The resolution of network intents to network service definitions is a key operation of intent-based networking. In this paper, we study the resolution of multi-domain network intents and propose EDAIR, an efficient distributed architecture that greatly improves accuracy of network intent resolution with little impact on performance. While previous state-of-the-art systems for multi-domain intent resolution consist on the sequential interrogation of all domains involved in the intent and the aggregation of their answers to construct a final network service definition, EDAIR defines an intelligent agent to represent each domain and constructs a distributed multi-agent system based on artificial intelligence. Only the required agents will be involved in intent resolution and the process stops as soon as a proper solution is achieved. We evaluated EDAIR to show that it is 48% more accurate and only increases the time needed to resolve intents by 38%, denoting its efficiency in the distributed operation.

Network virtualization offers enormous flexibility to construct network services tailored to the needs of network tenants and users. Network services can be implemented through composition of virtual network functions, which can be deployed in multiple sites, forming multi-site network services. However, network virtualization further induces the increasing complexity of the network, which, in turn, increases the difficulty of error avoidance in management operations. Thus, network management must be automated to tackle such complexity and eliminate errors. In this paper, we propose the intelligent network service automation system (INSA), and we demonstrate its high efficiency and accuracy in automated network service construction and management. INSA is designed to transparently orchestrate all elements of the management system using a control plane based on publish/subscribe messaging, known as a management service bus. This enables INSA to make highly accurate decisions using vast telemetry information without overloading the control plane. Additionally, INSA integrates key enablers such as efficient and scalable control plane networking, telemetry compression, distributed processing, hidden fault detection, and autonomic resource control. We evaluated INSA both theoretically and through a prototype implementation. The former proves our claims regarding the efficiency improvement provided by compression and distribution. The latter showcases INSA management accuracy and performance, and it also demonstrates how INSA manages multi-site network services. The evaluation results show that, in comparison with the state-of-the-art alternatives, INSA uses 15–36% less CPU time, possesses 80% higher scalability, and improves accuracy by 33%.

Network virtualization has become a central aspect of 6G networks, which enable the composition of virtual network functions (VNFs), which can be instantiated at single or multiple sites to create highly flexible virtual network services. As a result of the increased complexity imposed on network components, network service automation systems (NSASs) are expected to become key technology enablers for 6G, whose implementation requirements on the control plane that are not met by current approaches based on a central message broker. We propose to meet these requirements by extending NSAS with an efficient and scalable control plane network based on information-centric networking (ICN), named iCPN, which uses names instead of addresses for information transmission and exploits in-network cache. We evaluated an iCPN prototype, demonstrating that it uses 50% less bandwidth and 15-36% less computation time than common alternatives.

Workload prediction is the key to improving the quality of service (QoS) of various user applications running in autonomous large-scale networks. Machine learning (ML) technologies contribute to workload prediction because they provide fully automated data analytics. However, no ML algorithm can simultaneously provide perfect, timely, and generalized solutions at the same time. In this paper, we propose an adaptive optimized ML algorithm selection and installation (OMASI) method for workload prediction and predictive autoscaling to maintain the QoS of user applications. With OMASI, a central repository server collects information about the analytical objectives and optimization preferences of multiple distributively deployed ML engines and provides them with the best-suited ML algorithms for their data-analytics. We implemented and demonstrated OMASI on an experimental in-network computing testbed and evaluated the workload prediction accuracy of several available ML algorithms in terms of content downloading latency and throughput. The results show that OMASI can maintain the QoS of user applications, such as latency and throughput, by reducing the CPU usage prediction error by up to 86%.

A network digital twin (NDT) is a virtual object that mirrors a network system (NS) so that a control and management system (CMS) may identify present and potential failures using artificial intelligence methods, resulting in enhanced NS resilience. In this study, we propose a method of enhancing an NDT by generating and transmitting telemetry knowledge objects (TKOs) instead of raw monitoring data. TKOs enforce data correction, provide rich information, and are inherently adaptable. We also apply the telemetry knowledge distributed processing (TKDP) approach to the CMS so that each NS element processes its own measurements to generate and transmit TKOs, thus avoiding the transmission of meaningless, wrongly formed, or incomplete data. Moreover, distributing the processing capacity allows the target NDTs to reflect more complex NS states than when using centralized processing. To maximize the impact of TKOs and TKDPs, we followed the current standardization specifications for network telemetry and NDT construction. Finally, we demonstrated the benefits of TKOs and TKDPs by evaluating the fidelity of an NDT that matches a typical scenario. We showed that the resultant NDT fidelity is much higher when using TKO and TKDP than when relying on current alternatives.

