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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.

Precision Agriculture is a clear standout of applying the most recent advances of intelligent systems. The motivation behind the adoption of such a systems is to reduce costs, increment treatments quality and efficiency, thus, increasing the quantity and the quality of agricultural products. In our study we used a histograms based on color indices to discriminate between three classes: soil, soybean and broad-leaf(weeds). This feature representation was tested with two classifiers Back-propagation neural network (BPNN), and Support Vector Machine (SVM). Our approach achieved a state of the art performance with an overall accuracy of 96.601% for BPNN, and 95.078% SVM.

With the advent of Big Data, new challenges have emerged regarding the evaluation of decision support systems (DSS). Existing evaluation benchmarks are not configured to handle a massive data volume and wide data diversity. In this paper, we introduce a new DSS benchmark that supports multiple data storage systems, such as relational and Not Only SQL (NoSQL) systems. Our scheme recognizes numerous data models (snowflake, star and flat topologies) and several data formats (CSV, JSON, TBL, XML, etc.). It entails complex data generation characterized within “volume, variety, and velocity” framework (3 V). Next, our scheme enables distributed and parallel data generation. Furthermore, we exhibit some experimental results with KoalaBench.

Video streaming has often suffered from a quality of experience (QoE) degradation related to playback interruptions and buffering events. Therefore, the video file should be prefetched before starting playback. This paper aims to optimize the QoE of video streaming by exploiting the trade-off between starvation probability and start-up delay. First, we analyze the starvation probability of different file distributions (i.e. Exponential distribution, Pareto distribution, Log-normal distribution and Uniform distribution). Second, we adopt Lagrange multiplier to identify the optimal start-up delay to prevent the repeated starvation events during the video playout. The simulation results show that the proposed approach reduces the startup-delay by 40%.

Recently, telecommunication industry has witnessed an explosive growth in the number of connected devices equipped with more than one wireless interface. These multimode devices are able to connect to different wireless technologies at once to benefit from a higher throughput, a better energy efficiency (EE) and an extended battery lifetime. In this work, we analyze the behavior of devices equipped with cellular and device-to-device (D2D) interfaces with multihoming capability. We investigate the energy efficiency maximization problem under cellular and D2D communications. Yet, we propose a game theoretic framework where the interaction among cellular devices is modeled as a non-cooperative game. Next, we analyze the Nash equilibria and derive conditions of existence. An energy efficient mode selection is then discussed. Furthermore, we provide some numerical examples.

Due to the exponential booming of wireless traffic demands on vehicular networks, the 79 GHz band has been allocated to automotive radar applications since 2013. In the mining Internet-of-Things (IoT) environment, this millimeter wave band enhances the detection capability of targets and accuracy in extreme conditions. This paper provides a new design of part of radar sensor used in mining industry; the antenna. The proposed design preserves expressly the performance aspects required for IoT communications, such as the gain and the radiation pattern. We present a novel lx4 H-slotted patch antenna array operating in the 79 GHz band using inset-feeding line. It is characterized by its small size (10.8 mm x 2.083 mm). The simulated results on CST microwave studio software including the return loss, voltage standing wave ratio make this suggested design an attractive candidate to be deployed for medium-range radar and short-range radar in 77–81 GHz.

Due to the high mobility of vehicles and the dynamic topology changes of vehicular networks, it is difficult to satisfy drivers needs through a single wireless network. Consequently, by integrating different wireless access networks such as LTE and IEEE 802.11p, the vehicular network is expected to be a good platform called Heterogeneous Vehicular Network that can meet various vehicular user requirements. In such networks, vehicles need to have mechanisms in place to decide which access network is the most suitable for every application that the vehicular user requires. Always-Best-Connected is considered as a special concept to allow users getting connected to Internet using the access network that best suits their needs or profile at any point in time. Clearly, this concept provides multiple access networks simultaneous for mobile users moving in heterogeneous access network environment. In this paper, we propose two approaches called graph theory-based approach, and signaling game-based approach. The first approach is based on graph theory while the second one based on signaling game theory to compute the suitable path which provides Always-Best-Connected service for a smart vehicle moving in vehicular networks.

