We propose a packet-based inverse multiplexing method to allow scalable network access with a bigger-pipe physical interface. The method is based on aggregation at the physical layer (APL) that fragments an original packet-flow and distributes the fragments among an adequate numbers of physical links or networks. It allows us to share wavelengths and/or bandwidth resources in optical networks. Its technical feasibility at the speed of newly standardized 100 Gb/s Ethernet (100 GbE) is successfully evaluated by implementing the inverse multiplexing logic functions on a prototype board. We demonstrate super-high-definition video streaming and huge file transfer by transmitting 100 GbE MAC frames over multiple 10 GbE physical links via inverse multiplexing.

This paper proposes a prioritized aggregation method that supports compressed video transmission on millimeter wave wireless personal area network (mmWave WPAN) systems. Frame aggregation is an effective means to improve system efficiency and throughput for wide band systems such as mmWave WPAN. It is required by the applications that the mmWave WPAN systems should provide Gbps or multiGbps transmission capability. The proposed scheme targets not only transmission efficiency but also support of compressed video transmission which currently is very popular. The proposal combines MAC layer aggregation with PHY layer skew modulation to facilitate the video transmission in a way that more important data is better protected. Simulation results show that the average peak signal to noise ratio (PSNR) performance is improved by 5 dB compared to conventional method, while the Gbps transmission requirement is fulfilled.

In-network data aggregation is one of the most important issues for achieving energy-efficiency in wireless sensor networks since sensor nodes in the surrounding region of an event may generate redundant sensed data. The redundant sensed data should be aggregated before being delivered to the sink to reduce energy consumption. Which node should be selected as a Data Aggregation Node (DAN) for achieving the best energy efficiency is a difficult issue. To address this issue, this letter proposes a scheme to select a DAN for achieving energy-efficiency in an event region. The proposed scheme uses an analytical model to select the sensor node that has the lowest total energy consumption for gathering data from sensor nodes and for forwarding aggregated data to a sink, as a DAN. Analysis and simulation results show that the proposed scheme is superior to other schemes.

A network of sensors can be used to obtain state based data from the area in which they are deployed. To reduce costs, the data sent via intermediate sensors to a sink are often aggregated. In this letter, we introduce Self-Construction of Aggregation Tree (SCAT) scheme which uses a novel data aggregation scheme utilizing the knowledge of the mobile node and the infrastructure (static node tree) in gathering the data from the mobile node. The static nodes can construct a near- optimal aggregation tree by themselves, using the knowledge of the mobile node, which is a process similar to forming the centralized aggregation tree.

Carrier aggregation is a potential technology for the LTE-Advanced system to support wider bandwidth than the LTE system. This paper analyzes the performance of carrier aggregation under elastic traffic, and compares it to that of a simpler approach for the same purpose, referred to as the independent carrier approach. The queueing behaviors of these two approaches are formulated as one fast versus multiple slow state-dependent Processor Sharing servers, respectively. Both analytical and simulation results show that when there are L component carriers with uniform bandwidth in the system, the performance of the carrier aggregation approach is L times better than that of the independent carrier approach in terms of the average user delay and throughput under the same traffic load.

We propose a scalable parallel interface that provides an ideal aggregated bandwidth link for an application. The scalable parallel interface uses time information to align packets and allows dynamic lane and/or path change, a large difference in transmission delays among lanes, and so on. The basic performance of the scalable parallel interface in 10 Gb/s 2 lanes is verified using an estimation board that is newly developed to evaluate the basic functions used in a Terabit LAN. The evaluation shows that the scalable parallel interface achieves a very low delay variation that is almost the same as that under back-to-back conditions. The difference in the delay variation between the scalable parallel interface and the back-to-back condition is approximately 10 ns when the transmission delay time varies from 10 µs to 1 s.

This paper proposes bidirectional packet aggregation and coding (BiPAC), a packet mixing technique which jointly applies packet aggregation and network coding in order to increase the number of supportable VoIP sessions in wireless multi-hop mesh networks. BiPAC applies network coding for aggregated VoIP packets by exploiting bidirectional nature of VoIP sessions, and largely reduces the required protocol overhead for transmitting short VoIP packets. We design BiPAC and related protocols so that the operations of aggregation and coding are well-integrated while satisfying the required quality of service by VoIP transmission, such as delay and packet loss rate. Our computer simulation results show that BiPAC can increase the number of supportable VoIP sessions maximum by around 87% as compared with the case when the packet aggregation alone is used, and 600% in comparison to the transmission without aggregation/coding. We also implement BiPAC in a wireless testbed, and run experiments in an actual indoor environment. Our experimental results show that BiPAC is a practical and efficient forwarding method, which can be implemented into the current mesh hardware and network stack.