Network function virtualization (NFV) technology enables the deployment of a distinct network slice composed of various virtualized network functions (VNFs) required for offering a communication service. As the user population or network traffic volume fluctuates, the capacity of the network slices must be dynamically adjusted. It is necessary to decide whether to place VNFs in appropriate locations such that the quality of services (QoS) is guaranteed while optimally utilizing the allocated resources. The above challenge can be addressed by solving an optimization problem, such as integer linear programming. However, as solving the optimization problem requires a considerable amount of time, it is not applicable in agile network control mechanisms. In this paper, we propose a network function placement algorithm based on salience, which is one of the characteristic metrics of links in a graph. Our proposal is aimed to provide the adequate placement in short time to achieve the following objectives of keeping the slice reconstruction cost low and avoiding the cases of QoS degradation. The simulation results show that our scheme performs equally well for different sized slices (i.e., the user population). We found that the network reconfiguration costs are low and almost the same for various sized slices when they are reconstructed by the proposed method in a substrate network of 200 nodes. Furthermore, we confirmed that the proposed method keeps the slice reconstruction cost low and avoids blocking of slice reconstruction even in the situations when a resource competition occurs among multiple slices.

Service function chaining (SFC) provides the plat-form for flexible resource management by dynamically allocating resources to virtual and/or container network functions (VNFs/CNFs). To meet the quality of service (QoS) requirements while facing increasing resource demands, the sys-tem will require the migration of the VNFs/CNFs from the current server to the others that offer sufficient resources. In this study, we formulate an integer linear programming (ILP) based optimization model to solve the function migration scheduling problem so that it meets QoS requirements of each service function (SF) chain. The remarkable points of this work are the following two points. The one is that we consider latency between VNFs/CNFs belonging to an SF chain, avoiding overhead due to their unnecessary migration and resource shortage. And the other is that we consider the case in which each VNF/CNF must be to be deployed strictly to a designated virtual machine (or container). To reduce complexity, we apply an encoder-decoder recurrent neural network (ED-RNN) as a machine learning model to the function migration scheduling problem. Performance evaluations show that the ED-RNN based approach achieves a similar performance as the ILP, while adding the benefits of very low complexity.

In this paper we propose a mechanism to automate the management of large networks based on the autonomic network management scheme. It incorporates a novel embedding algorithm that is 3 to 6 times faster than related work in finding out optimal embedding a virtual network (VN) or network slice (NS) to a substrate network (SN). It therefore finds out a configuration that assigns the elements of the VN/NS to the elements of the SN. The main novelty of this algorithm is the application of the knowledge from the VN/NS to get a configuration by improving a previous configuration, so less computing power is required than related work. The reasoning process is designed to consider the traffic of the network together with node load, so the resulting configuration optimizes the overall usage of the SN. Finally, the algorithm and heuristic also target the minimization of the number of operations required to obtain a new configuration from the current configuration, thus avoid the addition, removal, or movement of elements as much as possible. We demonstrate these claims by evaluating a proof-of-concept implementation of our solution and comparing it with the previous solutions.

Network function virtualization (NFV) technology enables us to deploy a distinct network slice composed of various virtualized network functions (VNFs) required for a given service, such as video on demand (VoD) and Internet-of-things (IoT). As a result, application service providers (ASPs) can easily deploy and provide their services to end users (EUs) by leasing resources formed as network slices. However, as the user population or the network traffic volume fluctuates, the capacity of network slices has to be adjusted dynamically. It requires to take decision about placing NFs in appropriate locations so that the quality of services (QoS), such as transmission or processing latency is guaranteed while optimally utilizing the reserved resources. One can address the above challenge by solving an optimization problem such as integer linear programming. However, the optimization problem takes much time to solve. In this paper, we propose a network function placement algorithm based on salience, which is one of characteristic metrics of links in graphs. Our objectives are two folds: keeping the slice reconstruction cost low, and avoiding the cases of QoS degradation. The simulation results show that our scheme performs equally well for different sizes of slices (i.e., the number of EUs). When slices are reconstructed with our scheme in a 200-node network, we found that the network reconfiguration costs are low and almost the same value for various sizes of slices.