Unmanned aerial vehicles (UAVs) were initially developed for military monitoring and surveillance tasks but found several interesting applications in the civilian domain. A promising application/technology is to use drone small cells (DSCs) to expand wireless communication coverage on demand. Rapid deployment along with limited operating costs are key factors that boost the development of DSCs for both military and civilian utilizations. DSCs are rapidly deployable to provide connectivity for temporary users (e.g. attendees of festivals, sporting events, or seminars), or over disaster areas to replace damaged communication infrastructure. UAVs are battery-powered, which makes energy consumption optimization a critical issue for acceptable performance, high availability, and an economically viable DCS deployment. In this article we focus on the scheduling of beaconing periods as an efficient means of energy consumption optimization. The conducted study provides a sub-modular game perspective of the problem and investigates its structural properties. We also provide a learning algorithm that ensures convergence of the considered UAV network with its unique Nash equilibrium operating point. Finally, we conduct extensive numerical investigations to assist our claims about the energy efficiency of the strategic beaconing policy (at Nash equilibrium).

To ensure connectivity in a large-scale networks, we consider a multiple drones flying mesh network (DFMN) built on IEEE 802.11s. In DFMN, drones fly above a target area, collecting data from Internet of Things (IoT) devices and forward it throughout drones in a multi-hop fashion till arriving to the processing unit. Moreover, each drone generates its own packets, and they mainly contain hello messages and beacon frames advertising their presence. In this paper, we propose a queueing framework which analyze the performance of such a mesh network in view of forwarding queues’ stability and end-to-end throughput.

Recently, UAV has provided a significant role to support the wireless network, thanks to the several advantages that can offer in comparison with the terrestrial base station. In this paper, we aim to deal with data collection and charging depletion ground IoT devices through UAV station, which is used as a flying base station. To extend the network lifetime, we present a novel use of UAV with energy harvesting module. Thus, the UAV can be used as an energy source to serve depleted IoT devices. On one hand, the UAV charges the depletion ground IoT devices starting initially with those with battery level under a certain threshold. On the other hand, the UAV station collects data from IoT devices that have sufficient energy to transmit their packets, and in the same phase, the UAV exploits the RF signals transmitted by IoT devices to extract and harvest energy. Furthermore, and as the UAV station has a limited coverage time due to its known energy constraint, we investigate in this work the trade- off between both times that the UAV reserves to devices in order to recharge them and to collect data. Numerical results evaluate different metrics of performances in which we examine the added value of UAV with energy harvesting module.

Information Centric Network emerges a paradigm shift from host centric to information centric communication model. ICN deployment faces a significant challenge in poorly developed telecommunications infrastructures where end-to-end paths are not guaranteed. In this context, delay-tolerant networks (DTN) were introduced as an initiative that efficiently handles network interruptions. This paper proposes a new incentive approach for content caching, by exploiting ICN and DTN advantages while avoiding their shortcomings. The proposed caching solution intends to achieve a logical trade-off between the dissemination rate and the energy efficiency in such environments. To this end, we propose a reputation-based content caching mechanism where DTN stations (relays) attempt to deliver content stored in ICN stations. Our approach is designed to ensure an efficient equilibrium between the overall delivery probability and the energy consumption. Furthermore, the network performance is exhibited through a numerical investigation. We conclude that the proposed mechanism can significantly enhance the caching and the energy efficiency by achieving an optimum dissemination rate.

Nowadays, Wireless communication with unmanned aerial vehicles (UAVs) has become extremely popular thanks to their flexible deployment and higher chance of line-of-sight (LoS) links to ground users. Despite the several advantages of using UAVs as flying base stations, still a number of technical challenges arise, including energy limitation. This encourages researchers to investigate the energy optimization challenges. In this paper we consider a finite network of unmanned aerial vehicles (UAVs) acting as base stations and serving a number of ground wireless users that are deployed according to a homogeneous Poisson Point Processes (PPP), each user with infinite buffer storage. We derive the coverage probability expression as well as the arrival rate for each user. We address the activity energy optimization problem aiming to enhance the coverage and rate performance of our proposed scenario. To facilitate simulations for such UAV systems, this article presents a realistic mobility model for a UAV movement emulation. Detailed description of this mobility model and it's associated movement patterns are characterized. Interesting throughput-energy trade-offs have been concluded theoretically and also with some numerical results.

