The Dancing problem requires a swarm of n autonomous mobile robots to form a sequence of patterns, i.e., perform a choreography. Existing work has proven that some crucial restrictions on choreographies and initial configurations (e.g., on repetitions of patterns, periodicity, symmetries, contractions/expansions) must hold so that the Dancing problem can be solved under certain robot models. Here, we prove that these necessary constraints can be dropped by considering the $$\mathcal {LUMI}$$ model (i.e., where robots are endowed with a light whose color can be chosen from a constant-size palette) under the quite unexplored sequential scheduler. We formalize the class of Universal Dancing problems which require a swarm of n robots starting from any initial configuration to perform a (periodic or finite) sequence of arbitrary patterns, only provided that each pattern consists of n vertices (including multiplicities). However, we prove that, to be solvable under $$\mathcal {LUMI}$$ , the length of the feasible choreographies is bounded by the compositions of n into the number of colors available to the robots. We provide an algorithm solving Universal Dancing by exploiting the peculiar capability of sequential robots to implement a distributed counter. Even assuming non-rigid movements, our algorithm ensures spatial homogeneity of the performed choreography.

Children with Unilateral Cerebral Palsy (UCP) often experience reduced spontaneous use of the non-dominant upper limb in daily life. Traditional clinical assessments, such as the Assisting Hand Assessment (AHA), provide valuable but clinical-environment-related and thus episodic measures of functional performance in structured settings. There remains a critical need for tools that enable continuous, ecologically valid, and objective monitoring of upper limb activity in real-world environments. In this study, we introduce the Daily AHA Biomarker (DAB), a novel digital biomarker derived from wearable sensor data, designed to estimate AHA scores based on spontaneous motor behavior. The DAB is intended to be used in clinical practice to monitor the movement of the upper extremities of subjects with UCP in their naturalistic environments. Using bilateral wrist-worn accelerometers, we collected multiday time-series data from 80 children (54 with UCP and 26 with Typical Development). Our Machine Learning pipeline combines time-series classification and regression to predict AHA scores from unstructured, daily living-recorded data. The final DAB indicator showed high predictive accuracy ( $$R^2 = 0.709$$ ) and a strong correlation with the clinical AHA score and the Manual Ability Classification System (MACS) level.

In networked systems supporting mobile agents, a particularly dangerous security threat facing the agents is the presence of a black hole (Bh): a network host that destroys any incoming agent without leaving any trace. The problem, called Black hole search (Bhs), of efficiently determining the location of such a dangerous host has been extensively studied under a variety of different assumptions. In spite of their differences, the existing results share the same assumption that all the searching agents are reliable.In this paper, we start the investigation of the Bhs problem when some of the searching agents are faulty in a malicious way. More precisely, we consider that up to f of the k searching agents are Byzantine: they may behave in an arbitrary manner, actively misleading other agents; furthermore, they are in collusion with the black hole, and immune to its destructive power.We study under what conditions the Bhs problem can be solved in a synchronous network of arbitrary topology in spite of the malicious agents, examining the impact on complexity of two factors: the a-priori topological knowledge held by the agents, and the communication mechanism available to them.We prove that, with prior knowledge about the graph topology (i.e., a network map), Bhs can be solved by k ≥ 2f +2 agents in O(n + f) synchronous rounds both with whiteboards and with just local communication, where n is the number of nodes in the network.Without any knowledge about the topological structure, using whiteboard communication Bhs can be solved by k ≥ (f+1)(∆+ 1) agents in O(m+f) rounds; instead, using local communication, Bhs can be solved by k ≥ (f + 1)(∆ + 1) + 3f + 1 agents in O(m • n + f) rounds, where m is the number of links of the network and ∆ is the maximum degree of the network.In all cases, as we show, the bound on the total number k of agents is asymptotically optimal.