Network function virtualization (NFV) enables network operators to flexibly provide diverse virtualized functions for services such as Internet of things (IoT) and mobile applications. NFV platforms are required to offer stable and guaranteed quality-of-service (QoS)even during dynamically changing resource demands and traffic volumes. To meet the QoS requirements against time-varying network environments, infrastructure providers must dynamically adjust the amount of computational resources, such as CPU, assigned to virtual network functions (VNFs). To provide agile resource control and adaptiveness, predicting the virtual server load via machine learning technologies is an effective approach for proactive control. In this paper, we propose a traffic prediction framework based on ensemble learning, comprising weak regressors trained by ML models, such as recurrent neural networks (RNNs), random forest, and elastic net. It was observed that the prediction error tends to worsen with time because the gap of trends between the past and future traffics becomes wider. Therefore, to reduce the prediction errors, we propose an adjustment mechanism for regressors based on forgetting and dynamic ensemble. The evaluation result with real traffic data verified that the resource adjustment scheme based on the proposed traffic prediction framework keeps the frequency of over and under provisioning low, which is lesser by 45% in comparison to RNNs and autoregressive moving average (ARMA).

Network softwarization is a transformation trend for networks to converge in programmable service platforms able to host different requirements from multiple tenants. The concept of network slicing plays a key role in this trend. It offers isolated logical networks to different tenants over the same underlying infrastructure. Network slices hosting missioncritical services require fast reactions to changes in environment, adapting their capacity as needed to avoid service disruption and performance degradation. This represents a key challenge for the management plane, traditionally under the direct operation of human administrators, unable to provide a nimble enough response rate. To support tenants in driving lifecycle management operations over their network slices in a timely manner, datadriven, closed-loop mechanisms must be designed. In this paper we introduce a solution that takes advantage of events occurring outside the boundaries of a tenant-managed network slice to guide those adaptations. We finally demonstrate the feasibility of the proposal and describe an experimentation scenario to exploit such results.

IoT remote control is faced with scalability, secure communication and privacy preservation issues and conventional solutions (HTTPS) have disclosed poor scaling problem and privacy concerns. In this paper, we propose a novel lightweight and secure IoT remote monitoring mechanism using DNS with privacy preservation. Basically, the communication between IoT devices and gateways uses the conventional protocols as usual such as CoAP and MQTT while only the remote monitoring uses DNS protocol. That is, encrypted IoT data, after being encoded with base64, is stored as a DNS TXT record of the domain name of the IoT device and only the designated users are allowed to query and decrypt the data based on TSIG authentication of DNS protocol and asymmetric cryptography. We implemented a prototype system over name-bound virtual networks (NBVNs) in which all virtual nodes are registered in DNS automatically and the network traffic is restricted within each NBVN. Through the preliminary evaluations we confirmed the effectiveness of secure communication and privacy preservation in IoT remote monitoring in the proposed mechanism.

Service function chaining (SFC) enables network operators to flexibly provide diverse services such as Internet-of-Things (IoT) and mobile applications. SFC technologies are required to offer stable and guaranteed quality-of-service (QoS) even in the circumstances of dynamically-changing resource demands and traffic volumes. To meet QoS requirements against time-varying network environment, infrastructure providers need to dynamically adjust the amount of computational resources such as CPU assigned to virtual network functions (VNFs) in each service function chain. If the increased resource demand of a VNF cannot be satisfied from the available resources in the current node, the VNF should be migrated to another node. Related work has been tackling SFC embedding and scheduling issues by applying various techniques such as game theory, integer linear programming and heuristic methods. However, they have limitations that they cannot provide an agile operation of VNF migration as they require a large number of iterations. In order to overcome the limitations, we propose to utilize an encoder-decoder recurrent neural network and train it to solve the VNF migration scheduling problem. Through the simulation, we verify that the proposed method can agilely determine VNF redeployment locations by keeping the frequency of server overload and VNF migration minimum.

Software defined networking (SDN) and network function virtualization (NFV) technologies enable us to create, manage and operate network services more flexibly. Distributed placement of controllers reduces the response time of control operations and distributes load of controllers. Many prior studies have investigated several algorithms of controller placement in networks. However, those algorithms take longer computation time and require a lot of information. Thus, they are difficult to use, especially in large networks. To simplify the controller placement problem, we propose a salience-based approach, where the salience value of a link is defined as the number of shortest path trees that traverse the link. We found that by placing controller functions on the nodes having many high-salient links, the controller placement approach shortens the length of control paths and keeps controllers' load low even in large networks.