In an Internet of things (IoT) context, the existence of multiple technologies, that ensure communication between each node and the entire network, aims to improve the overall capacity to satisfy the growing number of connected devices. However, to meet a high performance, it is important to assign the right device to the right radio access technology (RAT). In this paper, we are interested in a radio access technology selection in an IoT context. We answer the question how to select the right RAT taking into account two main parameters: energy efficiency and cost to connect to a RAT, and using two different approaches. The first one is a fully distributed approach that allows to devices to autonomously select the best RAT that meets their preference requirement, but also satisfies the RATs access points utilities. The second approach relies on a centralized entity that ensures association between IoT devices and RATs using a binary knapsack optimization problem. The results obtained through simulations show that when it comes to energy efficiency and fairness among devices, the matching approach outperforms the knapsack based scheme.

Nowadays, unmanned aerial vehicles (UAVs) as flying base stations have attracted significant interest in telecommunications service field; Thanks to their low-cost, high maneuverability, flexible deployment and mobility advantages, they have become a main stream solution, to provide wireless communication in exceptional scenarios such as hard to reach areas or massive-attended events. Because UAVs are energy budget constrained, optimal availability control for energy efficiency is proposed in this paper. Within such a situation, the most important objective of a UAVs operator is to provide a service with acceptable availability and cost effective, so that, to realize a satisfactory benefit. The conducted study provides a non-cooperative duopoly game where UAVs Service Providers (SP) are competing to serve a geographical area. We focus on the scheduling of beaconing periods as an efficient mean of energy consumption optimization that results in temporary unavailability of network connectivity. A tractable comparative analysis for the game’s equilibrium, in terms of both Cournot and stackelberg frameworks is derived. We show that the benefit when adopting the leadership model is exhibiting some additional properties compared to the Cournot (anarchic) game. Some extensive simulations are given to confirm our proposal.

While trying to get a service from the Base station it happens that many devices will find themselves competing to get the cellular service, which means each device will use the maximum power possible to win the competition, but if there is another solution that will allow the device to get what it wanted with minimum of energy, how the device will react to it? In other words, we know that the communication through a Device-to-Device (D2D) link is done with a minimum of transmitted power, so why not optimize the power in a cellular communication by introducing D2D communication (i.e. switching from competition to cooperation). In this paper we will focus on studying the players'behavior based on quitting games(i.e. a non cooperative game theory), specifying both the pure and mixed Nash equilibrium during a competition between two devices while communicating with the base station, as well as calculating the probability of quitting and the average quitting time.

Founded in 2005 YouTube has became the most fast growing stream service, that has a great impact on the internet traffic distribution. Thus understanding YouTube's characteristics is crucial to network providers. In this survey we are going to present the factors that impact user's satisfaction, in other meaning the impact on the Quality of experience(QoE). Furthermore we will discuss how to determine the influencing factors on YouTube, how to measure its quality and how to improve it. Tomorrow YouTube will have to adapt to the changes that will occur because our environment is changing from regular to smart digital one.

Traditional vertical handover schemes postulate that vertical handovers VHOs of users come on an individual basis. This enables users to know previously the decision already made by other users, and then the choice will be accordingly made. However, in case of group mobility, almost all VHO decisions of all users, in a given group e.g., passengers on board a bus or a train equipped with smart phones or laptops, will be made at the same time. This concept is called group vertical handover GVHO. When all VHO decisions of a large number of users are made at the same time, the system performance may degrade and network congestion may occur. In this paper, we propose two fully decentralized algorithms for network access selection, and that is based on the concept of congestion game to resolve the problem of network congestion in group mobility scenarios. Two learning algorithms, dubbed Sastry Algorithm and Q-Learning Algorithm, are envisioned. Each one of these algorithms helps mobile users in a group to reach the nash equilibrium in a stochastic environment. The nash equilibrium represents a fair and efficient solution according to which each mobile user is connected to a single network and has no intention to change his decision to improve his throughput. This shall help resolve the problem of network congestion caused by GVHO. Simulation results validate the proposed algorithms and show their efficiency in achieving convergence, even at a slower pace. To achieve fast convergence, we also propose a heuristic method inspired from simulated annealing and incorporated in a hybrid learning algorithm to speed up convergence time and maintain efficient solutions. The simulation results also show the adaptability of our hybrid algorithm with decreasing step size-simulated annealing DSS-SA for high mobility group scenario. Copyright © 2015 John Wiley & Sons, Ltd.

