Federated learning has attracted much research attention due to its privacy protection in distributed machine learning. However, existing work of federated learning mainly focuses on Convolutional Neural Network (CNN), which cannot efficiently handle graph data that are popular in many applications. Graph Convolutional Network (GCN) has been proposed as one of the most promising techniques for graph learning, but its federated setting has been seldom explored. In this article, we propose FedGraph for federated graph learning among multiple computing clients, each of which holds a subgraph. FedGraph provides strong graph learning capability across clients by addressing two unique challenges. First, traditional GCN training needs feature data sharing among clients, leading to risk of privacy leakage. FedGraph solves this issue using a novel cross-client convolution operation. The second challenge is high GCN training overhead incurred by large graph size. We propose an intelligent graph sampling algorithm based on deep reinforcement learning, which can automatically converge to the optimal sampling policies that balance training speed and accuracy. We implement FedGraph based on PyTorch and deploy it on a testbed for performance evaluation. The experimental results of four popular datasets demonstrate that FedGraph significantly outperforms existing work by enabling faster convergence to higher accuracy.

Payment channel networks (PCN) have demonstrated its significant advantages in improving the scalability of blockchain. However, the existing work of PCN leads to serious privacy leakage problem that intermediate nodes along a payment path can collude to obtain the payment amounts and payment receivers. To address this problem, we propose to move PCN-related modules into the Trusted Execution Environment (TEE) commonly available on modern CPUs, so that adversaries cannot access the critical payment information protected by TEE, even though they compromise the software (e.g., blockchain clients or operating system) outside of TEE. An additional challenge is that adversaries can still infer payment receivers by observing the pattern of message transmissions among nodes. To hide payment receivers, we further propose to send redundant transactions to pseudo receivers to confuse adversaries. A fast algorithm with provable approximation ratio has been proposed to maximize the level of privacy protection under the constraint of communication overhead. Both experiments on a small-scale testbed and large-scale simulations are conducted to evaluate our proposal. The results show that our proposed solution outperforms existing work significantly.

Off-blockchain payment channels can significantly improve blockchain scalability by enabling a large number of micro-payments between two blockchain nodes, without committing every single payment to the blockchain. Multiple payment channels form a payment network, so that two nodes without direct channel connection can still make payments. A critical challenge in payment network construction is to decide how many funds should be deposited into payment channels as initial balances, which seriously influences the performance of payment networks, but has been seldom studied by existing work. In this paper, we address this challenge by designing PnP, a balance planning service for payment networks. Given estimated payment demands among nodes, PnP can decide channel balances to satisfy these demands with a high probability. It does not rely on any trusted third-parties, and can provide strong protection from malicious attacks with low overhead. It obtains these benefits with two novel designs, the cryptographic sortition and the chance-constrained balance planning algorithm. Experimental results on a testbed of 30 nodes show that PnP can enable 30% more payments than other designs.

In the past decade, network data communication has experienced a rapid growth, which has led to explosive congestion in heterogeneous networks. Moreover, the emerging industrial applications, such as automatic driving put forward higher requirements on both networks and devices. On the contrary, running computation-intensive industrial applications locally are constrained by the limited resources of devices. Correspondingly, fog computing has recently emerged to reduce the congestion of content-centric networks. It has proven to be a good way in industry and traffic for reducing network delay and processing time. In addition, device-to-device offloading is viewed as a promising paradigm to transmit network data in mobile environment, especially for autodriving vehicles. In this paper, jointly taking both the network traffic and computation workload of industrial traffic into consideration, we explore a fundamental tradeoff between energy consumption and service delay when provisioning mobile services in vehicular networks. In particular, when the available resource in mobile vehicles becomes a bottleneck, we propose a novel model to depict the users' willingness of contributing their resources to the public. We then formulate a cost minimization problem by exploiting the framework of Markov decision progress (MDP) and propose the dynamic reinforcement learning scheduling algorithm and the deep dynamic scheduling algorithm to solve the offloading decision problem. By adopting different mobile trajectory traces, we conduct extensive simulations to evaluate the performance of the proposed algorithms. The results show that our proposed algorithms outperform other benchmark schemes in the mobile edge networks.