In the era of 5G communication, various applications require network resources such as CPU, storage, and bandwidth in different amounts for executing different functions. 5G networks will Ue consist of virtual network slices, each slice containing a series of virtual network functions (VNF) placed in virtualized network nodes. There is a challenge of quickly provisioning bandwidth and computation resources for VNF to optimally satisfy their requirements [1]. Thus, VNF placement [2] has been studied as a service function chaining (SFC) problem to fmd out a set of nodes in the substrate network that satisfy the resource requirements and ensure minimum end to end communication latency of the path passing through the nodes.

The Autonomic Resource Control Architecture (ARCA) we have designed in previous work is intended to provide elastic resource adaptation in virtual computer and network systems by automating management tasks in edge/branch virtual networks. Our objective is to reduce the time required to continuously estimate or anticipate the amount of resources that a system requires by considering its workload together with event data notified by external detectors. However, analyzing such amount of data can take a long time and thus delay management decisions and system adaptation. Moreover, too frequent changes in the amount of allocated resources can hassle the underlying controllers. In this paper we propose a method to resolve these problems by filtering input data items to reduce their rate while smoothing non-persistent spikes to reduce the volatility of resource allocations. We evaluate it by running an algorithmic model with a set of input data following a Pareto distribution, analyzing the accuracy of different variations of the proposed method. We found that the accuracy of the resulting allocations is improved by 13% to 30%, depending on the specific configuration of the proposed method.

Service function (SF) chaining enables network operators and infrastructure providers to flexibly orchestrate virtualized network functions (NFs) at various places on software defined virtual networks. Meanwhile, with the rapid prevalence of Internet-of-Things (IoT) applications and mobile network services, quality-of-service (QoS) requirements are getting increasingly diverse and network traffic volume and pattern are varying rapidly. The time-varying network traffic requires the infrastructure provider to dynamically adjust the appropriate amount of computational resources (e.g. CPU) assigned to an NF to efficiently process the traffic in the SF chaining infrastructure. In this paper, to adequately allocate CPU resources (available in limited amount) to virtual networks (VNs) requiring to satisfy diverse QoS levels in response to traffic variation without manual operations, we propose autonomic arbitration of CPU resources along SF chains which combines two CPU resource adjustment methods: The first method performs resource arbitration among VNs within a node, and the second method migrates NFs among nodes within a VN. These methods autonomically adjust the amount of CPU resource allocated to each NF so that over utilization of CPU can be avoided as much as possible. Moreover, during the autonomic resource adjustments in SF chains, the proposed methods are able to maintain communication paths on the substrate network so that the service is not interrupted at all. We also propose differentiated resource allocation for QoS differentiation and transferable resource searching. Through computer simulation, we verify that our autonomic resource adjustment methods can reduce the occurrence of CPU-saturation by more than 90%.

Vehicles are considered to constitute a large portion of new devices getting connected to 5G or beyond networks. Connected vehicles should be enabled to communicate with each other and roadside infrastructure securely, reliably, and in ultra-low latency. Vehicles should be able to discover and identify other vehicles in their surrounding very quickly. For this purpose, we propose a directory service that can store a huge number of vehicle's records and provide the records to querying vehicles in a very short latency (mostly less than 10 milliseconds). Similarly, it can dynamically update the records within a few seconds, while fully complying with record owner-centric privacy policy. The preliminary experiment results obtained from a simple testbed implementation with computers demonstrate the feasibility of the proposed system to meet the target lookup and update performance of connected vehicles.

Current network service requirements are increasing the pressure on the level of flexibility and reliability that must be provided by the virtual computer and network systems that host them while enforcing close-to-optimum resource allocations to minimize both monetary and operational costs. A key requirement is the fast detection and adaptation to workload changes, which is beyond human abilities, so the operation must be automated. To do so we propose to use an autonomic resource control architecture. In this paper, we formalize the architecture and discuss how it is able to detect the CPU load changes of a networked service by analyzing heterogeneous observations, including both resource load measurements and external events, and adapt (increase or decrease) the allocated resources consequently. The key benefit of our proposal is that, in most situations, including external information into the analysis, such as seismometer measurements, allows the control architecture to detect new situations earlier than analyzing only load measurements. We demonstrate such claims with the results obtained from the execution of a proof-of-concept implementation of the architecture we propose on an emergency scenario.

