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Emerging Internet-of-Things (IoT) applications are driving increasing demand for advanced services in wireless networks, prompting the development of new technologies to address the associated challenges. Energy efficiency of IoT standards is a key feature targeted by research efforts and industrial activities, leading to an extensive and growing number of innovative solutions. Low Power Wide Area Networks (LPWANs) define a class of wireless communication technologies seen as highly relevant for future IoT development given its long communication range, low-cost devices and interesting energy management. Long Range Wide Area Network (LoRaWAN) is acknowledged to be the dominant IoT communication technology. It has allowed broad deployment and unlocked new IoT applications such as smart cities, asset tracking, etc. This article provides a comprehensive tutorial on the LoRa standard, and surveys existing solutions, hot topics and future insights for building energy efficient IoT infrastructures and IoT devices. Indeed, energy efficiency is one of the key factors for successful and sustainable deployments of IoT applications. More precisely, this article discusses how to meet LoRa/LoRaWAN energy efficiency across physical layer, medium access control layer, and network layer. Subsequently, extensive pioneering solutions from related literature are compared and assessed. Finally, insightful conclusions are drawn, and open problems are listed at the end of this article.

The operational and technological structures of radio access networks have undergone tremendous changes in recent years. A displacement of priority from capacity–coverage optimization (to ensure data freshness) has emerged. Multiple radio access technology (multi-RAT) is a solution that addresses the exponential growth of traffic demands, providing degrees of freedom in meeting various performance goals, including energy efficiencies in IoT networks. The purpose of the present study was to investigate the possibility of leveraging multi-RAT to reduce each user’s transmission delay while preserving the requisite quality of service (QoS) and maintaining the freshness of the received information via the age of information (AoI) metric. First, we investigated the coordination between a multi-hop network and a cellular network. Each IoT device served as an information source that generated packets (transmitting them toward the base station) and a relay (for packets generated upstream). We created a queuing system that included the network and MAC layers. We propose a framework comprised of various models and tools for forecasting network performances in terms of the end-to-end delay of ongoing flows and AoI. Finally, to highlight the benefits of our framework, we performed comprehensive simulations. In discussing these numerical results, insights regarding various aspects and metrics (parameter tuning, expected QoS, and performance) are made apparent.

Nowadays, unmanned aerial vehicles (UAVs) are being used in several novel applications, especially in the telecommunication domain. However, ensuring UAV communication and networking for the purpose of a specific application is still challenging. Indeed, due to the mobility of a UAV in a vast area, permanent connectivity over the backhaul is very sporadic and might be lost. In this article, we consider an aerial mesh network where each UAV can serve as a flying base station to boost terrestrial base station in case of damaged infrastructure case for example, or/and provide connectivity for uncovered or poorly covered nodes, and behaves as a relay to establish communication between two components owing to a lack of reliable direct communication link between them. We then detail a case study where a UAV-fleet is used to collect data from the ground Internet-of-Things (IoT) devices and forward it to the cloud for further processing passing by a remote gateway. We aim here to build a queueing framework, including network layer, MAC layer, and physical layer, and investigate both uplink and downlink communication links. Next, we derive some closed forms allowing us to predict the network performance in terms of traffic intensity at every UAV of the aerial mesh network, end-to-end (E2E) throughput, and E2E delay of ongoing streams. Next, we conduct extensive simulations to illustrate the benefit of our framework. Results discussion and numerous insights on parameter setting, target quality of service, and design consideration are also drawn.

As season-long weeds competition produces important yield losses, early detection of these plants is essential to sustain productivity. Machine vision as a non-destructive surveying technique requires features that can describe weeds in a real field case. Colours and shapes provide good results in controlled conditions. However, when different crops or weeds appear in clusters, such solutions fail to meet satisfactory performance. Therefore, considering features that are less specific to field conditions is crucial for integrated weed management. In this study, we provide effective use of the Histogram of Oriented Gradients (HOG) to improve its performance for weed detection. The concept is based on the Bag-of-Visual-Words (BOVW) approach. We use the HOG blocks as keypoints to generate the visual-words, and the features vectors are the histograms of these visual-words. Next, we use the Backpropagation Neural Network to detect weeds and classify plants for three different crop fields. Namely, we consider sugar-beet, soybean, and carrot as target crops. Results demonstrate that the proposed weed detection system can locate weeds for site-specific treatment and selective spraying of herbicides. The proposed BOVW-based HOG can discriminate between weeds and crops with an accuracy of 97.7%, 93%, and 96.6% in sugar-beet, carrot and soybean fields respectively. For plant classification, our method can classify plants with an accuracy of 90.4%, 92.4%, and 94.1% in sugar-beet, carrot and soybean fields respectively. Our results turn out 37.6% better than the classical HOG that produces an accuracy ranging from 71.2% to 83.3% in weed detection and 49.1%–82.1% in plant classification.

