Traffic-Adaptive and Link-Quality-Aware Communication in Wireless Sensor Networks

This paper is a summary of the main contribu- tions of the PhD thesis published in [1]. The main research contributions of the thesis are driven by the research question how to design simple, yet efficient and robust run-time adaptive resource allocation schemes within the commu- nication stack of Wireless Sensor Network (WSN) nodes. The thesis addresses several problem domains with con- tributions on different layers of the WSN communication stack. The main contributions can be summarized as follows: First, a a novel run-time adaptive MAC protocol is intro- duced, which stepwise allocates the power-hungry radio interface in an on-demand manner when the encountered traffic load requires it. Second, the thesis outlines a metho- dology for robust, reliable and accurate software-based energy-estimation, which is calculated at network run- time on the sensor node itself. Third, the thesis evaluates several Forward Error Correction (FEC) strategies to adap- tively allocate the correctional power of Error Correcting Codes (ECCs) to cope with timely and spatially variable bit error rates. Fourth, in the context of TCP-based communi- cations in WSNs, the thesis evaluates distributed caching and local retransmission strategies to overcome the perfor- mance degrading effects of packet corruption and trans- mission failures when transmitting data over multiple hops. The performance of all developed protocols are eval- uated on a self-developed real-world WSN testbed and achieve superior performance over selected existing ap- proaches, especially where traffic load and channel condi- tions are suspect to rapid variations over time.

Online social networks: why we disclose

On online social networks such as Facebook, massive self-disclosure by users has attracted the attention of industry players and policymakers worldwide. Despite the impressive scope of this phenomenon, very little is understood about what motivates users to disclose personal information. Integrating focus group results into a theoretical privacy calculus framework, we develop and empirically test a Structural Equation Model of self-disclosure with 259 subjects. We find that users are primarily motivated to disclose information because of the convenience of maintaining and developing relationships and platform enjoyment. Countervailing these benefits, privacy risks represent a critical barrier to information disclosure. However, users’ perception of risk can be mitigated by their trust in the network provider and availability of control options. Based on these findings, we offer recommendations for network providers.

Privacy concerns and identity in online social networks

Driven by privacy-related fears, users of Online Social Networks may start to reduce their network activities. This trend can have a negative impact on network sustainability and its business value. Nevertheless, very little is understood about the privacy-related concerns of users and the impact of those concerns on identity performance. To close this gap, we take a systematic view of user privacy concerns on such platforms. Based on insights from focus groups and an empirical study with 210 subjects, we find that (i) Organizational Threats and (ii) Social Threats stemming from the user environment constitute two underlying dimensions of the construct “Privacy Concerns in Online Social Networks”. Using a Structural Equation Model, we examine the impact of the identified dimensions of concern on the Amount, Honesty, and Conscious Control of individual self-disclosure on these sites. We find that users tend to reduce the Amount of information disclosed as a response to their concerns regarding Organizational Threats. Additionally, users become more conscious about the information they reveal as a result of Social Threats. Network providers may want to develop specific mechanisms to alleviate identified user concerns and thereby ensure network sustainability.

The use of unmanned aerial vehicles and wireless sensor networks for spraying pesticides

The application of pesticides and fertilizers in agricultural areas is of crucial importance for crop yields. The use of aircrafts is becoming increasingly common in carrying out this task mainly because of their speed and effectiveness in the spraying operation. However, some factors may reduce the yield, or even cause damage (e.g., crop areas not covered in the spraying process, overlapping spraying of crop areas, applying pesticides on the outer edge of the crop). Weather conditions, such as the intensity and direction of the wind while spraying, add further complexity to the problem of maintaining control. In this paper, we describe an architecture to address the problem of self-adjustment of the UAV routes when spraying chemicals in a crop field. We propose and evaluate an algorithm to adjust the UAV route to changes in wind intensity and direction. The algorithm to adapt the path runs in the UAV and its input is the feedback obtained from the wireless sensor network (WSN) deployed in the crop field. Moreover, we evaluate the impact of the number of communication messages between the UAV and the WSN. The results show that the use of the feedback information from the sensors to make adjustments to the routes could significantly reduce the waste of pesticides and fertilizers.

Simulating Cortical Development as a Self Constructing Process: A Novel Multi-Scale Approach Combining Molecular and Physical Aspects

Current models of embryological development focus on intracellular processes such as gene expression and protein networks, rather than on the complex relationship between subcellular processes and the collective cellular organization these processes support. We have explored this collective behavior in the context of neocortical development, by modeling the expansion of a small number of progenitor cells into a laminated cortex with layer and cell type specific projections. The developmental process is steered by a formal language analogous to genomic instructions, and takes place in a physically realistic three-dimensional environment. A common genome inserted into individual cells control their individual behaviors, and thereby gives rise to collective developmental sequences in a biologically plausible manner. The simulation begins with a single progenitor cell containing the artificial genome. This progenitor then gives rise through a lineage of offspring to distinct populations of neuronal precursors that migrate to form the cortical laminae. The precursors differentiate by extending dendrites and axons, which reproduce the experimentally determined branching patterns of a number of different neuronal cell types observed in the cat visual cortex. This result is the first comprehensive demonstration of the principles of self-construction whereby the cortical architecture develops. In addition, our model makes several testable predictions concerning cell migration and branching mechanisms.

