Modular caterpillar robots are becoming a universal platform for advances in robotics research, especially when simulated in complex environments. This research utilizes the implementation of the “WEBOT” simulator to investigate a modular caterpillar robot developed based on the “Yamor” prototype. Three bio-inspired approaches with different control methods are compared to assess their impact on the motion function of caterpillar-like robots. Performance is evaluated using parameters such as average speed, stability, and total distance over different media. We tested these three approaches in six environments: Earth (clean soil), Earth (desert), Earth (water), Earth (oil), Mars, and the Moon. We also considered functionality in hazardous conditions where random problems can occur during movement. For this reason, we included the accidental detachment of a module to investigate the behavior of the robot after detachment.

Satellites, high-altitude drones, and 5 G links in space now support critical services such as air traffic control, financial transactions, and weather alerts. However, most of this equipment was not originally designed to face modern cyber threats. Ground stations connected to the cloud can be breached, GPS signals can be jammed, and some components in the supply chain may include hidden malware. There is still no shared list of known vulnerabilities and no secure environment for testing spacerelated cyber defense. This paper identifies eleven key research gaps. These include secure routing, onboard attack detection, automated recovery, trusted supply chains, post-quantum encryption, zero-trust implementation, and real-time service impact monitoring. For each topic, we describe the challenge, explain why it matters, and propose a guiding research question. We also explore how a team of small, task-specific artificial intelligence agents, what we call an agentic (multi-agent) approach, could improve onboard defense without relying on large hardware.The paper ends with a proposed five-year roadmap. This includes flight tests of post-quantum and quantum key distribution (QKD) links, open-access cyber-ranges for practical testing, better rules for sharing vulnerabilities, and initial deployments of multi-agent security on operational spacecraft. Moving toward these proactive, modular defenses will help prevent outages like the KA-SAT incident.

Large-scale complex images contain a large number of crowded and fine-grained features that are prone to information loss during Deep Learning's (DL) downsampling process. While most recent contributions focused on discriminative tasks like small object detection, fewer papers tackled complex image processing for generative tasks. This paper presents a new pipeline for extracting and clustering significant sub-images from complex, unannotated image datasets. The pipeline is composed of three main steps: (1) identifying clusters of keypoints using K-means, (2) extracting and refining sub-image frames with a region-based expansion algorithm, and (3) classifying extracted frames. Subsequently, it enables data augmentation, simplifies domain complexity, and supports conditional training, tailoring the learning process to specific contexts, and enhancing the quality of image synthesis models. Furthermore, we analyze the structure of complex image domains by investigating intra-domain gap between extracted clusters.

Energy consumption prediction in smart buildings is essential for optimizing energy efficiency, reducing costs, and minimizing environmental impact. This paper proposes a novel approach combining federated learning with edge-fog-cloud computing to predict energy consumption across multiple smart buildings while preserving data privacy. The architecture involves training local models on edge devices, aggregating them at fog nodes, and performing higher-level aggregation in the cloud. Differential privacy is integrated into the federated learning process to ensure data confidentiality. The study utilizes two years of data collected from 12 houses, comprising 17,428 records, and employs LSTM neural networks for time-series prediction. The results demonstrate significant improvements in prediction accuracy (MSE: 14.55, MAE: 2.83, R2: 0.93, Accuracy: 93.28%), along with reduced latency (1.97 seconds) and efficient execution time (5904.36 seconds). Comparative analysis explores the performance of different machine learning models in energy consumption prediction, highlighting the strengths and limitations of each approach. This work contributes to advancing sustainable energy management practices in smart building ecosystems.

Dense wireless networks, such as electromagnetic nanonetworks, are characterized by high number of resource-constrained nanonodes close to each other. Testing communication protocols for these networks is an important challenge as real-world experimentation is complex and theoretical analysis is often too limited. Therefore, simulations are widely used as an alternative due to its cost-effectiveness, reproducibility, and efficiency. However, their accuracy depends on how they simulate the real conditions of the network. For instance, simulation accuracy depends widely on the packet loss model used. This paper addresses this challenge, by comparing the Unit Disc Graph (UDG) and shadowing packet loss models in the context of flooding protocols. The obtained results show that while both models can ensure similar outcomes in some cases, they diverge significantly in others, particularly when data delivery is not guaranteed across the entire network. Finally, this study shows the importance of selecting appropriate packet loss models in simulations to ensure reliable and generalizable protocol evaluation.

Programmable matter in the context of microrobots is like mobile microcomputers with size of a millimeter, which can move around each other, communicate to form different shapes. Current challenges are as much theoretical as technological. The main research challenges in computer science focus on clustering, synchronization time, decision-making, analysis of detected data, security and the autoconfiguration process, which today remains the most fundamental. When the number of modules is very high, the self-reconfiguration challenge becomes more crucial for applications requiring rapid transition between two forms. Clustering can enable parallel transitions by allowing geographically close modules to make intra-cluster transitions, thus reducing reconfiguration complexity, the number of transitions and the time required. Clustering-based autoconfiguration solutions are available, but they do not include the security aspect. In this paper, we propose a security protocol for clustering-based self-reconfiguration solutions. This solution relies on resource-efficient cryptographic mechanisms to implement a robust authentication solution that underpins a flexible key management mechanism coupled with a confidential data exchange of the resulting structure

The most challenging task in the daily life of blind individuals is to navigate safely. This challenge is prevalent in both outdoor and indoor environments. The lack of visual cues when blind people navigate makes reaching destination a very difficult and stressful task to achieve. In our prior research, we established a connection between stress levels and the challenges encountered by visually impaired individuals during outdoor navigation. To address this, we developed an outdoor navigation system capable of initially determining the least stressful route between two points, and subsequently delivering real-time navigation instructions and obstacle detection. This paper introduces a novel concept: a hybrid navigation system designed to help visually impaired people navigate in unfamiliar environments, both indoors and outdoors, enabling them to successfully reach their destination while minimizing stress.

