Swarm UIs provide assistance to support users in their tasks and are increasingly explored in HCI. This paper studies the extent to which this assistance impacts users’ sense of agency. A reduced sense of agency can lead to non-use of the interface or a diminishing sense of responsibility regarding the consequences of users’ actions. We conduct three experiments studying the impact of three factors on the sense of agency: the level of assistance, the task difficulty, and the predictability of modules. Our nine assistance levels vary in system autonomy and module coordination (proxy vs. no proxy). We find that higher assistance reduces users’ sense of agency, and this effect is not impacted by task difficulty. Predictability only impacts the least assistive interaction techniques. Our results will foster users’ acceptance, responsibility, and use of swarm UIs.

In this paper, we explore the field of self-reconfigurable modular robots, representing a significant advance in robotic technology. These robots have many capabilities, offering high adaptability and flexibility for a variety of applications. However, computing the stability is challenging as it is computationally intensive, it needs to be distributed and fast, as close as possible of real-time. In this article, we introduce a distributed algorithm designed to overcome these challenges while taking mechanical constraints into account. At the heart of this algorithm is the notion of the "support polygon", which enables the stability of a modular robot to be assessed in real time. The algorithm is based on a fully distributed tree partitioning approach, facilitating efficient communication and collaboration between modules. The algorithm also uses a polygon merging approach to reduce the number of messages when creating the polygon support, thus significantly reducing response time. In fact, the response time of the method used is very small compared to other research. We also present simulation results on a simulator, VisibleSim, as well as experimental validation on real robotic modules, which underlines the practical viability of the approach. Overall, this work lays a solid base for further advances aiming to guarantee the stability of modular robots.

In Industry 4.0, achieving semantic interoperability is a significant problem due to the complexities of current automation systems and the numerous standards involved. The study explores how Artificial Intelligence (AI) and semantic interoperability connect within the Internet of Things (IoT) framework to overcome barriers to technology adoption. The main goal is to analyze how AI’s adaptive and predictive abilities might transform semantic interoperability by studying AI-driven methodologies to provide a flexible and efficient solution. The main objective of the paper is to leverage Named Entity Recognition (NER) AI models to streamline the identification of entities within the Internet of Things (IoT) for achieving semantic interoperability. It tests a Natural Language Processing (NLP) translator on data representations not seen during training, and the outcome highlights the efficiency of NLP in correctly understanding and processing these representations.

Sampling Industrial IoT data streams aims to generate a sample for future data analysis tasks. Several variables influence the efficacy of the constructed sample, including the sampling algorithm and its complexity, the sampling rate selected, and how the sampled data are processed at the gateway. In this paper, we propose a lightweight feature-based priority sampling technique for optimizing Industrial IoT multivariate time series prior to deep learning model classification. The selection of an effective sampling algorithm and rate, coupled with efficient data processing, poses a significant challenge with a key objective of balancing communication overhead reduction and precision maintenance. Our technique minimizes data transmission at the IoT device level, enhancing energy efficiency and improving classification performance by noise reduction through selective feature sampling. Comparative evaluation with existing sampling techniques using a benchmark dataset indicates superior performance in terms of data reduction and classification accuracy trade-offs. Notably, our approach enhances the accuracy of a ResNet model and reduces its processing time.

Localizing each module in a modular self-reconfigurable robot (MSR) is of paramount importance. In MSR, the communication graph is directly mapped to the real topology which makes the localization problem easy to solve. However, some types of connectors can lose the orientation of the modules, making the problem intractable. In this work, we propose to build a coordinate system for 3D lattice-based modular robots using a multiagent system. We present DisCo algorithm, that uses one agent per module which can only communicate with its connected neighbors and that does not need a central coordination system. We show that the agents can tackle any kinds of 3D lattice and we illustrate it with a Face Centered Cubic lattice (12 neighbors) and a cubic lattice (6 neighbors). Using communications and only four states, DisCo can also deduce the orientation of modules if the connectors do not provide this information.

Modular Tangible User Interfaces (TUIs) –i.e., UIs made of small-scale physical modules– offer novel opportunities for tangible interaction thanks to their highly customizable form factor. Such modular TUIs were proposed with different shape of modules and bonding strength between them. The problem we address in this paper is the lack of knowledge of how bonding strength and shape of the modules impact usability. We present the first study exploring the impact of bonding strength and module shape on subjective user ratings when interacting with a magnetic modular prototype. We assessed three levels of bonding strength (low, mid, high) and two shapes (cubes and rounded cubes) in a controlled user study. Participants performed eight common manipulations found in the literature for (non-)modular TUIs. Experimental results showed that (1) cubic modules are overall easier and more satisfying to manipulate, except for precision and bending tasks, (2) low strength impairs UI solidity, but high strength impairs precision tasks with cubic modules.