A Service Function Chain (SFC) is an ordered Network Function (NF) chain to process flows or packets for the end-to-end delivery of network services. In the context of Network Function Virtualization (NFV) and Software-Defined Networking, which are promising technologies for next generation networks, the Virtualized Network Function (VNF) can be deployed on either generic physical machines or virtual machines. A challenging problem is to determine where and how to place these VNFs of an SFC request in the network. In this paper, we first formulate this VNFs placement problem as an Integer Linear Programing (ILP) model and then propose an enhanced VNF placing scheme based on layered graphs to achieve better reliability. To improve the reliability, our scheme avoids placing more than one VNF of an SFC on the same node to protect the SFC from a single point of failure. We have conducted a numerical analysis and computer simulation for the feasibility validation of our scheme. The performance results, in terms of end to end delay of SFC and computation time cost on different topologies, show that our scheme performs well in different scenarios.

In this paper, we propose a mechanism for autoconfiguration of name-bound virtual networks (NBVNs) for Internet of Things (IoT). Some IoT standardization groups have defined APIs for IoT device communications, in which they indicate the correspondent nodes and resources by names. However, current technologies for the construction of Virtual Networks (VNs) rely on VLANs, IP routing, and OpenFlow control, thus they do not provide a name-based solution. Our proposal fills this gap. We first define business players that construct and use the NBVNs. They are Application Service Provider (ASP), Virtual Network Operator (VNO), and Infrastructure Provider (InP). We then specify the roles of ASP/VNO/InP, including the tasks that must be performed by their managers, and the required interactions for the purpose of autoconfiguration. Subsequently, we describe the system we developed to implement the operations of ASP/VNO/InP and thus construct NBVNs automatically. For each NBVN, the required IPv6 addresses are automatically allocated and assigned to the network nodes, including the IoT devices. Moreover, data forwarding and name resolution mechanisms are also configured automatically. Thus, the proposed system constructs area-and/or time-bound VNs for offering network services to event-centric IoT applications, such as outdoor concerts and sporting events. Finally, we demonstrate that the constructed NBVNs are capable to configure thousands of addresses and name entries within a minute, thus IoT devices can communicate among them by using their correspondent node names instead of their addresses. Moreover, both ASP and VNO can operate the servers, switches, and routers pertaining to the NBVNs by also using their names.

To enable billions of devices to communicate with each other simultaneously, small end-to-end latency and high scalability and reliability are the essential characteristics for the Internet of Things (IoT). The transition from today's Internet to IoT has demanded a new information and communications infrastructure. In this paper we implement and deploy a scalable and reliable inter-domain infrastructure, LORIF proposed in our previous work, to meet the requirements of IoT. Based on two local network testbeds and an intercontinental network testbed, we evaluate the scalability, resilience, and mobility of LORIF. The results demonstrate that LORIF can provide low end-to-end latency and high scalability and reliability.

Network virtualization techniques enable network operators to implement and provide multiple virtual networks (VNs) on a common substrate network infrastructure. The network resources should be able to be dynamically reallocated among VNs or migrated from a place to another one in various situations, such as to construct a new urgent VN in case of occurrence of some urgent contingency, or to satisfy quality of service (QoS) requirements of non-urgent VNs in case of time-varying network traffic conditions as much as possible. In this paper, we propose methods to automatically and dynamically select and migrate resources of non-urgent VNs. In particular, our proposal applies reinforcement learning for the selection of resources from alternate places to satisfy the QoS requirements. We evaluate the performances in terms of the satisfaction level of QoS requirement with various frequency of network traffic variation in a given duration and learning parameter configurations. Simulation results show that the proposed dynamic resource migration method can increase the number of times that non-urgent VNs' QoS requirements are satisfied in comparison with a static resource assignment method. Moreover, we show that, depending on traffic variation and parameter configuration, applying reinforcement learning can increase the number of times the QoS requirement is satisfied compared to the dynamic method with completely random resource selection.

