Abstract Let V be a set of n vertices, $${\mathcal M}$$ M a set of m labels, and let $${\textbf{R}}$$ R be an $$m \times n$$ m × n matrix ofs independent Bernoulli random variables with probability of success p; columns of $${\textbf{R}}$$ R are incidence vectors of label sets assigned to vertices. A random instance $$G(V, E, {\textbf{R}}^T {\textbf{R}})$$ G ( V , E , R T R ) of the weighted random intersection graph model is constructed by drawing an edge with weight equal to the number of common labels (namely $$[{\textbf{R}}^T {\textbf{R}}]_{v,u}$$ [ R T R ] v , u ) between any two vertices u, v for which this weight is strictly 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 $$G(V, E, {\textbf{R}}^T {\textbf{R}})$$ G ( V , E , R T R ) we wish to find a partition of V into two sets so that the total weight of the edges having exactly one endpoint in each set is maximized. In particular, we initially prove that the weight of a maximum cut of $$G(V, E, {\textbf{R}}^T {\textbf{R}})$$ G ( V , E , R T R ) is concentrated around its expected value, and then show that, when the number of labels is much smaller than the number of vertices (in particular, $$m=n^{\alpha }, \alpha <1$$ m = n α , α < 1 ), a random partition of the vertices achieves asymptotically optimal cut weight with high probability. Furthermore, in the case $$n=m$$ n = m and constant average degree (i.e. $$p = \frac{\Theta (1)}{n}$$ p = Θ ( 1 ) n ), we show that with high probability, a majority type randomized 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 formally prove a connection between the computational problem of finding a (weighted) maximum cut in $$G(V, E, {\textbf{R}}^T {\textbf{R}})$$ G ( V , E , R T R ) and the problem of finding a 2-coloring that achieves minimum discrepancy for a set system $$\Sigma $$ Σ with incidence matrix $${\textbf{R}}$$ R (i.e. minimum imbalance over all sets in $$\Sigma $$ Σ ). We exploit this connection by proposing a (weak) bipartization algorithm for the case $$m=n, p = \frac{\Theta (1)}{n}$$ m

Human Gesture Recognition (HGR) using smart wearable IoT devices has emerged as a new field in human-centered computing regarding various domains. Though there are many research works related to data processing methodologies and Neural Networks architectures in this field, a lack of research on how to efficiently identify and interpret the AI models’ exports into human gestures is observed. This paper proposes an innovative end-to-end approach of how to solve and evaluate effectively a major part of HGR problems in a real-world scenario, in real-time. This is achieved with the effective utilization of data processing methods, the adoption, and extension of a cutting-edge Deep Learning model architecture, as well as the introduction and implementation in practice of innovative methods, both for interpretation and evaluation, that increase the trustworthiness of the model’s predictions.As a case study, we deployed the introduced pipeline into a real-world scenario of gestures’ identification and classification regarding allergic symptoms. We adopted multidisciplinarity by collaborating with recognized allergists that validated the whole approach in real patients via two pilot phases. As a result, by delivering a real-world application of our approach, we achieved a superior performance concerning the reliability of the pipeline, being 91.6% in our laboratory pilot phase and 81.4% in patients’ pilot data. Lastly, it is worth mentioning here that our framework can be employed in most HGR problems with minor modifications in data processing and learning procedure configuration.