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.

Energy efficiency is crucial in modern smart building management. Effective energy management not only reduces operational costs but also promotes environmental sustainability by reducing carbon emissions. Additionally, optimizing energy usage improves occupant comfort and productivity, contributing to a healthier and more sustainable built environment. Recently researchers have focused on cutting-edge technologies to develop efficient models that predict energy consumption and ensure a tradeoff between occupant, provider and environment needs. Among such technologies, Internet of Things (IoT), edge/fog computing, and federated Learning (FL) have significantly have proven their efficiency in this domains. In this paper, we provide a performance analysis of such technologies for energy consumption prediction in smart buildings. According to a set of defined criteria, we select some recent proposed techniques and we study their performance through various assessment metrics. Our idea behind such comparison is to identify promising techniques while using Friedman test and furthermore highlight further open research problems in the domain. Real-world data has been used to measure and evaluate each approach providing valuable insights for practical implementation and deployment in smart building environments.

Evaluation of recommendation systems continues evolving, especially in recent years. There have been several attempts to standardize the assessment processes and propose replacement metrics better oriented toward measuring effective personalization. However, standard evaluation tools merely possess the capacity to provide a general overview of a system’s performance; they lack consistency and effectiveness in their use, as evidenced by most recent studies on the topic. Furthermore, traditional evaluation techniques fail to detect potentially harmful data on small subsets. Moreover, they generally lack explainable features to interpret how such minor variations could affect the system’s performance. This proposal focuses on data clustering for recommender evaluation and applies a cluster assessment technique to locate such performance issues. Our new approach, namedgroup validation, aids in spotting critical performance variability in compact subsets of the system’s data and unravels hidden weaknesses in predictions where such unfavorable variations generally go unnoticed with typical assessment methods. Group validation for recommenders is a modular evaluation layer that complements regular evaluation and includes a new unique perspective to the evaluation process. Additionally, it allows several applications to the recommender ecosystem, such as model evolution tests, fraud/attack detection, and the capacity for hosting a hybrid model setup.

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.

This paper presents a heterogeneous federated ensemble model for intrusion detection system, employing a semisupervised novelty detection technique - the baseline K-means. The technique learns normal traffic from baseline data and utilizes the Mahalanobis distance to detect anomalous packets. To mitigate the false-positive rate inherent in anomaly-based intrusion detection system, we propose an ensemble approach that integrates local novelty detection models dedicated to each worker in both weighed and voting-based strategies. The federated design augments each worker’s detection capability without increasing the false positive rate. Our extensive experiments showcase the system’s robustness and adaptability over traditional standalone IDS, with marked improvements in precision, recall, and F1score under varying sampling rates. We made this project’s code publicly available on Github for replicability.

Generative Adversarial Networks (GANs) have shown potential for generating images, but have limitations when applied to complex datasets. To address these limitations, class-conditional training is employed, as it performs better and maintains a high level of semantic diversity. In this work, we propose a new method for training generative models on complex images by extracting rules defining the relationships between objects in the image, cropping significant sub-regions based on these rules, and training the models in a conditional setting using the extracted rules as labels. The proposed approach is evaluated and the results demonstrate its effectiveness by increasing the training dataset size, and then feeding it to conditional training. As a result, synthesized samples maintain asset fine-grained details and the visibility of small instances.

Getting around independently on a daily basis is a challenge for blind people. Indeed, when walking outdoors, blind people must avoid many obstacles to reach their destination safely. The difficulty comes from the great variety of the configuration of the environment, with obstacles that can be static or dynamic, and varying levels of danger. Even if the blind person is already familiar with the environment in which they move, the inherent dynamics of the many objects and actors in the environment are still stressful. This article tackles the question of whether there is a link between physiological stress signals and the obstacles that blind users face when navigating paths and routes in daily life. We designed and proposed two prototypes using biological sensors connected to a cane for blind people to collect data in several scenarios. Methods and analysis that were applied on the collected data in order to detect stress will be discussed along with all the results achieved. This work shows that stress can be identified and detected when a blind person is navigating a path, and even that the stress factors causing this stress can be related to obstacles along the path.

Programmable Matter (PM) based on modular robots is a material which can be reprogrammed to have different shapes and to change its physical properties on demand. It can be deployed in several domains and has a variety of applications in construction, surgery, environmental science, space exploration, etc. PM is composed of a big number of limited resources connected robots called modules or particles to form its shape. These modules communicate with each other and move around each other dynamically in order to switch from one configuration to another. Due to the limited resources of modules and the high number of packets that transit within the system, it is very challenging to ensure packet delivery with high reliability. In this paper, we are using a Bidirectional Hetero-Associative Memory (BHAM) networks to improve the reliability and fault tolerance in PM. The idea is to let modules sending packets with smaller size without loosing any information. Furthermore, this model is also capable to remove noise from received packets. The proposed approach is tested on a real programmable matter blinky blocks platform as well as via simulations. We studied two versions of artificial neural networks based on storage capacity. The experimental results show that the studied approach is efficient in reducing the size of packets that transit in the system thus reducing energy consumption and it is capable to detect and remove noise and correct noisy packets.

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.

