Heterogeneous Links Research Articles

Digitization and Automation: Firm Investment and Labor Outcomes

Digitization and automation are thought to be transforming the economy, but evidence on their adoption and impact is limited. This paper analyzes determinants and effects of firms' investment in these technologies using administrative data from Germany. The main result is that while technology typically substitutes for workers, in several service industries the complementarity effect dominates. This is shown using two approaches: (1) labor scarcity increases investment in technology on average but impedes it in selected industries; (2) technology typically reduces employment but increases it in selected industries. For identification, I instrument labor scarcity with population aging and use a difference-in-differences design that combines industry-level technological trends with local intensity of technology adoption. Additional results show that financial constraints impede technology adoption and that the new technology is skill-biased. Overall, the paper unwinds the heterogeneous link between new technologies and labor, highlighting the importance of analyzing a broad set of technologies and studying patterns of their adoption by firms.

ADYTIA: Adaptive and Dynamic TCP Interface Architecture for heterogeneous networks

SummaryDue to the widespread popularity and usage of Internet of things (IoT)‐enabled devices, there is an exponential increase in the data traffic generated from these IoT devices. Most of these devices communicate with each other using heterogeneous links having constraints such as latency, throughput, and interference from concurrent transmissions. This results in an extra burden on the underlying communication infrastructure to manage the traffic within these constraints between source and destination. However, most of the existing applications use different Transmission Control Protocol (TCP) variants for traffic management between these devices and are dependent on the stage of the sender, irrespective of the application types and link characteristics. Each operating system (OS) has different TCP variant for all applications, irrespective of path characteristics. Hence, a single TCP variant cannot select the best suitable link, which results in degradation in throughput compared to the existing default. Moreover, it cannot use the full capacity of the available link for different applications and network links, especially in heterogeneous network such as IoT. To cope up with these challenges, in this paper, we propose an Adaptive and Dynamic TCP Interface Architecture (ADYTIA). ADYTIA allows the usage of different TCP variants based on application and link characteristics, irrespective of the physical links of the entire path. It allows the usage of different TCP variants based on their design principle across heterogeneous technologies, platforms, and applications. ADYTIA is implemented on NS‐2 and Linux kernel for real testbed experiments. Its ability to select the best suitable TCP variant results in 20% to 80% improvement in throughput compared with the existing default and single TCP variant on Linux and Windows.

Heterogeneous links between urban form and mobility: A comparison of São Paulo, Istanbul and Mumbai

This paper presents determinants of travel demand in three important cities in emerging economies: São Paulo, Istanbul and Mumbai. By comparison, similarities and differences of travel demand among the cities are identified and discussed with regard to their geographical, institutional and spatio-physical conditions. Special attention is paid to the hitherto understudied impact of the built environment on travel in emerging economy cities. Drawing on identical household surveys carried out in each city, the study reveals that gender, social status, car ownership and geographical location are consistently associated with mode choice. Yet, the relative importance of those characteristics differs in each city in line with their distinct socio-cultural realities. Trip duration appears to be more affected by built-environment characteristics, once mode choice is taken into account. But, again, potential influences of the built environment operate in different ways in São Paulo, Istanbul and Mumbai. In particular, there appears to be a closer relationship between transport and land-use in Mumbai. The variation-finding, comparative design reveals plural associations of life situation, the built environment and travel, and thus evinces specific interactions that require contextual policy attention to achieve sustainable and inclusive urban mobility.

A new routing strategy limited by heterogeneous link bandwidth

Traffic capacity of a network is very vital to a variety of complex networks, such as communication networks and road networks, in which the bandwidth of every link is limited or finite. In this work, inspired by the deployment process of nodes and links in real networks, we assume the bandwidth of every link is composed of a constant part and a degree-related one that can be updated iteratively with the network hardware update. We propose a link bandwidth-based routing mechanism to enhance the network traffic capacity. Extensive simulations in both scale-free networks and random networks are done to confirm the effectiveness of our proposed method. Comparing results with the shortest path routing and a weighted routing, our method achieves better network traffic capacity among all used routing strategies without obvious extra cost including the network diameter, average path length and average packet traveling time. Our work studies network routing from a very new perspective and might have potential applications in real network systems such as the communication networks.