In the era of information, the services of fog computing environment with content delivery are expected to offer users the better satisfaction of Quality-of- Experience (QoE) than that in a conventional environment. Nevertheless, the dataflow and new demands from users increase along with the promising of content-centric computing system in fog computing environment. Therefore, the satisfaction of QoE will become the major challenge. In this article, to enhance the satisfaction of QoE, we propose QoE models to evaluate the quality of service in fog computing environment concerning both system and users. The value of QoE does not only refer to the system cost, but also the Mean Opinion Score (MOS) of users. Therefore, our models could capture influential factors from system cost based on system states and services for users. Specially, we mainly focus on issues of cache allocation and transmission rate. Under this fog computing environment, aiming to the capacity of cache allocation among fog nodes and handle transmission rates under a constrained total system cost and MOS, we devote our efforts to the following two aspects. First, we formulate the QoE as a joint resource allocation problem under different transmission rates to acquire best QoE. Then, we propose a dynamic algorithm based on shortest path tree (SPT), which is suitable for fog computing environment with content delivery frequently. Simulation results reveal that the benefit for using the dynamic allocation (DA) method to allocate resource can achieve high QoE performance.

In the age of big data, the services in pervasive edge environment are expected to offer end-users better Quality of Experience (QoE) than that in a normal edge environment. Nevertheless, various types of edge devices with storage, delivery, and sensing are coming into our environment and produce the high-dimensional big data accompanied by a volume of pervasive big data increasingly with a lot of redundancy. Therefore, the satisfaction of QoE becomes the primary challenge in high dimensional big data on the basis of pervasive edge environment. In this paper, we first propose a QoE model to evaluate the quality of service in pervasive edge environment. The value of QoE does not only include the accurate data, but also the transmission rate. Then, on the basis of the accuracy, we propose a Tensor-Fast Convolutional Neural Network (TF-CNN) algorithm based on Deep Learning, which is suitable for pervasive edge environment with high-dimensional big data analysis. Simulation results reveal that our proposals could achieve high QoE performance.

This paper studies the secure transmission rate issue between sensors and the remote controller to defend the jamming and eavesdropping attacks in green cyber-physical transportation systems. In this system, the traffic sensor transmits the transportation state information to the remote controller via wireless networks. Due to the broadcast characteristics of the wireless communication, the systems are vulnerable to the eavesdropping and jamming attacks. In this paper, we study how to maximize the secure transmission rate between sensors and the controller in the presence of a malicious eavesdropper and a jammer. Specifically, the malicious jammer is smart and can choose the optimal power strategy to maximize the side effect with the knowledge of sensor's transmission power. For the purpose of achieving the maximum utility, the optimal strategy is determined via adjusting the sensor's transmission power according to the control feedback conditions. We consider the single-antenna model and the multiantenna model to formulate this problem as an optimization problem based on a Stackelberg game. We then prove the existence of the Stackelberg equilibrium via the interaction between the sensor and the jammer. Moreover, we present two algorithms to obtain the optimal transmission strategy, i.e., a stochastic algorithm with feedback and renewed intelligent simulated annealing. Finally, extensive simulations and trace experimental results are presented to verify our theoretical analysis.

When a large-scale disaster happens, efficient network connection and communication becomes difficult due to serious damage of existing network infrastructures. Meantime, people have strong demands of information sharing with each other for evacuation and disaster-relief activities in such a disaster environment. To serve these heavy communication demands, establishing local area networks (LANs) consisting of portable servers has been considered as one of the most promising solutions. Based on the established LANs, people can share disaster-related information in covered area. However, due to the lack of stable Internet connection, these LANs are isolated and cannot be synchronized in real time. To tackle this problem, in this paper, we propose an intermittent data synchronization scheme by introducing moving vehicles as relays to exchange data between isolated data servers after disasters. With the objective of maximizing the synchronized weighted data volume under the capability constraints of the mobile relay, we formulate a stochastic programming problem for trajectory planning. We leverage queueing theory and the Lyapunov-drift technique to solve this problem in an online setting, which is practical for a real disaster environment. Our theoretical analysis shows that the performance gap of our proposed online algorithm is (1/V) of the optimum. Additionally, extensive simulations and comparisons with other algorithms are conducted to show the superior performance of our proposed online algorithm.

