content-Centric Networking (CCN) router mounting on an Unmanned Air Vehicle (UAV). Moreover, we design and develop a Flying Router (FR), which combines a CCN router and a UAV, and we propose a detail design of communication architecture in which multiple FRs are able to process routing cooperatively among disjoint networks. In addition, we examine feasibility of the proposed architecture by carrying out a basic experiment using a prototype of an FR.

Packet classification has become increasingly complex and important to network equipment intended for future use. A recent trend to achieve complex packet classification is to use software-based methods, which tend to be slower than hardware-based methods. For search, this typically means using ternary content-addressable memory (TCAM) to make classification feasible. However, TCAM is not well-suited to the long (in bits) and sparse rules used for running advanced applications that require complicated classification. We propose a multi-dimension search engine (MDSE) that is optimized for use with long, sparse rules, and we propose a multi-dimensional TCAM scheme, which is an MDSE constructed to operate on TCAM. Through fine-grained simulations with real traffic, we show that our proposed search engine can reduce the power consumed by network equipment by about 85%.

Software-defined Networking (SDN) is a new network architecture that decouples the control plane from the data plane. Scalability of the control plane with respect to network size and update frequency is an important problem that has been addressed by previous studies from a variety of viewpoints. However, the solutions found in these studies may be only locally optimized solutions. To find a globally optimized solution, a broader viewpoint is required: one in which various SDN architectures can be evaluated and compared. In this paper, we propose an abstract model of SDN architectures, which enables multiple SDN architectures to be compared under a unified evaluation condition, and discuss the modeling of SDN architecture and its variations to find the optimal design from a global viewpoint. We first propose a generic model of SDN architectures and derive variations in terms of composition unit (single or multiple), processing principle (sequential or parallel), or location (intra- or inter-node). We then show that existing SDN architectures can be represented as one of the variations of our abstract model with fitted parameters. Finally we discuss how variation of components affects performance and show, using message-driven simulations, that our model enables comprehensive performance comparisons of different SDN designs represented as parameterized models.

We analyze subjective assessments by comparative evaluations of bulk data transmission by using two psychological methods (the method of successive categories and the constant method). From the results of the first experiment, the thresholds at which participants downloading a data file began to feel dissatisfaction with service degradation are 15.7Mbps and 11.6Mbps obtained by the two different methods when the throughput without the service degradation is approximately 22Mbps. In the second experiment, we investigate the threshold of user satisfaction for various network environments. The threshold is 63% of the throughput of the user's usual network environment. Moreover, from the viewpoint of download time, users feel dissatisfaction with the quality when download time becomes 1.5 times longer. These values can be used to more effectively allocate network resources and thereby achieve higher service quality.

SUMMARY Ternary Content Addressable Memory (TCAM) is a special type of memory used in routers to achieve high-speed packet forwarding and classification. Packet forwarding is done by referring to the rules written in the routing table, whereas packet classification is performed by referring to the rules in the Access Control List (ACL). TCAM uses more transistors than Random Access Memory (RAM), resulting in high power consumption and high production cost. Therefore, it is necessary to reduce the entries written in the TCAM to reduce the transistor count. In this paper, we propose a new TCAM architecture by using Range Matching Devices (RMD) integrated within the TCAM’s control logic with an optimized prefix expansion algorithm. The proposed method reduces the number of entries required to express ACL rules, especially when specifying port ranges. With less than 10 RMDs, the total number of lines required to write port ranges in the TCAM can be reduced to approximately 50%.

Recent studies have described the topologies of various networks including the Internet are categorized as scale-free networks. Scale-free network is extremely vulnerable to node attacks. However, the suitability of the topology of the Internet for communications has not been studied. We investigate whether the current Internet is optimized in both aspects of communication efficiency and attack tolerance. For this, we define three metrics to represent the capabilities of the network, which are Clustering coefficient, Efficiency, and Reachability. As a result, we found that the value of ?, a scaling exponent in power law function representing the degree distribution of a scale-free network, may be reduced in the present Internet. To reduce the value of ?, we propose four strategies for re-organizing a network. However, in real network, we cannot control the user's preference directly. We use a diffusion model based on social behavior dynamics. Furthermore, we show the characteristics of the re-organized networks, and discuss which strategy is more appropriate for achieving a desired network.

This article presents a report on APNOMS 2008, which was held October 22---24, 2008 in Beijing, China. The theme of APNOMS 2008 was "Challenges for Next Generation Network Operations and Service Management."

The end-to-end round trip time (RTT) is one of the most important communication characteristics for Internet applications. From the viewpoint of network operators, RTT may also become one of the important metrics to understand the network conditions. Given this background, we should know how a factor such as a network incident influences RTTs. It is obvious that two or more factors may interfere in the observed delay characteristics, because packet transmission delays in the Internet are strongly dependent on the time-variant condition of the network. In this paper, we propose a modeling method by using mixed distribution which enables us to express delay characteristic more accurately where two or more factors exist together. And, we also propose an inferring method of network behavior by decomposition of the mixed distribution based on modeling results. Furthermore, in experiments we investigate the influence caused by each network impact factor independently. Our proposed method can presume the events that occur in a network from the measurements of RTTs by using the decomposition of the mixed distribution.