Cognitive radio technology allows the reuse of the underutilized frequency spectrum on an opportunistic and non-interfering basis by means of introducing, besides the legitimate primary users of the spectrum, a new kind of users called cognitive or secondary users. Thus, reliable spectrum sensing is critical to dynamically detect available licensed frequency bands and mitigate the primary signals, but it remains realistically difficult to carry out. In fact, although distributed collaborative sensing has turned out to be fruitful for the cognitive radio environment, its accuracy is often affected by the selfish and autonomous behavior of users. In this article, we model distributed spectrum sensing and channel allocation as a non-cooperative game, and apply the minority game to bring forth and study the cooperative behavior of users. The novelty brought by our study consists of alleviating the number of users contending for primary channels by giving them the opportunity to choose between the two, either sensing the channel or being inactive during the time slot. To address the trade-off faced by the SUs, we evaluate the performance of two secondary systems in a green communications context: energy consumption and transmission delay.

This article exhibits a reward-based incentive mechanism for file caching in delay-tolerant networks. In delay-tolerant networks, nodes use relay’s store-carry-forward paradigm to reach the final destination. Thereby, relay nodes may store data in their buffer and carry it till an appropriate contact opportunity with destination arises. However, the relays are not always willing to assist data forwarding due to a limited energy or a low storage capacity. Our proposal suggests a reward mechanism to uphold and to sustain cooperation among relay nodes. We model this distributed network interaction as a non-cooperative game. Namely, the source node offers to the relay nodes a positive reward if they accept to cache and to forward a given file successfully to a target destination, whereas the relay nodes may either accept or reject the source deal, depending on the reward attractiveness and on their battery status (their actual energy level). Next, full characterizations of both pure and mixed Nash equilibria are provided. Then, we propose three fully distributed algorithms to ensure convergence to the Nash equilibria (for both pure equilibrium and mixed equilibrium). Finally, we validate our proposal through extensive numerical examples and many learning simulations and draw some conclusions and insightful remarks.

The vision of ubiquitous computing in interactive mobile cloud applications and Internet of Things (IoT) based systems is still difficult to achieve. The difficulty lies in the use of cloud services in mobile devices, which impacts the issues of performance, scalability, availability, and lack of resources in mobile computing environments. Despite the astonishing advancement achieved in IoT technology, there is still much to do. Some IoT-based systems, which rely on a variety of mobile devices, need to work even when the connection is temporarily unavailable or under-degraded. Besides, mobile cloud service providers can reduce network latency by moving some of their services close to the user. To cope with this challenge, we propose in this chapter the usage of small clouds known as cloudlets, and we describe two cloudletbased architectures, which allow leveraging the geographical proximity of cloud services to mobile users. We model the network latency of the different components of the two architectures using a continuous-time Markov chain (CTMC). These components are essentially the user nodes, the cloudlets, and the principal cloud. For each architecture, we simulate queries submitted by mobile users to a search engine, and we estimate the incurred delay by using the CTMC state models.

In this paper, we deal with the store-and-forward paradigm for self-organizing Delay Tolerant Networks (DTN). To overcome the decentralized nature and the infrastructureless constraint of such a network, highly distributed design and efficient incentive mechanisms are needed in order to convince relay nodes to disseminate the content. Here, we exhibit a new way to set the store-and-forward scheme based on the emerging matching game theory. This approach serve to match between on one hand different kinds of files generated by a source node and on the second hand relay nodes that may forward these files. In order to make incentive for cooperation, the source node offers a strategic reward to relay nodes that have accepted to forward a given file. Moreover, each relay and file can be defined by a context, i.e. its characteristics. Based on that, the source would prefer maximize the overall delivery probability at the same time as the relay would try to guarantee the highest possible reward while considering its battery status. Our matching-game-based scheme promises an efficient tradeoff between the overall delivery probability and the energy consumption. For practical considerations, we propose an algorithmic solution to achieve a stable matching between the sets of source files and the set of relay stations. Extensive simulations show that our scheme outperforms the legacy two-hop routing and illustrate the impact of preferences of each set involved in the game, and how such a tool can meet a high delivery rate at a reasonable energy budget.