Modular robots are defined as autonomous kinematic machines with variable morphology. They are composed of several thousands or even millions of modules that are able to coordinate to behave intelligently. Clustering the modules in modular robots has many benefits, including scalability, energy-efficiency, reducing communication delay, and improving the self-reconfiguration process that focuses on finding a sequence of reconfiguration actions to convert robots from an initial shape to a goal one. The main idea of clustering is to divide the modules in an initial shape into a number of groups based on the final goal shape to enhance the self-reconfiguration process by allowing clusters to reconfigure in parallel. In this work, we prove that the size-constrained clustering problem is NP-complete, and we propose a new tree-based size-constrained clustering algorithm called “SC-Clust.” To show the efficiency of our approach, we implement and demonstrate our algorithm in simulation on networks of up to 30000 modules and on the Blinky Blocks hardware with up to 144 modules.

In this paper, we propose a new self-reconfiguration scheme for modular robots based on a metamodule design that allows to form a 3D porous structure. The porous structure enables a parallel flow of modules inside it without blocking. The metamodule can also be used to fill its internal volume with an additional number of modules allowing the structure to be compressible and expandable. Hence, it is a potential for improving the self-reconfiguration process. We first present the metamodule model and the porous structure built using it. Then, we describe an algorithm to self-reconfigure the structure from an initial shape to a given goal shape. We evaluated the algorithm in simulation on structures composed of up to 2,700 modules. We studied the performance in term of parallelism, showed that the number of communications is proportional to the number of motions and the execution time varies linearly with the diameter of the configuration.

A Modular Self-Reconfigurable Robot (MSR) is an Internet of Robotic Things object (IoRT) composed of an ensemble of independent communicating robotic modules that can self-reconfigure to change their initial shape into a goal one. Self-reconfiguration is known to be an intricate and complex task and faults such as broken connections, loss of power, incomplete motions … are likely to occur during the self-reconfiguration process. However, existing work on self-reconfiguration considers fault-free robotic modules and does not apply any fault-tolerance mechanisms. In this paper, we propose a fault-tolerance mechanism that can be applied to a broken interface which results in communication failures in the context of the self-reconfiguration of a 3D Catom robot using the deterministic scaffold assembly algorithm. We introduce a new module role: the Helper module. The Helper module serves as a communication bridge between two modules attached by a broken interface. We showed in simulation the efficiency of our approach dealing with communication failures caused by broken interfaces.

Shape-changing User Interfaces (UIs) explore the ability of a UI to change its physical shape to support multiple interaction modalities for users’ input and/or system’s output. An approach currently studied to implement such interfaces at a high resolution is based on mm-sized, round, and self-actuated modules. The problem we tackle in this paper is to find the range of usable sizes of such modules, to better inform the trade-off between usability and technological feasibility. We assessed four sliders in a controlled user study: a standard slider and three sliders made of mock-up rounded modules of 1 mm, 2.5 mm, and 5 mm. Experimental results show that (1) 5 mm modules significantly impair performance for the pursuit task and subjective perception for both tasks, (2) performance increases when the size of modules decreases, but (3) users reportedly enjoyed the haptic feedback provided by 1 mm to 2.5 mm modules. These results provide deeper understanding on the impact of the size of modules on performance and subjective perception to inform current technological development of physical user interfaces made of small robotic modules.

Programmable matter is a material that can change its physical properties at will, whether it is its shape, density or conductivity. It can be implemented as an ensemble of micro-robots arranged in space to form a specific shape and having their own computing power. This technology behaves as a distributed system. Each micro-robot is called a module and the whole forms a modular robot. This paper tackles the self-reconfiguration problem by presenting a deterministic planning algorithm that can decide which positions can be filled over multiple iterations using virtual forces. The proposed algorithm implements the Hungarian method to optimize the planning by minimizing the total number of movements of the robots and preventing positions from being blocked. Each module embeds the same algorithm and coordinates with the others using neighbor-to-neighbor communications. Simulation results are conducted to show the effectiveness of the proposed approach.

As in all distributed systems, having an accurate access to a global notion of time is vital for a modular robot's modules to coordinate their activities and accomplish their goal. In this paper, we present and compare two methods for clock skew compensation to enhance the performance of network-wide time synchronization in modular robots with neighbor-to-neighbor communication. The first one, Adaptive Rate Search (ARS), uses a light weight adaptive search method to adapt the drift rate of local clocks. The second one, combines Linear Regression with Bayes estimation (LR+) to reduce the accumu-lative error induced during the propagation of synchronization messages on large number of hops. We evaluate both methods with Blinky Block robots: using a real modular robots system and simulation. The results show that both methods LR+ and ARS present a significant error reduction compared to least-square linear regression used in previous state of the art synchronization protocol for modular robots with neighbor-to-neighbor communication.