The Dancing problem requires a swarm of n autonomous mobile robots to form a sequence of patterns, aka perform a choreography.Existing work has proven that some crucial restrictions on choreographies and initial configurations (e.g., on repetitions of patterns, periodicity, symmetries, contractions/expansions) must hold so that the Dancing problem can be solved under certain robot models.Here, we prove that these necessary constraints can be dropped by considering the LUMI model (i.e., where robots are endowed with a light whose color can be chosen from a constant-size palette) under the quite unexplored sequential scheduler.We formalize the class of Universal Dancing problems which require a swarm of n robots starting from any initial configuration to perform a (periodic or finite) sequence of arbitrary patterns, only provided that each pattern consists of n vertices (including multiplicities).However, we prove that, to be solvable under LUMI, the length of the feasible choreographies is bounded by the compositions of n into the number of colors available to the robots.We provide an algorithm solving the Universal Dancing problem by exploiting the peculiar capability of sequential robots to implement a distributed counter mechanism.Even assuming non-rigid movements, our algorithm ensures spatial homogeneity of the performed choreography.

In networked environments supporting mobile agents , a pressing problem is the presence of network sites harmful for the agents. In this paper we consider the danger posed by a node that destroys any incoming agent without leaving any trace. Such a dangerous node is known in the literature as a black hole ( Bh ). The problem of a team of system agents determining its location, known as black hole search ( Bhs ), has been extensively studied in the literature under a variety of assumptions, both in synchronous and asynchronous settings. The main complexity parameter of Bhs is the number of system agents (called size ) needed to solve the problem; other parameters are the number of moves (called cost ) performed by the agents, and the time until termination. In the existing literature, with only a couple of exceptions, all results are based on a common assumption that the network is static , i.e. its topology does not change in time. We consider instead the Bhs when the network is dynamic : the link structure of the graph changes over time. While time-varying graphs have been the focus of intense research in the last two decades, very little is known on the problem of locating the Bh in such networks. In this paper, we contribute to fill this research gap by studying Bhs in dynamic ring networks, focusing on the 1-interval connectivity adversarial dynamics. Feasibility and complexity of the problem depend on many factors, specifically on the size n of the ring, whether or not n is known, and the type of inter-agent communication (whiteboards, tokens, face-to-face, visual). In this paper, we provide a complete feasibility characterization presenting size optimal algorithms. Furthermore, we establish lower bounds on the cost and time of size-optimal solutions and show that our algorithms achieve those bounds.

Programmable Matter (PM) has been widely investigated in recent years. It refers to some kind of substance with the ability to change its physical properties (e.g., shape or color) in a programmable way. In this paper, we refer to the SILBOT model, where the particles live and move on a triangular grid, are asynchronous in their computations and movements, and do not possess any direct means of communication (silent) or memory of past events (oblivious). Within SILBOT , we aim at studying Spanning problems, i.e., problems where the particles are required to suitably span all over the grid. We first address the Line Formation problem where the particles are required to end up in a configuration where they all lie on a line, i.e., they are aligned and connected. Secondly, we deal with the more general Scattering problem: starting from any initial configuration, we aim at reaching a final one where no particles occupy neighboring nodes. Furthermore, we investigate configurations where some nodes of the grid can be occupied by unmovable elements (i.e., obstacles) from both theoretical and experimental view points.

The ongoing digital revolution in the healthcare sector, emphasized by bodies like the US Food and Drug Administration (FDA), is paving the way for a shift towards person-centric healthcare models. These models consider individual needs, turning patients from passive recipients to active participants. A key factor in this shift is Artificial Intelligence (AI), which has the capacity to revolutionize healthcare delivery due to its ability to personalize it. With the rise of software in healthcare and the proliferation of the Internet of Things (IoT), a surge of digital data is being produced. This data, alongside improvements in AI's explainability, is facilitating the spread of person-centric healthcare models, aiming at improving health management and patient experience. This paper outlines a human-centered methodology for the development of an AI-as-a-service platform with the goal of broadening access to personalized healthcare. This approach places humans at its core, aiming to augment, not replace, human capabilities and integrate in current processes. The primary research question guiding this study is: "How can Human-Centered AI principles be considered when designing an AI-as-a-service platform that democratizes access to personalized healthcare?" This informed both our research direction and investigation. Our approach involves a design fiction methodology, engaging clinicians from different domains to gather their perspectives on how AI can meet their needs by envisioning potential future scenarios and addressing possible ethical and social challenges. Additionally, we incorporate Meta-Design principles, investigating opportunities for users to modify the AI system based on their experiences. This promotes a platform that evolves with the user and considers many different perspectives.