Robust Channel Allocation with Heterogeneous Requirements for Wireless Mesh Backbone Networks

When multiple mobile sensors and actuators share a common wireless mesh backbone network of defence systems, the channel allocation mechanism must guarantee the heterogeneous link requirements under conditions of uncertainty. In this paper, a robust channel allocation mechanism is proposed by exploiting partially overlapped channels for directional multi-channel wireless mesh networks. The approach relies on a chance-constrained optimization problem, in which the objective is to minimize the spectrum usage of the network, and the constraints are the signal-to-interference-plus-noise ratio requirements of links with uncertainty. We convert the proposed integer non-linear optimization problem into a mixed-integer convex problem by using efficient transition and approximation. The optimal channel allocation is obtained by solving the proposed optimization problem which adapts to the heterogeneous link and robustness requirements. The simulation results show that the proposed method ensures the heterogeneous link requirements under uncertain conditions while minimizing the spectrum usage of the network.

Data collection in sensornets with heterogeneous duty cycles: pursuit of efficient opportunity

For sensornets with heterogeneous duty cycles, sensor nodes wake up with different sleeping periods, which combined with unreliable links, making it very challenging to enhance the network performance. This paper presents a new opportunistic routing protocol, called EOF (Efficient Opportunistic Forwarding), which mainly involves a forwarding metric that can efficiently exploit the single-hop forwarding opportunity and a delay-aware forwarder selection scheme. Different from previous opportunistic routings for sensornets, EOF comprehensively considers the effect of heterogeneous duty cycles and link unreliability on the overall network performance. The experimental results show that compared to the state-of-the-art protocol, EOF can achieve better overall network performance with almost identical energy cost, especially when it is applied to the sensornet with significant heterogeneity of duty cycle.

Network Learning via Multiagent Inverse Transportation Problems

Despite the ubiquity of transportation data, methods to infer the state parameters of a network either ignore sensitivity of route decisions, require route enumeration for parameterizing descriptive models of route selection, or require complex bilevel models of route assignment behavior. These limitations prevent modelers from fully exploiting ubiquitous data in monitoring transportation networks. Inverse optimization methods that capture network route choice behavior can address this gap, but they are designed to take observations of the same model to learn the parameters of that model, which is statistically inefficient (e.g., requires estimating population route and link flows). New inverse optimization models and supporting algorithms are proposed to learn the parameters of heterogeneous travelers’ route behavior to infer shared network state parameters (e.g., link capacity dual prices). The inferred values are consistent with observations of each agent’s optimization behavior. We prove that the method can obtain unique dual prices for a network shared by these agents in polynomial time. Four experiments are conducted. The first one, conducted on a four-node network, verifies the methodology to obtain heterogeneous link cost parameters even when multinomial or mixed logit models would not be meaningfully estimated. The second is a parameter recovery test on the Nguyen–Dupuis network that shows that unique latent link capacity dual prices can be inferred using the proposed method. The third test on the same network demonstrates how a monitoring system in an online learning environment can be designed using this method. The last test demonstrates this learning on real data obtained from a freeway network in Queens, New York, using only real-time Google Maps queries.

Mining user interests over active topics on social networks

Inferring users’ interests from their activities on social networks has been an emerging research topic in the recent years. Most existing approaches heavily rely on the explicit contributions (posts) of a user and overlook users’ implicit interests, i.e., those potential user interests that the user did not explicitly mention but might have interest in. Given a set of active topics present in a social network in a specified time interval, our goal is to build an interest profile for a user over these topics by considering both explicit and implicit interests of the user. The reason for this is that the interests of free-riders and cold start users who constitute a large majority of social network users, cannot be directly identified from their explicit contributions to the social network. Specifically, to infer users’ implicit interests, we propose a graph-based link prediction schema that operates over a representation model consisting of three types of information: user explicit contributions to topics, relationships between users, and the relatedness between topics. Through extensive experiments on different variants of our representation model and considering both homogeneous and heterogeneous link prediction, we investigate how topic relatedness and users’ homophily relation impact the quality of inferring users’ implicit interests. Comparison with state-of-the-art baselines on a real-world Twitter dataset demonstrates the effectiveness of our model in inferring users’ interests in terms of perplexity and in the context of retweet prediction application. Moreover, we further show that the impact of our work is especially meaningful when considered in case of free-riders and cold start users.