With the rapid population growth, increasing agricultural productivity is an extreme requirement to meet demands. Early identification of crop diseases is essential to prevent yield loss. Nevertheless, it is a tedious task to manually monitor leaf diseases, as it demands in-depth knowledge of plant pathogens as well as a lot of work, and excessive processing time. For these purposes, various methods based on image processing, deep learning, and machine learning are developed and examined by researchers for crop leaf disease identification and often have obtained significant results. Motivated by this existing work, we conducted an extensive comparative study between traditional machine learning (SVM, LDA, KNN, CART, RF, and NB) and deep transfer learning (VGG16, VGG19, InceptionV3, ResNet50, and CNN) models in terms of precision, accuracy, f1-score, and recall on a dataset taken from the PlantVillage Dataset composed of diseased and healthy crop leaves for binary classification. Moreover, we applied several activation functions and deep learning optimizers to further enhance these CNN architectures’ performance. The classification accuracy (CA) of leaf diseases that we obtained by experimentation is quite impressive for all models. Our findings reveal that NB gives the least CA at 60.09%, while the InceptionV3 model yields the best CA, reaching an accuracy of 98.01%.

The next generation of wireless communications consists of heterogeneous networks that operate over different technologies and standards. Co-existence of multiple technologies aims to enhance the network capacity in order to satisfy the increasing number of connected devices. However, in order to meet high performance, it is important to assign the right device to the right RAT. In this article, we propose a novel distributed approach to solve the RAT selection problem, in a self-organized IoT context. Our scheme is mainly based on tools from MG theory. We show that a stable association between connected IoT devices and available RATs can be met while using a fully distributed algorithm that respects connected devices requirements and RAT constraints as well. The proposed MG assignment allows IoT devices, with limited energy budget, to improve their energy efficiency and to reduce the data transmission cost over the serving RAT at very light signaling overhead. Moreover, extensive simulations show that the MG based approach outperforms the conventional throughput maximization association in terms of energy efficiency and throughput. Our scheme also exhibits some nice load balancing features.

LoRaWAN (Long Range Wide Area Network) is one of the most popular low power wide area networks technologies for the Internet of Things (IoT). It provides higher coverage, lower energy consumption and cost. A key parameter of LoRa modulation is the Spreading Factor (SF), which can be tuned to achieve a desired tradeoff between data rate and range. The six LoRa spreading factors (SF7 through SF12) are inherently orthogonal, which implies that communications using different SFs can coexist simultaneously on the same frequency channel without impacting performance. However, multiple devices using the same SF must compete for channel access and packet collisions must be managed. In this work, we present a comprehensive framework for Slotted-Aloha-based LoRa with random SF selection. Detailed analysis of the steady state of the system allows numerical derivation of the optimal retransmission probability. Furthermore, we assess the system's achievable performance in terms of average throughput and expected delay.

The role of Wireless Sensor Networks in the ongoing industrial development is becoming crucial on daily basis. It is undoubtedly the skeleton of the current global digital transformation and the fourth industrial revolution. WSN has grown into an emerging field of research due to its tremendous opportunities and several challenges. Machine to Machine (M2M), energy consumption, and wireless transmission are the most challenging areas of research with a plethora of solid papers that have been published in the last decade. Unquestionably, Low-Energy Adaptive Clustering Hierarchy (LEACH) is the most famous clustering protocol in the literature that allows the creation of self-organizing networks. However, the protocol presents several drawbacks in terms of cluster balancing, random CH selection, and single-hop communication. In this paper, we propose a hybrid energy-aware multi-hop clustering algorithm for WSN's self-organization and energy efficiency. The approach is based on the combination of K-medoids and LEACH clustering approach with a trade-off philosophy for the network clustering and eventual energy enhancement. The technique applies different energy ranges for a self-aware gateways' selection, along with the application of K-medoids, and LEACH for the building of dynamic clusters. The results are compared to the LEACH and K-medoids algorithms. Extensive simulation runs have shown a very good improvement in the network's performance based on the first dead node, the network lifetime, and the energy dissipation metrics. The algorithm's results show an improvement of 158% comparing to LEACH and 834% comparing to K-medoids in terms of the first dead node, while the network performance is enhanced by 151% comparing to LEACH and 33% comparing to K-medoids.