Understanding user behavior in the context of recommender systems remains challenging for researchers and practitioners. Inconsistent and misleading user information, which is often concealed in datasets, can inevitably shape the recommendation results in certain distorted ways despite utilizing recommender models with enhanced personalizing capabilities. Naturally, the quality of data that fuels those recommenders should be extremely reliable and free of any biases that might be invisible to a model, irrespective of its type. In this article, we introduce two modern forms of noise that are intrinsically hard to detect and eliminate; one is malicious in nature and will be termed Burst while the other is unique in that it forms its own category and will be referred to as Opt-out. Additionally, with the aim of segregating the nature of noise behind such threats, we present a distinct case study on Burst and Opt-out to illustrate how the detection of those threats can be challenging compared to that of traditional noise and with the current detection methods. Finally, we expound on the ability of such threats to bias the output of recommenders in their own unique way while primarily retaining data that is not fundamentally erroneous.

Fire brigades’ operations vary with time, climate, season, occasions, etc. For example, the frequency of accidents is greater during the day than at night. Thus, adjusting the need to the demand of fire departments by categories of operations can lead to a reduction of material, financial and human resources. Therefore, it can be very helpful during the financial and economic crisis most countries face. It also helps firefighters to be well prepared by knowing the type and number of human resources needed for the next operation. The aim of this study is to predict the number of firefighters’ interventions of 14 different categories varying between emergency and non-emergency deployments. The experiments in this study on the dataset provided by the fire and rescue service, SDIS 25, in the Doubs-France region showed that it is not necessary to improve the prediction when more explanatory variables are added. Some characteristics are not informative and may reduce the accuracy of the results.

Portfolio-based algorithm selection can help in choosing the best suited algorithm for a given task while leveraging the complementary strengths of the candidates. Solving the Multi-Agent Path Finding (MAPF) problem optimally has been proven to be NP-Hard. Furthermore, no single optimal algorithm has been shown to have the fastest runtime for all MAPF problem instances, and there are no proven approaches for when to use each algorithm. To address these challenges, we develop MAPFASTER, a smaller and more accurate deep learning based architecture aiming to be deployed in fleet management systems to select the fastest MAPF solver in a multi-robot setting. MAPF problem instances are encoded as images and passed to the model for classification into one of the portfolio's candidates. We evaluate our model against state-of-the-art Optimal-MAPF-Algorithm selectors, showing +5.42% improvement in accuracy while being 7.1× faster to train. The dataset, code and analysis used in this research can be found at https://github.com/jeanmarcalkazzi/mapfaster.

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 wireless sensor network (WSN) is a set of special-ized devices that commonly monitor environmental and physical conditions. A critical aspect of applications with WSNs is their limited resources especially in multivariate sensor features when transmitting large amount of data from the nodes to the base station. The aim is then to optimize power consumption during data transmission by using data reduction methods. In this article, we study multivariate data reduction at node's level. We propose a new efficient model based on reducing collected data by aggregation and polynomial regression. We evaluate and compare our method with existing data aggregation techniques, and with the following well-known compression techniques (xz, bzip2, brotli and gzip). The simulation results show that our approach outperforms the existing methods and offers a good approximation of data quality with small approximation errors.

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.

Forecasting the number of fire department deployments for different types of operations is important to size the need to the demand and hence, improve emergency response efficiency and reduce financial and material resources. Fire department operations are not considered hazardous because they are somewhat related to time and date. Fires are more likely to occur in the fall than in the winter, and floods are more risky in the winter than in the spring. Car accidents are also logically more likely to occur during the day than at night, when most people are resting at home. This work focuses on predicting the target value of fire calls by creating 14 different subsets of data for each type of possible category (childbirth, fire, suicide, traffic accident, drown, fire on public road, water-flood, heating, emergency aid to people, help for people, public road accident, brawl, witness, and wasp). The methodology was based on the Departmental Fire and Rescue Doubs (SDIS 25) in France, where two machine learning techniques were then implemented to verify the feasibility of the experiments. Although the results can be improved by adding additional explanatory variables, the results were promising.

Most research on energy efficiency in wireless sensor networks considers that the communication subsystem consumes significantly more energy than the sensing and computing ones. In order to verify this widely adopted premise, an experimental study has been conducted on a Pysense sensor shield that utilizes WiFi and LoRa. This paper compares the energy consumption of each subsystem and two data-driven energy conservation algorithms that employ different strategies. The findings of this work indicate that lowering the energy consumption of the communication subsystem is only advantageous when using WiFi but was less effective and promising when using LoRa. Additionally, it demonstrates the importance of simultaneously optimizing the activation of multiple subsystems to minimize energy consumption. The findings of this study, as well as the source code, are available on Github: https://github.com/BouTayehGaby/WSN-energy-consumption-benchmark.

Emergency medical services (EMS) provide crucial prehospital care, such as in the case of cardiac arrest, where the victim requires immediate first-aid. For this reason, it is vital to improving EMS response time. This article proposes a novel methodology based on machine learning (ML) techniques to predict both the victims’ mortality and their need for transportation to health facilities using data gathered from the start of the emergency call until the Departmental Fire and Rescue Service of the Doubs (SDIS25) is notified. We first analyzed SDIS25 calls to find out associations between the call processing times and victims’ mortality, and to measure the variables’ importance. Next, we validated our proposed ML-based methodology, where mortality could be predicted with accuracy and area under the receiver operating characteristic curve (AUC) scores of 96.44% and 96.04%, respectively, while the need for transportation achieved an accuracy and AUC scores of 73.62% and 78.91%, respectively. What is more, we found out that it was still possible to predict both targets perturbating the input data by applyingk-anonymity and differential privacy techniques. In conclusion, the results showed the potential of ML for EMS, which can be used as a decision-support tool to early identify mortality and the use of resources (transportation) and, thus, help EMS to save more lives and avoid service disruptions.

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.