Some sleep disorders, such as sleep apnea, restless legs syndromes (RLS), and periodic limb movement disorder (PLMD), require a full-night sleep monitoring for diagnosis. Conventional sleep monitoring devices are disturbing and inconvenient for daily scene applications. In this poster paper, we propose a sleep monitoring system by embedding RFID tags into bed cloth and realize two main functions: breath monitoring and body movement detection. We apply a finite impulse response low pass filter to get smooth breath signal wave and use a convolutional neural network (CNN) algorithm to identify the movement of person objects. Finally, we conduct experiments to evaluate the breath monitoring in a real world scenario. The experiment results show that our monitoring system can monitor breath with a high accuracy.

Big data analysis is usually casted into parallel jobs running on geo-distributed data centers. Different from a single data center, geo-distributed environment imposes big challenges for big data analytics due to the limited network bandwidth between data centers located in different regions. Although research efforts have been devoted to geo-distributed big data, the results are still far from being efficient because of their suboptimal performance or high complexity. In this paper, we propose a traffic-aware task placement to minimize job completion time of big data jobs. We formulate the problem as a non-convex optimization problem and design an algorithm to solve it with proved performance gap. Finally, extensive simulations are conducted to evaluate the performance of our proposal. The simulation results show that our algorithm can reduce job completion time by 40%, compared to a conventional approach that aggregates all data for centralized processing. Meanwhile, it has only 10% performance gap with the optimal solution, but its problem-solving time is extremely small.

This paper focuses on the security issue in Cyber-Physical Systems. The sensor transmits the state estimation information to the remote controller via wireless networks. Due to the broadcast characteristics of wireless communication, the systems are vulnerable to the eavesdropping attacks and jamming attacks. In this paper, we study how to maximize the secure transmission rate between sensors and controller with the presence of malicious eavesdropper and jammer. The malicious jammer is smart and can choose the optimal power strategy to maximize the side effect with the knowledge of sensor's transmission power. When the sensor adjusts its transmission power to achieve the maximum utility, the control feedback is used to adjust the optimal strategy in CPS. We formulate this proposed problem as a Stackelberg game such that the optimal power allocation strategy is achieved. We further prove the existence of Stackelberg equilibrium by obtaining the interaction between the sensor and the jammer. To tackle this optimization problem, we present a stochastic algorithm with feedback (SAF) algorithm. As a result, the optimal transmission strategy is obtained. Finally, extensive simulations are presented to verify our theoretical analysis.

Big data analytics has attracted close attention from both industry and academic because of its great benefits in cost reduction and better decision making. As the fast growth of various global services, there is an increasing need for big data analytics across multiple data centers (DCs) located in different countries or regions. It asks for the support of a cross-DC data processing platform optimized for the geo-distributed computing environment. Although some recent efforts have been made for geo-distributed big data analytics, they cannot guarantee predictable job completion time, and would incur excessive traffic overthe inter-DC network that is a scarce resource shared by many applications. In this paper, we study to minimize the inter-DC traffic generated by MapReduce jobs targeting on geo-distributed big data, while providing predicted job completion time. To achieve this goal, we formulate an optimization problem by jointly considering input data movement and task placement. Furthermore, we guarantee predictable job completion time by applying the chance-constrained optimization technique, such that the MapReduce job can finish within a predefined job completion time with high probability. To evaluate the performance of our proposal, we conduct extensive simulations using real traces generated by a set of queries on Hive. The results show that our proposal can reduce 55 percent inter-DC traffic compared with centralized processing by aggregating all data to a single data center.

After big disasters, a damaged area can be out of contact because of severe damage of existing network infrastructures. Meanwhile, high demands for network connections to the disaster area will arise to collect damage information and disseminate rescue instructions. In this paper, we propose to construct a network for disaster management using mobile stations equipped with sensors and network interfaces. They are controlled by a disaster management center via wide area network technology, and conduct various disaster management tasks, such as damage sensing, information collection and message dissemination. To address the challenges of unpredictable tasks and limited number of mobile stations with working capability constraints, we propose an online algorithm that schedules mobile stations for disaster management tasks with weights in each time slot, without any knowledge of future task arrivals. Our objective is to maximize the total weight of finished tasks under constraints of maximum working capability of mobile stations. We prove that the performance of proposed online algorithm is no worse than e-1/e of optimal solutions. Extensive simulations are conducted to evaluate our proposed algorithm.