Cloud computing is a digital era technology which uses the Internet to maintain data as well as applications in cloud data centers. However, this technology still meet numerous challenges and suffers from several attacks. For this reason, we proposed recently a new scheme called “klm-based profiling and preventing security attacks (klm-PPSA)” to detect both known and unknown attacks. In this study, we exhibit a comparative study of the klm-PPSA model using separately two accurate and interpretable machine learning algorithms: regularized class association rules (RCAR) and classification based on associations (CBA). Moreover, considering an interesting data set, three case studies of the proposal with three different implementations of the $klm$ security factors are given ( $k$ -PPSA, km-PPSA and klm-PPSA models). The experiments for each case study with run-time measurement were done. The obtained results show that: compared to $k$ -PPSA and km-PPSA models, the klm-PPSA model gives the highest performances in terms of sensitivity with both CBA and RCAR but with a processing time seven times more than CBA. However, RCAR gives an accuracy and specificity better than the CBA for all the models. Eventually, klm-PPSA system is able to detect and prevent several types of known and unknown attacks.

The impressive progress in sensing technology over the last few years has contributed to the proliferation and popularity of the Internet of Things (IoT) paradigm and to the adoption of Sensor Clouds for provisioning smart ubiquitous services. Also, the massive amount of data generated by sensors and smart objects led to a new kind of services known as Data-as-a-Service (DaaS). The quality of these services is highly dependent on the quality of sensed data (QoD), which is characterized by a number of quality attributes. DaaS provisioning is typically governed by a Service Level Agreement (SLA) between data consumers and DaaS providers. In this work, we propose a game-based approach for DaaS Provisioning, which relies on signaling based model for the negotiation of several QoD attributes between DaaS providers and data consumers. We consider that these entities are adaptive, rational, and able to negotiate the QoD offering even in the case of incomplete information about the other party. We use in the negotiation between the two parties a Q-learning algorithm for the signaling model and a Multi Attributes Decision Making (MADM) model to select the best signal. Moreover, we empirically validate the MADM model using Shannon’s entropy. The results obtained in the case of a multi-stages negotiation scenario show the convergence towards the pooling equilibrium.

Parking is at the heart of any modern city, considering its spatial influence and its role in urban mobility. It is also a key component of its urban landscape management and a critical component of an efficient transportation system. However, parking is still a source of environmental nuisance and quality of living collapse. There are many challenging issues that arise, such as land consumption, occupancy of public space, economic impact, pedestrian safety and social fairness. This becomes even more alarming in dense and ultra-dense urban environments where the availability of parking places significantly affects the traffic fluidity, and where a lack of parking spots is the main trigger of parking/traffic congestion. In this article, we propose a set of dynamic and zone-aware pricing strategies to solve the parking issues and traffic congestion. As a case study, we consider the administrative district of Casablanca city, the economic capital of Morocco. Our scheme is suitable for mixed-use areas, as it takes into account the presence of different driver profiles with different parking needs. Here, we aim to improve the rotation of attractive spots (located nearby zones of interest) and set a usage-based parking assignment via appropriate incentives. Inherently, this is equivalent to use strategic pricing to strategically control the parking dwell time, which ensures a usage-based fair sharing of public space among users and improves the traffic conditions in the target area.

