The reliability of low-power wireless communications is compromised by the constant radio spectrum usage increase needed to support new applications. To allow reliable signal quality, more flexible countermeasures are needed to allow communication protocols despite crowded radio spectrum. This paper proposes to integrate Machine Learning (ML) in the internal processing of the Bluetooth Low Energy (BLE) radio. The objective is to predict the optimal gain of the Automatic Gain Control (AGC) index according to internal radio processing metrics produced during the previous received packets. The integration of a Bootstrap Aggregation Tree (BAgged Tree) in a simulated BLE radio shows reduction of 11% of the mean Packet Error Rate (PER) compared to the original performance.

Blockchains are used to store and transmit digital and secure information. This is made possible by creating a chain of chronologically and cryptographically linked data blocks. An oracle is often used to feed the blockchain to ensure the data inserted are reliable. The nature of data sent to the oracle can vary a lot from one application to another. In the scope of the Internet of Things, data provided by sensors may be corrupted when the sensor is damaged or has been corrupted. In this case, it is important for the oracle to include an efficient tool able to detect these outliers. The Grubbs test is such an efficient tool. This article aims at presenting the impact of the use of the Grubbs test on the performance of the oracle. It shows that not only it allows to significantly increase the quality of the data inserted in the blockchain by identifying outliers, it also keeps the algorithmic complexity of the oracle as low as possible.

A method to improve dynamic service provision is presented, catering to variable future demands while minimizing energy consumption and waiting times, maximizing customer satisfaction. The approach involves dynamic service dispatch reconfiguration at predefined intervals, addressing optimal solutions through iterative single-variable resolution. We propose a pseudo-optimal minimization problem approximating the optimal solution without requiring demand statistics, offering a simple expression. The research establishes effectiveness via a mathematical model and practical implementation using a Google-provided dataset, illustrating real-world applicability.

The effective detection of plant diseases is crucial for the optimal management of agricultural systems. In this paper, we present our contributions in the context of detecting plant diseases in an industrial greenhouse [1], focusing specifically on tomatoes. Our main objectives are to develop and validate a detection system using the YOLOv8 model and to explore its potential for practical application in a real-world setting. To facilitate our research, we introduce a novel dataset comprising images of tomato leaves affected by various diseases. This dataset serves as a valuable resource for training and evaluating our detection model. We employ the YOLOv8 architecture, a state-of-the-art object detection framework, and experiment with different parameters to assess its performance in accurately detecting diseased areas on tomato leaves. Through extensive experimentation, we compare the performance of the YOLOv8 model using various parameters, such as different training strategies, data augmentation techniques, and hyperparameter configurations. The results provide insights into the optimal settings for achieving high detection accuracy and robustness. Furthermore, we demonstrate the practical utility of our developed model by conducting a real-life implementation within an industrial green-house. This exemplifies the integration of our detection system into an operational environment, showcasing its potential to assist greenhouse operators in early disease detection, monitoring, and decision-making processes. Our preliminary findings demonstrate promising disease detection capabilities on tomato leaves inside greenhouses, achieving an mAP50 score of 0.8 using our best model. Although there is room for improvement, these initial results indicate significant potential.

The goal of this article is to present a hardware and software architecture developed for the evaluation of road anomaly detection algorithms, its different components and the solutions we developed to optimize the use of the bandwidth during communications. It also presents some significant results obtained with the new lightweight road-anomaly detection methods we developed in the scope of this architecture.

Nowadays, botnet-based attacks are the most preva-lent cyber-threats type. It is therefore essential to detect this kind of malware using efficient bots detection techniques. This paper presents our security anomalies detection system, based on a model that we named Combined Forest. Our approach consists of merging some pre-processed Decision Trees to highlight different kinds of botnet by detecting their intrinsic exchanges. Using a supervised data approach, each tree is built from a labelled dataset. In order to achieve this, we aggregate the IP-flows into Traffic-flows to extract key features and avoid over-fitting. Then, we tested different machine learning algorithms and selected the most suitable one. After that, many experiments have been done to determine the best parameters and design the most accurate, adantative and efficient model.