In this paper, we address the problem of energy-aware user satisfaction in self-organizing networks. Our main objective is to meet with the users requirements while reducing energy consumption. Accordingly, we aim at seeking satisfaction equilibria, mainly the efficient satisfaction equilibrium (ESE). We first define conditions of existence and uniqueness of ESE. Considering information-theoretic transmission rate based measure, we fully characterize the ESE and prove that, whenever it exists, it is a solution of a linear system. We show that, at the ESE, no player can increase its Quality of Service without degrading the energy performance. Finally, in order to reach the ESE, we propose a fully distributed scheme based on the Banach-Picard algorithm and show, through simulation results, its qualitative properties.

A new opportunistic cross-layer MAC protocol involving channel allocation and packet scheduling for cognitive radio networks is proposed. Cognitive radio allows secondary users SUs to exploit the available portions of the licensed spectrum bands without interfering with primary users. In particular, we consider a cognitive radio system, where SUs are equipped with two transceivers: a control transceiver and a software-defined radio transceiver. Data traffic characteristics of SUs are considered to ameliorate system performance. So, we propose a mechanism of resource reservation to improve QoS requirements that favors successful SUs to transmit data during x time slots without interfering with primary users. The key novelty of this paper is giving priority for SUs with important data traffic and which frequently solicits data channels to transmit for the remaining time of the ongoing time slot and for the next time slots directly after checking the channel availability. We develop a new analytical model to evaluate delay parameter for two scenarios with and without resource reservation and we then investigate the impact of those scenarios on the energy consumption. We show through simulations that cognitive radio performances increase noticeably with the proposed scheme. Copyright © 2014 John Wiley & Sons, Ltd.

In cognitive radio networks, secondary users (SUs) face two conflicting objectives. Each SU seeks to minimize the sensing duration while maximizing the detection probability of primary users (PU) to avoid interfering with their transmissions. Both objectives have a substantial effect on energy efficiency. This paper investigates a noncooperative setting for selecting the sensing duration when multiple SUs operate in the same network. Here, each SU has a certain throughput requirement. The interaction among SUs is captured via a satisfaction strategic game with explicitly stated throughput demands. We prove that depending on the throughput requirements, either zero, one or two Satisfaction Equilibria (SE) exist. We then provide a fully distributed learning algorithm (SELA) to discover them. Extensive simulation results show the validity of the proposed SELA and illustrate the relationship between the throughput demand and the sensing duration.

In this paper, we present the Enhanced Learning Based Random Walk (ELBRW) recommender system for Places of Interest (POI), which leverages contextual information for providing more relevant POI recommendations. The ELBRW considers a model of contextual factors namely POI crowdedness based on a discrete-time Markov chain and combines user interests and "mobility homophily" for POI recommendation in Location- Based Social Networks (LBSNs). By comparing it to the Learning Based Random Walk (LBRW), a context- free recommender system, the performed experiments using LBSNs data provide promising results in terms of POI recommendation quality.

In this paper, we propose a green solution for cellular users located in hyper dense co-channel deployments of LTE small cell networks (SCNs), randomly distributed within LTE macro cell networks (MCNs). Our solution is based on a distributed sharing time access algorithm executed by small base stations (SBSs), and multi-homing capabilities of macro cellular users to improve the energy efficiency of cellular users and to satisfy their QoS throughput requirements. The theoretical analysis is validated by simulations. Our results demonstrate the improved energy efficiency of cellular users compared to the other access control mechanisms.

Unmanned Aerial Vehicles (UAV) were initially developed for military monitoring and surveillance tasks. However, they recently found several interesting applications in the civilian domain. One promising application is to use UAV for military operations behind enmy lines. Rapid deployment along with limited operating costs are key factors that boost the development of UAVs for both military and civilian utilizations. UAVs are battery-powered which makes energy consumption optimization a critical issue for acceptable performance, high availability and an economically viable UAVs deployment. In this paper, we focus on tuning the beaconing probability as an efficient mean of energy consumption optimization. The conducted study provides markov decision process perspective of the problem. Also, we conduct extensive numerical investigations to assist our claims about the energy efficiency of the optimal beaconing policy.

