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.

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.

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.

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.

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.

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.

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.

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.

Recommender systems are nowadays widely implemented in order to predict the potential objects of interest for the user. With the wide world of the internet, these systems are necessary to limit the problem of information overload and make the user’s internet surfing a more agreeable experience. However, a very accurate recommender system creates a problem of over-personalization where there is no place for adventure and unexpected discoveries: the user will be trapped in filter bubbles and echo rooms. Serendipity is a beneficial discovery that happens by accident. Used alone, serendipity can be easily confused with randomness; this takes us back to the original problem of information overload. Hypothetically, combining accurate and serendipitous recommendations will result in a higher user satisfaction. The aim of this paper is to prove the following concept: including some serendipity at the cost of profile accuracy will result in a higher user satisfaction and is, therefore, more favourable to implement. We will be testing a first measure implementation of serendipity on an offline dataset that lacks serendipity implementation. By varying the ratio of accuracy and serendipity in the recommendation list, we will reach the optimal number of serendipitous recommendations to be included in an accurate list.

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.

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%.

In collaborative filtering recommender systems, the users rate items, and this process helps in understanding their preferences. The systems can suffer from the cold-start problem, which refers to the absence or insufficiency of ratings for new users. This can be solved by using active learning strategies, which can be non-personalized or personalized, and which were evaluated and tested previously using different datasets and metrics. In this paper, we present a clearer study by implementing the main non-personalized single-heuristic strategies (random, popularity, co—coverage, variance, entropy, entropy0) on the same dataset, and by evaluating them using the same metrics, in order to have a better comparison. We use the public MovieLens dataset in the experimentations and the results show that the random strategy performs the worst, whereas the entropy0 leads to the best results. All strategies except the random strategy lead to very close results at a certain point, where ratings for almost the same items will have been elicited.

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.

Researchers agree on a basic, general and abstract definition that mobile cloud computing is a combination of mobile and cloud computing. Nevertheless, the location of the computation power is still controversial and varies from centralized servers located somewhere in the internet to a collection of mobile devices interested in processing certain tasks. Interestingly, all mobile cloud architectures tried to meet the expectations of mobile cloud through application layer solutions. Although of its innovation, mobile networks' physical layer is not optimized for similar applications, thus it affects the system's overall performance and cause mobile cloud architectures to fail in satisfying its goals such as increase in delay, increase in battery consumption etc. In this paper, we will discuss mobile cloud from telecommunication perspective to harmonize the physical layer of mobile networks (LTE and UMTS) with the application layer by proposing an innovative architecture. This new architecture keeps the mobile operator at the core of mobile cloud computing and offers revenue-making business model that motivates the operator to invest in this technology.

Service continuity in heterogeneous wireless access technologies is a main issue. The challenge is to preserve continuous services while moving between these technologies. Making the decision to which network to switch (handoff) is one of the main key. In order to achieve the service continuity, we must rely on seamless vertical handoff techniques with a minimum processing-delay. In this paper we propose a vertical handoff decision scheme to enhance the service mobility using the simple additive weighting (SAW) method in a distributed manner, under heterogeneous environments. Our main goal is to reduce the overload and the processing-delay in the mobile terminal, by delegating the calculation of handoff metrics for network selection to the target visiting networks.

Next generation wireless networks will take advantage of the popularity and the data rates offered by unlicensed wireless networks such as WiFi (WirelessFidelity) or WiMAX (WorldwideInteroperabilityforMicrowaveAccess) to enhance cellular services Le. UMTS (Universal Mobile Telecommunication System). Although currently exist approaches that provide service mobility, they rely on the assumption that cellular operators also own or manage the WiFi/WiMAX networks. Nevertheless, in spite of the business-related and technical differences of network operators/service providers we consider that service mobility between cellular and independent unlicensed wireless networks is feasible. In this article we present the SIP embedded Attribute Certificates to enable service mobility in heterogeneous multi- operator wireless networks.

The network layer operations in ad hoc networks - namely packet routing and forwarding - are solely performed by the nodes themselves and consequently rely on the cooperation among them. In such networks, to best manage packet forwarding to the aimed destination node raises a need for continuous monitoring of the network service that is likely to be obtained from the other node cooperation. In this paper, we survey reputation mechanisms based on forwarding monitoring in ad hoc networks and we identify and analyze the relevant issues concerning the evaluation of forwarding operations. Finally, we highlight the need for transposing these results to hybrid ad hoc networks, with the aim to monitor the availability of the Internet access.

In this paper, we describe several methods of DHCP authentication. We propose an extension to DHCP protocol in order to allow a strict control on equipments by using a strong authentication. This extension, called E-DHCP (Extended-Dynamic Host Configuration Protocol) is based on two principles. The first one is the defimition of a new DHCP option that provides simultaneously the authentication of entities (client/server) and DHCP messages. The technique used by this option is based mainly on the use of asymmetric keys encryption RSA, X.509 identity certificates and attribute certificates. The second principle is the attribution of PMI (Privilege Management Infrastructure) attribute authority server functionalities to DHCP server. This server creates an attribute certificate to the client, which ensures the relation between the identity certifiicate of the client and the allocated IP address. This attribute certificate will be then used in the access control.