Security Games for Node Localization through Verifiable Multilateration

Most applications of wireless sensor networks (WSNs) rely on data about the positions of sensor nodes, which are not necessarily known beforehand. Several localization approaches have been proposed but most of them omit to consider that WSNs could be deployed in adversarial settings, where hostile nodes under the control of an attacker coexist with faithful ones. Verifiable multilateration (VM) was proposed to cope with this problem by leveraging on a set of trusted landmark nodes that act as verifiers. Although VM is able to recognize reliable localization measures, it allows for regions of undecided positions that can amount to the 40 percent of the monitored area. We studied the properties of VM as a noncooperative two-player game where the first player employs a number of verifiers to do VM computations and the second player controls a malicious node. The verifiers aim at securely localizing malicious nodes, while malicious nodes strive to masquerade as unknown and to pretend false positions. Thanks to game theory, the potentialities of VM are analyzed with the aim of improving the defender's strategy. We found that the best placement for verifiers is an equilateral triangle with edge equal to the power range R, and maximum deception in the undecided region is approximately 0.27R. Moreover, we characterized-in terms of the probability of choosing an unknown node to examine further-the strategies of the players

Renal tubular function in children and adolescents with Gitelnian's syndrome, the hypocalciuric variant of Bartter's syndrome

Renal tubular function was studied in 14 patients with Gitelman's syndrome and 14 control subjects. Apart from the biochemical hallmarks of Gitelman's syndrome, namely alkalaemia, hyperbi carbonataemia, hypokalaemia, hypomagnesaemia (with increased magnesium over creatinine ratio), increased urinary chloride over creatinine ratio, and low urinary calcium over creatinine, the patients were found to have hyperproteinaemia, hypochloraemia, high total plasma calcium concentration, reduced plasma ionized calcium concentration, and high urinary sodium excretion. A statistically significant negative linear relationship between plasma magnesium concentration and magnesium excretion corrected for glomerular filtration was observed in patients. The fractional calcium clearance and the urinary excretion of calcium corrected for glomerular filtration was significantly decreased in patients. In patients the urin ary osmolality after overnight water deprivation ranged from 526 to 1067 mmol/kg. Glucosuria and aminoacid uria were similar in patients and controls. The results of the study demonstrate the renal origin of hypomag nesaemia and hypocalciuria in Gitelman's syndrome. The failure to demonstrate hyperaminoaciduria, hyperglucosuria, hyperphosphaturia, hyperuricosuria, and severely impaired urinary concentrating ability provide evidence for a defect residing in the distal convoluted tubul

To explore or to exploit? Learning humans' behaviour to maximize interactions with them

Assume a robot operating in a public space (e.g., a library, a museum) and serving visitors as a companion, a guide or an information stand. To do that, the robot has to interact with humans, which presumes that it actively searches for humans in order to interact with them. This paper addresses the problem how to plan robot's actions in order to maximize the number of such interactions in the case human behavior is not known in advance. We formulate this problem as the exploration/exploitation problem and design several strategies for the robot. The main contribution of the paper than lies in evaluation and comparison of the designed strategies on two datasets. The evaluation shows interesting properties of the strategies, which are discussed

Robust Multi-Agent Pickup and Delivery with Delays

Multi-Agent Pickup and Delivery (MAPD) is the problem of computing collision-free paths for a group of agents such that they can safely reach delivery locations from pickup ones. These locations are provided at runtime, making MAPD a combination between classical Multi-Agent Path Finding (MAPF) and online task assignment. Current algorithms for MAPD do not consider many of the practical issues encountered in real applications: real agents often do not follow the planned paths perfectly, and may be subject to delays and failures. In this paper, we study the problem of MAPD with delays, and we present two solution approaches that provide robustness guarantees by planning paths that limit the effects of imperfect execution. In particular, we introduce two algorithms, k-\mathbf{TP} and p-\mathbf{TP}, both based on a decentralized algorithm typically used to solve MAPD, Token Passing (TP), which offer deterministic and probabilistic guarantees, respectively. Experimentally, we compare our algorithms against a version of TP enriched with online replanning. k-\mathbf{TP} and p-\mathbf{TP} provide robust solutions, significantly reducing the number of replans caused by delays, with little or no increase in solution cost and running time

Delayed and time-variant patrolling strategies against attackers with local observation capabilities

Surveillance of graph-represented environments is an application of autonomous patrolling robots that received remarkable attention during the last years. In this problem setting, computing a patrolling strategy is a central task to guarantee an effective protection level. Literature provides a vast set of methods where the patrolling strategies explicitly consider the presence of a rational adversary and fully informed attacker, which is characterized by worst-case (for the patroller) observation capabilities. In this work, we consider an attacker that does not have any prior knowledge on the environment and the patrolling strategy. Instead, we assume that the attacker can only access local observations on the vertex potentially under attack. We study the definition of patrolling strategies under the assumption that the attacker, when planning an attack on a particular location, tries to forecast the arrivals of the patroller on that particular location. We model our patrolling strategies with Markov chains where we seek the generation of arrivals that are difficult to forecast. To this end we introduce time-variance in the transition matrix used to determine the patrollers movements on the graph-represented environment