In this paper we propose an alternative to the security protocols generally developed for VANETs (Vehicular Ad-hoc NETworks), which often rely on asymmetric keys and PKIs (Public Key Infrastructures). Instead, we propose a solution whose architecture is based on Physical Unclonable Functions (PUFs) inside the vehicles. We design a protocol with these PUF functions to securely spread a secret key by means of a Road Side Unit (RSU). This secret key will be used by the vehicles to warn neigboring vehicles of an emergency situation. The key is regularly changed by the RSU but its value evolves slowly over time in a random way. Thus a message using an old (but not too old) key can be accepted as valid. We define the whole protocol where a vehicle first identifies itself to the Road Side Unit for authentication. Then the RSU sends back a key which will be used to send alerts if an emergency situation occurs on the road. The RSU manages the evolution of this key by just randomly changing one bit at each change. Thus an alert can be accepted if the key used to issue an alert is "close" to the last key issued by the RSU. We investigate the advantages of this new scheme and evaluate the implications in terms of exchanges between the vehicles (and their PUFs) and the network connecting the RSUs.

Indoor Localization has attracted interest in both academia and industry for its wide range of applications. In this paper, we propose an indoor localization solution based on Channel State Information (CSI). CSI is a fine-grain measure of the effect of the channel on the transmitted signal. It is computed for each subcarrier and each antenna in the Multiple-Input-Multiple-Output (MIMO) antenna case. It is also becoming a trend for indoor position fingerprinting. By using a K-nearest neighbor learning method a highly accurate indoor positioning is achieved. The input feature is the magnitude component of CSI which is preprocessed to reduce noise and allow for a quicker search. The euclidean distance between CSI is the criteria chosen for measuring the closeness between samples. The method is applied to a CSI dataset estimated at an 8 × 2 MIMO antenna that is published by the organizers of the Communication Theory Workshop Indoor Positioning Competition. The proposed method is compared with three other methods all based on deep learning approaches and tested with the same dataset. The K-nearest neighbor method presented in this paper achieves a Mean Square Error (MSE) of 2.4 cm which outperforms its counterparts.

Ensuring a decent Quality of Experience (QoE) is fundamental for service providers, in particular mobile networks operators, when designing their current and future connectivity solutions. With this aim in view, they are compelled to cope with potential QoE detractors such as Inter-Cell Interference (ICI) which is expected to be a liability with a foreseen network densification. This issue was anticipated in Long Term Evolution (LTE) networks and many solutions leveraging cooperation schemes between the access nodes to alleviate the ICI's effects can be found in the literature, notably for uplink (UL) transmissions which pose a greater challenge. Among the cooperative models dealing with UL ICI, Joint Detection (JD) is particularly interesting since it promises substantial throughput gains while maintaining a high spectral efficiency. However, its practical feasibility is still unclear. In the following work, we propose a platform gathering a set of architectural, functional and technical requirements to endow realistic LTE networks with JD capabilities.

Data Centers (DCs) need to periodically configure their servers in order to meet user demands. Since appropriate proactive management to meet demands reduces the cost, either by improving Quality of Service (QoS) or saving energy, there is a great interest in studying different proactive strategies based on predictions of the energy used to serve CPU and memory requests. The amount of savings that can be achieved depends not only on the selected proactive strategy but also on user-demand statistics and the predictors used. Despite its importance, it is difficult to find theoretical studies that quantify the savings that can be made, due to the problem complexity. A proactive DC management strategy is presented together with its upper bound of energy cost savings obtained with respect to a purely reactive management. Using this method together with records of the recent past, it is possible to quantify the efficiency of different predictors. Both linear and nonlinear predictors are studied, using a Google data set collected over 29 days, to evaluate the benefits that can be obtained with these two predictors.

Next Generation Mobile Network, 5G, will leverage heavily on Network Function Virtualisation for flexible and cost efficient deployments. 5G technology is to provide numerous innovative business opportunities through a key concept known as Network Slicing. This approach will require efficient placement and chaining of virtual network functions (VNFs) composing a network slice. The goal is to maximize network resources availability with minimal cost as network infrastructure is shared among different network slices. This paper proposes a time and resource-efficient VNF placement and chaining method, UA*, a Utility-based placement algorithm that extends the A* algorithm node classification set. UA* considers slice performance requirements and policies. Our simulations evaluate UA* and compares its performance with state of the art approaches.

