Network lifetime maximization with cross-layer design in wireless sensor networks

This paper investigates a cross-layer design approach for minimizing energy consumption and maximizing network lifetime (NL) of a multiple-source and single-sink (MSSS) WSN with energy constraints. The optimization problem for MSSS WSN can be formulated as a mixed integer convex optimization problem with the adoption of time division multiple access (TDMA) in medium access control (MAC) layer, and it becomes a convex problem by relaxing the integer constraint on time slots. Impacts of data rate, link access and routing are jointly taken into account in the optimization problem formulation. Both linear and planar network topologies are considered for NL maximization (NLM). With linear MSSS and planar single-source and single-sink (SSSS) topologies, we successfully use Karush-Kuhn-Tucker (KKT) optimality conditions to derive analytical expressions of the optimal NL when all nodes are exhausted simultaneously. The problem for planar MSSS topology is more complicated, and a decomposition and combination (D&C) approach is proposed to compute suboptimal solutions. An analytical expression of the suboptimal NL is derived for a small scale planar network. To deal with larger scale planar network, an iterative algorithm is proposed for the D&C approach. Numerical results show that the upper-bounds of the network lifetime obtained by our proposed optimization models are tight. Important insights into the NL and benefits of cross-layer design for WSN NLM are obtained.

Gaussian process quantile regression using expectation propagation

Direct quantile regression involves estimating a given quantile of a response variable as a function of input variables. We present a new framework for direct quantile regression where a Gaussian process model is learned, minimising the expected tilted loss function. The integration required in learning is not analytically tractable so to speed up the learning we employ the Expectation Propagation algorithm. We describe how this work relates to other quantile regression methods and apply the method on both synthetic and real data sets. The method is shown to be competitive with state of the art methods whilst allowing for the leverage of the full Gaussian process probabilistic framework.

Design and analysis of distributed utility maximization algorithm for multihop wireless network with inaccurate feedback

Distributed network utility maximization (NUM) is receiving increasing interests for cross-layer optimization problems in multihop wireless networks. Traditional distributed NUM algorithms rely heavily on feedback information between different network elements, such as traffic sources and routers. Because of the distinct features of multihop wireless networks such as time-varying channels and dynamic network topology, the feedback information is usually inaccurate, which represents as a major obstacle for distributed NUM application to wireless networks. The questions to be answered include if distributed NUM algorithm can converge with inaccurate feedback and how to design effective distributed NUM algorithm for wireless networks. In this paper, we first use the infinitesimal perturbation analysis technique to provide an unbiased gradient estimation on the aggregate rate of traffic sources at the routers based on locally available information. On the basis of that, we propose a stochastic approximation algorithm to solve the distributed NUM problem with inaccurate feedback. We then prove that the proposed algorithm can converge to the optimum solution of distributed NUM with perfect feedback under certain conditions. The proposed algorithm is applied to the joint rate and media access control problem for wireless networks. Numerical results demonstrate the convergence of the proposed algorithm. © 2013 John Wiley & Sons, Ltd.

Design and analysis of distributed utility maximization algorithm for multihop wireless network with inaccurate feedback

Distributed network utility maximization (NUM) is receiving increasing interests for cross-layer optimization problems in multihop wireless networks. Traditional distributed NUM algorithms rely heavily on feedback information between different network elements, such as traffic sources and routers. Because of the distinct features of multihop wireless networks such as time-varying channels and dynamic network topology, the feedback information is usually inaccurate, which represents as a major obstacle for distributed NUM application to wireless networks. The questions to be answered include if distributed NUM algorithm can converge with inaccurate feedback and how to design effective distributed NUM algorithm for wireless networks. In this paper, we first use the infinitesimal perturbation analysis technique to provide an unbiased gradient estimation on the aggregate rate of traffic sources at the routers based on locally available information. On the basis of that, we propose a stochastic approximation algorithm to solve the distributed NUM problem with inaccurate feedback. We then prove that the proposed algorithm can converge to the optimum solution of distributed NUM with perfect feedback under certain conditions. The proposed algorithm is applied to the joint rate and media access control problem for wireless networks. Numerical results demonstrate the convergence of the proposed algorithm. © 2013 John Wiley & Sons, Ltd.