In this paper, we propose a multidisciplinary Gesture Recognition case study using a Machine Learning approach for the detection and classification of allergic rhinitis-related gestures. Allergic diseases and especially allergic rhinitis are among the most common diseases in the world, mostly underappreciated, causing considerable impairment of daily activities, including job, and school productivity. For this reason, close monitoring and early recognition of symptoms worsening are considered essential. We hypothesize that recognizing allergic rhinitis to patients by such an approach may be a useful tool for such purpose.In our study, for the first time, the most common allergic rhinitis gestures are identified, based on patients' description and specialists' experience. Our data is retrieved by a large pool of active allergic rhinitis patients attending three specialized outpatient clinics in Greece. Gestures are recorded with the help of a wristband Bluetooth device incorporating a 3-axis accelerometer and a 3-axis gyroscope. Feature engineering and several signal processing methods are then applied to the raw sensor data (which are treated as 6-dimensional signals), and valuable features are extracted related to the time and frequency domains.To improve the performance of the Machine Learning models, we utilize Principal Component Analysis (PCA), and we also use functions such as Grid Search and Randomized Search, in order to achieve higher recognition accuracy by hyperparameter optimization.With these features and steps of processing, we built a classifier that can uniquely identify 15 allergic rhinitis gestures with an accuracy of 93% in a challenging variety of moves in the patient's head (nose, eye, ear). It is worth noting that allergic rhinitis gestures are more subtle, varied and spontaneous than other moves that have been considered in the literature so far. To the best of our knowledge, this is the first time that a Machine Learning approach is successfully applied in such a challenging field like respiratory diseases.

Temporal graphs abstractly model real-life inherently dynamic networks. Given a graph G, a temporal graph with G as the underlying graph is a sequence of subgraphs (snapshots) Gt of G, where t≥1. In this paper we study stochastic temporal graphs, i.e. stochastic processes G whose random variables are the snapshots of a temporal graph on G. A natural feature observed in various real-life scenarios is a memory effect in the appearance probabilities of particular edges; i.e. the probability an edge e∈E appears at time step t depends on its appearance (or absence) at the previous k steps. We study the hierarchy of models of memory-k, k≥0, in an edge-centric network evolution setting: every edge of G has its own independent probability distribution for its appearance over time. We thoroughly investigate the complexity of two naturally related, but fundamentally different, temporal path problems, called Minimum Arrival and Best Policy.

This papers investigates the effective control of electromagnetic radiation (EMR) in wireless adhoc communication networks. In particular, we focus on networks with wireless provision of energy, via the emerging technology of wireless power transfer (WPT). Our aim is to propose algorithmic methods towards optimizing the trade-off among the (potentially high) radiation levels in the network and the efficiency of power transfer. After formally defining EMR and relevant performance metrics, we critically discuss selected abstract radiation models (such as a well-studied scalar model) and identify their strengths and limitations. In particular, we highlight a recent vectorial representation of wireless power, which allows a very precise management of radiation, as well as a peer to peer model of wireless power exchange with negligible radiation levels. Under these models, we present selected algorithmic methods and heuristics for effective radiation control, such as adaptive schemes for charger configuration in highly mobile systems, the precise phase management of the wireless power waves and the evaluation and handling of overlaps in the wireless power transmission.

Recent technological advances in the domain of Wireless Power Transfer (WPT) have enabled the employment of previously unrealistic methods for power management in wireless systems. At the same time, some of the classical scalar models have proved incapable of capturing the multi-dimensional aspects of WPT that are similar to the superposition of wave functions. In this work, we consider the vector model which is by now a widely accepted model for WPT and its validity has been confirmed experimentally in the literature. Under the vector model, we study the problem of power maximization in a wireless network consisting of wireless chargers. We take the state of the art one step further by assuming that chargers can use phase-shifting to adjust their output in order to improve the total power provided by the network of chargers at selected points in the network area. Even though the technology for phase-shifting already exists, researchers have only recently tried to study it from an algorithmic perspective and algorithmic solutions are nearly inexistent. In this paper, we provide a rigorous formulation for the problem of power maximization as a semi-definite program with rank constraints and we present efficient centralized and distributed solutions, and also heuristics where only local information is available.