Blind Adaptive Digital Backpropagation for Fiber Nonlinearity Compensation

Fully blind adaptive digital backpropagation (DBP) based on a steepest descent algorithm is proposed. For homogeneous links, a two-parameter blind adaptive (TBA) scheme is presented, which assumes that all DBP steps have the same nonlinear scaling factor and filter bandwidth parameter. For heterogeneous links, a multiparameter blind adaptive (MBA) scheme is proposed to achieve the optimal performance by adapting chromatic dispersion, nonlinear scaling factor, and bandwidth parameter of each DBP step. The principles and performance of the proposed schemes are presented for low-pass filter assisted DBP, denoted as LDBP, in single carrier systems and cross-phase modulation model based DBP in subcarrier-multiplexing (SCM) systems, denoted as SCM-DBP. In a 34.94 Gbaud dual-polarization 16QAM single channel transmission experiment with a homogeneous 2560 km link, we demonstrate that both TBA-LDBP and TBA-SCM-DBP can converge to optimal performance with various system configurations. In addition, for heterogeneous links, we show in simulations and experiments that MBA-LDBP and MBA-SCM-DBP achieve 0.2–1.4 dB improvement compared to their TBA counterparts.

Alternative Scheduling Decisions for Multipath TCP

In this letter, we discuss the impact of packet scheduling decisions on the performance of Multipath Transmission Control Protocol (MPTCP) flows. We propose, implement, and evaluate three alternative schedulers, namely, Highest Sending Rate (HSR), Largest Window Space (LWS) and Lowest Time/Space (LTS). We evaluate and compare them to the current MPTCP’s scheduler, Lowest Latency (LL), in an experimental environment. To analyze the performance of the multipath throughput improvement of these schedulers, we conducted a vast set of 40 different experiments that combine homogeneous and heterogeneous link parameterizations, considering packet loss rate, delay, and transmission rate. Experimental results showed that the proposed alternatives achieved a higher performance than LL in most of the experiments, allowing better alternatives for improving multipath throughput with paths under loss, delay, and varied transmission rates.

Aggregating LTE and Wi-Fi: Toward Intra-Cell Fairness and High TCP Performance

The data explosion and resource scarcity of mobile cellular networks require new paradigms to effectively integrate heterogeneous radio resources. Of many candidate approaches, smart aggregation of LTE and Wi-Fi radios is a promising solution that bonds heterogeneous links to meet a mobile terminal’s bandwidth need. Motivated by the existence of a significant number of carrier operated Wi-Fi APs, we propose an easily deployable mechanism, called LTE-W, which efficiently utilizes LTE and Wi-Fi links only with the minimum change of eNodeBs, LTE backhaul networks, and mobile terminals. LTE-W, which is a link-level aggregation mechanism, has the following two key components: 1) mode selection and 2) bearer-split scheduling. First, in the mode selection, LTE-W internally decides who should be served by either LTE-only or LTE-Wi-Fi aggregation considering intra-cell fairness rather than just following users’ intention of aggregation. For the users’ preference to be offered the aggregation service, we choose a bearer (roughly defined in LTE as a set of flows with a similar QoS) as a basic unit of aggregation and propose a smart intra-bearer scheduling algorithm that splits a bearer’s traffic into LTE and Wi-Fi links, considering the performance of TCP flows that take two heterogeneous wireless links. We evaluate our mechanism using the NS-3 with LENA, under various configurations, including nodes with mobility and HTTP traffic, and compare it with a transport-level aggregation mechanism, multipath TCP (MPTCP), demonstrating that LTE-W significantly improves MPTCP, e.g., up to 75% in terms of Jain’s fairness index.

An improved approximate network blocking probability model for all-optical WDM Networks with heterogeneous link capacities

Abstract Currently, analytical models are used to compute approximate blocking probabilities in opaque and all-optical WDM networks with the homogeneous link capacities. Existing analytical models can also be extended to opaque WDM networking with heterogeneous link capacities due to the wavelength conversion at each switch node. However, existing analytical models cannot be utilized for all-optical WDM networking with heterogeneous structure of link capacities due to the wavelength continuity constraint and unequal numbers of wavelength channels on different links. In this work, a mathematical model is extended for computing approximate network blocking probabilities in heterogeneous all-optical WDM networks in which the path blocking is dominated by the link along the path with fewer number of wavelength channels. A wavelength assignment scheme is also proposed for dynamic traffic, termed as last-fit-first wavelength assignment, in which a wavelength channel with maximum index is assigned first to a lightpath request. Due to heterogeneous structure of link capacities and the wavelength continuity constraint, the wavelength channels with maximum indexes are utilized for minimum hop routes. Similarly, the wavelength channels with minimum indexes are utilized for multi-hop routes between source and destination pairs. The proposed scheme has lower blocking probability values compared to the existing heuristic for wavelength assignments. Finally, numerical results are computed in different network scenarios which are approximately equal to values obtained from simulations.