Maximization of a Linear Utility Function over the Set of the Housing Market Short-Term Equilibria

AMS subject classification: 90C05, 90A14.

A Customer's Expectation and Perception of Hotel Service Quality in Cyprus

In recent years, hotels in Cyprus have encountered difficult economic times due to increasing customer demands and strong internal industry development competition. The hospitality industry’s main concern globally is to serve its customer S needs and desires, most of which are addressed through personal services. Hence, the hotel businesses that are able to provide quality services to its ever-demanding customers in a warm and efficient manner are those businesses which will be more likely to obtain a long term competitive advantage over their rivals. Ironically, the quality of services frequently cannot fully appreciated until something goes wrong, and then, the poor quality of services can have long lasting lingering effects on the customer base and, hence, often is translated into a loss of business. Nevertheless, since the issue of delivery of hospitality services always involves people, this issue must center around the management of the human resource factor, and in particular, on the way which interacts with itself and with guests, as service encounters. In the eyes of guests, hospitality businesses will be viewed successful or failure, depending on [he cumulative impact of the service encounters they have experienced on a personal level. Finally, since hotels are offering intangible and perishable personal service encounters, managing these services must be a paramount concern of any hotel business. As a preliminary exercise, visualize when you have last visited a hotel, or a restaurant, and then, ask yourself these questions: What did you feel about the quality of the experience? Was it a memorable one, which you would recommend it to others, or there were certain things, which could have made the difference? Thus, the way personalized services are provided can make the deference in attracting arid retaining long-term customers

Water for the Wayuu: Procurement and maximization of a limited resource among indigenous pastoralists on the Guajira Peninsula of northern Colombia

For the Wayuu of the Guajira Peninsula of northern Colombia, water procurement has historically been challenging. The ancestral territory of this indigenous pastoral society is windy and arid, with low rainfall, high temperatures and an absence of perennial rivers or streams. In the past, the Wayuu adapted to these environmental conditions by practicing transhumance during the prolonged dry seasons, digging spring wells and artificial ponds and by following guiding principles for water usage. Since the 1930s, the government has made efforts to build additional wind-powered wells and ponds for a growing native population. Notwithstanding, these water solutions have only partly met the necessities; public water sources are limited or unreliable and few attempts are made to generate safe drinking water. Furthermore, the ubiquitous practice of animal husbandry places added pressure on existing sources; livestock consume more water than the human populations in the areas visited. Rapid assessments in four Wayuu areas on the peninsula were conducted by the author and an interdisciplinary team working for the Cerrejón Foundation for Water in La Guajira from 2010 to 2013. The assessments were part of a larger pilot project to design and implement a sustainability plan for reservoir-based water supply systems in the region. This study brings cultural practices and local knowledge to the forefront as key elements for the success of water works and other development projects carried out in Wayuu territory.

Expectation Modulates Episodic Memory Formation via Dopaminergic Circuitry

Episodic memory formation is shaped by expectation. Events that generate expectations have the capacity to influence memory. Additionally, whether subsequent events meet or violate expectations has consequences for memory. However, clarification is still required to illuminate the circumstances and direction of memory modulation. In the brain, the mechanisms by which expectation modulates memory formation also require consideration. The dopamine system has been implicated in signaling events associated with different states of expectancy; it has also been shown to modulate episodic memory formation in the hippocampus. Thus, the studies included in this dissertation utilized both functional magnetic resonance imaging (fMRI) and behavioral testing to examine when and how the dopaminergic system supports the modulation of memory by expectation. The work aimed to characterize the activation of dopaminergic circuitry in response to cues that generate expectancy, during periods of anticipation, and in response to outcomes that resolve expectancy. The studies also examined how each of these event types influenced episodic memory formation. The present findings demonstrated that novelty and expectancy violation both drive dopaminergic circuitry capable of contributing to memory formation. Consistent with elevated dopaminergic midbrain and hippocampus activation for each, expected versus expectancy violating novelty did not differentially affect memory success. We also showed that high curiosity expectancy states drive memory formation. This was supported by activation in dopaminergic circuitry that was greater for subsequently remembered information only in the high curiosity state. Finally, we showed that cues that generate high expected reward value versus high reward uncertainty differentially modulate memory formation during reward anticipation. This behavioral result was consistent with distinct temporal profiles of dopaminergic action having differential downstream effects on episodic memory formation. Integrating the present studies with previous research suggests that dopaminergic circuitry signals events that are unpredicted, whether cuing or resolving expectations. It also suggests that contextual differences change the contribution of the dopaminergic system during anticipation, depending on the nature of the expectation. And finally, this work is consistent with a model in which dopamine elevation in response to expectancy events positively modulates episodic memory formation.

