This paper discusses the collection of sensor data for power distribution systems. In current power distribution systems, this is usually performed solely by the Remote Terminal Unit (RTU) which is located at the root of a power distribution network. The recent rise of distributed power sources, such as photovoltaic generators, raises the demand to increase the frequency of data collection because the output of these distributed generators varies quickly depending on the weather. Increasing data collection frequency in turn requires shortening the time required for data collection. The paper proposes the use of aggregation points for this purpose. An aggregation point can collect sensor data concurrently with other aggregation points as well as with the RTU. The data collection time can be shortened by having the RTU receive data from aggregation points, instead of from all sensors. This approach then poses the problem of finding the optimal location of aggregation points. To solve this problem, the paper proposes a Mixed Integer Linear Problem (MILP) formulation of the problem. The MILP problem can then be solved with off-the-shelf mathematical optimization software. The results of experiments show that the proposed approach is applicable to rather large scale power distribution systems.

Capital expenditure (CAPEX) reduction and efficient wavelength allocation are critical for the future access networks. Elastic lambda aggregation network (EλAN) based on WDM and OFDM technologies is expected to realize efficient wavelength allocation. In this paper, we propose adaptive bandwidth allocation (ABA) algorithm for EλAN under the conditions of crowded networks, in which modulation format, symbol rate and the number of sub-carriers are adaptively decided based on the distance of PON-section, QoS and bandwidth demand of each ONU. Network simulation results show that the proposed algorithm can effectively reduce the total bandwidth and achieve steady high spectrum efficiency and contribute to the further reduction of CAPEX of future optical access networks.

Software-Defined Networking (SDN) can be applied for managing application flows dynamically by a logically centralized SDN controller and SDN switches. Because one SDN switch can support just a few thousand forwarding rule installations per second, it is a barrier to dynamic and scalable application flow management. For this reason, it is essential to reduce the number of application flows if they are to be successfully managed. Nowadays, since much attention has been paid to developing a network service that reduces application delay, the allowable delay of application flows has become an important factor. However, there has been no work on minimizing the number of flows while satisfying end-to-end delay of flows. In this paper, we propose a method that can aggregate flows and minimize the number flows in a network while ensuring all flows satisfy their allowable delay in accordance with QoS or SLA. Since the problem is classified as NP-hard, we propose a heuristic algorithm. We compared the aggregation effect of the proposed method, simple aggregation method and optimal solution by simulation. In addition, we clarify the characteristics of the proposed method by performing simulations with various parameter settings. The results show that the proposed method decreases the number of rules than comparative aggregation method and has very shorter computational time than optimal solution.

This paper presents indoor and outdoor experiments that confirm 4-Gbps throughput based on 400-MHz bandwidth transmission when applying carrier aggregation (CA) with 4 component carriers (CCs) and 4-by-4 single-user multiple-in multiple-out multiplexing (MIMO) in the 15-GHz frequency band in the downlink of 5G cellular radio access. A new radio interface with time division duplexing (TDD) and radio access based on orthogonal frequency-division multiple access (OFDMA) is implemented in a 5G testbed to confirm ultra-high speed transmission with low latency. The indoor experiment in an entrance hall shows that the peak throughput is 4.3Gbps in front of the base station (BS) antenna where the reference signal received power (RSRP) is -40dBm although the channel correlation at user equipment (UE) antenna is 0.8. The outdoor experiment in an open-space parking area shows that the peak throughput is 2.8Gbps in front of a BS antenna with a high RSRP although rank 2 is selected due to the high channel correlation. The results also show that the average throughput of 2Gbps is achieved 120m from the BS antenna. In a courtyard enclosed by building walls, 3.6Gbps is achieved in an outdoor-to-outdoor environment with a high RSRP and in an outdoor-to-indoor environment where the RSRP is lower due to the penetration loss of glass windows, but the multipath rich environment contributes to realizing the low channel correlation.

Ground-truth identification - the process, which infers the most probable labels, for a certain dataset, from crowdsourcing annotations - is a crucial task to make the dataset usable, e.g., for a supervised learning problem. Nevertheless, the process is challenging because annotations from multiple annotators are inconsistent and noisy. Existing methods require a set of data sample with corresponding ground-truth labels to precisely estimate annotator performance but such samples are difficult to obtain in practice. Moreover, the process requires a post-editing step to validate indefinite labels, which are generally unidentifiable without thoroughly inspecting the whole annotated data. To address the challenges, this paper introduces: 1) Attenuated score (A-score) - an indicator that locally measures annotator performance for segments of annotation sequences, and 2) label aggregation method that applies A-score for ground-truth identification. The experimental results demonstrate that A-score label aggregation outperforms majority vote in all datasets by accurately recovering more labels. It also achieves higher F1 scores than those of the strong baselines in all multi-class data. Additionally, the results suggest that A-score is a promising indicator that helps identifying indefinite labels for the post-editing procedure.