Wireless sensor networks have become an emerging technology that is reshaping the industry 4.0. An exponential evolution with an enormous application in diverse areas such as aerospace engineering, healthcare, and smart farming. WSNs have also grown into a very hot area of interest. Therefore, several types of research have been conducted around the world interesting in energy efficiency, wireless communication, network lifetime, and scalability. Energy consumption is one of the most challenging areas in WSNs, with wireless propagation losses, limited power sources, and random sensor’s implementation, researchers during the last decade interested in developing new approaches serving for power consumption’s reduction and control. Clustering is one of several paradigms that are explored to manage power use and expand the network’s lifetime. In this paper, a multi-hop and dynamic k-medoids based algorithm for energy saving is proposed. The algorithm is using the elbow method for an optimized K parameter selection. The k-medoids clustering approach aims to reduce distances between cluster heads and relative cluster members at each round, for better communication’s management and an eventual optimization in the power dissipation during wireless transmission. The results of the proposed algorithm are compared to the results from Leach, Heed, and k-medoids. Extensive simulation runs shown an improvement of 30% compared to normal K-medoids in terms of first dead node and an improvement of 108% in terms of network lifetime.

Cloud computing is an innovative process that delivers on-demand services over the internet. Virtualization is considered as the key concept of cloud computing since it handles running multiple virtual resources in a single physical resource. Mapping the virtual machine (VM) to the appropriate physical machine (PM) is called virtual machine placement (VMP). In this context, the dilemma of placing VMs in the cloud environment presents a significant challenge that has been wholly addressed but not yet totally fixed. This paper provides a multi-objective decision-making approach for VMP in a cloud computing infrastructure. We propose a conic scalarization method to solve the optimization problem. Simulation results prove that the offline algorithm yields good results compared to online deterministic algorithms.

We present a novel approach to predict and characterize the antenna’s radiation pattern using an asymmetric lemniscate. Directional antennas are usually approximated by a circular sector where side lobes and back lobes are ignored. Our model is simple, accurate and considers all antenna’s lobes. Here, we present the three-bar linkage system allowing us to construct the target pattern. Then, we derive the polar equation of the asymmetric lemniscate. Finally, we provide some illustrative examples of antennas where our model generates the exact shape of the real radiation pattern.

The accurate localization of wireless devices plays an important role in several real-time Internet of Things (IoT) applications. In a network composed of many IoT sensors, a distributed collaborative localization approach can give more accurate localization performance based on a decentralized and low-complexity processing. However, the presence of Non-Line of Sight links between IoT devices detrimentally impacts the localization accuracy. In this paper, we propose a distributed localization algorithm based on a convex relaxation of the Huber loss function. Moreover, to reduce the algorithm convergence time, an iterative stochastic gradient descent algorithm is proposed. Through numerical simulations, we show that the proposed algorithm when used with optimal relaxation parameters of the Huber loss function achieves very low root mean square error and outperforms existing algorithms in the literature. Finally, we validate our proposed scheme using real experimental data.

In an increasingly urbanized world, road transportation plays an important role in today's economy. To reduce congestion due to parking searches, the concept of smart parking was introduced. One of the essential and interesting features of smart parking is the guidance to a vacant spot. To do so, in such parking, each spot is generally equipped with an IoT sensor. These sensors form an IoT wireless sensor network (WSN). The battery lifetime remains one of the main limitations of such a network. Here, we propose an algorithm for indoor guidance to a vacant spot based on finding a battery level balance between the parking sensors. A comparison between five parking strategies including our proposal is presented. Although still under improvement, the results of the first simulations highlight two majors points. The first concerns the extension of the first sensor fall's point. The second regards the number of sensors' battery replacement. Indeed, starting with the same initial battery level, when using our proposal, every sensor will have the same number of battery replacement when the other strategies operate more replacement. The proposed lifetime-efficient guidance allows reducing significantly the number of interventions relating to the batteries replacement of IoT parking sensors and push back the fall of the first sensor so extend the parking overall lifetime.

Network slicing assisted 5G is driven by providing a wide range of services that aims to satisfy various pre-service requirements. There is consensus that network slicing is a key enabler for the service-oriented 5G, that aims to cope with the increasing complexity of these networks. One of the major objectives of network slicing is to provide a different level of resource isolation, through resource abstraction and virtualization and the ability to efficiently share network resources. In this paper, we focus on video streaming services in the presence of other services with different QoS requirements. We propose a novel approach for resource sharing that provides interslice protection, flexibility, load-driven elasticity, and network efficiency. In particular, we design two-level multi-scale allocation schedulers for an efficient and low complexity RAN slicing by exploiting the characteristic of adaptive traffic such as video streaming service. Our mathematical analysis and simulation results confirm the benefits of resource abstraction and exhibit the added value of our solution.