Due to the emerging demand for Internet of Things (IoT) applications, indoor positioning has become an invaluable task. We propose NDR, a novel lightweight deep learning solution to the indoor positioning problem. NDR is based on Noise and Dimensionality Reduction of Channel State Information (CSI) of a Multiple-Input Multiple-Output (MIMO) antenna. Based on preliminary data analysis, the magnitude of the CSI is selected as the input feature for a Multilayer Perceptron (MLP) neural network. Polynomial regression is then applied to batches of data points to filter noise and reduce input dimensionality by a factor of 14. The MLP's hyperparameters are empirically tuned to achieve the highest accuracy. NDR is compared with a state-of-the-art method presented by the authors who designed the MIMO antenna used to generate the dataset. NDR yields a mean error 8 times less than that of its counterpart. We conclude that the arithmetic mean and standard deviation misrepresent the results since the errors follow a log- normal distribution. The mean of the log error distribution of our method translates to a mean error as low as 1.5 cm.

The need for monitoring the quality of roads is undeniable. There are many different approaches for road quality controls. Among them, the one making use of smartphones has recently become very popular. Nevertheless, building a system that saves energy for smartphones and adapts to real conditions remains a big challenge. In this article, we introduce a lightweight architecture to sense and analyze potholes based on data collected with smartphones. In this model, we improve some algorithms for real-time road-anomaly detection using smartphones. The efficiency of these new algorithms has been verified by experiments with both cars and scooters.

The automatic identification of road anomaly can be used to reduce the risk for drivers and the maintenance cost of the roads. Nowadays, with the wide popularity of smartphones that include multiple sensors, several solutions have been proposed to detect these anomalies. This paper proposes a method to synthesize pothole data sent from smartphones to identify pothole location with a high accuracy. The effectiveness of the approach has been determined by means of simulations.

Nowadays, the information systems security is a crucial issue for the survival of any company, so this justifies the use of intrusion detection systems (IDS) or the intrusion prevention systems (IPS). These systems are essentially based on the analysis of the network data content (frames), in search of traces of known attacks. Currently, IDS/IPS become the main element of security networks and hosts, they can both detect and respond to an attack in real time or off-line. Even this, having a completely secure network is practically impossible. In this article, we try to propose an improvement of intrusion detection systems based on Machine Learning techniques. These rapidly expanding techniques have shown that predictions and machine learning could be improved, which could significantly improve the reliability of detection against polymorphic and unknown threats. Simulation results showed that security intrusion detection is improved with the use of Machine Learning techniques.

Data collected from an operational Google data center during 29 days represent a very rich and very useful source of information for understanding the main features of a data center. In this paper, we highlight the strong heterogeneity of jobs. The distribution of job execution duration shows a high disparity, as well as the job waiting time before being scheduled. The resource requests in terms of CPU and memory are also analyzed. The knowledge of all these features is needed to design models of jobs, machines and resource requests that are representative of a real data center.

Traces collected from an operational Google data center over 29 days represent a very rich and useful source of information for understanding the main features of a data center. In this paper, we characterize the strong heterogeneity of jobs and the medium heterogeneity of machine configurations. We analyze the off-periods of machines. We study the distribution of jobs per category, per scheduling class, per priority and per number of tasks. The distribution of job execution durations shows a high disparity, as does the job waiting time before being scheduled. The resource requests in terms of CPU and memory are also analyzed. The distribution of these parameter values is very useful to develop accurate models and algorithms for resource allocation in data centers.

Checkpointing-Aided Parallel Execution (CAPE) is a framework that is based on checkpointing technique and serves to automatically translates and execute OpenMP programs on distributed-memory architectures. In some comparisons with MPI, CAPE have demonstrated high-performance and the potential for fully compatibility with OpenMP on distributed-memory systems. However, it should be continued to improve the performance, flexibility, portability and capability. This paper presents the new execution model for CAPE that improves its performance and makes CAPE even more flexible.

CAPE (Checkpointing-Aide Parallel Execution) is an approach tried to port OpenMP programs on distributed memory architectures like Cluster, Grid or Cloud systems. It provides a set of prototypes and functions to translate automatically and execute OpenMP program on distributed memory systems based on the checkpointing techniques. This solution has shown that it has achieved high performance and complete compatibility with OpenMP. However, it is in research and development stage, so there are many functions that need to be added, some techniques and models need to be improved. This paper presents approaches and techniques that have been applied and will be applied to optimize checkpoints and execution model of CAPE.