Our experiences in East Japan Earthquake show that disasters will cause a large-scale network interruption due to serious damage of existing network infrastructures. To enable Internet connection before network service restoration, which is usually time-consuming, we propose to use a mobile relay to carry data for several isolated communities formed after disasters. Specifically, we consider that only one community has the Internet connection, and data from Internet need to be carried to other communities by the mobile relay. The data downloading performance of each community is evaluated by the utility of obtained data minus the penalty of corresponding latency. With the objective of maximizing the poorest performance among communities, we formulate a max-min problem to optimize the trajectory of the mobile relay and its carried data volume for each community. Due to the NP-hardness of this problem, we propose a genetic algorithm by representing the trajectories of mobile relay as chromosomes that evolve to approximate the optimal solution. The fitness of each chromosome is evaluated by optimizing the data volume carried for each community. Extensive simulations are conducted to show that our proposed algorithm significantly outperforms existing algorithms.

Delay tolerant networks (DTN) have shown its great successes in many mobile applications with intermittent connectivity. To increase contact opportunities that play an important role in routing performance and energy efficiency of DTN, the approaches of deploying static relay nodes, which are also referred to as throwboxes, has been widely adopted. In this paper, we study a relay deployment problem with the objective of minimizing communication latency among all nodes in DTN, which has been little studied so far. Upon the concept of latency graph that we propose to model the message delivery latency in DTN, the problem of relay placement for latency minimization (RPLM) is defined and proven NP-hard. To solve the RPLM problem, we propose a heuristic algorithm with low complexity, and conduct extensive simulations to show that it significantly outperforms other two existing schemes.

After a decade of extensive research on application-specific wireless sensor networks (WSNs), the recent development of information and communication technologies makes it practical to realize the software-defined sensor networks (SDSNs), which are able to adapt to various application requirements and to fully explore the resources of WSNs. A sensor node in SDSN is able to conduct multiple tasks with different sensing targets simultaneously. A given sensing task usually involves multiple sensors to achieve a certain quality-of-sensing, e.g., coverage ratio. It is significant to design an energy-efficient sensor scheduling and management strategy with guaranteed quality-of-sensing for all tasks. To this end, three issues are investigated in this paper: 1) the subset of sensor nodes that shall be activated, i.e., sensor activation, 2) the task that each sensor node shall be assigned, i.e., task mapping, and 3) the sampling rate on a sensor for a target, i.e., sensing scheduling. They are jointly considered and formulated as a mixed-integer with quadratic constraints programming (MIQP) problem, which is then reformulated into a mixed-integer linear programming (MILP) formulation with low computation complexity via linearization. To deal with dynamic events such as sensor node participation and departure, during SDSN operations, an efficient online algorithm using local optimization is developed. Simulation results show that our proposed online algorithm approaches the globally optimized network energy efficiency with much lower rescheduling time and control overhead.

To characterize the delivery performance of message dissemination in Disruption/Delay Tolerant Networks, various methods have been proposed. However, existing work shares a common simplification that the pairwise meeting rate between any two mobile nodes is exponentially distributed. In this paper, instead of relying on such assumption, we jointly consider the deactivation-rate over relay nodes and the number of sinks deployed in the network as the primary system parameters. Then, an ODE-based theoretical framework is proposed in a stochastic manner, which enables to describe how these two parameters affect the performance of a message delivery process using controlled epidemic routing. Via extensive experiments, the high accuracy of our analytical framework are verified.

In this paper, we investigate the minimization problem of average delay of multi-antenna AP based SIMO (single-input and multiple-output) system by optimizing the antenna allocation. We first obtain the upper bound of transmission bandwidth by deriving the expected error probability of coherent detection for SIMO under Rayleigh fading, and then develop the expression of average delay of terminals sharing the same channel via contention by applying CTMC (continuous-time Markov chains). Upon the above results, we formulate the optimization of antenna allocation as a non-linear integer programming problem and present a polynomial time algorithm based on integer partition and minimum matching to support optimized allocation. We prove the correctness of our theoretical derived results by extensive simulations. The performance comparison results show that the proposed antenna allocation algorithm outperforms other heuristic based algorithms under different traffic models.