Shape-changing User Interfaces attract growing interest in Human-Computer Interaction. Modular robotics offer a great opportunity for their implementation. However, the current theoretical and technical advances of modular robotics are fragmented and little centered on the user. To unify existing work and center future research on the user, we perform a systematic literature review enabling us to build a unifying space for the design of modular shape-changing user interfaces.Our aim is to bridge the gap between HCI and robotics. We relate properties of different domains and identify inconsistencies to structure the design space. Towards this aim, we conduct a thorough cross-disciplinary survey to propose: 1) a set of design properties at the scale of the interface (macro-scale) and at the scale of the modules (micro-scale) and 2) the impact of these properties on each other. This paper can be used to describe and compare existing modular shape-changing UIs and generate new design ideas by building upon knowledge from robotics and HCI.

The vision for programmable matter is to create a material that can be reprogrammed to have different shapes and to change its physical properties on demand. They are autonomous systems composed of a huge number of independent connected elements called particles. The connections to one another form the overall shape of the system. These particles are capable of interacting with each other and take decisions based on their environment. Beyond sensing, processing, and communication capabilities, programmable matter includes actuation and motion capabilities. It could be deployed in different domains and will constitute an intelligent component of the IoT. A lot of applications can derive from this technology, such as medical or industrial applications. However, just like any other technology, security is a huge concern. Given its distributed architecture and its processing limitations, programmable matter cannot handle the traditional security protocols and encryption algorithms. This article proposes a new security protocol optimized and dedicated for IoT programmable matter. This protocol is based on lightweight cryptography and uses the same encryption protocol as a hashing function while keeping the distributed architecture in mind. The analysis and simulation results show the efficiency of the proposed method and that a supercomputer will need about 5.93 × 10 25 years to decrypt the message.

We present a distributed framework for predicting whether a planned reconfiguration step of a modular robot will mechanically overload the structure, causing it to break or lose stability under its own weight. The algorithm is executed by the modular robot itself and based on a distributed iterative solution of mechanical equilibrium equations derived from a simplified model of the robot. The model treats intermodular connections as beams and assumes no-sliding contact between the modules and the ground. We also provide a procedure for simplified instability detection. The algorithm is verified in the Programmable Matter simulatorVisibleSim, and in real-life experiments on the modular robotic systemBlinky Blocks.

Modular robots are autonomous systems with variable morphology, composed of independent connected computational elements, called particles or modules. Due to critical resource constraints and limited capabilities, globally unique identifier (ID) assignment to each particle is a very challenging task in modular robots. However, having a unique ID in each one remains essential for various operations and applications in this domain. For instance, it is required to establish communications between nodes and implement routing protocols. It helps in saving energy consumption and enhancing the security mechanisms. In this paper, we propose a distributed unique ID assignment method for modular robots. It is a three phases based algorithm. The first phase consists in discovering the system while building a logical tree. The second phase finds the total size of particles in the system needed for several operations in modular robots, and the third one is dedicated to the unique ID assignment. After fully optimizing the distributed algorithm, the effects of various system shapes and leader positions on the energy and time complexity are studied, while proposing fitting solutions for different requirements.

Modular robots are defined as autonomous kinematic machines with variable morphology. They are composed of several thousands or even millions of modules which are able to coordinate in order to behave intelligently. Clustering the modules in modular robots has many benefits, including scalability, energy-efficiency, reducing communication delay and improving the self-configuration processes that focuses on finding a sequence of reconfiguration actions to convert robots from an initial configuration to a goal one. The main idea is to divide the nodes in an initial shape into some clusters based on the final goal shape in order to reduce the time complexity and enhance the self-reconfiguration tasks. In this paper, we propose a robust clustering approach based on a distributed density-cut graph algorithm to divide the networks into a pre-defined number of clusters based on the final goal shape. The result is an algorithm with linear complexity that scales to large modular robot systems. We implement and demonstrate our algorithm on a real Blinky Blocks system and evaluate it in simulation on networks of up to 30,000 modules.

This paper addresses the self-reconfiguration problem in large-scale modular robots for the purpose of shape formation for object representation. It aims to show that this process can be accelerated without compromising on the visual aspect of the final object, by creating an internal skeleton of the shape using the previously introduced sandboxing and scaffolding techniques, and then coating this skeleton with a layer of modules for higher visual fidelity. We discuss the challenges of the coating problem, introduce a basic method for constructing the coating of a scaffold layer by layer, and show that even with a straightforward algorithm, our scaffolding and coating combo uses much fewer modules than dense shapes and offers attractive reconfiguration times. Finally, we show that it could be a strong alternative to the construction of dense shapes using traditional self-reconfiguration algorithms.