With the emergence of network function virtualization (NFV) and Software-Defined Networking (SDN) technologies, virtual network functions (VNF) can be interconnected to deliver different mobile services in 5G communication networks. Our primary purpose is to find the optimal VNF placement reducing resource consumption while providing specific latency and throughput for slicing services. We consider VNFs as M/M/1/C queues chained together to serve customer requests. The problem relies on finding an efficient orchestration and placement of VNFs. In this context, we propose a decision algorithm based two-procedures, the first one, named orchestration phase, aims to manage the reuse of VNFs having the same required functions through a dynamic logit method, and the second one is related to the new placement of VNFs. In this paper, we focus on a simple scenario of delivering video streaming service traversing a standard chain of four VNFs. Simulation results prove the performance of our proposed algorithm in terms of End-to-End delay and dropping probability compared to greedy and affinity algorithms.

Power control is originally used in wireless networks, to compute optimal transmit power and deal with undesired interference. It is also a flexible mechanism that can provide Quality of Service (QoS) and strategically allow to meet the user requirements. This paper introduces a QoS-aware Satisfactory Power Allocation (SPA) for ultra dense networks, using a mean field perspective. In this setting, the user devices are partitioned into several classes based on their throughput requirements. Now, instead of seeking to maximize their QoS, the user devices from each class only aims to meet their respective throughput demands. Yet, by leveraging stochastic geometry analysis and mean field approximation, we investigate the uplink power control problem in a large scale ultra dense network that guarantees a satisfactory performance per class. Next, we formulate the problem as a mean field optimal control where the optimality conditions are derived using Lagrangian dual formulation. Finally, the effectiveness of the SPA policies is illustrated via extensive numerical analysis, and many insightful discussions are presented.

Reaching their destination at the lowest cost and the minimum delay is the main drivers’ goal. However, and due to unobservable or partially observed traffic congestion, most of the drivers hardly meet this goal under mono-modality. In this paper, we develop a graph-theoretic framework allowing to find out optimal multi-modal paths considering the driver preference and constraints (time, cost, comfort, etc.) Yet, our Intelligent Transport System (ITS) allows a driver to use his/her own car while traveling. Then, a parking station might be proposed before the driver continues traveling towards the final destination, through other suitable modalities and transportation facilities. This proposed path is mainly multi-modal and includes the best combination of transport-parking modes to reach a given destination. Our early simulation runs show promising results, as the optimal paths combine the three modalities (driving, walking, and riding) to respond to drivers’ mobility requests.

IoT sensors in smart parking, according to their sleep duration, wake up automatically and make the sensing to determine parking spot status (vacant/occupied). These periodic awakenings, if they are necessary and useful when the parking spot is vacant, they are much less when it is occupied. Here we propose a new algorithm based on occupancy-aware concept to adopt an opportunistic sleep duration. When the spot is vacant, the sensor will wake up according to the conventional sleep duration. However, once the parking place is detected occupied, the sensor will not wake up according to this time but according to another time greater than the previous sleep time duration. The first results obtained by our approach are promising. Thanks to our new occupancy-aware algorithm, the battery lifetime of IoT parking sensors is more than tripled when the occupancy rate is important.

Food security has been always a critical challenge for humankind, especially for developing countries. Different players around the world are engaged at different levels to resolve and overcome this challenge. Smart farming is one of the areas of interest in which Internet of Things (IoT) is presented as one of many paradigms that can be explored to manage crops in real-time. Crop's Management that is practicable through introducing of new practices, surveying important parameters and eventually improving the quality of crops. In this paper, we present an IoT-based low-cost architecture for smart farming based on wireless sensors network technology. The architecture supports the plug-and-play nodes approach. The system is based on the implementation of the change point detection algorithm and leach protocol for network clustering. This solution supports near real-time monitoring, data processing, and aid to improve decision-making. Heterogeneous wireless sensor nodes timely survey parameters such as soil moisture, ambient temperature, air quality, etc. Generated data is periodically transmitted to the relevant cluster heads. The Base Station gathers data from the cluster heads for eventual processing and storing. Extensive simulation runs show an improvement of 137% in terms of first dead node and improvement of 123% in terms of network lifetime.