The monitoring of mobile ad hoc networks is an observation task that consists of analysing the operational status of these networks while evaluating their functionalities. In order to allow the whole network and applications to work properly, the monitoring task has become of considerable importance. It must be carried out in real-time by performing measurements, logs, configurations, etc. However, achieving continuous energy-efficient monitoring in mobile wireless networks is very challenging considering the environment features as well as the unpredictable behavior of the participating nodes. This paper outlines the challenges of continuous energy-efficient monitoring over mobile ad hoc networks. We propose two strategies that can reduce the energy consumption and extend the lifetime of the monitoring system. We also formulate the continuous monitoring problem decision as a Markov Decision Process (MDP). The experimental results obtained by simulations, clearly show that our proposals can reduce significantly the energy consumption and increase the whole network lifetime.

Firefighters are equipped with an immobility detector device also called the Personal Alert Safety System (PASS) that is integrated into the user's Self-Contained Breathing Apparatus (SCBA). If a firefighter remains motionless for a certain period of time, a loud audible alert is triggered to notify the Firefighter Assist and Search Team (FAST) deployed in the area of intervention that the wearer of the PASS device is in trouble and in need of rescue. However, this device is not reliable enough since it triggers frequently false positives which lead to developing a tolerance for sounding alarms among the crew. As a consequence, they do not seem to be concerned about it as they should and the alarms are just ignored sometimes. In this paper, we propose a PERsonal LPWAN sYstem (PERLY) prototype for state assessment and localization of Firefighters. The latter's specifications were set by personnel from the fire and emergency response department of the Doubs brigade. The aim was to make the system more reliable compared to the PASS, to add additional important functionalities, and to minimize the false positive alarms.

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.

Recommender systems have been upgraded, tested, and applied in many, often incomparable ways. In attempts to diligently understand user behavior in certain environments, those systems have been frequently utilized in domains like e-commerce, e-learning, and tourism. Their increasing need and popularity have allowed the existence of numerous research paths on major issues like data sparsity, cold start, malicious noise, and natural noise, which immensely limit their performance. It is typical that the quality of the data that fuel those systems should be extremely reliable. Inconsistent user information in datasets can alter the performance of recommenders, albeit running advanced personalizing algorithms. The consequences of this can be costly as such systems are employed in abundant online businesses. Successfully managing these inconsistencies results in more personalized user experiences. In this article, the previous works conducted on natural noise management in recommender datasets are thoroughly analyzed. We adequately explore the ways in which the proposed methods measure improved performances and touch on the different natural noise management techniques and the attributes of the solutions. Additionally, we test the evaluation methods employed to assess the approaches and discuss several key gaps and other improvements the field should realize in the future. Our work considers the likelihood of a modern research branch on natural noise management and recommender assessment.

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.

With the growth of artificial intelligence techniques the problem of writer identification from historical documents has gained increased interest. It consists on knowing the identity of writers of these documents. This paper introduces our baseline system for writer identification, tested on a large dataset of latin historical manuscripts used in the ICDAR 2019 competition. The proposed system yielded the best results using Scale Invariant Feature Transform (SIFT) as a single feature extraction method, without any preprocessing stage. The system was compared against four teams who participated in the competition with different feature extraction methods: SRS-LBP, SIFT, Pathlet, Hinge, Co-Hinge, QuadHinge, Quill, TCC and oBIFs. An unsupervised learning system was implemented, where a deep Convolutional Neural Network (CNN) was trained using patches extracted from SIFT descriptors. Then the results were encoded using a multi - Vector of Locally Aggregated Descriptors (VLAD) and applied an Exemplar Support Vector Machine (E-SVM) at the end to compare the results. Our system achieved best performance using a single feature extraction method with 91.2% mean Average Precision (mAP) and 97.0% accuracy.

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.

Nowadays, most Internet of Things (IoT) devices collect multiple features and produce multivariate time series. In an IoT application, the mining and classification of the collected data have become crucial tasks. Hybrid LSTM-fully convolutional networks (MLSTM-FCN) provide state-of-the-art classification results on multivariate time series benchmarks. This paper examines the use of the DenseNet architecture, originally proposed for computer vision applications, for the classification of multivariate time series. More precisely, this paper proposes a hybrid LSTM-DenseNet model that is able to achieve the performance of the state-of-the-art models and surpass them in many situations, based on the results obtained from various experiments on 15 benchmark datasets. Thus, this paper suggests the 1D DenseNet as a potential tool to be considered by machine learning engineers and data scientists for IoT time series classification task.

With the advent of the industrial revolution, human beings have developed drastically over the past decades. By 2020, wireless communications would connect more than twenty-five billion devices. Low Power Wide Area (LPWA) technologies are becoming popular as a result of the fast development of the Internet of Things (IoT) market. In this paper, we propose a wearable LoRa-based system for remote safety monitoring of people performing activities in remote areas with no network coverage. The designed system is supposed to detect possible heart problems and/or a “man-down” situation. It then transmits an emergency alert containing information about the state of the concerned individual and its location via LoRa to the surrounding recipients. The proposed system composed of a GPS enabled IoT device, a smart-watch and a smart-phone, has been validated in a remote area in the city of Belfort in France. The obtained results demonstrate the feasibility of such a system.

LPWAN refers to highly energy-efficient wireless communication over very long distances. Nonetheless, even with the increased range, 1-hop connectivity can be difficult to achieve in real-world deployment scenario, especially for remote and rural areas where density of gateways is low and where devices/gateway are usually deployed for a specific application. Therefore a smart and transparent 2-hop approach has been proposed in a previous work to leverage these connectivity issues. This article extents this approach with similarity detection features in order to (i) reduce the power consumption when waking-up to relay packets and (ii) reduce the radio activity time when running under duty-cycle regulated constraints.