Distributed self-reconfiguration in large-scale modular robots is a slow process and increasing its speed a major challenge. In this article, we propose an improved and asynchronous version of a previously proposed distributed self-reconfiguration algorithm to build a parametric scaffolding structure. This scaffold can then be coated to form the desired final object. The scaffolding is built through a continuous feeding of modules into the growing shape from an underneath reserve of modules which shows a reconfiguration time improved by a factor of 3√N compared to the previous and synchronous version of the algorithm, therefore attaining an O(N1/3) reconfiguration time, with N the number of modules in the system. Our algorithm uses a local motion coordination algorithm and pipelining techniques to ensure that modules can traverse the structure without collisions or creating deadlocks. Last but not least, our algorithm manages uncertainty in the motion duration of modules without negatively impacting reconfiguration time.

Among the diversity of the existing modular robotic systems, we consider in this paper the subset of distributed modular robotic ensembles composed of resource-constrained identical modules that are organized in a lattice structure and which can only communicate with neighboring modules. These modular robotic ensembles form asynchronous distributed embedded systems. In many algorithms dedicated to distributed system coordination, a specific role has to be played by a leader, i.e., a single node in the system. This leader can be elected using various criteria. A possible strategy is to elect a center node, i.e., a node that has the minimum distance to all the other nodes. Indeed, this node is ideally located to communicate with all the others and this leads to better performance in many algorithms. The contribution of this paper is to propose the k-BFS SumSweep algorithm designed to elect an approximate-center node. We evaluated our algorithm both on hardware modular robots and in a simulator for large ensembles of robots. Experimental results show that k-BFS SumSweep is often the most accurate approximation algorithm (with an average relative accuracy between 90% to 100%) while using the fewest messages in large-scale systems, requiring only a modest amount of memory per node, and converging in a reasonable length of time.

Terahertz band (0.1-10 THz) provides very large bandwidth, enabling multimedia transmission at short distance. In macro world, ultra broadband communication networks at THz band (TeraNets) provides very large bandwidth for wireless multimedia sensor networks (WMSN). Similarly, recent development in nano-technology (nano-antenna and nano-transceiver) shows that electromagnetic nanocommunications at THz band support very large bandwidth too, which enables the development of wireless multimedia nano-sensor networks (WMNSN). For both WMSN and WMNSN, the major challenges are simple and energy efficient transmission, since the network consists of a large number of nodes with limited battery capacity. In this paper, we propose a simple, energy efficient and robustness-aware image compression for pulse-based WMSN and WMNSN. We investigate the system performance in terms of image quality, energy efficiency, perpetual operation in nanocommunications and transmission robustness against error. The results show that for these networks, with the trade-off of image quality, the proposed method outperforms JPEG, JPEG 2000, GIF and PNG in all used metrics, for example the energy reduction compared to uncompressed is 77% for JPEG and 90% for our method.

Recent advances in robotics technology have made it practical to deploy a large number of inexpensive robots in a wide range of application domains. In many of those applications, a group of autonomous robots is required to form a predefined geometric shape such as a line or a circle. This problem, namely pattern formation problem, is one of the most important coordination problems in multi-robot systems. A particular pattern extensively studied in literature is the uniform circle, and the corresponding problem is called uniform circle formation. In uniform circle formation, a set of simple mobile robots (asynchronous, autonomous), starting from arbitrary positions on the plane, have to arrange themselves on the vertices of a regular polygon eventually. Towards addressing the problem, existing works usually make conveniently strong assumptions, i.e., the robots are regarded as mass points and have unlimited sensing and communication range. The question of whether the robots with actual size and limited sensing and communication range could form a uniform circle, to our knowledge, has remained open. In this paper, we propose a new approach towards addressing this issue. Three phases, consensus on the circle, circle formation, and uniform transformation, constitute our approach. Inside our approach, there are some new distributed algorithms such as convex hull construction and cardinality estimation. Simulation result, theoretical analysis, and successful deployment have shown the effectiveness and practicability of our approach.

Internet of Things (IoT) is a network composed of a variety of heterogeneous things and objects such as Connected Wearable Devices (sensors, MEMS, microrobots, PDA, ...), Connected Cars, Connected Homes, Connected Cities, and the Industrial Internet. These things use generally wireless communication to interact and cooperate with each other to reach common services and goals. IoT(T, n) is a wireless network of things composed of T things with n items (information) distributed on it. The aim of the permutation routing is to route to each thing, its items, so it can accomplish its task. In this paper, we present an agent-based broadcast protocol for mobile Internet of Things that uses few communication channels. The main idea is to partition things into groups according to the number of channels. In each group, an agent manages a set of things. This new protocol performs efficiently with respect to the number of broadcast rounds and runs without conflict and collision on the communication channels. We give an estimation of the upper and the lower bounds of the number of broadcast rounds in the worst case. This paper is the first to present efficient broadcast protocol for the internet of things.