Team-maxmin equilibrium: Efficiency bounds and algorithms

The Team-maxmin equilibrium prescribes the optimal strategies for a team of rational players sharing the same goal and without the capability of correlating their strategies in strategic games against an adversary. This solution concept can capture situations in which an agent controls multiple resources-corresponding to the team members-that cannot communicate. It is known that such equilibrium always exists and it is unique (except degenerate cases) and these properties make it a credible solution concept to be used in real-world applications, especially in security scenarios. Nevertheless, to the best of our knowledge, the Team-maxmin equilibrium is almost completely unexplored in the literature. In this paper, we investigate bounds of (in) efficiency of the Team-maxmin equilibrium w.r.t. the Nash equilibria and w.r.t. the Maxmin equilibrium when the team members can play correlated strategies. Furthermore, we study a number of algorithms to find and/or approximate an equilibrium, discussing their theoretical guarantees and evaluating their performance by using a standard testbed of game instances

Community based activity center to support independently living elders

A novel platform that integrates an activity center and a virtual community is here described. It is part of a heterogeneous system aimed to provide monitoring, stimulation and assistance at the point of need to independent living elders, especially those who are at risk of falling into frailty. Combination of specific technological components has allowed to achieve a modular, responsive and dynamical design of the interfaces and their behaviors making their use natural. The explicit request to do activities with peers makes this platform a natural social engine. Preliminary results from use at home are reported and discussed

Development and Adaptation of Robotic Vision in the Real-World: the Challenge of Door Detection

Mobile service robots are increasingly prevalent in human-centric, real-world domains, operating autonomously in unconstrained indoor environments. In such a context, robotic vision plays a central role in enabling service robots to perceive high-level environmental features from visual observations. Despite the data-driven approaches based on deep learning push the boundaries of vision systems, applying these techniques to real-world robotic scenarios presents unique methodological challenges. Traditional models fail to represent the challenging perception constraints typical of service robots and must be adapted for the specific environment where robots finally operate. We propose a method leveraging photorealistic simulations that balances data quality and acquisition costs for synthesizing visual datasets from the robot perspective used to train deep architectures. Then, we show the benefits in qualifying a general detector for the target domain in which the robot is deployed, showing also the trade-off between the effort for obtaining new examples from such a setting and the performance gain. In our extensive experimental campaign, we focus on the door detection task (namely recognizing the presence and the traversability of doorways) that, in dynamic settings, is useful to infer the topology of the map. Our findings are validated in a real-world robot deployment, comparing prominent deep-learning models and demonstrating the effectiveness of our approach in practical settings

Stability of the antimalarial drug dihydroartemisinin in under physiologically-relevant conditions : implications for clinical treatment, pharmacokinetic and in vitro assays

Artemisinins are peroxidic antimalarial drugs known to be very potent but chemically highly unstable; they degrade in the presence of ferrous iron, Fe(II)-heme or biological reductants. Less documented is how this translates into chemical stability and antimalarial activity across a range of conditions applying to in vitro testing and clinical situations. Dihydroartemisinin (DHA) is studied here because it is both an antimalarial drug on its own and the main metabolite of other artemisinins. The behavior of DHA in PBS, plasma or erythrocytes lysate at different temperatures and pH ranges was examined. The antimalarial activity of the residual drug was evaluated using the chemosensitivity assay on P. falciparum, and the extent of decomposition of DHA was established through use of HPLC-ECD analysis. The role of the Fe(II)-heme was investigated by blocking its reactivity using carbon monoxide. A significant reduction in the antimalarial activity of DHA was seen after incubation in plasma and to a lesser extent in erythrocytes lysate: activity was reduced by half after 3 hours and almost completely abolished after 24 hours. Serum-enriched media also affected DHA activity. Effects were temperature and pH-dependent and paralleled the increased rate of decomposition of DHA from pH 7 upwards and in plasma. These results suggest that particular care should be taken in conducting and interpreting in vitro studies, prone as they are to experimental and drug storage conditions. Disorders such as fever, hemolysis or acidosis associated with malaria severity may contribute to artemisinins instability and reduce their clinical efficacy

Bilevel programming methods for computing single-leader-multi-follower equilibria in normal-form and polymatrix games

The concept of leader-follower (or Stackelberg) equilibrium plays a central role in a number of real-world applications bordering on mathematical optimization and game theory. While the single-follower case has been investigated since the inception of bilevel programming with the seminal work of von Stackelberg, results for the case with multiple followers are only sporadic and not many computationally affordable methods are available. In this work, we consider Stackelberg games with two or more followers who play a (pure or mixed) Nash equilibrium once the leader has committed to a (pure or mixed) strategy, focusing on normal-form and polymatrix games. As customary in bilevel programming, we address the two extreme cases where, if the leader\u2019s commitment originates more Nash equilibria in the followers\u2019 game, one which either maximizes (optimistic case) or minimizes (pessimistic case) the leader\u2019s utility is selected. First, we show that, in both cases and when assuming mixed strategies, the optimization problem associated with the search problem of finding a Stackelberg equilibrium is NP-hard and not in Poly-APX unless P= NP. We then consider different situations based on whether the leader or the followers can play mixed strategies or are restricted to pure strategies only, proposing exact nonconvex mathematical programming formulations for the optimistic case for normal-form and polymatrix games. For the pessimistic problem, which cannot be tackled with a (single-level) mathematical programming formulation, we propose a heuristic black-box algorithm. All the methods and formulations that we propose are thoroughly evaluated computationally