V-BAG: A virtual bandwidth aggregation scheme for Internet of Things data

In order to meet the two crucial requirements of Internet of Things (support of a large number of Internet of Things devices and low power consumption), this article proposes a novel and efficient virtual bandwidth aggregation scheme for Internet of Things data. Since many Internet of Things applications require reliable Internet of Things data transmission, a Transmission Control Protocol is chosen as a transport protocol in this article. The proposed scheme consists of three salient functions: packet scheduler, estimator, and ACK buffer. The packet scheduler provides an efficient heterogeneous link selection using the proposed scheduling algorithm. The estimator provides an accurate delay and bandwidth estimation of heterogeneous wireless links using network layer information. Finally, the ACK buffer substantially boosts the performance of the scheduler and estimator with a simple ACK buffering scheme. Our extensive analysis using a numerical method and simulation corroborates the outstanding performance of the proposed scheme. Because of the salient functions of the proposed scheme, it can provide maximum capacity for future Internet of Things data transmission.

Reliable network design under supply uncertainty with probabilistic guarantees

ABSTRACTThis paper proposes a bi-level risk-averse network design model for transportation networks with heterogeneous link travel time distributions. The objective of the network design is to minimise the total system travel time (TSTT) budget (TSTTB), which consists of the mean TSTT and a safety margin. The design is achieved by selecting optimal link capacity expansions subject to a fixed expansion budget. Users’ selfish behaviour and risk attitude are captured in the lower level traffic assignment constraints, in which travellers select routes to minimise their own path travel time budget. The properties of the design problem are analysed analytically and numerically. The analysis shows that despite the lack of knowledge of travel time distributions, the probabilities that the actual TSTT and the actual path travel time are, respectively, within the optimal TSTTB and the minimum path travel time budget under optimal design have lower bounds. The lower bounds are related to the system manager's and travellers’ risk aversion. The optimal TSTTB is proven to be bounded below even when the link expansion budget is unlimited.

Heterogeneous Link Prediction Technique in Personalized E-Learning System using SVM

Personalized learning system is an online educational system; it is used to predict the learner's most prepared learning styles. Preferences of learning styles are found through way of linking different types of materials over the web. Deal of these website links, the link prediction is introduced; it is a problem technique in many online social networks, online E-learning centers, its forums and communities. In general, the link prediction techniques deal with homogeneous way of predicting the user's learning style in online social networks and educational systems. However, a new challenging problem in link prediction is to analyze heterogeneous learner's (e.g. male and female) based on their preference of learning styles in selecting the links. A day-by-day research works are being incorporated in the technology-enhanced between heterogeneous entity and their perception level. This research work mainly focus on examining the information of different learning styles and prediction of more preferred learning styles links by classification models through Weka tool. The experiment was examined using various data mining classification models, where Support vector machine (SVM) classification model has achieved good performance result and has predicted the learner's potential by analyzing the most representative learner's’ features of perception level and gender. Finally the research work has concluded how these representative learner's features are incorporated with learning style dimensions as well as better learning model is used on the system.

Multi-level networked knowledge: representation and DL-reasoning

Integrating pre-existing, heterogeneous, and complementary ontologies, and exploiting them jointly in reasoning remains a major challenge. Ontology alignments make explicit the correspondences between terms from different ontologies and must be taken into account in reasoning. Two forms of correspondences can be introduced: mappings represent predefined relations such as subsumption, equivalence, or disjointness, that have a fixed semantics in all interpretations; links can relate complementary ontologies by introducing terms defined by experts, and their semantics varies according to interpretations. Different experts can introduce different terms according to their points of view, which brings semantically heterogeneous links. Thus, integrating pre-existing networks of aligned ontologies requires aligning terminologies from different alignments, so as to form higher level alignments. This generates networked knowledge that can in turn be aligned with other networked knowledge. As a result, we talk of multi-level networked knowledge, a concept that we formalise here and for which we propose a possible formal semantic for automating reasoning tasks. This semantic consists of reducing reasoning on networked knowledge to reasoning over DL formalisms for which we have reasoning procedures. The proposed approach is implemented and tested in order to compare results, for different networks.