Many distributed algorithms require a specific role to be played by a leader, a single node in the system. The choice of this node often has a direct impact on the performance. In particular, selecting a central node as the leader can significantly improve algorithm efficiency. Classical distributed algorithms require global information about the connectivity network to elect a centroid node. Thus, they are not suitable for large-scale distributed embedded systems with scarce computation, memory and energy resources. We present E2ACE, an Effective and Efficient Approximate-Centroid Election algorithm that uses O(1) memory space per node, O(d) time and O(mn^2) messages of size O(1), where n is the number of nodes, m the number of connections and d the diameter of the system. We evaluate our algorithm on the Blinky Blocks system using simulations. Experimental results show that E2ACE scales well in terms of accuracy, execution time and number of messages. We show that E2ACE is more accurate than the only existing algorithm with similar complexity results.

Modular self-reconfigurable robots are composed of independent connected modules which can self-rearrange their connectivity using processing, communication and motion capabilities, in order to change the overall robot structure. In this paper, we consider rolling cylindrical modules arranged in a two-dimensional vertical hexagonal lattice. We propose a parallel, asynchronous and fully decentralized distributed algorithm to self-reconfigure robots from an initial configuration to a goal one. We evaluate our algorithm on the millimeter-scale cylindrical robots, developed in the Claytronics project, through simulation of large ensembles composed of up to ten thousand modules. We show the effectiveness of our algorithm and study its performance in terms of communications, movements and execution time. Our observations indicate that the number of communications, the number of movements and the execution time of our algorithm is highly predictable. Furthermore, we observe execution times that are linear in the size of the goal shape.

In this paper, we propose the Modular Robot Time Protocol (MRTP), a network-wide time synchronization protocol for modular robots. Our protocol achieves its performance by combining several mechanisms: central time master election, low-level time-stamping and clock skew compensation using linear regression. We evaluate our protocol on the Blinky Blocks hardware. Experimental results show that MRTP can potentially manage real systems composed of up to 27,775 Blinky Blocks. We observe that the synchronization precision depends on the hardware, the hop distance to the time master, the synchronization periods and the number of synchronization points used for the linear regressions. Furthermore, we show that our protocol is able to keep a Blinky Blocks system synchronized to a few milliseconds, using few network resources at runtime, even-though the Blinky Blocks hardware clocks exhibit very poor accuracy and resolution.

A modular robots is composed of many independent connected modules which are able to achieve common goals through communications. A modular self-reconfigurable robot can move and reorganize its modules to modify its shape. In this paper, we consider a modular self-reconfigurable robot made from cubic modules (blocks) that are able to slide along their faces. Sliding motions imply complex cooperations, for example, crossing an angle needs at least three synchronized blocks. Based on this kind of hardware, we propose a distributed rule-based algorithm which plans and moves the blocks to reach a final configuration. We propose the use of motion rules that drastically simplify the complexity of the sliding movements and we define a special kind of metamodule to fasten the reconfiguration. We evaluate our algorithm in a simulator in order to study its behavior in the case of large modular robots composed of more than 10,000 modules. We test its robustness with more than 120 different kinds reconfigurations scenarii, representing more than 338 millions of movements for the blocks without any problem.

Programmable matter i.e. matter that can change its physical properties, more likely its shape according to an internal or an external action is a good example of a cybermatics component. As it links a cyberized shape to real matter, it is a straight example of cyber-physical conjugation. But, this interaction between virtual and real worlds needs two elements. The first one is to find a way to represent the cyberized object using programmable matter and the second is to be able to adapt the matter to the cyberized changes. This article presents the progresses made in these two topics within the Claytronics project.

The programmable material is one of the most challenging problems in micro-robot networking. In addition to the problems that arise by the miniaturization of millimeter-scale mobile devices, the conception of the distributed asynchronous algorithms allowing the coordination of large number of robots remains a very complex task. Micro-robot network represents one of the implementations of the Internet of things, where a set of micro-robots react to an order submitted on a wireless downlink channel specifying a global goal. This goal corresponds to a target shape in the case of shape-shifting problem. Programmable materials have many applications in the field of paintable displays, prototyping, locomotion, etc. We propose in this paper an original flexible distributed algorithm allowing to reorganize a modular micro-robot network into a desired target shape (physical topology). The efficiency of such an algorithm is assessed on the basis of the memory requirements, the communication load, and the number of performed movements to reach the final shape. The proposed algorithm shows a great flexibility concerning the range of target shapes that can be achieved, in part because there is no need for an explicit description of the final shape. To assess the computational performances of the presented algorithm, we proposed a linear programming model of the shape-shifting problem that provides a lower bound of optimized criteria. The comparison of our results with those given by the relaxed linear programming proves the efficiency of our approach.