In this paper, we investigate the uplink power allocation problem in a large scale environment for user devices with multi-homing capabilities. We introduce an analytical model for multi-homing ultra-dense heterogeneous networks, which takes into account spatial randomness and user device diversity. Coupling stochastic geometry analysis and mean-field approximation, we formulate the problem as a mean-field optimal control with two populations. Then, the optimality conditions are derived using Lagrangian dual formulation to obtain the mean-field equilibrium. Finally, by using a finite difference method, we illustrate the optimal transmit power for both uni-homed and dual-homed devices.

The parking plays a fundamental role in urban transport policy development, as an important factor impacting driver’s behavior and a major source of traffic problems. In this paper, we present some novel parking pricing schemes to solve the parking spots scarcity and enhance the traffic condition in urban areas. We capture the traveler behavior in term of joint route and parking selection using a simple congestion game. Each traveler aims to minimize his/her expected travel cost by choosing an optimal strategy (route and parking spot). We show how an efficient pricing can incentivize the travelers to optimally choose their route-parking while reducing the traffic congestion.

Millimeter-wave communications have recently attracted significant interest in future wireless networks regarding its wide bandwidth that achieves high data rates. However, the major challenge lies in the beam alignment problem that may induce a substantial loss in the received power, notably when narrow beams are employed. Therefore, this paper jointly addresses the problem of beam alignment and power allocation in a non-orthogonal multiple access (NOMA) mmWave system. Unlike the conventional orthogonal multiple access (OMA), we study the case where two users are joined for NOMA transmission. Next, to mitigate this combined issue, we propose an optimization formulation owing to maximize the sum rate. We compare two types of antennas: sectorized and lemniscate antenna patterns under NOMA and OMA schemes. Simulation results prove the performance of NOMA-lemniscate based beam alignment and power allocation scheme compared to the conventional OMA scheme.

Cloud computing is the newly emerged technology adopted by many organizations due to its different benefits. Unfortunately, despite all the benefits offered by the cloud, there are certain concerns regarding the security issues related to the cloud platform which can threaten its widespread adoption. In this study, we suggest a scalable model to profile and prevent security attacks in the application layer of a cloud environment using an accurate and interpretable machine learning algorithm called regularized class association rules. The proposed model is based, first, on three additional security factors $(k, l$ and $m)$ , second, on the traditional authentication methods such as passwords and biometrics including keystroke to grant access to the cloud services/resources for an authorized user. Moreover, a case study of the proposal is given in order to validate the model and its usefulness. Eventually, a simulation was done to test the model performances.

The proliferation of connected objects has revolutionized the traditional Internet, giving rise to the emerging Internet of Things (IoT). The IoT ecosystem is very large, and it includes smart interconnections among sensors and devices with applications in both the industrial world and customers' daily lives. As a unified standard for IoT is still under development, many challenges related to IoT must be discussed and addressed, especially those related to energy efficiency. This article tackles the challenge of energy efficiency in IoT from a novel perspective. It shows that instead of maximizing the QoS, which is generally energy costly, better energy efficiency can be achieved by targeting satisfactory QoS levels only. The approach aims to enhance energy efficiency while ensuring a desired QoS threshold. This is supported by a game theoretical solution concept referred to as the satisfaction equilibrium. Moreover, as IoT objects require self-configuring techniques to maintain the network scalability and flexibility, this article introduces fully distributed schemes in order to reach efficient satisfaction equilibria in both slow- and fast-fading channel contexts. The proposed schemes can also be adapted to achieve the maximum performance of IoT applications that desire the highest QoS levels. The performance of these algorithms is illustrated through a smart home use case scenario.