After a decade of extensive research on application-specific wireless sensor networks (WSNs), the recent development of information and communication technologies make it practical to realize software-defined sensor networks (SDSNs), which are able to adapt to various application requirements and to fully explore the resources of WSNs. In SDSNs, wireless sensor nodes can be dynamically reprogrammed for different sensing tasks via the over-the-air-programming technique. For a given sensing task, it is usually required to guarantee certain quality-of-sensing, e.g., coverage ratio. Intuitively, the more sensors are deployed with a program, the higher quality-of-sensing of the corresponding task can be achieved. However, this is at the expense of high reprogramming energy consumption. In this paper, we investigate how to design an energy-efficient reprogramming strategy with guaranteed quality-of-sensing for a sensing task. To this end, two issues will be tackled: 1) the subset of sensors that shall be reprogrammed, i.e., reprogramming sensor selection and 2) the program distribution routing. They are jointly considered and formulated as an integer linear programming (ILP) problem, based on which an algorithm with low computation complexity is then proposed. The high efficiency of our algorithm is validated by extensive simulation studies.

Cooperative communication (CC) has been proposed to increase the wireless channel capacity and reliability by employing multiple single-antenna nodes to form a virtual antenna array. Many efforts focus on exploiting the benefits of CC among multiple source-destination pairs with an unrealistic assumption that each of them communicates over a dedicated channel without interference. In this paper, we investigate the transmission scheduling problem for multiple source-destination pairs under the assistance of a set of dedicated relay nodes on a single channel. By applying the protocol interference model, we propose a concept of cooperative link to characterize the interference regions of CC. Due to the NP-completeness of optimal scheduling, LP (linear programming) based heuristic algorithms are proposed to maximize the minimum transmission rate under a given relay assignment. Then, without specifying a relay node for each source-destination pair, we study the max-min rate problem by jointly considering transmission scheduling and relay assignment. Heuristic algorithms are proposed to solve this more challenging problem. Finally, extensive simulations are conducted to show that the proposed algorithm outperforms direct transmission substantially.

To understand the delivery performance of message dissemination in Disruption Tolerant Networks (DTNs), various methods have been proposed in the literature. However, existing work shares a common simplification that the pairwise meeting rate between any two mobile nodes is exponentially distributed. In this paper, instead of relying on such assumption, we jointly consider the transmission range and Random Direction Mobility (RDM) model to stochastically analyze delivery performance of epidemic routing in terms of percolation ratio and delivery delay. Furthermore, we study a controlled epidemic routing, in which any message stays at a mobile node longer than a predefined lifetime should be removed from the node. It can be considered as an age-structure process described by the Susceptible-Infectious-Recovered (SIR) model. To the best of our knowledge, we are the first to characterize the message propagation process by applying the Delay Differential Equations (DDEs) in DTNs. The correctness of our analysis is validated by extensive simulations.

After a decade of extensive research on application-specific wireless sensor networks (WSNs), the recent development of information and communication technologies make it practical to realize new WSNs paradigm known as software-defined sensor networks (SDSNs). SDSNs are able to adapt to various application requirements and to fully explore the communication, computation and sensing resources of WSNs. Sensor nodes in SDSNs can be dynamically reprogrammed for different sensing tasks via the over-the-air-programming technique. In this paper, we introduce the concept of SDSNs and outline several pioneering related work and enabling technologies for the realization of SDSNs.

A function alternation mechanism is proposed. It enables sensor nodes to take over the damaged functions of their neighboring sensor nodes automatically. By introducing the mechanism in a wireless sensor network (WSN), we can realize a WSN that never stops monitoring, and does not require maintenance after it is deployed, even if some sensor nodes suddenly die. In this paper, after explaining the details of the function alternation mechanism along with some evaluation results, we introduce a mission-critical application that utilizes the proposed function alternation mechanism for monitoring disaster-hit regions.