Hybrid analog digital (HDA) architectures have been widely used in communication systems for analog source over discrete-time memoryless Gaussian channels. They employ a linear coding scheme in the analog parts, while considering separately the design of source and channel codes in the digital parts. To the best of our knowledge, none of the previous HDA schemes exploit the advantages of maintaining a joint source and channel coding design in the digital segment. In this paper, we investigate the effect of the analog parts on various outputs of the digital modules in a HDA communication system, and introduce a novel HDA architecture with adaptation for the digital parts. Such adaptation allows our system to exploit the joint effect of the analog components and the channel noise on outputs of the digital components, while simultaneously taking into consideration the unequal distribution of source code outputs. Our simulations illustrate that the proposed HDA system provides robust and graceful performance (on both bandwidth compression and expansion modes) for a wide range of channel conditions.

This paper proposes a new scheduling scheme which based on user perception, Channel- and QoS-Aware (known as E-MQS scheduler) for real-time traffics in LTE downlink direction. The proposed scheduling scheme is based on the extension of the E-model and the consideration of Maximum Queue Size (MQS) as a an essential factor for the metric. The proposed scheduling scheme is evaluated in LTE heterogeneous traffic with mobility. The simulation results show that the proposed scheduler not only satisfies QoS requirements of real-time services but also outperforms the Frame Level Scheduler (FLS), Modified Largest Weighted Delay First (M-LWDF) and Exponential/Proportional Fair (EXP/PF) schedulers in terms of delay, cell throughput, Fairness Index (FI) and Spectral Efficiency (SE), especially for Video flow. The proposed scheduler also significantly improves the Packet Loss Rate (PLR) in comparison with the M-LWDF and EXP/PF schedulers for both VoIP and Video flows.

Hybrid digital-analog (HDA) architectures have been widely developed for efficient digital transmission of analog speech, audio or video data. By considering the advantage of both digital and analog components, HDA systems gain better performances than purely analog and digital schemes in a wide range of channel conditions. However, HDA systems described in previous works are mostly designed for continuous-valued sources. In this paper, we address the problem of transmission of discrete sources over noisy channels. In particular, our work focuses on digital speech data in PCM format. We proposed two analog schemes, linear mapping, and non-linear mappings. The linear analog mapping employs an equal error protection scheme while the non-linear mapping takes into account the heterogeneous nature of error values to provide better protection to important values. The experiment results show that our HDA systems provide a better performance on a wide range of channel qualities in comparison with traditional purely digital systems.

Location-based routings for Vehicular Ad-hoc Networks (VANETs) use location information in routing decisions. However, location-based routing protocols need location services to query the location information of the communication partner node so that packets could be forwarded properly. In this paper, we propose a proactive flooding-based location service which name is Semi-Flooding Location Service (SFLS). The basic design objective in our proposal is to minimize the number of update packets sent over the whole network. We employed a conditional update technique that manages the number of location updates at the forwarder nodes. This significantly reduces the overhead of location updates. To study the effectiveness of our algorithm, a mathematical model is developed, and some numerical results are provided. Results demonstrated that SFLS achieves its design goals.

This paper proposes a new scheduling scheme which based on the extended E-model, Channel- and QoS- Aware (known as E-MQS scheduler) for real-time traffics in LTE downlink direction. The real-time services (VoIP, Video, etc.) are very sensitive to network impairments such as delay, packet loss, jitter, etc. The proposed scheduling scheme is based on the extension of the E-model and the consideration of Maximum Queue Size (MQS) as a factor for the metric. Since this scheduling scheme considers Mean Opinion Score (MOS) values, thus, it gets higher user perception. The simulation results show that the proposed scheme has the performance which not only satisfies QoS requirements of real- time services but also outperforms the Frame Level Scheduler (FLS), Modified Largest Weighted Delay First (M-LWDF) and Exponential/Proportional Fair (EXP/PF) schedulers in terms of delay, cell throughput, Fairness Index (FI) and Spectral Efficiency (SE), especially for Video flow. Our proposed scheduler also significantly improves the Packet Loss Rate (PLR) in comparison with the M-LWDF and EXP/PF schedulers for both VoIP and Video flows. The performance evaluation is compared in terms of Delay, PLR, Throughput, FI and SE for FLS, M-LWDF, EXP/PF schedulers and our proposed one.