The wireless sensor network is a resource-constrained self-organizing system that consists of a large number of tiny sensor nodes. Due to the low-cost and low-power nature of sensor nodes, sensor nodes are failure-prone when sensing and processing data. Most presented fault-tolerant research for wireless sensor networks focused on crash faults or power faults and less on Byzantine faults. Hence, in this paper, we propose a power-saving data aggregation algorithm for Byzantine faults to provide power savings and high success rates even in the environment with high fault rates. The algorithm utilizes the concept of Byzantine masking quorum systems to mask the erroneous values and to finally determine the correct value. Our simulation results demonstrate that when the fault rate of sensor nodes is up to 50%, our algorithm still has 48% success rate to obtain the correct value. Under the same condition, other fault-tolerant algorithms are almost failed.

Traditional traffic analysis is can be performed online only when detection targets are well specified and are fairly primitive. Local processing at measurement point is discouraged as it would considerably affect major functionality of a network device. When traffic is analyzed at flow level, the notion of flow timeout generates differences in flow lifespan and impedes unbiased monitoring, where only n-top flows ordered by a certain metric are considered. This paper proposes an alternative manner of traffic analysis based on source IP aggregation. The method uses flows as basic building blocks but ignores timeouts, using short monitoring intervals instead. Multidimensional space of metrics obtained through IP aggregation, however, enhances capabilities of traffic analysis by facilitating detection of various anomalous conditions in traffic simultaneously.

In a grid computing environment, the network characteristics such as bandwidth and latency affect the task performance. The demands for bandwidth of wide-area networks become large and it reaches more than 100 Gbps. In this article, we focus on parallel routes transmission, such as link aggregation, to realize large bandwidth network. The performance of grid computing with parallel routes transmission is evaluated on the emulated wide-area network.

The aggregation of Wi-Fi links has been identified as one way of taking advantage of available channels to achieve higher speed data transmission in future cognitive radio networks. However variations in link quality make it difficult to achieve stable performance from aggregated Wi-Fi links. In this paper we present a method for controlling aggregation of Wi-Fi links based on monitoring of link status. We first discuss the requirements for detecting bad-links which degrade the performance of aggregated Wi-Fi links. We then describe the implementation of an enhanced link-status detection algorithm based on monitoring of signal strength and number of retransmissions. In particular, we address the problems of monitoring and recovering links after they have been dropped from use, and adjusting decision thresholds to adapt to changing wireless conditions. Finally, we report the results of tests which demonstrate the effectiveness for attaining efficient aggregation of Wi-Fi links for high throughput under varying wireless conditions.

In this paper, proactive data filtering (PDF) algorithm is proposed for data aggregation (or data fusion) in wireless sensor networks. The objective of the algorithm is to further reduce the energy consumption when sensor nodes perform data aggregation. In many applications, the sensor field will be overwhelmed by unnecessary and redundant sensory information when the sink node disseminates a query throughout the sensor field. In order to reduce the energy consumption, our scheme employs intelligent decision logic in the sensor node which delays or deactivates the transmission of its response. A performance evaluation shows that data aggregation with the PDF significantly improves energy-efficiency.

This paper describes the experimental evaluation of a testbed with a simple decision-feedback channel tracking scheme for MIMO-OFDM systems. The channel tracking scheme periodically estimates the channel state matrix for each subcarrier from received signals and replicas of the transmitted signal. The estimated channel state matrices, which are obtained at mutually different timings, are combined based on maximum ratio combining and used for MIMO signal detection. The testbed was implemented on field programmable gate arrays (FPGAs) of 1/5 scale, which confirms the implementation feasibility of the channel tracking scheme. The packet error rate (PER) and mobility performance of the testbed were measured. The testbed employed a 22 MIMO channel, zero-forcing algorithm for MIMO signal detection, 16QAM for the subcarrier modulation scheme, and coding rate of 1/2. The proposed scheme suppressed the increase in the required SNR for PER of 10-2 to less than 1 dB when the relative velocity between the transmitter and the receiver was less than 45 km/h assuming 5 GHz band operation. In addition, the proposed scheme offers 6.3% better throughput than the conventional scheme. The experimental results demonstrate that the channel tracking scheme implemented in the testbed effectively tracks the fluctuation of a MIMO channel.

To achieve scalability and security, large networks are often structured hierarchically as a collection of domains. In hierarchical networks, the topology and QoS parameters of a domain have to be first aggregated before being propagated to other domains. However, topology aggregation may distort useful information. Although spanning tree aggregation can perfectly encode attribute information of symmetric networks, it can not be applied to asymmetric networks directly. In this paper, we propose a spanning tree based attribute aggregation method for asymmetric networks. The time complexity of the proposed method and the space complexity of its resulted aggregated topology are the same with that of the spanning tree aggregation method in symmetric networks. This method can guarantee that the attributes of more than half of the links in the networks are unaltered after aggregation. Simulation results show that the proposed method achieves the best tradeoff between information accuracy and space complexity among the existing asymmetric attribute aggregation methods.