Data reduction is a well known efficient technique to reduce energy consumption in wireless sensor networks (WSN). It consists in reducing the amount of data sensed and transmitted to the sink. In this paper, we propose an energy-efficient two-levels data reduction technique based on a clustering architecture. At the first level, each sensor sends a set of representative points to the cluster-head (CH) at each period, instead of sending the raw data. When data points are received by the CH, it uses the Euclidean distance in order to eliminate redundant data generated by neighboring sensor nodes, before sending them to the sink. To validate our approach, we applied our technique on real underwater sensor data and we compared them with other existing data reduction methods. The results show the effectiveness of our technique in terms of improving the energy consumption and the network lifetime, without loss in data fidelity.

Wireless sensor networks (WSNs) continue their ascending developement to be among the leaders of technology. Furthermore, images are of paramount importance in several applications based on WSNs. Capturing, processing and transmitting the image face several challenges, mainly because of their highly needed power consumption. The huge number of images sensed and transmitted in a Wireless Video Sensor Network (WVSN) increases the dataflow on the overall network. A WVSN consists of three different layers: the video-sensor node, the coordinator and the sink. Sending images at the same time from different sensor nodes to a coordinator causes several problems. Besides energy consumption and bandwidth usage that represent the two major challenges in WSN, the queue of images on the coordinator can cause slower detection of intrusions and thus slower reaction from the coordinator. These reasons increase the need of a mac-layer protocol to control the network. We propose a new modified communication protocol based on the S-MAC protocol. This solution consists of adding a priority bit to the S-MAC protocol. Our approach is validated by experimentation using raspberry pi 3 and by simulation in OMNET++.

Recently, interest in Wireless Body Sensor Networks composed by low-power devices which are placed in, on or around the body has been increased. Wireless Body Sensor Networks open up tremendous healthcare and wellness applications such as continuous monitoring of a patient's vital signs. One of the fundamental challenges in Wireless Body Sensor Networks is energy consumption due to wireless transmission of collected data. In this paper, we aim to extend the life-time of battery-powered biosensors by applying a data reduction technique that works efficiently under constrained processing, storage, and energy resource conditions. The presented technique is a lossless transform-based compression technique based on the Discrete Wavelet Transform using the lifting scheme extended with Lagrange polynomial interpolation. To evaluate our approach, we have run multiple series of simulations on real sensor data. The results show that our proposed method reduces the amount of data by up to 90% without losing any information.

Today, we can witness wireless sensor networks (WSNs) in action almost everywhere. Their applications are ubiquitous covering environment, medical care, military, surveillance, etc. While the potential benefits of WSNs are real and significant, there remains two major challenges to fully realize this potential: big data collection and limited sensor energy. To overcome these problems, filtering techniques over data routed to the sink should be used in such a way that they do not discard useful information. In this paper, we propose a new filtering technique dedicated to periodic sensor applications. The first filter is applied at the sensor nodes and aims to reduce their raw data based on the Pearson coefficient metric. The second filter is applied at intermediate nodes, called aggregators. It uses K-nearest neighbor clustering algorithm in order to eliminate data redundancy collected by neighboring nodes. The evaluation of our technique is made based on experiments on telosB sensors. The obtained results show the relevance of our technique, in terms of energy consumption and data accuracy, compared to other proposed methods.

In the recent years, many researches have been done on Wireless Body Sensor Networks, consisting of wearable devices that provide personalized healthcare through continuous monitoring of the patients' health condition. One of the major difficulties in WBSNs is the power consumption due to wireless transmission of sensed data. Data reduction can be considered a direct way to reduce the power consumption due to data transmission. However, most of the data reduction techniques suffer when the variation of the collected samples is high, or when the data are noisy. In this paper, we propose to enhance a data reduction scheme based on an adaptive sampling technique using dynamically adapted risk level by combining it with the Discrete Wavelet Transform lifting scheme for noise filtering. To assess our approach, we have run different series of simulation on real sensor data. The results show that combining the lifting scheme method with adaptive sampling increased the data reduction percentage by up to 50%.

Recently, Wireless Video Sensor Networks (WVSNs) have been one of the most used technologies for surveillance, event tracking, nature catastrophe and other sudden events. Those networks are composed of small embedded camera motes which help to extract the needed information for the monitored zone of interest. A WVSN is divided into 3 different layers: the video sensor-node layer, the coordinator layer and the sink. Every video sensor-node is in charge of capturing the raw data of images and videos and sending it to the coordinator for further analysis before sending the analyzed data to the sink. In a normal scenario, the load of collected images and videos from different sensor nodes on the same network is huge. Sending all the images from all the sensor nodes to the coordinator consumes a lot of energy on every sensor, and may cause a bottleneck. In this paper, some processing and analysis are added based on the similarity between frames on the sensor-node level to send only the important frames to the coordinator. Kinematic functions are defined to predict the next step of the intrusion and to schedule the monitoring system accordingly. Compared to a fully scheduling approach based on predictions, this approach minimizes the transmission on the network. Thus, it reduces the energy consumption and the possibility of any bottleneck while guaranteeing the detection of all the critical events at the sensor-node level as shown in the experiments.

The use of wireless sensor network for industrial applications has attracted much attention from both academic and industrial sectors. It enables a continuous monitoring, controlling, and analyzing of the industrial processes, and contributes significantly to finding the best performance of operations. Sensors are typically deployed to gather data from the industrial environment and to transmit it periodically to the end user. Since the sensors are resource constrained, effective energy management should include new data collection techniques for an efficient utilization of the sensors. In this paper, we propose adaptive data collection mechanisms that allow each sensor node to adjust its sampling rate to the variation of its environment, while at the same time optimizing its energy consumption. We provide and compare three different data collection techniques. The first one uses the analysis of data variances via statistical tests to adapt the sampling rate, whereas the second one is based on the set-similarity functions, and the third one on the distance functions. Both simulation and real experimentations on telosB motes were performed in order to evaluate the performance of our techniques. The obtained results proved that our proposed adaptive data collection methods can reduce the number of acquired samples up to 80% with respect to a traditional fixed-rate technique. Furthermore, our experimental results showed significant energy savings and high accurate data collection compared to existing approaches.