Self-initiated expatriates and their career success

Purpose This paper aims to provide conceptual clarity by distinguishing self‐initiated expatriates (SIEs) from company‐assigned expatriates (AEs), and skilled migrants; most importantly, it introduces an overarching conceptual framework based on career capital theory to explain SIEs’ career success. Design/methodology/approach This conceptual framework is based on a review of the relevant literature on SIE, expatriation, career studies, cross‐cultural studies, migration, and other related areas. Findings Protean career attitude, career networks, and cultural intelligence are identified as three major types of career capital influencing SIEs career success positively; the predicting relationships between these are mediated by cultural adjustment in the host country. Cultural distance acts as the moderator, which highlights the influence of macro‐contextual factors on SIEs’ career development. Research limitations/implications The current paper applied career capital theory and did not integrate the impact of family and labour market situation on SIEs’ career development. Further research should test the proposed framework empirically, and integrate the impact of family‐ and career‐related factors into a holistic approach. Practical implications When constructing international talent acquisition and retention strategies, organizations and receiving countries should understand the different career development needs and provide SIEs with opportunities to increase career capital during expatriation. Furthermore, the current framework suggests how to adjust to the host country in order to meet career development goals. Originality/value The multi‐level and sequential framework adds value by identifying specific types of career capital for SIEs and providing a conceptual underpinning for explaining how they interact and foster SIEs’ career success. Moreover, the framework embraces SIEs from both developed and developing economies.

The Exploitation of Versatile Building Blocks for the Self-Assembly of Novel Molecular Magnets

Using molecular building blocks to self-assemble lattices supporting long-range magnetic order is currently an active area of solid-state chemistry. Consequently, it is the realm of supramolecular chemistry that synthetic chemists are turning to in order to develop techniques for the synthesis of structurally well-defined supramolecular materials. In recent years we have investigated the versatility and usefulness of two classes of molecular building blocks, namely, tris-oxalato transition-metal (M. Pilkington and S. Decurtins, in “Magnetoscience—From Molecules to Materials,” Wiley–VCH, 2000), and octacyanometalate complexes (Pilkington and Decurtins, Chimia 54, 593 (2001)), for applications in the field of molecule-based magnets. Anionic, tris-chelated oxalato building blocks are able to build up two-dimensional honeycomb-layered structural motifs as well as three-dimensional decagon frameworks. The discrimination between the crystallization of the two- or three-dimensional structures relies on the choice of the templating counterions (Decurtins, Chimia 52, 539 (1998); Decurtins et al. Mol. Cryst. Liq. Cryst. 273, 167 (1995); New J. Chem. 117 (1998)). These structural types display a range of ferro, ferri, and antiferromagnetic properties (Pilkington and Decurtins, in “Magnetoscience—From Molecules to Materials”). Octacyanometalate building blocks self-assemble to afford two new classes of cyano-bridged compounds namely, molecular clusters and extended three dimensional networks (J. Larionova et al., Angew. Chem. Int. Ed. 39, 1605 (2000); Pilkington et al., in preparation). The molecular cluster with a MnII9MoV6 core has the highest ground state spin value, S=51/2, reported to-date (Larionova et al., Angew. Chem. Int. Ed. 39, 1605 (2000)). In the high-temperature regime, the magnetic properties are characterized by ferromagnetic intracluster coupling. In the magnetic range below 44 K, the magnetic cluster signature is lost as possibly a bulk behavior starts to emerge. The three-dimensional networks exhibit both paramagnetic and ferromagnetic behavior, since the magnetic properties of these materials directly reflect the electronic configuration of the metal ion incorporated into the octacyanometalate building blocks (Pilkington et al., in preparation). For both the oxalate- and cyanide-bridged materials, we are able to manipulate the magnetic properties of the supramolecular assemblies by tuning the electronic configurations of the metal ions incorporated into the appropriate molecular building blocks (Pilkington and Decurtins, in “Magnetoscience—From Molecules to Materials,” Chimia 54, 593 (2000)).

Molecular-Based Magnetism in High-Spin Molecular Clusters and Three-Dimensional Networks Based on Cyanometalate Building Blocks

The field of molecule-based magnets is a relatively new branch of chemistry, which involves the design and study of molecular compounds that exhibit a spontaneous magnetic ordering below a critical temperature, Tc. One major goal involves the design of materials with tuneable Tc's for specific applications in memory storage devices. Molecule-based magnets with high magnetic ordering temperatures have recently been obtained from bimetallic and mixed-valence transition metal μ-cyanide complexes of the Prussian blue family. Since the μ-cyanide linkages permit an interaction between paramagnetic metal ions, cyanometalate building blocks have found useful applications in the field of molecule-based magnets. Our work involves the use of octacyanometalate building blocks for the self-assembly of two new classes of magnetic materials namely, high-spin molecular clusters which exhibit both ferromagnetic intra- and intercluster coupling, and specific extended network topologies which show long-range ferromagnetic ordering.

A Demonstration of Mobility Prediction as a Service in Cloudified LTE Networks

Location prediction has attracted a significant amount of research effort. Being able to predict users’ movement benefits a wide range of communication systems, including location-based service/applications, mobile access control, mobile QoS provision, and resource management for mobile computation and storage management. In this demo, we present MOBaaS, which is a cloudified Mobility and Bandwidth prediction services that can be instantiated, deployed, and disposed on-demand. Mobility prediction of MOBaaS provides location predictions of a single/group user equipments (UEs) in a future moment. This information can be used for self-adaptation procedures and optimal network function configuration during run-time operations. We demonstrate an example of real-time mobility prediction service deployment running on OpenStack platform, and the potential benefits it bring to other invoking services.