This paper presents an uplink energy-efficient power control mechanism for massive cellular Internet of Things (IoT) devices using a Mean-Field Game (MFG) approach. In this setting, IoT devices are clustered around closed access small base stations supporting massive connectivity, in order to avoid data traffic congestion. Each IoT device adapts its transmit power to its energy level and selfishly attempts to satisfy its quality of service expressed in terms of signal to interference plus noise ratio, while reducing the power consumption. The power control is first modeled as a differential game then extend to a MFG considering two cases: Large scale network and ultra dense IoT network. The mean field interference are derived using stochastic geometry analysis. Therefore, the IoT devices can predict their optimal transmit power policies based only on their initial energy distribution. A finite difference algorithm is then developed to obtain the mean field equilibrium. The simulations illustrate the optimal transmit power and the mean field at the equilibrium for both orthogonal and non orthogonal multiple access.

LoRa (Long Range) is increasingly catching the attention of researchers, industry leaders and business influencers as one of the most promising standard for Internet of Things (IoT) ecosystem. LoRa uses slotted Aloha protocol allowing connected IoT devices to send data in a sporadic way and a fully distributed fashion without syncing with other devices. Under a massive IoT environment, LoRa experiences too many collisions which decreases the average throughput drastically and increases the expected delay. In this paper, we propose a scalable slotted Aloha access method for LoRa-empowered Massive IoT environments. Our scheme uses the option field of the LoRa frame, and proposes to iteratively reduce the number of contending devices till this field expires. The remaining devices will access the channel using the legacy slotted Aloha. Our approach does not require any change of the LoRa frame which is of great interest from implementation perspective. Analytic results and extensive numerical examples show clearly how our scheme outperforms the legacy LoRa. We also provide some insights on how to select the network parameters so as the whole network performance is maximized.

Using millimeter wave (mmWave) bands in 5G self-organizing networks has a significant potential to provide high bandwidth. However, the major challenge lies in initial access beamforming where mmWave communications suffer from deafness problem that may cause a significant loss in the received power especially when narrow beams are adopted. This paper tackles the problem of beam alignment in mmWave 5G. The problem is formulated as a non-cooperative game between transmitter and receiver where each player tries to align its beamforming direction in a way to obtain maximum throughput. We first provide a full characterization of pure Nash equilibria. Then, we propose a gradient descent algorithm that allows users to learn their optimal beamwidth. Simulation results prove the performance of our beam alignment model since mmWave frequencies deliver narrow beams with high gain and significant capacity.

Cloud computing (CC) is the emerging technology in the world for hosting and delivering services over the internet. It offers a variety of benefits such as cost saving, access to different services without any installation from anywhere and any time by internet, etc. Despite all the advantages offered by CC, this technology still susceptible to security threats. From user point of view, CC seems to be very insecure due to security attacks which threaten it and limit its widespread adoption. More motivations are required to provide more trustworthy solutions to secure the cloud as much as possible and preserve users trust. For that, we propose a new model called "profiling and preventing security attacks", abbreviated to PPSA, to detect and prevent the known attacks as well as the unknown attacks before accessing the cloud services/resources. Unlike the existing solutions, such as intrusion detection systems (IDSs), the proposed solution is able to be deployed in a wide area network in the basis of internet and prevent unknown attacks. In this study, we propose a new solution to profile and prevent security attacks in CC. Then, we define new security factors based on keystroke dynamic. Afterwards, we integrate a machine learning algorithm (classification based on associations) to our proposal in order to profile and predict security attacks and optimize as well the PPSA scheme. Eventually, the proposal is illustrated by a realistic case study.

The proliferation of connected objects is leading to new ways of using unmanned aerial vehicles (UAV) as wireless relay. As this novel application of drone in wireless communications is currently under development, many challenges must be dealt with, and discussed for the most efficient deployment. This paper proposes an analytic model of Drones Flying Mesh Network which act collaboratively in multi-hop manner to provide connectivity, gather and forward data to an end system. This model take into consideration topology, UAV parameters and the interaction between layers (PHY,MAC). Our main outcome in this work is to predict the average end-to-end throughput as well as stability of intermediate UAV queue under saturated network and using the balance rate. The proposed model was evaluated with simulation using Matlab. We show the interval values of model parameters within which the stability region is find out.