In recent years, the reduced cost and increased capacity of memory have resulted in a growing number of buffers in switches and routers. Consequently, today's networks suffer from bufferbloat, a term that refers to excess frame buffering resulting in high latency, high jitter, and low throughput. Although ring aggregation is an efficient topology for forwarding traffic from multiple, widely deployed user nodes to a core network, a fairness scheme is needed to achieve throughput fairness and avoid bufferbloat, because frames are forwarded along ring nodes. N Rate N+1 Color Marking (NRN+1CM) was proposed to achieve per-flow fairness in ring aggregation networks. The key idea of NRN+1CM is to assign a color that indicates the dropping priority of a frame according to the flow-input rate. When congestion occurs, frames are selectively discarded based on their color and the frame-dropping threshold. Through the notification process for the frame-dropping threshold, frames are discarded at upstream nodes in advance, avoiding the accumulation of a queuing delay. The performance of NRN+1CM was analyzed theoretically and evaluated with computer simulations. However, its ability to avoid bufferbloat has not yet been proven mathematically. This paper uses an M(n)/M/1/K queue model to demonstrate how bufferbloat is avoided with NRN+1CM's frame-dropping threshold-notification process. The M(n)/M/1/K queue is an M/M/1/K queuing system with balking. The state probabilities and average queue size of each ring node were calculated with the model, proving that the average queue size is suppressed in several frames, but not in the most congested queue. Computer simulation results confirm the validity of the queue model. Consequently, it was logically deducted from the proposed M(n)/M/1/K model that bufferbloat is successfully avoided with NRN+1CM independent of the network conditions including the number of nodes, buffer sizes, and the number and types of flows.

Content-centric networking (CCN) is an emerging networking architecture that is being actively investigated in both the research and industrial communities. In the latest version of CCN, a large number of interests have to be issued when large content is retrieved. Since CCN routers have to search several tables for each incoming interest, this could cause a serious problem of router workload. In order to solve this problem, this paper introduces a novel strategy of “grouping” multiple interests with common information and “packing” them to a special interest called the list interest. Our list interest is designed to co-operate with the manifest of CCN as its dual. This paper demonstrates that by skipping and terminating several search steps using the common information in the list interest, the router can search its tables for the list interest-based request with dramatically smaller complexity than the case of the standard interest-based request. Furthermore, we also consider the deployment of list interests and design a novel TCP-like congestion control method for list interests to employ them just like standard interests.

To reduce the server load and communication costs of machine-to-machine (M2M) systems, sensor data are aggregated in M2M gateways. Aggregation logic is typically programmed in the C language and embedded into the firmware. However, developing aggregation programs is difficult for M2M service providers because it requires gateway-specific knowledge and consideration of resource issues, especially RAM usage. In addition, modification of aggregation logic requires the application of firmware updates, which are risky. We propose a rule-based sensor data aggregation system, called the complex sensor data aggregator (CSDA), for M2M gateways. The functions comprising the data aggregation process are subdivided into the categories of filtering, statistical calculation, and concatenation. The proposed CSDA supports this aggregation process in three steps: the input, periodic data processing, and output steps. The behaviors of these steps are configured by an XML-based rule. The rule is stored in the data area of flash ROM and is updatable through the Internet without the need for a firmware update. In addition, in order to keep within the memory limit specified by the M2M gateway's manufacturer, the number of threads and the size of the working memory are static after startup, and the size of the working memory can be adjusted by configuring the sampling setting of a buffer for sensor data input. The proposed system is evaluated in an M2M gateway experimental environment. Results show that developing CSDA configurations is much easier than using C because the configuration decreases by 10%. In addition, the performance evaluation demonstrates the proposed system's ability to operate on M2M gateways.

Recently, many wireless sensor networks (WSNs) have employed mobile sensor nodes to collect a variety of data from mobile elements such as humans, animals and cars. In this letter, we propose an efficient mobile data aggregation scheme to improve the overall performance in gathering the data of the mobile nodes. We first propose a spatial mobile data aggregation scheme to aggregate the data of the mobile node spatially, which is then extended to a two-tier mobile data aggregation by supplementing a temporal mobile data aggregation scheme to aggregate the data of multiple mobile nodes temporally. Simulation results show that our scheme significantly reduces the energy consumption and gathering delay for data collection from mobile nodes in WSNs.