The raise of Software Defined Networking (SDN) opens a wide spectrum of improvements for network architectures targeting different problems. It includes the case of those architectures designed to overcome mobility and multi-homing problems in big networks, such as the Internet, as is the case of the HIMALIS network architecture. In this paper we present an approach for HIMALIS to benefit from SDN technologies by changing the way it addresses its control operations. We propose to leverage control operations of the HIMALIS architecture through an identity-based control plane that has been designed following the principles of SDN. Thus, our approach proposes to integrate the main elements of HIMALIS with a controller, which provides enormous benefits to the architecture in general, such as the possibility to perform control operations with global knowledge of the network status and the evolution of the functions provided by the control plane without changing the implementation of the end nodes. Finally, in order to demonstrate those benefits, we have built architectonic models for both HIMALIS and our proposal, executed different simulations with them running on different mobility scenarios, and extracted the results that show clear improvement of our proposal.

Conventional cellular mobile networks are likely to get disturbed easily when a natural disaster (earthquake, tsunami, flooding, etc.) hits an area because of their centralized control and high-power requirements. To address this issue, we propose a new mobile network architecture having distributed controls and ad-hoc configurations. This architecture exploits the locally available resources such as frequency spectrum and lively nodes to reconfigure the network and provide emergency services. We present its design and use cases.

Introduction of ID/locator split into the future Internet architecture can make distributed and dynamic mobility management functions simpler and native. Unlike conventional IP mobility solutions, the new approach can offer smooth mobility across heterogeneous network-layer protocols (e.g. IPv4, IPv6) and other benefits. This paper first gives an overview of new features that ID/locator split-based architecture can bring about besides overcoming the limitations of conventional IP mobility protocols and then discusses the architecture, implementation, and performance of HIMALIS as an example of ID/locator split-based architecture. The performance results validate that HIMALIS has end-to-end throughputs and latencies similar to those of the conventional TCP/IP network. Moreover, the results verify that HIMALIS is capable to provide seamless handover across heterogeneous network-layer protocols.

For the sake of better scalability and flexibility in the mobile and multihoming environments, future networks are expected to be based on the concept of ID/locator split. The ID/locator split architectures require storing, updating and retrieving of ID/locator mappings frequently, for which they need built-in security. To address this issue, this paper presents an integrated security scheme for securely storing, updating and retrieving hostnames to IDs and locators mapping records in two layers of name registries: domain name registries and host name registries. It then utilizes the mapping records retrieved from the registries for securing the network access, communication sessions, and mobility management functions. The scheme provides comprehensive protection of the ID/locator split architecture through an effective combination of asymmetric and symmetric cryptographic functions.

The ID/locator split-based approach has been widely recognized as a promising approach for the design of future networks. However, the existing ID/locator split architectures are still vulnerable to various attacks, such as impersonation attacks and man-in-the-middle attacks. They cannot be simply protected by the existing security mechanisms, which have the limitations especially on scalability. To solve these problems, we propose a proactive scheme for securing ID/locator split architecture, which embeds built-in security features to enable proactive protections of the architecture. Through this scheme, hosts register their information to the network securely, obtain trustworthy information of destination hosts, authenticate each other, and securely update their locators without requiring an involvement of a trusted third party (TTP). Compared to other existing security mechanisms, the proposed scheme does not require additional authentication mechanism and it can provide the thorough protections of the whole architecture.

Mobility is not natively supported in the current Internet because it uses IP addresses as host or session identifiers in the transport and application layers and as locators in the network layer. In the mobile networking environment, a host should be able to change its locator when it moves from one network to another, while keeping the host or session ID fixed. Thus, to provide inherent mobility support in the future Internet, which we call the New Generation Network, we propose HIMALIS (Heterogeneity Inclusion and Mobility Adaptation through Locator ID Separation) architecture. HIMALIS uses separate namespaces for host IDs and locators. It includes a new naming scheme for generating host names and IDs. It uses a logical control network to store and distribute bindings among host names, IDs and locators for supporting mobility. We verify the basic functions of the architecture by implementing and testing it in a testbed system.

In spite of recent advances of personal communication devices and access network technology, users are still facing the issues such as high device maintenance costs, complication of inter-device cooperation, illegal access to devices, and leakage of personal information. Consequently, it is difficult for users to securely construct a network with local as well as remote personal devices. We propose a User-driven Service Creation Platform (USCP), which enables users to construct a secure private network in a simple and intuitive way, making the most of the authentication mechanism in cellular networks. USCP separates signaling and data paths in a flat structure of a virtual network topology. In this paper, we describe the basic design of USCP.

