Radiation Aware Mobility Paths in Wirelessly Powered Communication Networks

Wireless power transfer (WPT) is an emerging technology that is used in ad hoc networks of battery-powered devices, to deliver energy and keep the network functional. Existing state-of-the-art studies have mainly focused on applying this technology, but the potential risk of electromagnetic radiation (EMR) exposure is really overlooked by them. This still holds for the general case of the RF Wireless Communication networks. Hence, we consider The Minimum Radiation Path Problem of finding the lowest radiation trajectory of an agent moving from a source to a destination point in a network plane. Different from previous works, we attempt to study (for the first time in the state-of-the-art) path radiation under a more realistic WPT model than the usual one-dimensional models, that have been used in the past and cannot capture interesting superadditive and cancellation effects between distinct electromagnetic sources. In the light of the above, we design and evaluate both an algorithm and a heuristic that achieve different trade-offs between radiation and trajectory length of a moving agent. Document type: Conference objec

Stochastic Models and Adaptive Algorithms for Energy Balance in Sensor Networks

We consider the important problem of energy balanced data propagation in wireless sensor networks and we extend and generalize previous works by allowing adaptive energy assignment. We consider the data gathering problem where data are generated by the sensors and must be routed toward a unique sink. Sensors route data by either sending the data directly to the sink or in a multi-hop fashion by delivering the data to a neighbouring sensor. Direct and neighbouring transmissions require different levels of energy consumption. Basically, the protocols balance the energy consumption among the sensors by computing the adequate ratios of direct and neighbouring transmissions. An abstract model of energy dissipation as a random walk is proposed, along with rigorous performance analysis techniques. Two efficient distributed algorithms are presented and analyzed, by both rigorous means and simulation. The first one is easy to implement and fast to execute. The protocol assumes that sensors know a-priori the rate of data they generate. The sink collects and processes all these information in order to compute the relevant value of the protocol parameter. This value is transmitted to the sensors which individually compute their optimal ratios of direct and neighbouring transmissions. The second protocol avoids the necessary a-priori knowledge of the data rate generated by sensors by inferring the relevant information from the observation of the data paths. Furthermore, this algorithm is based on stochastic estimation methods and is adaptive to environmental change

Adaptive wireless power transfer in mobile ad hoc networks

We investigate the interesting impact of mobility on the problem of efficient wireless power transfer in ad hoc networks. We consider a set of mobile agents (consuming energy to perform certain sensing and communication tasks), and a single static charger (with finite energy) which can recharge the agents when they get in its range. In particular, we focus on the problem of efficiently computing the appropriate range of the charger with the goal of prolonging the network lifetime. We first demonstrate (under the realistic assumption of fixed energy supplies) the limitations of any fixed charging range and, therefore, the need for (and power of) a dynamic selection of the charging range, by adapting to the behavior of the mobile agents which is revealed in an online manner. We investigate the complexity of optimizing the selection of such an adaptive charging range, by showing that two simplified offline optimization problems (closely related to the online one) are NP-hard. To effectively address the involved performance trade-offs, we finally present a variety of adaptive heuristics, assuming different levels of agent information regarding their mobility and energy

MAX CUT in Weighted Random Intersection Graphs and Discrepancy of Sparse Random Set Systems

Let $V$ be a set of $n$ vertices, ${\cal M}$ a set of $m$ labels, and let $\mathbf{R}$ be an $m \times n$ matrix of independent Bernoulli random variables with success probability $p$. A random instance $G(V,E,\mathbf{R}^T\mathbf{R})$ of the weighted random intersection graph model is constructed by drawing an edge with weight $[\mathbf{R}^T\mathbf{R}]_{v,u}$ between any two vertices $u,v$ for which this weight is larger than 0. In this paper we study the average case analysis of Weighted Max Cut, assuming the input is a weighted random intersection graph, i.e. given $G(V,E,\mathbf{R}^T\mathbf{R})$ we wish to find a partition of $V$ into two sets so that the total weight of the edges having one endpoint in each set is maximized. We initially prove concentration of the weight of a maximum cut of $G(V,E,\mathbf{R}^T\mathbf{R})$ around its expected value, and then show that, when the number of labels is much smaller than the number of vertices, a random partition of the vertices achieves asymptotically optimal cut weight with high probability (whp). Furthermore, in the case $n=m$ and constant average degree, we show that whp, a majority type algorithm outputs a cut with weight that is larger than the weight of a random cut by a multiplicative constant strictly larger than 1. Then, we highlight a connection between the computational problem of finding a weighted maximum cut in $G(V,E,\mathbf{R}^T\mathbf{R})$ and the problem of finding a 2-coloring with minimum discrepancy for a set system $\Sigma$ with incidence matrix $\mathbf{R}$. We exploit this connection by proposing a (weak) bipartization algorithm for the case $m=n, p=\frac{\Theta(1)}{n}$ that, when it terminates, its output can be used to find a 2-coloring with minimum discrepancy in $\Sigma$. Finally, we prove that, whp this 2-coloring corresponds to a bipartition with maximum cut-weight in $G(V,E,\mathbf{R}^T\mathbf{R})$.Comment: 18 page

Concepts and experiments on psychoanalysis driven computing

This research investigates the effective incorporation of the human factor and user perception in text-based interactive media. In such contexts, the reliability of user texts is often compromised by behavioural and emotional dimensions. To this end, several attempts have been made in the state of the art, to introduce psychological approaches in such systems, including computational psycholinguistics, personality traits and cognitive psychology methods. In contrast, our method is fundamentally different since we employ a psychoanalysis-based approach; in particular, we use the notion of Lacanian discourse types, to capture and deeply understand real (possibly elusive) characteristics, qualities and contents of texts, and evaluate their reliability. As far as we know, this is the first time computational methods are systematically combined with psychoanalysis. We believe such psychoanalytic framework is fundamentally more effective than standard methods, since it addresses deeper, quite primitive elements of human personality, behaviour and expression which usually escape methods functioning at “higher”, conscious layers. In fact, this research is a first attempt to form a new paradigm of psychoanalysis-driven interactive technologies, with broader impact and diverse applications. To exemplify this generic approach, we apply it to the case-study of fake news detection; we first demonstrate certain limitations of the well-known Myers–Briggs Type Indicator (MBTI) personality type method, and then propose and evaluate our new method of analysing user texts and detecting fake news based on the Lacanian discourses psychoanalytic approach.This publication is part of the Spanish I+D+i project TRAINERA (ref. PID2020-118011GB-C21), funded by MCIN/AEI/10.13039/ 501100011033Peer ReviewedPostprint (published version