Abstract A distributed algorithm $${\mathcal {A}}$$ A solves the Point Convergence task if an arbitrarily large collection of entities, starting in an arbitrary configuration, move under the control of $${\mathcal {A}}$$ A to eventually form and thereafter maintain configurations in which the separation between all entities is arbitrarily small. This fundamental task in the standard $$\mathcal {OBLOT}$$ OBLOT model of autonomous mobile entities has been previously studied in a variety of settings, including full visibility, exact measurements (including distances and angles), and synchronous activation of entities. Our study concerns the minimal assumptions under which entities, moving asynchronously with limited and unknown visibility range and subject to limited imprecision in measurements, can be guaranteed to converge in this way. We present an algorithm operating under these constraints that solves Point Convergence, for entities moving in two or three dimensional space, with any bounded degree of asynchrony. We also prove that under similar realistic constraints, but unbounded asynchrony, Point Convergence in the plane is not possible in general, contingent on the natural assumption that algorithms maintain the (visible) connectivity among entities present in the initial configuration. This variant, that we call Cohesive Convergence, serves to distinguish the power of bounded and unbounded asynchrony in the control of autonomous mobile entities, settling a long-standing question whether in the Euclidean plane synchronously scheduled entities are more powerful than asynchronously scheduled entities.

We consider Internet of Things (IoT) sensors deployed inside an area to be monitored. Drones can be used to collect the data from the sensors, but they are constrained in energy and storage. Therefore, all drones need to select a subset of sensors whose data are the most relevant to be acquired, modeled by assigning a reward. We present an optimization problem called Multiple-drone Data-collection Maximization Problem (MDMP) whose objective is to plan a set of drones' missions aimed at maximizing the overall reward from the collected data, and such that each individual drone's mission energy cost and total collected data are within the energy and storage limits, respectively. We optimally solve MDMP by proposing an Integer Linear Programming based algorithm. Since MDMP is NP-hard, we devise suboptimal algorithms for single- and multiple-drone scenarios. Finally, we thoroughly evaluate our algorithms on the basis of random generated synthetic data.

Omics data are being generated for different conditions, and can be a valuable resource for building novel predictive models for medical diagnosis. Given the reduced number of samples in each dataset, the application of Machine Learning (ML) models requires data integration. At the same time, multiple ML models are available, and the best option for data integration is not known. These challenges have been addressed typically in restricted settings, i.e., for one single disease at a time. However, a thorough comparison of models on integrated data, for different conditions, is still missing. In this paper we confront 7 classifiers on integrated data for 6 diseases, over 14 datasets. We compared the models on single and integrated datasets, employing different pre-processing techniques. We also evaluated the effect of feature selection, analyzing the robustness and relevance of the features extracted. We observed that, even if integration slightly reduces predictive power, the models are still able to produce good classifications. When testing generalization abilities on new datasets, sometimes the performance decreases drastically, depending on the disease studied.