As the urban population and the car ownership rate increase, traffic is becoming a serious urban problem. It remains difficult to reduce the large number of vehicles circling in search for free and cheaper lots in urban area. The driver’s choice for on-street lots, which are generally cheaper than off-street lots but also rare, causes congestion, impact negatively the parking revenues and increases the total cost of drivers' trips. However, we propose to intervene on price as effective tool to influence driver’s behavior, balance parking demand and enhance parking turnover in urban environment. This article highlights the interactions between parking price, real-time parking demand and parking time. We analyze the parking time under near-real-life conditions using the Discrete Batch Markovian Arrival Process (D-BMAP). Subsequently, we identify the optimal arrival rate that will seek to make the best use of parking resources. Finally, we propose to adopt a dynamic pricing policy that changes prices proportionally to the arrival rates on each parking and therefore reduces congestion (cruising time) and eliminates the driver’s preference for some parking.

Streaming service is continuously growing, which makes it the killer application of current 4G networks, as it demands more resources from mobile networks. The cellular network receives a large number of requests, most times for the same content, consuming the spectrum, energy, in addition to monetary costs inefficiently. In order to optimize spectrum utilization and reduce the induced costs, we propose a noncooperative game framework allowing to understand the user's behaviors. We observed a volunteer Dilemma-like situation when a mobile user could stream the requested video to its neighbors over a D2D link. Afterward, we provide a full description of both pure and mixed Nash equilibria (NE). Furthermore, to ensure convergence to NE points, we use linear reward-inaction and Gibbs Boltzmann learning algorithms. Finally, we show how our scheme could be exploited through extensive numerical simulation. Our framework capture the user's selfish behavior and provides a solution regarding setting and parameters allowing to reach high performance in terms of spectrum utilization, energy efficiency, and overall cost.

Algorithms are commonly perceived as difficult subjects. Many applications today require complex algorithms. However, the researchers look for ways to make them as simple as possible. In high time demanding fields, the process of sorting represents one of the foremost issues in the data structure for searching and optimization algorithms. In parallel processing, we divide program instructions among multiple processors by breaking problems into modules that can be executed in parallel, to reduce the execution time. In this paper, we proposed a novel parallel, re-configurable and adaptive sorting network of the BulkSort algorithm. Our architecture is based on simple and elementary operations such as comparison and binary shifting. The main strength of the proposed solution is the ability to sort in parallel without memory usage. Experimental results show that our proposed model is promising according to the required resources and its ability to perform a high-speed sorting process. In this study, we take into account the analysis result of the Simulink design to establish the required hardware resources of the proposed system.

Over the last few years, the evolution of network and user handsets' technologies, have challenged the telecom industry and the Internet ecosystem. Especially, the unprecedented progress of multimedia streaming services like YouTube, Vimeo and DailyMotion resulted in an impressive demand growth and a significant need of Quality of Service (QoS) (e.g., high data rate, low latency/jitter, etc.). Mainly, numerous difficulties are to be considered while delivering a specific service, such as a strict QoS, human-centric features, massive number of devices, heterogeneous devices and networks, and uncontrollable environments. Thenceforth, the concept of Quality of Experience (QoE) is gaining visibility, and tremendous research efforts have been spent on improving and/or delivering reliable and addedvalue services, at a high user experience. In this paper, we present the importance of QoE in wireless and mobile networks (4G, 5G, and beyond), by providing standard definitions and the most important measurement methods developed. Moreover, we exhibit notable enhancements and controlling approaches proposed by researchers to meet the user expectation in terms of service experience., Comment: 20 pages, 3 figures, Submitted to IEEE Access

In this paper, we are interested in building a joint availability and access cost policy for Unmanned Aerial Vehicles (UAVs)-empowered flying access networks. Indeed, we build a duopoly model to capture the adversarial behavior of UAVs operators in terms of their pricing and availability strategies. That is UAVs operators need to decide about the optimal beaconing period (period needed to send short messages advertising the existence of a UAV) while saving energy. Furthermore, they need to decide about the best price strategy in order to maximize their respective market share. Therefore, a tractable analysis for the game's Nash Equilibrium, both in terms of pricing and availability is derived. We show that this special game exhibits some very interesting properties as it is sub-modular with respect to the availability policy, whereas it is super-modular with respect to the service fee. Furthermore, we implement a learning scheme using best-response dynamics that allows operators to learn their joint pricing-availability strategies in a fast, accurate yet completely distributed fashion. Extensive simulations show the convergence of the proposed schemes to the joint pricing-availability Nash equilibrium and provide attractive insights on how the game parameters could be set to control the duopoly.