This article describes a stateless authentication protocol designed for sensor networks. A mutual authentication between a sensor and a sink can be useful in many applications such as the monitoring of electricity meters or surveillance and monitoring of industrial plants. The authentication protocol we propose can counter usual attacks on sensor networks. First, as it is based on a PUF function, it is efficient against physical node capture. An attacker can not get into the hardware of the sensor or the sink node to obtain the secret keys of the system even if the node is captured physically. Secondly, this protocol can also counter replay attacks since the authentication uses a time-stamp, and the time interval during which a replay attack could be launched can be controlled and greatly reduced. Thirdly, this protocol is stateless, and so the sink can be authenticated to many co-located sensors using a single authentication message. Moreover, the same message can combine the authentication with the transmission of encrypted or unencrypted data.

Sensor networks are often used to collect data in the environment. Nodes are used to sense the environment and sinks can be used then to collect data from the nodes and transfer them to a backend server for processing, analysis and/or visualization. As sensor nodes can collect critical data (e.g. for medical or military purposes), security is a crucial issue. This article presents a novel mechanism to perform a lightweight mutual authentication between a node and a sink based on simple processor operations and two messages only.

Sensor Networks (SN) have been developed for several domains which makes each person or organization has heterogeneous sensors. When considering that the data generated by sensor nodes should be permanently stored and permanently accessible from all over the world by scientists or doctors who need to be notified when some events are triggered, the data sharing, publication and notification become a crucial challenge. This paper present a convergence of social networks, cloud computing and sensor networks to resolve the aforementioned requirements while ensuring privacy and security of users.

This paper presents a Price-Reward learning scheme to encourage mutual coordination between mobile nodes and their wireless networks. In order to maximize the overall network coverage through cooperative diversity, a Nash-Stackelberg Multiplicative Weighted Imitative CODIPAS-RL scheme is proposed based on our previous work. The wireless network implements a 2-level Stackelberg game by introducing Price-Reward (λ,μ) parameters whereas the Reinforcement Learning (RL) scheme paves the way for mobile nodes to reach a Nash-Equilibrium state. The performance evaluation of the learning scheme for the presented scenario proves fast convergence towards the optimal solution by adopting different sets of actions for the selected strategies. This ensures QoS sustainability during handover situations by data relaying and avoids collisions among mobile nodes while accessing network resources.

This paper presents the architecture design and the performance evaluation of a composite-based back end and visualization systems. The first one is used to host composites representing sensor nodes, store, index and manage water quality measurements collected by wireless sensor networks (WSNs) deployed in rivers, lakes and coastal regions. Embedded communication systems in each sensor node enable ad hoc network operations to relay the measurements to its composite in charge of managing, processing and providing data to end users and large communities through a standard web RESTful API. The visualization system is in charge of providing web interfaces to scientifics and users to monitor and configure the wireless sensor network (WSN) and visualize measurements in a real-time manner. The focus is on both the back end and the visualization systems architecture description and on the evaluation of their performance.

We propose Price-Reward for Data Relaying and Handover in Wireless Networks, a handover and data relay management algorithm for wireless networks. The algorithm works in dynamic environment in which mobile nodes move randomly. The mathematical model presented explains how cooperative diversity helps to augment network coverage plus revenue and ensures connection reliability during mobility and handover situations.

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In this paper, we propose an architecture for Network of Information mobile node (NetInf MN). It bears characteristics and features of basic NetInf node architecture with features introduced in the LISP MN architecture. We also introduce a virtual node layer for mobility management in the Network of Information. Therefore, by adopting this architecture no major changes in the contemporary network topologies is required. Thus, making our approach more practical.