We start systematical analysis on the performance of managed mesh network (MMN), which provides communication function in the concept of community service platform in a local region. Towards the performance evaluation framework for MMN, we herein analyze the capacity provision of one base station (BS) under the constraints of physical transmission rate and available spectrum using queueing theory. In the related work of capacity analysis on mesh network, most of them have not put effort on the determination of exact capacity, or they investigated backhaul mesh network, which is different from MMN. Some other researchers investigated the architecture similar to MMN, but they have not investigated actual traffic processed in the transmission queue. Our modeling solves these problems and determines the exact capacity provided by each BS in an MMN. At the same time, we perform extensive simulations to verify the correctness of our modeling under a set of scenarios with different main parameters. After model verification, we apply our modeling results to a city and show how the capacity of one user at peak time changes with the deployment cost.

This paper identifies communication system functions and technologies that a local region, individuals living in that region, and various communities to which each individual belongs require and propose a community communication service platform for providing those functions and technologies. To provide a variety of services suited to the characteristics of a region or community, the local region itself or individuals, communities, or organizations that are not the conventional telecommunications carriers should be able to deploy, operate, and manage the communications network within that region and should be able to easily construct and provide various services on it. The proposed system, which is based on open specifications, establishes logical paths with a many-to-many mesh topology on a physical wireless mesh network to implement multiple services each of which uses the paths based on its own policy.

Abstract The advent of advanced mobile/wireless systems has been facilitating the battery‐powered mobile computing devices (nodes) to remain always connected to the internet. However, until now, the power‐drain rate of mobile nodes is very high in comparison with the available power of portable batteries. To reduce the energy consumption of mobile nodes, we present an integrated IP paging protocol (IIPP) by integrating the IP‐layer paging protocol based on Mobile IPv4 regional registration (MIPRR) with a power save mechanism. IIPP reduces the frequency of signaling messages between mobile nodes and networks. When not sending or receiving data for a certain time, mobile nodes enter power save mode (PSM), and consume very low power. We formulate analytical models and carry out simulations to evaluate the proposed IIPP. The results show that, compared to MIPRR, IIPP significantly reduces the average power consumption of the mobile node and signaling overheads in the network. Copyright © 2006 John Wiley & Sons, Ltd.

We present a new scheme for fast binding update with correspondent nodes to expedite the handover operation of mobile nodes in Mobile IPv6 protocol supporting networks. Our scheme is an extension of the Mobile IPv6 correspondent registration process that modifies some messages exchanged between a mobile node and a correspondent node. This scheme reduces the handover latency while providing the same level of security assurances for revealing the location information to the correspondent node as in the current Mobile IPv6 protocol. We also present an extension of the scheme to multihomed mobile nodes that have heterogeneous interfaces.

We present a graceful vertical handover scheme that provides graceful performance degradation during an upward vertical handover in heterogeneous overlay networks. This scheme harmonizes the functions performed by different layers, i.e., from the link layer to the application layer, and consequently smoothens the change in the throughput and reduces data losses in the network during a handover. By implementing the scheme in the ns-2 simulator, we present the results of the TCP performance with the graceful handover, which is significantly better than the performance without one

We evaluate the performance of IP paging with power save mechanism by formulating an analytical model and carrying out simulation study of integrated IP paging protocol (HPP) that integrates both the paging and power save functionality in IP layer. The results show that, compared to the mobile IP regional registration, the UPP significantly reduces the average power consumption of a mobile node and the signaling load in the access networks, while providing link layer independent mobility and power management functions.

Recently, IEEE 802.11 wireless networks have been widely deployed in public areas for mobile Internet services. In the public wireless LAN systems, paging function is necessary to support various advanced services (e.g. voice over IP and message applications) and to provide efficient power management scheme. In next-generation mobile networks, the so-called all-IP networks, the paging function will be supported in the IP layer (i.e. IP paging). When IP paging protocol is deployed in IEEE 802.11 wireless networks, it may utilize the power saving mechanism supported in the IEEE 802.11 standard for more efficient power management. However, since the current power saving mechanism is based on a periodical wake-up mechanism with a fixed interval, it is difficult to optimize the power saving performance. In this paper, we analyze the performance of IP paging protocol over IEEE 802.11 power saving mode (PSM). We define a wake-up cost and a paging delay cost. Then, we study the effect of varying the length of the wake-up interval and the session arrival rate. In addition, we analyze the distribution of the session blocking probability due to the coarse-grained wake-up interval. Also, we investigate the optimal wake-up interval to minimize the total cost through simulations. These results indicate that it is necessary to find the optimal wake-up interval in order to minimize the total cost while satisfying the given paging delay constraints.