Minimizing latency is a major issue for data aggregation in wireless sensor networks (WSNs). Hence, the proposed algorithms must achieve the minimum delay in data delivery while decreasing the energy consumption. In this paper, we propose a new version of the prefix frequency filtering technique (PFF) proposed by [1], which aims to minimize aggregation latency. PFF finds similar sets of data generated by nodes in order to reduce redundancy in data over the network, thus, nodes consume less energy. While in the enhanced version of the PFF technique, called PPSFF, we propose a positional filtering that exploits the order of readings both in the prefix and the suffix of a set and leads to upper bound estimations of similarity scores. Experiments on real sensor data show that our enhancement can significantly improve the latency of the PFF technique without affecting its performance.

Wireless Body Sensor Networks (WBSNs) are a low-cost solution allowing remote patient monitoring and continuous health assessment, thus reducing healthcare expenditure. In such networks, sensor nodes periodically collect vital signs and send them to the coordinator for fusion. However, sensor nodes have limited energy and processing resources and transmission is the most power-hungry task. In this paper, we target data reduction and energy consumption. We propose to locally adapt, in real-time, the sampling rate of a sensor node according to the variations in the vital sign being monitored and its risk. We propose to dynamically evaluate, in real-time, the risk of any vital sign given the information about the severity level of the patient's health condition and the severity level of the vital sign itself. We have tested our proposed approach on real health datasets in order to evaluate it. The results show that the percentage of detected critical events and the mean-square error (MSE) are both acceptable. In addition, the percentage of data reduction is around 50% implying a reduction of the energy consumption. Adjusting the risk of a vital sign, over time, ensures the adaptation of the sampling rate according to the overall health condition of the patient as well as the severity level of the collected measurements.

Data reduction is one of the most attractive way to conserve the limited energy resources of wireless sensor networks (WSNs). It aims to remove unnecessary data transmission. Therefore, data prediction and reduction mechanisms must be deployed at the source node in order to eliminate the redundant sensed data before sending them to the sink. In this paper, an energy efficient periodic distributed data reduction technique is proposed. Our technique allows each sensor node to search the variation between readings collected at each period based on the Kruskal-Wallis model. Then, the sensor selects a set of representative readings instead of sending the whole readings collected during a period to the sink. To evaluate the performance of our technique, simulations on a publicly available real sensor data followed by experiments in a real-world telosB sensor network testbed have been performed. Compared to other existing approaches, we are able to achieve up to 80% communication reduction while maintaining a high level of data accuracy.

Monitoring phenomena and environments is an emergent and required field in our today systems and applications. Hence, wireless sensor networks (WSNs) have attracted considerable attention from the research community as an efficient way to explore various kinds of environments. Sensor networks applications can be useful in different domains (terrestrial, underwater, space exploration, etc.). However, one of the major constraints in such networks is the energy consumption that increases when data transmission increases. Consequently, optimizing data transmission is one of the most significant criteria in WSNs that can conserve energy of sensors and extend network lifetime. In this article, we propose an efficient data transmission protocol that consists in two phases of data aggregation. Our proposed protocol searches, in the first phase, similarities between measures collected by each sensor. In the second phase, it uses distance-based functions to find similarity between sets of collected data. The main goal of these phases is to reduce the data transmitted from both sensors and cluster-heads (CHs) in a clustering-based scheme network. To evaluate the performance of the proposed protocol, experiments on real sensor data from both terrestrial and underwater networks have been conducted. Compared to other existing techniques, simulation and real experimentations show that our protocol can be effectively used to reduce data transmission and increase network lifetime, while still keeping data integrity of the collected data.

The rapid development of medical sensors has increased the interest in Wireless Body Area Network (WBAN) applications where physiological data from the human body and its environment is gathered, monitored, and analyzed to take the proper measures. In WBANs, it is essential to design MAC protocols that ensure adequate Quality of Service (QoS) such as low delay and high scalability. This paper investigates Medium Access Control (MAC) protocols used in WBAN, and compares their performance in a high traffic environment. Such scenario can be induced in case of emergency for example, where physiological data collected from all sensors on human body should be sent simultaneously to take appropriate action. This study can also be extended to cover collaborative WBAN systems where information from different bodies is sent simultaneously leading to high traffic. OPNET simulations are performed to compare the delay and scalability performance of the different MAC protocols under the same experimental conditions and to draw conclusions about the best protocol to be used in a high traffic environment.

Wireless Multimedia Sensor Networks (WMSNs) are composed of small embedded video sensors that allow continuous monitoring of a given territory. They collect and analyze frames from different video sensors deployed in the area of interest. One of the most important challenges in WMSN is the big data problem affecting the energy resources of the video sensors. Cameras and video-sensors send images and videos which costs in terms of memory storage, bandwidth and energy. In this paper, we propose a technique that adapts the frame rate at the level of each video-sensor. Our aim is to reduce the number of frames sent to the coordinator without losing any important information. Our approach is based on analyzing similarity between consecutive frames for each sensor. The proposed algorithm calculates the similarity by the aggregation of color and edge similarities between frames. The results of the proposed algorithm show a reduction in terms of energy consumption and sent data while guaranteeing the detection of critical events.

