Reliable link quality estimation in low-power wireless networks and its impact on tree-routing

Radio link quality estimation is essential for protocols and mechanisms such as routing, mobility management and localization, particularly for low-power wireless networks such as wireless sensor networks. Commodity Link Quality Estimators (LQEs), e.g. PRR, RNP, ETX, four-bit and RSSI, can only provide a partial characterization of links as they ignore several link properties such as channel quality and stability. In this paper, we propose F-LQE (Fuzzy Link Quality Estimator, a holistic metric that estimates link quality on the basis of four link quality properties—packet delivery, asymmetry, stability, and channel quality—that are expressed and combined using Fuzzy Logic. We demonstrate through an extensive experimental analysis that F-LQE is more reliable than existing estimators (e.g., PRR, WMEWMA, ETX, RNP, and four-bit) as it provides a finer grain link classification. It is also more stable as it has lower coefficient of variation of link estimates. Importantly, we evaluate the impact of F-LQE on the performance of tree routing, specifically the CTP (Collection Tree Protocol). For this purpose, we adapted F-LQE to build a new routing metric for CTP, which we dubbed as F-LQE/RM. Extensive experimental results obtained with state-of-the-art widely used test-beds show that F-LQE/RM improves significantly CTP routing performance over four-bit (the default LQE of CTP) and ETX (another popular LQE). F-LQE/RM improves the end-to-end packet delivery by up to 16%, reduces the number of packet retransmissions by up to 32%, reduces the Hop count by up to 4%, and improves the topology stability by up to 47%.

Bringing Context Awareness to IoT-Based Wireless Sensor Networks

IEEE International Conference on Pervasive Computing and Communications (PerCom). 23 to 26, Mar, 2015, PhD Forum. Saint Louis, U.S.A..

Poster/Demo Session Proceedings of the 12th European Conference on Wireless Sensor Networks (EWSN'15) C

Proceedings of the 12th European Conference on Wireless Sensor Networks (EWSN'15), 9-11 Feb 2015, Porto-Portugal.

A Modular Programming Approach for IoTBased Wireless Sensor Networks

Poster presented in 12th European Conference on Wireless Sensor Network (EWSN 2015). 9 to 11, Feb, 2015. Porto, Portugal.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Learning networks and moodle use in online courses: a social network analysis study

Dissertação para obtenção do Grau de Doutor em Ciências da Educação Especialidade em Tecnologias, Redes e Multimédia na Educação e Formação

Migrant networks and political participation in Mozambique

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics

Migrant networks and migrant intentions to return

Social ties are potentially an important determinant of migrants’ intention to return to their home country, and yet this topic has not been addressed in the existing economics literature on international migration. This study examines the absolute and relative importance of migrant social networks both at destination and at origin. We base our research on experimental data from Batista and Narciso (2013)1. By defining networks according to different characteristics of their members and migrant return intentions with respect to three different time horizons, we are able to dissect the network effect into its components. After controlling for unobserved heterogeneity and reverse causality biases we find that network at home seems to be the most important determinant of the migrant’s intention to return home within five and ten years.

Gendered networks and Mexican migration

In this paper, we investigate how the gendered origin of migrant networks (i.e. matrilineal vs. patrilineal) is associated with aspirations to migrate and subsequent migration behavior. Using longitudinal data from the Mexican Family Life Survey (MxFLS), we follow 3,923 married couples across 139 municipalities over the 2002-2005 period. We find that the networks of both the individual and her/his spouse are associated with aspiring to migrate to the United States. However, one’s own network matters most (i.e. matrilineal networks for women and patrilineal networks for men). On the other hand, in terms of behavior, only matrilineal networks predict a subsequent move to the U.S. for men and women/couples, who are assessed jointly. These findings suggest that our understanding of the role of migrant networks in perpetuating male-centered, labor migration does not necessarily translate once a union has formed. We make the case that future work would do well to account for not only the presence and composition of networks, but also their origin, which in certain circumstances may be the most relevant factor.