In this paper, we start the investigation of distributed computing by mobile agents in dangerous dynamic networks. The danger is posed by the presence in the network of a black hole (BH), a harmful site that destroys all incoming agents without leaving any trace. The problem of determining the location of the black hole in a network, known as black hole search (BHS), has been extensively studied in the literature, but always and only assuming that the network is static. At the same time, the existing results on mobile agents computing in dynamic networks never consider the presence of harmful sites. In this paper we start filling this research gap by studying black hole search in temporal rings, specifically focusing on 1-interval connectivity adversarial dynamics. The main complexity parameter of BHS is the number of agents (called size) needed to solve the problem; other parameters are the number of moves (called cost) performed by the agents, and the time until termination. Feasibility and complexity depend on many factors; the size n of the ring, whether or not n is known, and the type of inter-agent communication (whiteboards, tokens, face-to-face, visual). In this paper, we provide a complete feasibility characterization presenting size optimal algorithms. Furthermore, we establish lower bounds on the cost and time of size-optimal solutions and show that our algorithms achieve those bounds.

We consider distributed computations, by identical autonomous mobile entities, that solve the Point Convergence problem: given an arbitrary initial configuration of entities, disposed in the Euclidean plane, move in such a way that, for all ε>0, a configuration is eventually reached and maintained in which the separation between all entities is at most ε. The problem has been previously studied in a variety of settings. Our study concerns the minimal assumptions under which entities, moving asynchronously with limited and unknown visibility range and subject to limited imprecision in measurements, can be guaranteed to converge in this way. We present an algorithm that solves Point Convergence, provided the degree of asynchrony is bounded by some arbitrarily large but fixed constant. This provides a strong positive answer to a decade old open question posed by Katreniak. We also prove that, in an otherwise comparable setting, Point Convergence is impossible with unbounded asynchrony. This serves to distinguish the power of bounded and unbounded asynchrony in the control of autonomous mobile entities, settling at the same time a long-standing question whether in the Euclidean plane synchronous entities are more powerful than asynchronous ones.

In this paper we study the power of using lights, i.e. visible external memory, for distributed computation by autonomous robots moving in Look-Compute-Move (LCM) cycles. With respect to the LCM cycles, the most common models studied in the literature are the fully-synchronous (FSYNC), the semi-synchronous (SSYNC), and the asynchronous (ASYNC). In this paper we introduce in the ASYNC model, the weakest of the three, the availability of visible external memory: each robot is equipped with a light bulb that is visible to all other robots, and that can display a constant numbers of different colors, the colors are persistent, that is they are not automatically reset at the end of each cycle. We first study the relationship between ASYNC with visible bits and SSYNC. We prove hat asynchronous robots, when equipped with a constant number of colors, are strictly more powerful than traditional semi-synchronous robots. We also show that, when enhanced with visible lights, the difference between asynchrony and semi-synchrony disappears, this result must be contrasted with the strict dominance ASYNC

Consider a set of $n>2$ identical mobile computational entities in the plane, called robots, operating in Look-Compute-Move cycles, without any means of direct communication. The Gathering Problem is the primitive task of all entities gathering in finite time at a point not fixed in advance, without any external control. The problem has been extensively studied in the literature under a variety of strong assumptions (e.g., synchronicity of the cycles, instantaneous movements, complete memory of the past, common coordinate system, etc.). In this paper we consider the setting without those assumptions, that is, when the entities are oblivious (i.e., they do not remember results and observations from previous cycles), disoriented (i.e., have no common coordinate system), and fully asynchronous (i.e., no assumptions exist on timing of cycles and activities within a cycle). The existing algorithmic contributions for such robots are limited to solutions for $n \leq 4$ or for restricted sets of initial configurations of the robots; the question of whether such weak robots could deterministically gather has remained open. In this paper, we prove that indeed the Gathering Problem is solvable, for any $n>2$ and any initial configuration, even under such restrictive conditions.