Wireless Body Sensor Networks (WBSNs) are a low-cost solution for healthcare applications allowing continuous and remote monitoring. However, many challenges are addressed in WBSNs such as limited energy resources, early detection of emergencies and fusion of large amount of heterogeneous data in order to take decisions. In this paper, we propose a multisensor data fusion approach enabling one to determine the patient risk level based on vital signs scores. Consequently, a corresponding decision is taken routinely and each time an emergency is detected. This approach is based on early warning score systems, a fuzzy inference system and a technique determining the score of a vital sign given its past and current value. We evaluate our approach on real healthcare datasets.

Maintaining and improving the quality of life in ageing populations is a necessity. Hence, distant patient monitoring is a solution providing constant surveillance of vital signs and the detection of emergencies when they occur. In the past few years, wireless body sensor networks (WBSNs) emerged as a low cost solution for healthcare applications. In WBSNs, biosensors collect periodically physiological measures and send them to the coordinator where the data fusion process takes place. However, processing the huge amount of data captured by the limited lifetime biosensors and taking the right decisions when there is an emergency are major challenges in WBSNs. In this paper, we introduce a data fusion model using a decision matrix, an early warning score system and fuzzy set theory. We propose an algorithm at the coordinator level of the WBSN, aiming to take the appropriate decision when an emergency is detected.

Nowadays, Wireless Body Sensor Networks (WBSN) are emerging as a low cost solution for healthcare application to find new solutions, regarding patient monitoring which is becoming the elusive requirement. Quicker emergency detection is the main purpose to create a quicker reaction and treatment if required, such as an abnormal variation of the respiration rate, which satisfies the goal of extending life expectancy. This process can help all the chronic patients who are most of the time living alone or in nursing homes. However, the limited lifetime bio-medical sensors bring on the energy consumption challenge as one of the leading challenges in WBSN. Moreover, detecting locally an emergency is also one of the main challenges in WBSN. In this paper, we propose an adaptive sampling approach, based on fisher test theory, that estimates and adapts the sensing frequency based on previous readings and the patient criticality. The main goal is to optimize the energy consumption. Furthermore, we show how emergency alerts can be supported locally on each node of the network. To validate the effectiveness of our approach we conducted several series of simulations and built a simple energy saving comparison.

In the past few years, wireless body sensor networks (WBSNs) emerged as a low-cost solution for healthcare applications. In WBSNs, biosensors collect periodically physiological measurement and send them to the coordinator where the data fusion process takes place. However, processing the huge amount of data captured by the limited lifetime biosensors and taking the right decisions when there is an emergency are major challenges in WBSNs. In this paper, we introduce a biosensor data management framework, starting from data collection to decision making. First, we propose an adaptive data collection approach on the biosensor node level. This approach uses an early warning score system to optimize data transmission and estimates in real time the sensing frequency. Second, we present a data fusion model on the coordinator level using a decision matrix and fuzzy set theory. To evaluate our approach, we conducted multiple series of simulations on real sensor data. The results show that our approach reduces the amount of collected data, while maintaining data integrity. In addition, we show the impact of sampling and filtering data on the accuracy of the taken decisions and compare our data fusion approach with a basic decision tree algorithm.

The Internet of Things (IoT) has gained worldwide attention in recent years. It transforms the everyday objects that surround us into proactive actors of the Internet, generating and consuming information. An important issue related to the appearance of such a large-scale self-coordinating IoT is the reliability and the collaboration between the objects in the presence of environmental hazards. High failure rates lead to significant loss of data. Therefore, data survivability is a main challenge of the IoT. In this article, we have developed a compartmental e-Epidemic SIR (Susceptible-Infectious-Recovered) model to save the data in the network and let it survive after attacks. Furthermore, our model takes into account the dynamic topology of the network where natural death (crashing nodes) and birth are defined and analyzed. Theoretical methods and simulations are employed to solve and simulate the system of equations developed and to analyze the model.

In wireless sensor networks (WSNs), redundant collected measures and the resulting redundant packets to sendto the sink are likely to happen repeatedly. As transmission is an expensive issue in term of energy, eliminating data redundancy and reducing communication load can minimize energy consumption and extend the whole network lifetime. In this paper, we propose an adaptive protocol composed of two phases, called aggregation and transmission protocol (ATP), that operates on each sensor node separately in order to reduce its data transmission and to save energy. We consider a cluster-based scheme in which data is sent periodically from sensor nodes to their appropriate Cluster-Heads (CHs). The proposed protocol searches, during aggregation phase, similarities between data captured during a period p in order to eliminate redundancy from raw data. While during transmission phase, sensor node searches periodic correlation of data, using one way ANOVA model and Fisher test. The proposed protocol was successfully tested on real sensor data. The obtained results show that ATP can significantly minimize energy consumption, comparing to other existing data aggregation techniques, without affecting the quality of data.

Data aggregation in wireless sensor networks (WSN) has been proven as an effective technique for eliminating redundancy and forwarding only the extracted information from the raw data. Furthermore, by doing so data aggregation can often reduce the communication cost and extend the whole network lifetime. In this paper we study a new prefix-suffix filtering technique for data aggregation in periodic sensor networks (PSN). We investigate the problem of finding all pair of nodes generating similar data sets. We added a new suffix frequency filter technique to the existing prefix frequency filtering. Our goal is to integrate additional filtering technique in order to decrease the latency of the aggregation phase. Our simulation results show that our technique outperforms existing prefix filtering technique in reducing energy consumption.