Rate Maximization of Decode-and-Forward Relaying Systems with RF Energy Harvesting

We consider a three-node decode-and-forward (DF) half-duplex relaying system, where the source first harvests RF energy from the relay, and then uses this energy to transmit information to the destination via the relay. We assume that the information transfer and wireless power transfer phases alternate over time in the same frequency band, and their time fraction (TF) may change or be fixed from one transmission epoch (fading state) to the next. For this system, we maximize the achievable average data rate. Thereby, we propose two schemes: (1) jointly optimal power and TF allocation, and (2) optimal power allocation with fixed TF. Due to the small amounts of harvested power at the source, the two schemes achieve similar information rates, but yield significant performance gains compared to a benchmark system with fixed power and fixed TF allocation.

Joint Information and Jamming Beamforming for Secrecy Rate Maximization in Cognitive Radio Networks

In this paper, we consider the secure beamforming design for an underlay cognitive radio multiple-input singleoutput broadcast channel in the presence of multiple passive eavesdroppers. Our goal is to design a jamming noise (JN) transmit strategy to maximize the secrecy rate of the secondary system. By utilizing the zero-forcing method to eliminate the interference caused by JN to the secondary user, we study the joint optimization of the information and JN beamforming for secrecy rate maximization of the secondary system while satisfying all the interference power constraints at the primary users, as well as the per-antenna power constraint at the secondary transmitter. For an optimal beamforming design, the original problem is a nonconvex program, which can be reformulated as a convex program by applying the rank relaxation method. To this end, we prove that the rank relaxation is tight and propose a barrier interior-point method to solve the resulting saddle point problem based on a duality result. To find the global optimal solution, we transform the considered problem into an unconstrained optimization problem. We then employ Broyden-Fletcher-Goldfarb-Shanno (BFGS) method to solve the resulting unconstrained problem which helps reduce the complexity significantly, compared to conventional methods. Simulation results show the fast convergence of the proposed algorithm and substantial performance improvements over existing approaches.

Effect of Experience on Alibi Generation and Expectation

Members of the general population have high expectations of people who are asked to corroborate an alibi for the suspect of a crime. The general belief is that it is easy to provide an alibi if a person is innocent, and therefore guilt should be assumed when an alibi cannot be provided. The possibility that having to generate an alibi oneself could influence expectations was examined. Additionally, potential changes in opinion after being provided with positive or negative feedback were explored. Results showed a significant difference in expectations based on whether participants were correct or incorrect in identifying the suspect, that is, whether participants were able to provide an alibi. Those who were incorrect had lower expectations of themselves and of others than those who were correct. Making jurors aware of the difficulty in providing an alibi may lead to fairer treatment of suspects who have difficulty providing one.

Modeling height-diameter relationship in pinus pinaster ait. in the forest intervention zone of Lomba, NE-Portugal