We elaborate on two fundamental models for the emerging technology of Wireless Power Transfer in ad hoc communication networks. The first model is scalar, basically assuming that the received power by multiple transmitters is additive. The second model is vectorial, highlighting the detailed interference between RF waves of different power sources, thus, it is more precise (especially in the far field regions of dense charging systems) and allows addressing interesting superadditive (constructive) and cancellation (destructive) phenomena on the received power. Under these models, we present selected state of the art algorithms for key problems, such as how to deploy and configure the wireless chargers and how to achieve good trade-offs between efficient charging and electromagnetic radiation. We conclude with some future trends and directions in this fascinating topic.

We study how to efficiently transfer energy wirelessly in ad hoc networks of battery-limited devices, towards prolonging their lifetime. We assume a weak population of distributed devices which are exchanging energy in a "peer-topeer", manner with each other. We address a quite general case of diverse energy levels and priorities in the network and study the problem of how the system can efficiently reach a weighted energy balance state distributively. We present three protocols that achieve different performance trade-offs between energy balance quality, convergence time and energy efficiency.

In this paper, we study contention resolution protocols from a game-theoretic perspective. We focus on acknowledgment-based protocols, where a user gets feedback from the channel only when she attempts transmission. In this case she will learn whether her transmission was successful or not. Users that do not transmit will not receive any feedback. We are interested in equilibrium protocols, where no player has an incentive to deviate. The limited feedback makes the design of equilibrium protocols a hard task as best response policies usually have to be modeled as Partially Observable Markov Decision Processes, which are hard to analyze. Nevertheless, we show how to circumvent this for the case of two players and present an equilibrium protocol. For many players, we give impossibility results for a large class of acknowledgment-based protocols, namely age-based and backoff protocols with finite expected finishing time. Finally, we provide an age-based equilibrium protocol, which has infinite expected finishing time, but every player finishes in linear time with high probability.

Wireless energy transfer is an emerging technology that is used in networks of battery-powered devices in order to deliver energy and keep the network functional. Existing state-of-the-art studies have mainly focused on applying this technology on networks of relatively strong computational and communicational capabilities (wireless sensor networks, ad-hoc networks), also they assume one-directional energy transfer from special chargers to the network nodes. Different from these works, we here study (for the first time in the state-of-theart) interactive, "peer-to-peer" wireless charging in populations of much more resource-limited, mobile agents that abstract distributed portable devices. In this new model for interactive wireless charging, we assume that the agents are capable of achieving bi-directional wireless energy transfer acting both as energy transmitters and harvesters. We consider the cases of both loss-less and lossy energy transfer and provide an upper bound on the time needed to reach a balanced energy distribution in the population. We investigate the delicate impact of the diversity of energy levels on eventual energy balance achieved and highlight some key elements of the charging procedure. In the light of the above, we design and evaluate three interaction protocols that achieve different tradeoffs between energy balance, time and energy efficiency.

We study how to efficiently transfer energy wirelessly in ad hoc networks of battery-limited devices, towards prolonging their lifetime. In contrast to the state-of-the-art, we assume a much weaker population of distributed devices which are exchanging energy in a "peer to peer" manner with each other, without any special charger nodes. We address a quite general case of diverse energy levels and priorities in the network and study the problem of how the system can efficiently reach a weighted energy balance state distributively, under both loss-less and lossy power transfer assumptions. Three protocols are designed, analyzed and evaluated, achieving different performance trade-offs between energy balance quality, convergence time and energy efficiency.