This paper investigates a frame aggregation (FA) technique in the medium access control (MAC) layer for downlink multi-user multiple input multiple output (MU-MIMO) channels in wireless local area networks (WLANs), and proposes a high-efficient FA scheme that ehances system performance: transmission performance and fairness in communication between mobile terminals (MTs). The proposed FA scheme employs novel criteria for selecting receiving MTs and wireless frame setting with a frame size adaptation mechanism for MU-MIMO transmissions. The proposed receiving MT selection gives higher priority to the MTs expecting higher throughput in the next MU-MIMO transmission and having large amount transmission data while reducing signaling overhead, leading to improvements in system throughput and fairness in communication. The proposed wireless frame setting, which employs hybrid A-MSDU/A-MPDU FA, achieves frame error rate (FER) better than the requirement from communication services by using A-MSDU frame size adaptation. Through system-level simulation, the effectiveness of the proposed scheme is validated for downlink MU-MIMO channels in WLANs.

Some networks, such as wireless sensor networks, vehicle networks, etc., are often disconnected and thus fail to provide an end-to-end route for transmission. As a result, a new kind self-organized wireless network, i.e., Delay Tolerant Network (DTN) is proposed to transmit messages using a store-carry-forward method. To efficiently process aggregation queries, this paper proposes a subscription aggregation query processing method that combines query processing and transfer protocols. The basic idea is reducing the number of redundant copy transmissions, increasing the message delivery rate and reducing the transmission delay by matrix summation. Theoretical and experimental results show that the method can attain a good performance in the delay tolerant networks.

Cost aggregation is one of the most important steps in local stereo matching, while it is difficult to fulfill both accuracy and speed. In this letter, a novel cost aggregation, consisting of guidance image, fast aggregation function and simplified scan-line optimization, is developed. Experiments demonstrate that the proposed algorithm has competitive performance compared with the state-of-art aggregation methods on 32 Middlebury stereo datasets in both accuracy and speed.

The traffic of the future aggregation network will dynamically change not only in volume but also destination to support the application of virtualization technology to network edge equipment to achieve cost-effectiveness. Therefore, future aggregation network will have to accommodate this traffic cost-effectively, despite dynamic changes in both volume and destination. To correspond to this trend, in this paper, we propose an optical layer 2 switch network based on bufferless optical time division multiplexing (TDM) and dynamic bandwidth allocation to achieve a future aggregation network cost-effectively. We show here that our proposed network architecture effectively reduced the number of wavelengths and optical interfaces by application of bufferless optical TDM technology and dynamic bandwidth allocation to the aggregation network.

In this paper, we propose Configurable Query Processing Hardware (CQPH), an FPGA-based accelerator for continuous query processing over data streams. CQPH is a highly optimized and minimal-overhead execution engine designed to deliver real-time response for high-volume data streams. Unlike most of the other FPGA-based approaches, CQPH provides on-the-fly configurability for multiple queries with its own dynamic configuration mechanism. With a dedicated query compiler, SQL-like queries can be easily configured into CQPH at run time. CQPH supports continuous queries including selection, group-by operation and sliding-window aggregation with a large number of overlapping sliding windows. As a proof of concept, a prototype of CQPH is implemented on an FPGA platform for a case study. Evaluation results indicate that a given query can be configured within just a few microseconds, and the prototype implementation of CQPH can process over 150 million tuples per second with a latency of less than a microsecond. Results also indicate that CQPH provides linear scalability to increase its flexibility (i.e., on-the-fly configurability) without sacrificing performance (i.e., maximum allowable clock speed).

Ring aggregation networks are widely employed for metro access networks. A layer-2 ring with Ethernet Ring Protection is a popular topology for carrier services. Since frames are forwarded along ring nodes, a fairness scheme is required to achieve throughput fairness. Although per-node fairness algorithms have been developed for the Resilient Packet Ring, the per-node fairness is insufficient if there is bias in a flow distribution. To achieve per-flow fairness, N Rate N+1 Color Marking (NRN+1CM) was proposed. However, NRN+1CM can achieve fairness in case there are sufficient numbers of available bits on a frame header. It cannot be employed if the frame header cannot be overwritten. Therefore, the application range of NRN+1CM is limited. This paper proposes a Signaling based Discard with Flags (SDF) scheme for per-flow fairness. The objective of SDF is to eliminate the drawback of NRN+1CM. The key idea is to attach a flag to frames according to the input rate and to discard them selectively based on the flags and a dropping threshold. The flag is removed before the frame is transmitted to another node. The dropping threshold is cyclically updated by signaling between ring nodes and a master node. The SDF performance was confirmed by employing a theoretical analysis and computer simulations. The performance of SDF was comparable to that of NRN+1CM. It was verified that SDF can achieve per-flow throughput fairness without using a frame header in ring aggregation networks.