Wireless sensor networks are often deployed in public or otherwise untrusted and even hostile environments, which prompts a number of security issues. Although security is a necessity in other types of networks, it is much more so in sensor networks due to the resource-constraint, susceptibility to physical capture, and wireless nature. In this work we emphasize two security issues: (1) secure communication infrastructure and (2) secure nodes scheduling algorithm. Due to resource constraints, specific strategies are often necessary to preserve the network's lifetime and its quality of service. For instance, to reduce communication costs nodes can go to sleep mode periodically (nodes scheduling). These strategies must be proven as secure, but protocols used to guarantee this security must be compatible with the resource preservation requirement. To achieve this goal, secure communications in such networks will be defined, together with the notions of secure scheduling. Finally, some of these security properties will be evaluated in concrete case studies.

Patient monitoring is becoming a requirement for offering a better healthcare to an increasing number of chronic patients whether they are living alone, or in nursing homes. Thus, it is necessary to constantly monitor their vital signs to effectively control their health condition and to provide urgent treatment while an emergency such as an abnormal variation of heart rate occurs. In recent years, wireless body sensor networks have emerged as a low cost solution for healthcare applications. However, processing the huge amount of raw data captured by bio medical sensors is a major challenge for this type of networks, along with energy, as in almost all the cases the only source of energy is battery with limited lifetime. In this paper we address how emergency alerts can be supported locally on each node of the network. Our approach proposes some criticality models which allow to reduce considerably the amount of sent data through the network, thus improving power efficiency by sending only critical measures when needed. Simulation results are presented to validate the performance of the proposed approach.

In-network data aggregation becomes an important technique to achieve efficient data transmission in wireless sensor networks (WSN). Energy efficiency, data latency and data accuracy are the major key elements evaluating the performance of an in-network data aggregation technique. The trade-offs among them largely depends on the specific application. For instance, prefix frequency filtering (PFF) is a good recently example for an in-network data aggregation technique that optimizing energy consumption and data accuracy. The objective of PFF is to find similar data sets generated by neighboring nodes in order to reduce redundancy of the data over the network and thus to preserve the nodes energy. Unfortunately, this technique has a heavy computational load. In this paper, we propose an enhanced new version of the PFF technique called KPFF technique. In this new technique, we propose to integrate a K-means clustering algorithm on data before applying the PFF on the generated clusters. By this way we minimize the number of comparisons to find similar data sets and thus we decrease the data latency. Experiments on real sensors data show that our new technique can significantly reduce the computational time without affecting the data aggregation performance of the PFF technique.

Data collection from unreachable terrain and then transmit the information to the sink is a fundamental task in periodic sensor networks. Energy is a major constraint for this network as the only source of energy is a battery with limited lifetime. Therefore, in order to keep the networks operating for long time, adaptive sampling approach to periodic data collection constitutes a fundamental mechanism for energy optimization. The key idea behind this approach is to allow each sensor node to adapt its sampling rates to the physical changing dynamics. In this way, over-sampling can be minimised and power efficiency of the overall network system can be further improved. In this paper, we present an efficient adaptive sampling approach based on the dependence of conditional variance on measurements varies over time. Then, we propose a multiple levels activity model that uses behavior functions modeled by modified Bezier curves to define application classes and allow for sampling adaptive rate. The proposed method was successfully tested in a real sensor data set.

This paper presents an energy-efficient technique for data aggregation in periodic sensor networks. We investigate the problem of finding all pairs of nodes generating similar data sets such that similarity between each pair of sets is above a threshold t. We provide a frequency filtering approach to solve this problem. Our experiments demonstrate that our algorithm outperforms existing prefix filtering methods in reducing energy consumption.

In this paper, we deal with the problem of distributed data fusion in unsafe large-scale sensor networks. Data fusion application is the phase of processing the collected data by sensor nodes before sending it the end user. During this phase, resource failures are more likely to occur and can have an adverse effect on the application. To achieve/ensure the convergence of node states to the average of the initial measurements of the network even when sensor nodes are subject to failures, two algorithms are presented. We introduce first an efficient fault-tolerant scheme capable of supporting faults due to battery depletion. Next, we derive a more complex solution to resist to frequent and unexpected fail-silent/fail-stop node failures. We provide a comprehensive set of experimental results, that fully demonstrate the usefulness of the proposed schemes.

Energy is a major constraint in wireless sensor networks. Data Aggregation constitutes a fundamental mechanism for energy optimization. The idea is to minimize redundancy from the raw data captured by the sensors, minimizing the number of transmissions to the sink and thus saving energy. Since the data is often captured on a periodic basis, and sensor nodes detect common phenomena, a periodic based protocol that manages collected data sets can help to preserve the scarce energy. This paper proposes a new filtering technique for identifying duplicate sets of periodically captured data. We suggest a data aggregation model based on set joins similarity functions that conserves data integration while eliminating inherited redundancy. We show through the result that our approach offers significant data reduction by eliminating in-network redundancy and sending only necessary information to the sink.

A Wireless Video Sensor Network (WVSN) consists of a set of sensor nodes equipped with miniaturized video cameras. Unlike omni-directional sensors, the sensing region of a video node is limited to the field of view of its camera. In this paper, we study the problem of coverage by video sensors in randomly deployed WVSN. We focus on the performance of various fast cover set construction strategies for enabling efficient scheduling of nodes in mission-critical surveillance applications. Simulation results shows the performance of the various strategies in terms of percentage of coverage, network lifetime, intrusion stealth time and number of intrusion detection.

Multimedia data are applicable in various domains such as education, advertising, entertainment and communication. Multimedia data can be in the form of documents, and require adequate spatial and temporal presentation models. Sometimes, due to the noisy problems such as low bandwidth, or user preferences, replacing a media-element by another one can be achieved in order to provide the better quality of presentation. This paper deals with the problem of finding adequate multimedia presentation that fulfills spatio-temporal adaptation for switching between a set of alternative and semantically equivalent media elements.