Electromagnetic communication among nanosensors using time-spread on-off keying (TS-OOK) modulation in terahertz band promises very high transmission rates (up to several terabits per second). Due to scarce battery capacity in nanosensors, energy efficiency is a very important aspect in nanocommunication, as are also bandwidth expansion, multiuser interference, and robustness against transmission errors. This paper compares various low-weight codes found in the literature using metrics specific to nanocommunication. A small variation in such a code is also introduced and included in the comparison. Results show that there are tradeoffs among the various metrics used and, even if there is no clear winner method, the novel method has good results in almost all metrics.

Very small autonomous robots could cooperate to solve an almost infinite number of problems. Prototypes of such robots do already exist at the micro scale and even smaller ones are expected. Yet allowing them to wirelessly communicate considerably enhance their possible usages. In this paper, we revisit Vouivre, a nano-wireless simulation library we previously developed. Micro or nano-robots will be possibly be numerous and we needed to integrate new ideas to enhance the scalability of Vouivre. Using the specificities of the Terahertz radio channel as a foundation, we discuss and present an implementation that allows simulation of a larger number of concurrent transmissions between an even larger number of individual elements.

Modular robots are composed of many independent connected modules which are able to achieve common goals through communications. Many distributed algorithms have better performance if the modules that have to communicate with all the others, are placed at the center of the system. In this paper, we propose ABC-Center, an iterative algorithm for electing an approximate-center module in modular robots. ABC-Center uses O(1) space per module and O(kd) time, where k is the number of iterations required to terminate and d the diameter of the system. We evaluated our algorithm both on hardware modular robots and in a simulator for large ensemble of robots. The average expected eccentricity of the module elected by ABC-Center is less than 1.25 blocks off for random systems composed of up to 1000 modules. Furthermore, experiments show that our algorithm terminates after a few iterations. Hence, ABC-Center is scalable and adapted to modular robots with low memory resources.

In this paper we propose a distributed and robust parallel redeployment algorithm for MEMS micro robots. MEMS micro robots are low-power and low-memory capacity devices that can sense and act. To deal with the MEMS micro robots characteristics, in this paper, we present an efficient redeployment algorithm without predefined positions of the target shape, which reduces the memory usage to a constant complexity. This algorithm optimizes the energy consumption by minimizing the amount of displacement and the number of messages. This solution improves the memory usage (number of states), the execution time and the number of movements by using movement of different micro robots at the same time. In addition, we show how to predict the number of movement for each node to make the algorithm robust.

Novel platforms of modular robot systems have been developed with important applications in safety, transportation and sensing domains. In such systems, modular robots are able to change their organization in order to obtain different shapes. The conception of distributed programs allowing the "optimal" reorganization of a set of robots into a specific shape appears as a very challenging problem. In this paper we present an original distributed meta-algorithm for micro-robots shape-shifting problem. We show that this meta-algorithm, described as a general functioning schema, presents a good framework to easily conceive distributed algorithms for shape-shifting problems. We also prove the facility to instantiate the algorithm for special target shapes and we give an adaptation of the algorithm to reach any horizontally convex form. The presented meta-algorithm presents two main advantages: first, there is no need to exact positioning of the robots and secondly, the memory storage and communication requirements are significantly reduced.

In this paper we propose a distributed and efficient parallel self-reconfiguration algorithm for MEMS microrobots. MEMS microrobots perform various missions and tasks in a wide range of applications including odor localization, firefighting, medical service, surveillance and security, and search and rescue. To achieve these tasks the self-reconfiguration for MEMS microrobots is required. The self-reconfiguration with shared map does not scale. Because with the map (predefined positions of the target shape) each node should store all predefined positions of the target shape, therefore this is not always possible as MEMS nodes have a low-memory capacity. In this paper, we present an efficient self-reconfiguration algorithm without predefined positions of the target shape, which reduces the memory usage to a constant complexity. This algorithm improves the energy consumption by minimizing the amount of displacement and the number of messages.

Nowadays, video data transfers account for much of the Internet bandwidth and a huge number of users use it daily. However, despite its apparent interest, video streaming is still done in a suboptimal manner. Indeed, more and more high-definition and high-quality videos are nowadays stored on Internet but they are not accessible for everybody because a high and stable bandwidth is needed to stream them; also, during videoconferencing, the highest possible quality often exceeds the available bandwidth. Hence, a lower bitrate encoding is usually chosen but it leads to lower quality and network under-utilization too. This paper presents Video Adaptation at Application Layer (VAAL), a simple and efficient method designed to use optimally network resources and to ameliorate user video experience. It involves only the application layer on the server. The main idea of VAAL is that it checks Transmission Control Protocol-friendly transport protocol buffer overflows and adapts the video bitrate accordingly; as a result, the bitrate constantly matches the network bandwidth. It can be used together with Zigzag Avoidance Algorithm (ZAAL), a novel algorithm aiming to avoid quality oscillations. Experimental results show that the video adaptation using VAAL+ZAAL performs much better compared with the currently widely used static encoding, making it a strong candidate for hard real-time video streaming.