Networks and partnerships in third party logistics service production

Third party logistics, and third party logistics providers and the services they offer have grown substantially in the last twenty years. Even though there has been extensive research on third party logistics providers, and regular industry reviews within the logistics industry, a closer research in the area of partner selection and network models in the third party logistics industry is missing. The perspective taken in this study was of expanding the network research into logistics service providers as the focal firm in the network. The purpose of the study is to analyze partnerships and networks in the third party logistics industry in order to define how networks are utilized in third party logistics markets, what have been the reasons for the partnerships, and whether there are benefits for the third party logistics provider that can be achieved through building networks and partnerships. The theoretical framework of this study was formed based on common theories in studying networks and partnerships in accordance with models of horizontal and vertical partnerships. The theories applied to the framework and context of this study included the strategic network view and the resource-based view. Applying these two network theories to the position and networks of third party logistics providers in an industrial supply chain, a theoretical model for analyzing the horizontal and vertical partnerships where the TPL provider is in focus was structured. The empirical analysis of TPL partnerships consisted of a qualitative document analysis of 33 partnership examples involving companies present in the Finnish TPL markets. For the research, existing documents providing secondary data on types of partnerships, reasons for the partnerships, and outcomes of the partnerships were searched from available online sources. Findings of the study revealed that third party logistics providers are evident in horizontal and vertical interactions varying in geographical coverage and the depth and nature of the relationship. Partnership decisions were found to be made on resource based reasons, as well as from strategic aspects. The discovered results of the partnerships in this study included cost reduction and effectiveness in the partnerships for improving existing services. In addition in partnerships created for innovative service extension, differentiation, and creation of additional value were discovered to have emerged as results of the cooperation. It can be concluded that benefits and competitive advantage can be created through building partnerships in order to expand service offering and seeking synergies.

Routing protocol enhancement for handling node mobility in wireless sensor networks

In wireless sensor networks, the routing algorithms currently available assume that the sensor nodes are stationary. Therefore when mobility modulation is applied to the wireless sensor networks, most of the current routing algorithms suffer from performance degradation. The path breaks in mobile wireless networks are due to the movement of mobile nodes, node failure, channel fading and shadowing. It is desirable to deal with dynamic topology changes with optimal effort in terms of resource and channel utilization. As the nodes in wireless sensor medium make use of wireless broadcast to communicate, it is possible to make use of neighboring node information to recover from path failure. Cooperation among the neighboring nodes plays an important role in the context of routing among the mobile nodes. This paper proposes an enhancement to an existing protocol for accommodating node mobility through neighboring node information while keeping the utilization of resources to a minimum.

An adaptive cluster based routing scheme for mobile wireless sensor networks

Clustering schemes improve energy efficiency of wireless sensor networks. The inclusion of mobility as a new criterion for the cluster creation and maintenance adds new challenges for these clustering schemes. Cluster formation and cluster head selection is done on a stochastic basis for most of the algorithms. In this paper we introduce a cluster formation and routing algorithm based on a mobility factor. The proposed algorithm is compared with LEACH-M protocol based on metrics viz. number of cluster head transitions, average residual energy, number of alive nodes and number of messages lost

Channel Adaptive MAC Protocol with Traffic-Aware Distributed Power Management in Wireless Sensor Networks-Some Performance Issues

In Wireless Sensor Networks (WSN), neglecting the effects of varying channel quality can lead to an unnecessary wastage of precious battery resources and in turn can result in the rapid depletion of sensor energy and the partitioning of the network. Fairness is a critical issue when accessing a shared wireless channel and fair scheduling must be employed to provide the proper flow of information in a WSN. In this paper, we develop a channel adaptive MAC protocol with a traffic-aware dynamic power management algorithm for efficient packet scheduling and queuing in a sensor network, with time varying characteristics of the wireless channel also taken into consideration. The proposed protocol calculates a combined weight value based on the channel state and link quality. Then transmission is allowed only for those nodes with weights greater than a minimum quality threshold and nodes attempting to access the wireless medium with a low weight will be allowed to transmit only when their weight becomes high. This results in many poor quality nodes being deprived of transmission for a considerable amount of time. To avoid the buffer overflow and to achieve fairness for the poor quality nodes, we design a Load prediction algorithm. We also design a traffic aware dynamic power management scheme to minimize the energy consumption by continuously turning off the radio interface of all the unnecessary nodes that are not included in the routing path. By Simulation results, we show that our proposed protocol achieves a higher throughput and fairness besides reducing the delay

Routing Protocol Enhancement for handling Node Mobility in Wireless Sensor Networks

In wireless sensor networks, the routing algorithms currently available assume that the sensor nodes are stationary. Therefore when mobility modulation is applied to the wireless sensor networks, most of the current routing algorithms suffer from performance degradation. The path breaks in mobile wireless networks are due to the movement of mobile nodes, node failure, channel fading and shadowing. It is desirable to deal with dynamic topology changes with optimal effort in terms of resource and channel utilization. As the nodes in wireless sensor medium make use of wireless broadcast to communicate, it is possible to make use of neighboring node information to recover from path failure. Cooperation among the neighboring nodes plays an important role in the context of routing among the mobile nodes. This paper proposes an enhancement to an existing protocol for accommodating node mobility through neighboring node information while keeping the utilization of resources to a minimum.