Although the use of software-defined networking (SDN) enables routes of packets to be controlled with finer granularity (down to the individual flow level) by using traffic engineering (TE) and thereby enables better balancing of the link loads, the corresponding increase in the number of states that need to be managed at routers and controller is problematic in large-scale networks. Aggregating flows into macro flows and assigning routes by macro flow should be an effective approach to solving this problem. However, when macro flows are constructed as TE targets, variations of traffic rates in each macro flow should be minimized to improve route stability. We propose two methods for generating macro flows: one is based on a greedy algorithm that minimizes the variation in rates, and the other clusters micro flows with similar traffic variation patterns into groups and optimizes the traffic ratio of extracted from each cluster to aggregate into each macro flow. Evaluation using traffic demand matrixes for 48 hours of Internet2 traffic demonstrated that the proposed methods can reduce the number of TE targets to about 1/50 ∼ 1/400 without degrading the link-load balancing effect of TE.

TV white space (TVWS) brings potential opportunities to relieve the growing spectrum scarcity. Therefore organizations like the FCC have suggested the co-channel deployment of cellular networks (CNs) on condition that a keep-out distance from the protected region of TV receivers is maintained. However the consequent CN interference has not been described. In addition, considering the wide range of TV coverage, it is also inefficient and wasteful not applying the vacant spectra for secondary user (SU) communication by opportunistic access inside the TV coverage zone. In this paper, we first investigate the aggregate interference from CNs outside the protected area to find out how the interference is generated, and then research the available spectrum resource distribution for SUs inside the TV coverage zone under aggregate interference constraints to utilize TVWS more efficiently. Specifically, we model CN in three aspects. A close-form interference probability distribution function (PDF) is proposed. Since the PDF is too complex to analyze, we approximate it as Gaussian and prove the accuracy of our approximation with Kolmogorov-Smirnov test. Then, available spectra maximization is formulated as an optimization problem under both TV and SU receiver outage probability constraints. We find that available spectra demonstrate a volcano-shaped geographical distribution and optimal network-status-aware SU transmit power exists to maximize the spectra. Our analysis reveals the characteristics of interference in TVWS and contributes to the utilization improvement of white space.

Ontology mapping is important in many areas, such as information integration, semantic web and knowledge management. Thus the effectiveness of ontology mapping needs to be further studied. This paper puts forward a mapping method between different ontology concepts in the same field. Firstly, the algorithms of calculating four individual similarities (the similarities of concept name, property, instance and structure) between two concepts are proposed. The algorithm features of four individual similarities are as follows: a new WordNet-based method is used to compute semantic similarity between concept names; property similarity algorithm is used to form property similarity matrix between concepts, then the matrix will be processed into a numerical similarity; a new vector space model algorithm is proposed to compute the individual similarity of instance; structure parameters are added to structure similarity calculation, structure parameters include the number of properties, instances, sub-concepts, and the hierarchy depth of two concepts. Then similarity of each of ontology concept pairs is represented by a vector. Finally, Support Vector Machine (SVM) is used to accomplish mapping discovery by training and learning the similarity vectors. In this algorithm, Harmony and reliability are used as the weights of the four individual similarities, which increases the accuracy and reliability of the algorithm. Experiments achieve good results and the results show that the proposed method outperforms many other methods of similarity-based algorithms.

In this letter, we propose an energy-efficient in-network processing method for continuous grouped aggregation queries in wireless sensor networks. As in previous work, in our method sensor nodes partially compute aggregates as data flow through them to reduce data transferred. Different from other methods, our method considers group information of partial aggregates when sensor nodes forward them to next-hop nodes in order to maximize data reduction by same-group partial aggregation. Through experimental evaluation, we show that our method outperforms the existing methods in terms of energy efficiency.

Bandwidth aggregation (BAG) techniques have been researched for many years in an efforts to enhance throughput for multi-homed streaming service. However, despite of the considerable attention being devoted towards energy-efficient communications, the power efficiency for BAG has not been considered yet. To improve the power efficiency in multi-homed streaming service, this paper proposes Power Minimized Rate Allocation Scheme (PMRAS) with optimal rate allocation at each interface while guaranteeing an allowable packet loss rate. In developing PMRAS, we first formulate a power consumption model based on the network interface state (i.e. active and idle state). We adopt a Lagrangian algorithm to solve the convex optimization problem of power consumption. The performance results gained from a numerical analysis and simulations (NS-2) reveal that the proposed scheme offers superior performance over the existing rate allocation scheme for BAG with guaranteed required quality of service.