Over the last decades, research on microelectromechanical systems (MEMS) has focused on the engineering process which has led to major advances. Future challenges will consist in adding embedded intelligence to MEMS systems to obtain distributed intelligent MEMS. One intrinsic characteristic of MEMS is their ability to be mass-produced. This, however, poses scalability problems because a significant number of MEMS can be placed in a small volume. Managing this scalability requires paradigm-shifts both in hardware and software parts. Furthermore, the need for actuated synchronization, programming, communication and mobility management raises new challenges in both control and programming. Finally, MEMS are prone to faulty behaviors as they are mechanical systems and they are issued from a batch fabrication process. A new programming paradigm which can meet these challenges is therefore needed. In this article, we present CO2Dim, which stands for Coordination and Computation in Distributed Intelligent MEMS. CO2DIM is a common project between France and Hong-Kong building a new programming environment which includes a language, based on a joint development of programming and control capabilities, a simulator and real hardware.

MEMS micro robots are low-power and low memory capacity devices that can sense and act. One of the most challenges in MEMS micro robot applications is the self-reconfiguration, especially when the efficiency and the scalability of the algorithm are required. In the literature, if we want a self-reconfiguration of micro robots to a target shape consisting of P positions, each micro robot should have a memory capacity of P positions. Therefore, if P equals to millions, each node should have a memory capacity of millions of positions. Therefore, this is not scalable. In this paper, nodes do not record any position, we present a self-reconfiguration method where a set of micro robots are unaware of their current position and do not have the map of the target shape. In other words, nodes do not store the positions that build the target shape. Consequently, memory usage for each node is reduced to O(1). An algorithm of self-reconfiguration to optimize the communication is deeply studied showing how to manage the dynamicity (wake up and sleep of micro robots) of the network to save energy. Our algorithm is implemented in Meld, a declarative language, and executed in a real environment simulator called DPRSim.

Ad hoc networks operate mostly over open environments and are hence vulnerable to a large number of threats. This calls for providing advanced intrusion detection. To meet this requirement, we introduce IDAR, a signature- and log-based distributed intrusion detector dedicated to ad hoc routing protocols. Contrary to existing systems that observe packets, IDAR analyses the logs generated by the OLSR protocol and identifies patterns of misuse. This detector copes with the resource-constraints of devices by providing distributed detection. In particular, depending on the level of suspicion/gravity involved, in-depth cooperative investigation is launched. Simulation shows limited bandwidth usage, high detection and low false positives.

High-performance applications are often developed for a specific class of target platforms and executing them on the increasing variety of Cloud and grid environments requires substantial adjustments and reconciliation. The aim of our framework, called ADAPT (Adaptive Application and Platform Translation), is to allow adaptation of applications for execution on various computational resources. The overall objective is to enhance usability of cyber-infrastructure platforms by providing automated adaptations of applications and conditioning of target environments so that greater cross-utilization is achieved. This paper presents a proof-of-concept experiment of a possible use of ADAPT, viz. executing a C/MPI application originally written for clusters on the Microsoft Azure infrastructure. This MPI application-to-cloud adjustment is based on automatic identification of the application programming paradigm followed by applying an application transformation to enable execution on an alternative target.

An ongoing work is presented for accurately predicting the performance of distributed applications in heterogeneous systems. We are developing dPerf, a tool built using the Rose framework for performing static analysis and an automatic instrumentation on the input source code of programs written in C, C++ or Fortran. The accuracy in predicting program computation time resides in using hardware counters, as well as in applying two block benchmarking techniques that we propose in this paper. The current work makes use of a network simulator in order to calculate the communication time used in our approach. Afterwards, the computation and communication times are being summed up obtaining an estimation of the distributed application execution time. The approach is proven experimentally using NAS Integer Sort benchmark, the communications being simulated with SimGrid.

This paper presents a distributed control architecture to perform part recognition and closed-loop control of a distributed manipulation device. This architecture is based on decentralized cells able to communicate with their four neighbors thanks to peer-to-peer links. Various original algorithms are proposed to reconstruct, recognize and convey the object levitating on a new contactless distributed manipulation device. Experimental results show that each algorithm does a good job for itself and that all the algorithms together succeed in sorting and conveying the objects to their final destination. In the future, this architecture may be used to control MEMS-arrayed manipulation surfaces in order to develop Smart Surfaces, for conveying, fine positioning and sorting of very small parts for micro-systems assembly lines.