This paper proposes a simple and feasible decision-feedback channel tracking scheme for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems designed for wireless local area networks (LANs). In the proposed scheme, the channel state matrix for each subcarrier is tentatively estimated from a replica matrix of the transmitted signals. The estimated channel matrices, each derived at a different timing, are combined, and the previously estimated channel matrices are replaced with the latest ones. Unlike conventional channel tracking schemes based on a Kalman filter, the proposed scheme needs no statistical information about a MIMO channel, which makes the receiver structure quite simple. The packet error rate (PER) performances for the proposed scheme are evaluated on computer simulations. When there are three transmit and receive antennas, the subcarrier modulation scheme is 64 QAM, and the coding rate is 3/4, the proposed scheme keeps the SNR degradation at PER of 1e-2 less than 0.1 dB when the velocity of receiver is 3 km/h in an indoor office environment at 5 GHz band. In addition, compared to the conventional channel tracking scheme based on known pilot symbols, the proposed scheme improves throughput performance by 13.8% because it does not need pilot symbols. These results demonstrate that the proposed channel tracking scheme is simple and feasible for implementation in MIMO-OFDM systems based on wireless LANs.

A novel method of voice frame aggregation for wireless mesh networks is presented. In the method, the degree of aggregation is automatically regulated by the congestion level on the wireless link. On the IEEE 802.11-based mesh network, it is shown to yield approximately twice the call capacity, while incurring no additional delay for frame aggregation.

Energy-saving is crucial in wireless sensor networks. In this letter, we address the issue of lossless packing aggregation with the aim of reducing energy lost in cluster-model wireless sensor networks. We propose a performance model based on the bin packing problem to study the packing efficiency. It is evaluated in terms of control header size, and validated by simulations.

This paper presents the characteristics and modeling of VoIP traffic for a real network. The new model, based on measured data, shows a significant difference from the previously proposed models in terms of parameters and their effects. It is found that the effects of background noise and ringing tones have essential influences on the model. The observed distributions of talkspurt and silent durations have long-tail characteristics and considerably differ from the existing models. An additional state called "Long burst", which represents the background noise at the talker's place, is added into the continuous-time Markov process model. The other three states, "Talk", "Short silence" and "Long silence", represent the normal behavior of the VoIP user. Models for conversational speech containing the communication during the dialogue are presented. In the case of the VoIP traffic aggregation, the simplified models, which neglect the conversation's interaction, are proposed. Depending on the occurrences of background noise during the speech, the model is classified as "noisy speech" or "noiseless speech". The measured data shows that the background noise typically increases the data rate by 60%. Simulation results of aggregated VoIP traffic indicate the self-similarity, which is analogous to the measured data. Results from the measurements support the fact that except the ringing duration the conversations from both the directions can be modeled in identical manner.

Recently, the number of multi-homed hosts is getting large, which are equipped with multiple network interfaces to support multiple IP addresses. Although there are several proposals that aim at bandwidth aggregation for multi-homed hosts, few of them support mobility. This paper proposes a new framework called AMS: Aggregate-bandwidth Multi-homing Support. AMS provides functions of not only bandwidth aggregation but also mobility by responding to the changes of the number of connections during communication without the support of underlying infrastructure. To achieve efficient data transmission, AMS introduces a function called address pairs selection to select an optimal combination of addresses of the peer nodes. We implemented AMS in the kernel of NetBSD and evaluated it in our test network, in which dummynet was used to control bandwidth and delay. The measured results showed that AMS achieved ideal bandwidth aggregation in three TCP connections by selecting optimal address pairs.

In most research work for sensor network routings, perfect aggregation has been assumed. Such an assumption might limit the application of the wireless sensor networks. We address the impact of aggregation efficiency on energy consumption in the context of GIT routing. Our questions are how the most efficient aggregation point changes according to aggregation efficiency and the extent to which energy consumption can decrease compared to the original GIT routing and opportunistic routing. To answer these questions, we analyze a two-source model, which yields results that lend insight into the impact of aggregation efficiency. Based on analytical results, we propose an improved GIT: "aggregation efficiency-aware GIT," or AGIT. We also consider a suppression scheme for exploratory messages: "hop exploratory." Our simulation results show that the AGIT routing saves the energy consumption of the data transmission compared to the original GIT routing and opportunistic routing.