In this paper we initiate the study of populations of agents with very limited capabilities that are globally able to compute order statistics of their arithmetic input values via pair-wise meetings. To this extent, we introduce the Arithmetic Population Protocol (APP) model, embarking from the well known Population Protocol (PP) model and inspired by two recent papers in which states are treated as integer numbers. In the APP model, every agent has a state from a set Q of states, as well as a fixed number of registers (independent of the size of the population), each of which can store an element from a totally ordered set S of samples. Whenever two agents interact with each other, they update their states and the values stored in their registers according to a joint transition function. This transition function is also restricted; it only allows (a) comparisons and (b) copy / paste operations for the sample values that are stored in the registers of the two interacting agents. Agents can only meet in pairs via a fair scheduler and are required to eventually converge to the same output value of the function that the protocol globally and stably computes. We present two different APPs for stably computing the median of the input values, initially stored on the agents of the population. Our first APP, in which every agent has 3 registers and no states, stably computes (with probability 1) the median under any fair scheduler in any strongly connected directed (or connected undirected) interaction graph. Under the probabilistic scheduler, we show that our protocol stably computes the median in O(n^6) number of interactions in a connected undirected interaction graph of n agents. Our second APP, in which every agent has 2 registers and O(n^2 log{n}) states, computes to the correct median of the input with high probability in O(n^3 log{n}) interactions, assuming the probabilistic scheduler and the complete interaction graph. Finally we present a third APP which, for any k, stably computes the k-th smallest element of the input of the population under any fair scheduler and in any strongly connected directed (or connected undirected) interaction graph. In this APP every agent has 2 registers and n states. Upon convergence every agent has a different state; all these states provide a total ordering of the agents with respect to their input values.

This paper addresses wirelessly networked populations of nodes (agents) that can both transmit and receive wireless power among each other, interacting locally in a peer to peer manner. In this setting, we study the important problem of network formation, in particular how the agents can distributively create a star structure. Extending the state of the art, we introduce energy considerations in network formation: in addition to the star construction, our goal is to achieve a certain target energy distribution among the agents. We assume a generalized, more realistic energy loss factor which may differ for each pairwise power exchange.

Summary Motivated by the problem of efficiently collecting data from wireless sensor networks via a mobile sink, we present an accelerated random walk on random geometric graphs (RGG). Random walks in wireless sensor networks can serve as fully local, lightweight strategies for sink motion that significantly reduce energy dissipation but introduce higher latency in the data collection process. In most cases, random walks are studied on graphs like Gn,p and grid. Instead, we here choose the RGG model, which abstracts more accurately spatial proximity in a wireless sensor network. We first evaluate an adaptive walk (the random walk with inertia) on the RGG model; its performance proved to be poor and led us to define and experimentally evaluate a novel random walk that we call γ‐stretched random walk. Its basic idea is to favour visiting distant neighbours of the current node towards reducing node overlap and accelerate the cover time. We also define a new performance metric called proximity cover time that, along with other metrics such as visit overlap statistics and proximity variation, we use to evaluate the performance properties and features of the various walks. Copyright © 2013 John Wiley & Sons, Ltd.

Rapid technological advances in the domain of Wireless Energy Transfer (WET) pave the way for novel methods for energy management in Wireless Distributed Systems and recent research efforts have already started considering network models that take into account these new technologies. In this paper, we follow a new approach in studying the problem of efficiently charging a set of rechargeable nodes using a set of wireless energy chargers, under safety constraints on the electromagnetic radiation incurred. In particular, we define a new charging model that greatly differs from existing models in that it takes into account real technology restrictions of the chargers and nodes of the system, mainly regarding energy limitations. Our model also introduces non-linear constraints (in the time domain), that radically change the nature of the computational problems we consider. In this charging model, we present and study the Low Radiation Efficient Charging Problem (LREC), in which we wish to optimize the amount of "useful" energy transferred from chargers to nodes (under constraints on the maximum level of imposed radiation). We present several fundamental properties of this problem and provide indications of its hardness. Finally, we propose an iterative local improvement heuristic for LREC, which runs in polynomial time and we evaluate its performance via simulation. Our algorithm decouples the computation of the objective function from the computation of the maximum radiation and also does not depend on the exact formula used for the computation of the electromagnetic radiation in each point of the network, achieving good trade-offs between charging efficiency and radiation control, it also exhibits good energy balance properties. We provide extensive simulation results supporting our claims and theoretical results.