This paper introduces an approach to realize service-oriented architecture (SOA) governance using a messaging network. The proposed solution uses a messaging network with a structured overlay. The messaging network is XML-based, and is an actual implementation of a content-based network. The proposed scheme provides a network-centric approach to ensure SOA governance by use of a common proactive scheme on the network infrastructure.

In this paper, we propose a sensing function allocation method that is based on a distributed graph coloring and a slot allocation algorithm. By using this method, a dynamic function allocation can be carried out in order to balance the distribution of each sensing function in a target monitoring area. The experimental results show that the proposed algorithm can allocate sensing functions such that 92% of the target monitoring area is covered and the actual sensing time is reduced by approximately 57% when many sensor nodes are deployed in the target area.

This paper proposes a novel IP multihoming scheme in which fast packet processing nodes switch and distribute the traffic on an application flow basis. The newly developed node, called A-BOX, classifies each incoming packet up to the layer 4(L4) or L7(payload) header, edits it as necessary, and forwards it to an outgoing port according to the header's entry. The NAT-like sandwiched path formed between A-BOXs acts like one path defined for an application flow, and so suits applications that have QoS(quality of service) requirements. A-BOX realizes wire rate forwarding by applying hardware techniques including FPGAs. A-BOX can measure the traffic on a real time basis for each flow and distribute the flow to achieve the application's requirements. The software interface of A-BOX means that the network can be transparently controlled for each application flow. We clarify the feasibility of A-BOX's basic methods, which include network condition monitoring via individual flow with huge background traffic, switching a specified flow in the multihoming condition, and traffic sharing/bandwidth aggregation.

The Adaptive Computing Systems (ACS) program was initiated by Defense Advanced Research Projects Agency (DARPA) of the United States in 1996. With the advent of FPGAs, has emerged a new class of computing systems that contain configurable hardware. This session begins by a presentation by the first ACS program manager of its motivation, original goals, and objectives. It is then followed by presentations of four specific projects under the ACS program. Future activities surrounding the ACS community will be discussed at the end.

An active network node architecture, called Active Packet Editing (APE), is proposed. The main concept of APE is to accelerate functions essential to active network operation, such as packet classification and NAT (Network Address Translation). The twofold architecture of APE combines a software active packet processor with a high-speed hardware packet editor. Based on preset rules (pattern, action), the packet editor classifies and modifies packets passing through the node. Upon the receipt of active packets, the software active packet processor dynamically configures the packet editor. This paper focuses on introducing the high-speed packet editing mechanism which utilizes FPGAs (Field Programmable Gate Arrays) and CAMs (Content Addressable Memories). A prototype network node based on the architecture performs Gigabit-class throughput with packet editing.

We developed a flexible network node that is tuned for high-speed and multilayer packet manipulation. The key idea is a dynamic function assignment mechanism; each packet processing task is assigned to several processing modules in an on-the-fly manner and incoming packets are processed in them. With this mechanism, we can freely arrange the modules and add extra ones if more processing power is needed. In addition, the processing modules are realized using field programmable gate arrays (FPGAs) and micro processing units (MPUs). Thus, the functionality of each module can be dynamically changed at anytime. In this paper, the system concept and its implementation are described with an example application.

The paper describes the concept and implementation of a telecom emulator that features both reconfigurability and high-speed processing. The emulator can be easily transmuted into any telecom system as a real node. It has two innovative system design concepts. The first is to divide the specification into simplified processes based on the open system interconnection (OSI) reference model. The second is the use of a sophisticated hardmacro and its software-callable driver. We implemented a prototype system called ATTRACTOR and applied it to some telecom applications. The applications were able to be implemented in a short design time and were operated in real computer network environments.

A novel distributed-resource abstraction environment is introduced. You can access any resource in a computer network as a memory mapped I/O device, as if it was attached to the local bus of your PC. This network technology gives us several benefits. From the application development viewpoint, no network-related programming is required, and we don't need to modify the applications even if the network topologies and protocols are changed. On the other hand, network maintenance and upgrading can be done anytime without worrying about the application users, because the environment completely separates or hides the network from the applications. The API (Application Program Interface), a resource abstraction mechanism, and a directory service are implemented. In addition, a reconfigurable hardware technology is adopted to perform autonomous network control using a lour layer protocol. Furthermore, we introduce a testbed that allows heterogeneous resources to be utilized, and demonstrate the feasibility of our concept using some applications.