In the search of new architecture for distributed computing, peer-to-peer is studied as a new way forward. But, its intrinsic properties like the absence of centralized topology or the dynamic reorganization of the network make it difficult to reach high performances. Furthermore, it is not trivial to execute applications on an existing testbed which brings together a sufficient number of nodes. That is why it is necessary to use a simulation environment which will be used to bypass bottlenecks and to correct the parts of the applications which slow down the execution time. In this context, we have proposed P2PPerf: a simulation tool which aims at predicting performance and the execution time of a distributed application before its finalization. This article presents some experiments conducted with P2PPerf to evaluate its accuracy. The emphasis is put on the use of real execution platforms with the Grid'5000 platform and with the Wrekavoc tool.

In previous research work, we have developed a centralized security operation center (SOC) [2] and a distributed SOC [4]. These environments are very useful to react to intrusions or to analyze security problem because they provide a global view of the network without adding any kinds of software on network components. They therefore lack the possibility to have a real-time metric which measures the security health of the different sites. The idea is to have, in one look, an indication of the security level of all the sites of the network. In this article, we propose to define such a metric which gives the user 3 states for a given network.

The Smart Surface1project aims at designing an integrated micro-manipulator based on an array of micromodules connected in a 2D array network. Each micromodule has a sensor, an actuator and a processing unit. One of the aims of the processing unit is to recognize the shape of the part that is put on top of the smart surface. This recognition or more precisely this differentiation is done through a distributed algorithm that we call a criterion. The aim of this article is to present the ECO framework, which is able to test exhaustively the efficiency of different differentiation criteria, in terms of differentiation efficiency, memory and processing power needed. The tests show that ECO is of great help for choosing the best criteria to implement inside our smart surface.

Abstract Peer‐to‐peer paradigm is more and more studied by the distributed computing community. Indeed, this type of architecture has interesting properties like the absence of centralized topology, fault tolerance or dynamic reorganization of the network. However, managing these networks is complex and the acceleration of the distributed applications is not ensured. That is why it is necessary to predict the performance as soon as possible in design and development phases, to bypass bottlenecks and to correct part of the applications that slow down the execution time. In this context, we propose P2PPerf: a simulation tool that aims at predicting performance and the execution time of a distributed application before its finalization. P2PPerf has been tested on JNGI: a P2P distributed computing application using the JXTA platform. Copyright © 2007 John Wiley & Sons, Ltd.

Detecting all kinds of intrusions efficiently requires a global view of the monitored network. Built to increase the security of computer networks, traditional IDS are unfortunately unable to give a global view of the security of a network. To overcome this situation, we are developing a distributed SOC (security operation center) which is able to detect attacks occurring simultaneously on several sites in a network and to give a global view of the security of that network. In this article, we present the global architecture of our system, called DSOC as well as several methods used to test its accuracy and performance.

The aim of P2P computing is to build virtual computing systems dedicated to large-scale computational problems. JXTA (JuXTApose) proposes an underlying infrastructure on which JNGI (Jerome, Neelakanth, Greg, and Ilya: first names of the creators), one of the first P2P decentralized computing frameworks is built. In order to test this framework, we have built a tool named P2PPerf which allows us to study the behavior of JNGI and to optimize it according to our simulation results.

This paper describes a streaming architecture simulation model above network simulator 2 (NS2) which allows to define specific transport properties. Multimedia contents are specific because they are time-dependent and they can undergo small deterioration if necessary. We simulate such a congestion control that has the ability to decrease the multimedia quality in case of network congestion in order to decrease packet losses and packet delivery delays. We integrate this video TFRC congestion control inside DCCP (datagram congestion control protocol) and TFRC (TCP friendly rate control). The transcoding of the multimedia contents is realized thanks to the NetMoVie simulation model which is an RTP mixer. We compare the adaptive transport solution to the classic transport solution without any adaptive mechanism. The peak signal-to-noise ratio (PSNR) of the received multimedia contents is measured and compared for better visualization.

Widrow's interference canceller adapted by the normalized LMS (NLMS) is a standard approach for separating signals from multiple speakers, for example from the driver (target) and the codriver (interference) in a car. In practice, the adaptation must be carried out only when the interferer is dominant, i.e. only when some estimate of the signal-to-interference ratio (SIR) is below a certain threshold. In this paper, we present the implicitely controlled LMS (ILMS), a modification of the NLMS. ILMS adaptation is performed continuously using a variable step-size, whose design implicitly detects dominance of the interferer over target activity. Specific measures are taken to guarantee the stability during adaptation. Theoretical analysis of the ILMS transient convergence and stability conditions prove significant improvement with respect to the original NLMS. Experimental results on real in-car data assess the predicted behavior.