This research attempts a first step towards investigating the aspect of radiation awareness in environments with abundant heterogeneous wireless networking. We call radiation at a point of a 3D wireless network the total amount of electromagnetic quantity the point is exposed to, our definition incorporates the effect of topology as well as the time domain, data traffic and environment aspects. Even if the impact of radiation to human health remains largely unexplored and controversial, we believe it is worth trying to understand and control. We first analyze radiation in well known topologies (random and grids), randomness is meant to capture not only node placement but also uncertainty of the wireless propagation model. This initial understanding of how radiation adds (over space and time) can be useful in network design, to reduce health risks. We then focus on the minimum radiation path problem of finding the lowest radiation trajectory of a person moving from a source to a destination point of the network region. We propose three heuristics which provide low radiation paths while keeping path length low, one heuristic gets in fact quite close to the offline solution we compute by a shortest path algorithm. Finally, we investigate the interesting impact on the heuristics' performance of diverse node mobility.

Through recent technology advances in the field of wireless energy transmission, Wireless Rechargeable Sensor Networks (WRSN) have emerged. In this new paradigm for WSNs a mobile entity called Mobile Charger (MC) traverses the network and replenishes the dissipated energy of sensors. In this work we first provide a formal definition of the charging dispatch decision problem and prove its computational hardness. We then investigate how to optimize the trade-offs of several critical aspects of the charging process such as a) the trajectory of the charger, b) the different charging policies and c) the impact of the ratio of the energy the MC may deliver to the sensors over the total available energy in the network. In the light of these optimizations, we then study the impact of the charging process to the network lifetime for three characteristic underlying routing protocols; a greedy protocol, a clustering protocol and an energy balancing protocol. Finally, we propose a Mobile Charging Protocol that locally adapts the circular trajectory of the MC to the energy dissipation rate of each sub-region of the network. We compare this protocol against several MC trajectories for all three routing families by a detailed experimental evaluation. The derived findings demonstrate significant performance gains, both with respect to the no charger case as well as the different charging alternatives; in particular, the performance improvements include the network lifetime, as well as connectivity, coverage and energy balance properties.

We study the problem of energy-balanced data propagation in wireless sensor networks. The energy balance property is crucial for maximizing the time the network is functional, by avoiding early energy depletion of a large portion of sensors. We propose a distributed, adaptive data propagation algorithm that exploits limited, local network density information for achieving energy-balance while at the same time minimizing energy dissipation. We investigate both uniform and heterogeneous sensor placement distributions. By a detailed experimental evaluation and comparison with well-known energy-balanced protocols, we show that our density-based protocol improves energy efficiency significantly while also having better energy balance properties.

In this work, we overview some results concerning communication combinatorial properties in random intersection graphs and uniform random intersection graphs. These properties relate crucially to algorithmic design for important problems (like secure communication and frequency assignment) in distributed networks characterized by dense, local interactions and resource limitations, such as sensor networks. In particular, we present and discuss results concerning the existence of large independent sets of vertices whp in random instances of each of these models. As the main contribution of our paper, we introduce a new, general model, which we denote G(V, χ, f). In this model, V is a set of vertices and χ is a set of m vectors in ℝm. Furthermore, f is a probability distribution over the powerset 2χof subsets of χ. Every vertex selects a random subset of vectors according to the probability f and two vertices are connected according to a general intersection rule depending on their assigned set of vectors. Apparently, this new general model seems to be able to simulate other known random graph models, by carefully describing its intersection rule.

We investigate random intersection graphs, a combinatorial model that quite accurately abstracts distributed networks with local interactions between nodes blindly sharing critical resources from a limited globally available domain. We study important combinatorial properties (independence and hamiltonicity) of such graphs. These properties relate crucially to algorithmic design for important problems (like secure communication and frequency assignment) in distributed networks characterized by dense, local interactions and resource limitations, such as sensor networks. In particular, we prove that, interestingly, a small constant number of random, resource selections suffices to make the graph Hamiltonian and we provide tight evaluations of the independence number of these graphs.