This paper describes a project dedicated to developing an improved (in terms of usability) version of our previous telecommunication-oriented field programmable gate array (FPGA), and its applications. To achieve this goal, we adopt several challenging design strategies. First, we determine the new FPGA architecture based on a quantitative evaluation carried out to optimize the interaction between the FPGA and CAD algorithms. In addition, we create a new chip design environment that allows semi-automatic test pattern generation and cross-checking between logic and layout design. Furthermore, a dedicated CAD system is developed based on a consideration of the evaluation results and the characteristics of the FPGA. As a result of these design strategies, the FPGA and CAD system are well-balanced, and even though the FPGA has very rich routing resources, the routing process can be finished quickly without sacrificing application-circuit performance. The FPGA is applied to several reconfigurable systems for telecommunications, and is found to offer the required functions and good performance.

The paper describes a transmutable telecom system called ATTRACTOR, which uses more than 100 FPGAs, and features both reconfigurability and high performance. There are two key innovations. One is the board-level modularity concept that allows different functions to be implemented on different boards individually. The other is a high-speed serial link mechanism that provides excellent inter-board communication without sacrificing performance. Thus this system can be completely reconstructed to suit different applications by arranging different board combinations and reconfiguring each board. Using ATTRACTOR, we realized an IP packet forwarder, which is very speed-intensive telecom function, that has to decide the next-hop address of each incoming IP packet and send it out on-the-fly. It was confirmed that the function works well in a real ATM-LAN environment.

No abstract available.

This paper discusses how a distributed environment providing effortless networking can be efficiently implemented in a network system. To formalize the distributed environment, we introduce the concept of "Virtual BUS". It is similar to the concept of the computer system's bus architecture. We define user behavior as accessing the resources in a network through his/her own bus. Based on this simple formalization, we discuss the key issues in implementing distributed environments to support different requirements for realizing the quality of service desired. We implement an experimental effortless networking environment based on the Virtual BUS concept and show that the concept realizes effortless networking while guaranteeing QoS.

Recently, a lot of reconfigurable systems (RSs) utilizing FPGAs have been reported. The aim is to achieve high performance with low production cost, or to produce non-"von Neumann" machines. In this paper, we overviews RSs. FPGAs, the key components of RSs, are introduced. Second, we categorize RSs into several types. Next, applications are described. Then, hot topics related to RSs are mentioned. Finally, some open problems are described.

We have developed an architecture-oriented routing method for a telecommunications FPGA (Field-Programmable Gate Array) which has rich hierarchical routing resources. Our routing method consists of four routing procedures, each of which are related to a specific part of the routing resource architecture of the FPGA. It can accomplish routing results almost five times faster than a reference method that does not consider the routing resource architecture. The key idea in our approach is to reduce the number of possible resource candidates by referring to both the logical net structure and the FPGA resource architecture. We apply different routing algorithms according to the amount and architecture of candidate routing resources. Experiments show that our architecture-oriented method is more effective than conventional single-algorithm-based methods.

In designing FPGAs, it is important to achiev e a good balance bet w een the number of logic blocks, suc h has Look-Up Tables (LUTs), and wiring resources. It is difficult to find an optimal solution. In this paper, w e presen t an FPGA design methodology to efficiently find well-balanced FPGA architectures. The method covers all aspects of FPGA development from the architecture-decision process to physical implementation. It has been used to develop a new FPGA that can implement circuits that are twice as large as those implementable with the previous version but with half the number of logic blocks. This indicates that the methodology is effectiv e in dev eloping well-balanced FPGAs.

This paper presents a novel system architecture that combines tightly coupled field programmable gate arrays (FPGA's) and a microprocessing unit (MPU) that we have developed. This system architecture comprises three main programmable devices which yield high flexibility. These devices are a reduced instruction set computer (RISC)-type MPU with memories, programmable interconnection devices, and FPGA's. This system supports various styles of coupling between the FPGA's and the MPU which makes several data processing operations more effective. Furthermore, we indicate the most suitable applications for the system. They are telecommunication data processes involving complex protocol operations and network control algorithms. In this paper, two applications of the system are given. One is for operation, administration, and management (OAM) cell processing on an asynchronous transfer mode (ATM) network. The other is a dynamic remote reconfiguration protocol that enables the functions of the transport data processing system to be updated or changed on-line.