Originally, OpenMP was designed to develop parallel applications on shared-memory architectures. One of the advantages that made the success of OpenMP is the simplicity of the associated programming model. Check pointing Aided Parallel Execution (CAPE) is a paradigm that uses check pointing techniques to run parallel programs on distributed-memory architectures. In order to show its effectiveness, it has been used to develop a compiler to run OpenMP programs on distributed-memory architectures. The first prototype we developed proved the feasibility of the paradigm but the use of complete checkpoints led to poor performance. This was mainly due to the large amount of data to transfer and process. This paper presents the new prototype we developed for CAPE based on the discontinuous incremental check pointing technique and an analysis its performance.

Mobile devices are carried by people, which are characterized by their need to socialize. The social character of mobile networks may have an influence in modeling the mobility of their nodes, and consequently in the performance evaluation of the network. In this paper, we introduce social behaviors into existing mobility models so as to mimic real motion of users and evaluate their impact on network performance. An example illustrates the paper by introducing weighted social attraction points into the Manhattan mobility model.

Location-based routing for Mobile Ad hoc Networks (MANETs) use location information in routing decisions. However, location-based routing protocols need location services to query the location information of the communication partner node so that packets could be forwarded properly. In this paper, we propose a proactive flooding-based location service which named Semi-Flooding Location Service (SFLS). The basic design issue in our proposal is to minimize the number of update packets sent over the whole network. We employed a conditional update technique that manages the number of the updates at the forwarder nodes. This way reduces significantly the overhead of location updates. To study the effectiveness of our algorithm, a mathematical model is developed, and some numerical results are generated. Results demonstrated that SFLS achieves its design goals.

Mobility models are used in the network simulation area to reproduce the typical behaviour of nodes. If lots of mobility models have been proposed in the literature, very few are taking into account the social behaviour of users. In this paper, we propose a modelling framework to introduce social behaviours into existing mobility models so as to evaluate their impact on network performance. An example illustrates the paper by introducing social attraction points into the Manhattan mobility model.

Thanks to an easy way to express parallel opportunities, OpenMP has become the reference to develop applications for shared-memory architectures. As a result, several works have tried to provide an OpenMP compiler for distributed architectures. This article presents how the Turing eXtended Language (TXL) has been interfaced with the GCC compiler to implement the Checkpointing Aided Parallel Execution (CAPE) concepts in order to provide an OpenMP compiler for distributed architectures.

RFID systems aim at identifying objects in a wide variety of environments without any contact between tags that hold identities and readers. In order to secure RFID systems as much as possible, most solutions do not only require tags to authenticate themselves to readers, but they also require readers to authenticate themselves to tags to avoid identity theft. Many solutions have tried to cope with this mutual authentication problem. In this article, we present an original lightweight mutual authentication protocol which requires only two messages and does not use complex cryptographic functions.

This paper presents the design of the lightweight communication interface called RWAPI over the Infini- Band interconnect for clusters of PCs. RWAPI has been developed to provide performance to higher applications on a wide variety of architectures. Since the specifications of the InfiniBand interconnect provides many ways to transfer data, we are discussing some issues regarding the choices between InfiniBand capabilities. We implemented RWAPI using the grid-oriented architecture called GRWA and evaluated the communication performance. We obtained a very low latency and a throughtput very close to the maximum user bandwidth for messages as small as 4 kilo-bytes.

Radio Frequency Identification (RFID) technology is a method to remotely store and retrieve data using a small microchip called the RFID tag. This makes identification, authentication of objects and people possible without any physical contact between a tag and its reader. As of today, RFID have been applied to a wide range of problems: supply chain management to replace barcode, access control in restricted areas such as laboratories and airports... As a result, billions of tags will be deployed within the next years. However, RFID development is threatened by privacy and security risks. The limited capabilities of RFID tags make privacy and security enforcement a special challenge. This article presents a new method to ensure a mutual authentication between tags and readers using only two messages and without the use of complex cryptography. Our proposition focuses on low-capability tags which cannot use classic cryptographic security methods.

Software latency has often been a problem in gigabit net-working, since it makes the whole communication rather slow even on fast hardware. Previous work, such as BIP, try to solve the problem by sacrifying other aspects such as security. This article presents PAPI, a new software interface aimed at security study on HSL, a gigabit-class network. The basic version shows performance at least equivalent to the best software interfaces available today. Finer analysis shows that hardware is now the limiting factor: software latency is no longer the problem in communications.