In many network applications, the computation takes place on the minimum-cost spanning tree (MST) of the network G; unfortunately, a single link or node failure disconnects the tree. The ALL NODES REPLACEMENT (ANR) problem is the problem of precomputing, for each node u in G, the new MST should u fail. This problem has been extensively investigated for serial and parallel settings, and efficient solutions have been designed for those environments. The situation is surprisingly different in distributed settings. In fact, no distributed solution exists to date which performs better than the brute-force repeated application of MST construction. In this paper, we consider for the first time the problem of computing all the replacement minimum-cost spanning trees distributively. We design a solution protocol, and we prove that the total amount of communication exchanges taking place is O(n), each exchange using at most O(n) data items. Hence, the total amount of data items communicated during the computation (the data complexity) is O(n^2). We also show how the simpler problem ALL EDGES REPLACEMENT (AER) dealing with single edge failures, which can be solved with the same costs using some existing techniques. Also for the AER problem, efficient solutions exist in the serial and parallel setting but, prior to this work, no distributed solution other than brute force was known.

Abstract Mobile agents operating in networked environments face threats from other agents as well as from the hosts (i.e., network sites) they visit. A black hole is a harmful host that destroys incoming agents without leaving any trace. To determine the location of such a harmful host is a dangerous but crucial task, called black hole search. The most important parameter for a solution strategy is the number of agents it requires (the size); the other parameter of interest is the total number of moves performed by the agents (the cost). It is known that at least two agents are needed; furthermore, with full topological knowledge, Ω(n log n) moves are required in arbitrary networks. The natural question is whether, in specific networks, it is possible to obtain (topology‐dependent but) more cost efficient solutions. It is known that this is not the case for rings. In this article, we show that this negative result does not generalizes. In fact, we present a general strategy that allows two agents to locate the black hole with O(n) moves in common interconnection networks: hypercubes, cube‐connected cycles, star graphs, wrapped butterflies, chordal rings, as well as in multidimensional meshes and tori of restricted diameter. These results hold even if the networks are anonymous. © 2006 Wiley Periodicals, Inc. NETWORKS, Vol. 47(2), 61–71 2006

In this paper we present a self-stabilizing algorithm for the intruder problem. The problem can be formulated as follows: an enemy unit, or intruder, is trying to sneak through a field patrolled by an arbitrary number of friendly autonomous (i.e. robotic) units. These units must reach the intruder and block it by surrounding it. Our solution to this problem, provided as an algorithm for the autonomous patrolling units, makes minimal assumptions on their capabilities. In particular, we assume they are completely asynchronous, and moreover that they have no observable identities, no memory, and no means to explicitly communicate with each other. Each unit needs only to be capable of observing the current position of its fellows and of the intruder. All these features, while making the task harder, give to the algorithm the nice property of self-stabilization, thus improving its robustness. For example, if any unit is knocked out, all the others automatically adjust their behavior, in order to still complete the task. By concentrating on extremely simple units, we are also able to investigate which capabilities are really needed to solve this problem, with obvious cost benefits (especially if the units are deployed in a hostile environment). In the paper, we first present a computational model for our robotic "cops", followed by the description of the algorithm we propose. We also show results of computer simulations, providing quantitative measures on the efficiency of the algorithm.

Protecting agents from host attacks is a pressing security concern in networked environments supporting mobile agents. In this paper, we consider a black hole: a highly harmful host that disposes of visiting agents upon their arrival, leaving no observable trace of such a destruction. The task to identify the location of the harmful host is clearly dangerous for the searching agents. We study under what conditions and at what cost a team of autonomous asynchronous mobile agents can successfully accomplish this task; we are concerned with solutions that are generic (i.e., topology-independent). We study the size of the optimal solution (i.e., the minimum number of agents needed to locate the black hole), and the cost of the minimal solution (i.e., the number of moves performed by the agents executing a size-optimal solution protocol). We establish tight bounds on size and cost depending on the a priori knowledge the agents have about the network, and on the consistency of the local labellings. In particular, we prove that: with topological ignorance Δ + 1 agents are needed and suffice, and the cost is Θ(n2), where Δ is the maximal degree of a node and n is the number of the nodes in the network; with topological ignorance but in presence of sense of direction only two agents suffice and the cost is Θ(n2); and with complete topological knowledge only two agents suffice and the cost is Θ(n log n). All the upper-bound proofs are constructive.