This paper presents a novel system architecture applicable to high-performance and flexible transport data processing which includes complex protocol operation and a nehvork control algorithm. We developed a new tightly coupled Held Programmable Gate Array (FPGA) and Micro-Processing Unit (MPU) system named. Yet Another Re-Definable System (YARDS). It comprises three programmable devices which equateto high flexibility. These devices are the RISC-type MPU with memories, programmable inter-connection devices, and FPGAs. Using these, this system supports various styles of coupling between the FPGAs and the MPU which are suitable for constructing transport data processing. In this paper, two applications of the systemin the telecommunications field are given. One is an Operation, Administration, and Management (OAM) cell operations on an AsynchronousTransfer Mode (ATM) network. The other is a dynamic configuration protocol enables the updateor changeof the functions of the transport data processing system on-line. This is the first approach applying the FPGA/MPU hybrid system to the telecommunications field.

This paper proposes a new processor architecture for manipulating protocols in digital signal transport systems. To realize flexible and high-performance digital signal transport systems, the architecture has unique application-specific hardware with a core CPU. It is derived from an analysis of functions in real systems. A computer simulation confirms the efficiency of the architecture.

This paper discusses a new approach to efficient system design verification that uses an FPGA based real-time emulator. The architecture of a real-time emulator suitable for high-speed digital transmission system emulation and an experimental system are described. The proposed system consists of an emulation engine using the custom designed multi-chip module FPGA called PROTEUS-MCM, a program control unit, and a physical interface. The system can be used not only for real-time emulation of high-speed transmission systems, but also as initial systems for actual communication networks.

The paper discusses a new architecture for reconfigurable real time signal transport systems that uses FPGAs and describes an experimental system design. The basic architecture of the reconfigurable transport system is proposed based on the requirements for real time signal transport in a typical telecommunication network. The proposed system consists of reconfigurable modules using the custom designed FPGA called PROTEUS, a program control module for hardware/software coprocessing, and line interface modules including an automatic connection control mechanism. The system can be used not only for the initial installation stages of actual communication systems, but also for rapid prototyping tools or emulators.

A new FPGA (Field Programmable Gate Array) is developed for high-speed digital telecommunication systems. As architecture is based on the fundamental characteristics extracted from an analysis of actual systems. The FPGA has several unique features for realizing high-speed transport data processing. It allows us to build the high-performance components that are frequently used in transport data processing. In addition, its inter-chip connection mechanism enables us to build flexible multi-FPGA modules. Furthermore, we introduce a dedicated CAD system for the FPGA. We design several actual transport processing circuits on the FPGA using the CAD system and evaluate them. Experimental results show that the device has the potential to realize practical systems.

This paper presents a new technique for improving the performance of a synchronous circuit configured as a look-up table based FPGA without changing the initial circuit configuration; only the register location is altered. It improves clock speed and data throughput at the expense of latency. One of the most significant benefits realized by this approach is that the time-consuming and user-uncontrollable reconfiguration processes, i.e., remapping, replacement, and rerouting, are unnecessary when improving circuit performance. After applying our technique to some benchmark circuits, the average performance improvement was 33% for six combinational circuits, and 25% for 18 sequential circuits.>

This paper discusses a new architecture for programmable hardware targeted at high-speed digital telecommunication systems and describes a preliminary design. The basic architecture of the programmable hardware is proposed based on the characteristics of functions and an analysis of logic used in actual communication subsystems performing high-speed bit level operations. The proposed architecture, called PROTEUS, includes a pipeline structure of logic and latch groups, and a 2-stage logic block structure that consists of small LUTs and wide gates. The design strategy of a prototype chip and the CAD techniques used to achieve the required performance are also discussed.>

Conductor, an integration system for VLSI design tools and their human-interface builder, is described. The main advantage of this system is its unique process control mechanism. Once VLSI designers specify their tasks by using a flow-chart based graphic editor, the system can handle the design process and maintain the design data produced by each tool.>