Distributed denial-of-service attacks on public servers have recently become more serious. Most of them are SYN flood attacks, since the malicious attackers can easily exploit the TCP specification to generate traffic making public servers unavailable. We need a defense method which can protect legitimate traffic so that end users can connect the target servers during such attacks. In this paper, we propose a new framework, in which all of the TCP connections to the victim servers from a domain are maintained at the gateways of the domain (i.e., near the clients). We call the nodes maintaining the TCP connection defense nodes. The defense nodes check whether arriving packets are legitimate or not by maintaining the TCP connection. That is, the defense nodes delegate reply packets to the received connection request packets and identify the legitimate packets by checking whether the clients reply to the reply packets. Then, only identified traffic are relayed via overlay networks. As a result, by deploying the defense nodes at the gateways of a domain, the legitimate packets from the domain are relayed apart from other packets including attack packets and protected. Our simulation results show that our method can protect legitimate traffic from the domain deploying our method. We also describe the deployment scenario of our defense mechanism.

This article presents a report on APNOMS 2007, which was held October 10---12, 2007 in Sapporo, Japan. The theme of APNOMS 2007 was "Managing the Next Generation Networks and Services."

Distributed denial-of-service attacks on public servers have recently become more serious. The most effective way to prevent this type of traffic is to identify the attack nodes and detach (or block) attack nodes at their egress routers. However, existing traceback mechanisms are currently not widely used for several reasons, such as the necessity of replacement of many routers to support traceback capability, or difficulties in distinguishing between attacks and legitimate traffic. In this paper, we propose a new scheme that enables a traceback from a victim to the attack nodes. More specifically, we identify the egress routers that attack nodes are connecting to by estimating the traffic matrix between arbitral source-destination edge pairs. By monitoring the traffic variations obtained by the traffic matrix, we identify the edge routers that are forwarding the attack traffic, which have a sharp traffic increase to the victim. We also evaluate the effectiveness of our proposed scheme through simulation, and show that our method can identify attack sources accurately.

In recent years, high-performance routers are needed as well as increasing the link capacity to keep up with the Internet traffic explosion. Routers are preferable to store statistics of the incoming/outgoing traffic to realize the intelligent or policy-based services in the Internet. In this paper, we propose a new packet classification solution, which uses the combination of CAM and DRAM memories. Unlike other algorithms, we do not search the classifier by selecting the front bits, but select bits arbitrary from the classifier as the search key in the CAM. For this purpose, we propose new bit selection methods. Through simulation results, we show that our bit selection methods can decrease the delay of classification.

Web services are a unified framework that enables the exchange of information among distributed applications using existing World Wide Web communications technologies. Web services offer the benefit of simplifying the sharing of information among different applications that are operated via remote terminals. However, when multiple services exist that provide the same functionality, a mechanism for automatically selecting the most appropriate service from those on offer is necessary. Unfortunately, the majority of automatic selection mechanisms are designed to use a fixed set of indicators as the selection criteria and are therefore unable to adapt to the diverse demands of users. In this paper we propose a new mechanism for automatically performing such service selection that integrates a wide range of different indicators. Specifically, we assign attributes to each of the services that can be used as evaluation criteria; we then gather these attributes at the user's client. We can compute a rating for each service on the basis of the attributes that have been collected and select the service that is assigned the highest rating. In this paper we introduce the concept of an attribute database (an attribute repository) and define a new communications protocol to enable the registration and searching of attributes in order to deal with variation in the attributes used by different services. In addition, using a simulation of a distributed applications environment, we perform evaluations and demonstrate that the proposed selection mechanism is able to reduce execution times as compared to a random selection mechanism. We also implement the proposed mechanism in an experimental environment and show that it can be used in practice for automatically selecting services on the basis of attribute values. © 2007 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 90(8): 40–49, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecja.20284 Copyright © 2004 Wiley Periodicals, Inc.Copyright © 2007 Wiley Periodicals, Inc.

In the preceding works on the tree networks composed of binary symmetric channel (BSC), the path diversity effects are discussed in [8]–[10]. In [9], it is shown that the error probability of majority decision at the fusion center is almost constant regardless of network size. This constant property of error probability has not been discussed theoretically. In this paper, a new approach of signal-to-noise ratio (SNR) is proposed for the theoretical explanation on the constant property. A random tree network is presented to model a network with a large number of nodes randomly deployed over a field. The error probability of random tree network is analyzed for data fusion of end-nodes by SNR, which is shown to be useful to explain the constant property. Based on the analytical expressions, the effects of system parameters on error probability of random tree network are demonstrated.

Signal-to-noise ratio (SNR) is an important parameter of channel quality. The performance of adaptive communications in time-varying channels is affected significantly by the SNR estimation accuracy. In this paper, the new methods of SNR estimation for PSK and QAM are proposed. The methods are based on maximum likelihood estimation (MLE) using the probability density function (pdf) of received signal envelope, and are used in unknown signal power conditions without the carrier synchronization. The proposed methods are shown to estimate SNR efficiently by numerical results of normalized mean square error (NMSE) of SNR.

The identification of network application is important for network management such as traffic controls and anomaly traffic detection. To apply application-based traffic control promptly, the technique to progressive application identification is important. We previously proposed a method of timeliness identification of application, which can achieve to use first several packets of flow to identify applications with high accuracy whatever the traffic is encrypted or unencrypted. However, our previous method still has a challenge in terms of timeliness because it uses a single classifier with same traffic features to identify all applications, though some traffic features can be used earlier. In this paper, we propose a method of multistage application identification, which classifies progressively applications into groups with different granularity of groups. We first demonstrate that the accuracy of existing application identification is getting worse significantly, by increasing the number of application to identify. Second, propose a method of multistage identification method, and then explain group classification algorithm to classify applications into groups. Our experiment shows that our proposed method can identify applications earlier than a method of identification using a single classifier.