Matching structure and bargaining outcomes in buyer–seller networks

We examine the relationship between the matching structure of a bipartite (buyer–seller) network and the (expected) shares of the unit surplus that each connected pair in this network can create. We show that in different bargaining environments, these shares are closely related to the Gallai–Edmonds Structure Theorem. This theorem characterizes the structure of maximum matchings in an undirected graph. We show that the relationship between the (expected) shares and the Structure Theorem is not an artefact of a particular bargaining mechanism or trade centralization. However, this relationship does not necessarily generalize to non-bipartite networks or to networks with heterogeneous link values.

Low-Overhead Control Channels in Wireless Networks

Low-latency, low-overhead and reliable control channels are essential to the efficient operation of wireless networks. However, control channels that utilize current in-band and out-of-band designs do not fully meet this requirement. In this paper, we design and implement Aileron, a novel control channel based on automatic modulation recognition that carries control frames over an OFDM(A) PHY by varying the modulation rate of the OFDM subcarriers. With Aileron, the control information is embedded into the modulation type , not as the actual symbol value. The thus-transmitted information can be received with little to no frame synchronization. We have evaluated Aileron using both extensive simulations and real-world measurements, and discovered that control frames can be transmitted with more than 80 percent accuracy using only 10 OFDM blocks on a channel with SNR of merely 10dB. In order to demonstrate its applicability and efficiency in realistic networking scenarios, we also show how Aileron can be used in conjunction with a fine-grained channel access protocol to increase throughput under heterogeneous link conditions.

OLSRv2 for Community Networks: Using Directional Airtime Metric with external radios

In this paper we present novel approaches to routing in Community Networks. We combine recent advances in OLSR development regarding modularization, scalability, extensibility, and metrics with a node architecture concept based on radio and router separation adopted to Community Networks. This node architecture consists of a single router with several external radios acting as wireless bridges and connecting to the router via standard Ethernet, which we refer to as Hybrid Node Design. Furthermore, we propose the use of our Directional Airtime (DAT) routing metric that is suitable for the heterogeneous link characteristics often found in Community Networks. By using this metric we achieve a more stable route selection process and improved throughput in comparison to the OLSR-based approach currently used in Community Networks. In addition, we present some enhanced features unique to our implementation that include means to increase the self-configuration capabilities in IPv4, IPv6, and dual-stack configurations. As a consequence, we recommend that it is the right time for Community Networks to start the migration process from OLSRv1 to OLSRv2.For the implementation process and the protocol evaluation we followed a testbed-driven approach. It was supported by the Confine testbed both, in a virtualized version, as well as in a physical deployment in a dense setup at Fraunhofer FKIE. The physical testbed consists of over 20 indoor research nodes on two floors implementing our hybrid node design. Link layer information from the radio devices to the router is provided by the DLEP protocol, which was designed especially for wireless mesh networks. The obtained link layer information is used by OLSRv2 for routing metric computation. We propose the use of this architecture in Community Networks in order to increase scalability and simplify network administration.

Average latency and link utilization analysis of heterogeneous wormhole NoCs

We present a novel methodology for analysis of the average end-to-end latency and link utilization of wormhole NoCs with heterogeneous link capacities and heterogeneous number of virtual channels per unidirectional link. Our analysis methodology supports different heterogeneous NoC topologies for which no analysis framework has been developed in the past. The average end-to-end latency per flow is separated to three components: (1) The time it takes the head-flit to leave the source queue (queuing time at the source); (2) The time it takes the head-flit to reach the destination module (path acquisition time) and (3) The time it takes the rest of the packet to leave the network (transfer time). Furthermore, we take into account the interaction between intersecting flows. The average link utilization is calculated based on the path acquisition time and transfer time. To that end, our analysis introduces a set of implicit equations, which can be efficiently solved iteratively. We demonstrate the accuracy and the efficiency of our approximate analysis by comparing the analysis results to accurate simulations for several synthetic examples and SoC multimedia applications in heterogeneous networks. In addition, we compare the analysis results with HNOCS simulation results for a chip-multi-processor (CMP) using SPLASH-2 and PARSEC traces for both homogeneous and heterogeneous NoC configurations.