In this work, the relationship between diameter at breast height (d) and total height (h) of individual-tree was modeled with the aim to establish provisory height-diameter (h-d) equations for maritime pine (Pinus pinaster Ait.) stands in the Lomba ZIF, Northeast Portugal. Using data collected locally, several local and generalized h-d equations from the literature were tested and adaptations were also considered. Model fitting was conducted by using usual nonlinear least squares (nls) methods. The best local and generalized models selected, were also tested as mixed models applying a first-order conditional expectation (FOCE) approximation procedure and maximum likelihood methods to estimate fixed and random effects. For the calibration of the mixed models and in order to be consistent with the fitting procedure, the FOCE method was also used to test different sampling designs. The results showed that the local h-d equations with two parameters performed better than the analogous models with three parameters. However a unique set of parameter values for the local model can not be used to all maritime pine stands in Lomba ZIF and thus, a generalized model including covariates from the stand, in addition to d, was necessary to obtain an adequate predictive performance. No evident superiority of the generalized mixed model in comparison to the generalized model with nonlinear least squares parameters estimates was observed. On the other hand, in the case of the local model, the predictive performance greatly improved when random effects were included. The results showed that the mixed model based in the local h-d equation selected is a viable alternative for estimating h if variables from the stand are not available. Moreover, it was observed that it is possible to obtain an adequate calibrated response using only 2 to 5 additional h-d measurements in quantile (or random) trees from the distribution of d in the plot (stand). Balancing sampling effort, accuracy and straightforwardness in practical applications, the generalized model from nls fit is recommended. Examples of applications of the selected generalized equation to the forest management are presented, namely how to use it to complete missing information from forest inventory and also showing how such an equation can be incorporated in a stand-level decision support system that aims to optimize the forest management for the maximization of wood volume production in Lomba ZIF maritime pine stands.

Implementation and Validation of a Self-Consumption Maximization Energy Management Strategy in a Vanadium Redox Flow BIPV Demonstrator

This paper presents the results of the implementation of a self-consumption maximization strategy tested in a real-scale Vanadium Redox Flow Battery (VRFB) (5 kW, 60 kWh) and Building Integrated Photovoltaics (BIPV) demonstrator (6.74 kWp). The tested energy management strategy aims to maximize the consumption of energy generated by a BIPV system through the usage of a battery. Whenever possible, the residual load is either stored in the battery to be used later or is supplied by the energy stored previously. The strategy was tested over seven days in a real-scale VRF battery to assess the validity of this battery to implement BIPV-focused energy management strategies. The results show that it was possible to obtain a self-consumption ratio of 100.0%, and that 75.6% of the energy consumed was provided by PV power. The VRFB was able to perform the strategy, although it was noticed that the available power (either to charge or discharge) varied with the state of charge.

Mathematical Programming Models for Influence Maximization on Social Networks

In this dissertation, we apply mathematical programming techniques (i.e., integer programming and polyhedral combinatorics) to develop exact approaches for influence maximization on social networks. We study four combinatorial optimization problems that deal with maximizing influence at minimum cost over a social network. To our knowl- edge, all previous work to date involving influence maximization problems has focused on heuristics and approximation. We start with the following viral marketing problem that has attracted a significant amount of interest from the computer science literature. Given a social network, find a target set of customers to seed with a product. Then, a cascade will be caused by these initial adopters and other people start to adopt this product due to the influence they re- ceive from earlier adopters. The idea is to find the minimum cost that results in the entire network adopting the product. We first study a problem called the Weighted Target Set Selection (WTSS) Prob- lem. In the WTSS problem, the diffusion can take place over as many time periods as needed and a free product is given out to the individuals in the target set. Restricting the number of time periods that the diffusion takes place over to be one, we obtain a problem called the Positive Influence Dominating Set (PIDS) problem. Next, incorporating partial incentives, we consider a problem called the Least Cost Influence Problem (LCIP). The fourth problem studied is the One Time Period Least Cost Influence Problem (1TPLCIP) which is identical to the LCIP except that we restrict the number of time periods that the diffusion takes place over to be one. We apply a common research paradigm to each of these four problems. First, we work on special graphs: trees and cycles. Based on the insights we obtain from special graphs, we develop efficient methods for general graphs. On trees, first, we propose a polynomial time algorithm. More importantly, we present a tight and compact extended formulation. We also project the extended formulation onto the space of the natural vari- ables that gives the polytope on trees. Next, building upon the result for trees---we derive the polytope on cycles for the WTSS problem; as well as a polynomial time algorithm on cycles. This leads to our contribution on general graphs. For the WTSS problem and the LCIP, using the observation that the influence propagation network must be a directed acyclic graph (DAG), the strong formulation for trees can be embedded into a formulation on general graphs. We use this to design and implement a branch-and-cut approach for the WTSS problem and the LCIP. In our computational study, we are able to obtain high quality solutions for random graph instances with up to 10,000 nodes and 20,000 edges (40,000 arcs) within a reasonable amount of time.