Driven by the unprecedented growth in the number of connected devices and mobile data traffic, 5G wireless network is expected to afford a full scale Internet of Things. Various wireless access networks will continue to coexist; one concept is allowing a device to virtually and simultaneously be connected to other devices using all available network resources at its location which is multi-homing. In this paper, we draw an overall picture of multi-homing based solution for provisioning Quality of Service and Quality of Experience taking into consideration the economics issues and the challenging problems that must be solved in the future.

This paper presents some interesting findings about consumer confusion vis-a-vis real price for offered services and its impact on Telecommunications market dynamics. Noting that pricing strategies have a critical influence on consumer choice, customer satisfaction and customer retention in the telecommunications sector, our study focuses on the analysis of the service providers' strategic behavior. Yet, we construct a simple oligopolistic model to capture the interactions among service providers and end users. Next, we analyze the behavior of service providers in terms of their pricing strategies within the framework of non-cooperative game theory. It aims to evaluate the impact of consumer confusion on the competition and operators profitability. Here, rational service providers are competing with each other to maximize their respective payoffs in presence of both a fraction of confused consumers while others are non confused. We provide some interesting results regarding the Nash equilibrium of this game. More precisely, we showed existence and uniqueness of the Nash equilibrium under some conditions. Furthermore, we introduce two learning algorithms that may lead operators to learn their strategic pricing schemes in a complete distributed manner. Extensive simulations show convergence of a proposed scheme to the Nash equilibrium and give some insights on how the game parameters may vary the oligopoly outcome.

In the context of emerging technologies, Cloud Computing (CC) was introduced as a new paradigm to host and deliver Information Technology Services. In such an environment, privacy and security issues are critical areas that still require to be deeply explored. Yet, highly secured systems are generally met by computationally expensive systems. Such a system may also degrade the user experience and its willingness to adopt it. The aims of this paper are threefold: First, to integrate a Class-Association Rules (CARs) into the process of Multi-factor Authentication Based on Multimodal Biometrics (MFA-MB) for CC; Second, defines a new metric to measure the User Experience and; Third, exhibits an algorithm to authenticate cloud SaaS/PaaS Users with an enhanced MFA-MB scheme. Since, CARs are used to predict the most expected Multimodal Biometric Authentication (MBA) in the basis of Users' authentication habits, mined from their historical authentication data sets, which guarantees continuously improving their Experience. The integration of CARs in the CC authentication process allows also to identify the actual context (Time, Place, Device, etc.) which impacts the choice of biometrics used in MBA according to one User's situation. Therefore, this will help to increase the authentication security level using MBA at a decreased time and improved user experience. Integration of CARs is illustrated by a realistic case of Bimodal Biometric Authentication.

Cognitive radio allows flexible self-organising solution for spectrum scarcity management. It can be used to improve spectrum utilisation while protecting larger range of coexisting users. In this article, we present a new spectrum leasing solution using the matching theory. Based on this Nobel Prize winning framework, a novel distributed scheme, for joint resource management and business side, is built. Yet, we exhibit a new scheme, whereby multiple primary users (PUs) and secondary users (SUs) interact with one another to select their best possible partner optimising their own benefits. To efficiently use available spectrum, each SU seeks to be allocated a channel with sufficient bandwidth and low cost. Numerical investigations illustrate how stable the matching scheme and how it outperforms standard resource allocation. It is also compared with flat rate multiple access scheme via implementing the two schedulers in terms of network size, SUs' QoS demand and bandwidth fee.

Nowadays, practitioners as well as researchers skilled in the field of Information Technology (IT) attach great importance to the security of IT services. However, the traditional authentication techniques based on single factors such as passwords and tokens suffer from problems related to their robustness. In a recent work, we proposed a new technique called “Multi-factor Authentication based on Multimodal Biometrics (MFA-MB)” to overcome the drawbacks related to these techniques and also the problems related to the biometrics using single traits. Thus, this paper aims to model and develop an Autonomous Multimodal Biometric Authentication System called “AMBAS” using discrete-time Markov chains in order to decrease the complexity of the multimodal biometric system developed and used in the MFA-MB scheme. Eventually, giving the self-control to the AMBAS, offered thus as a new and unique solution, will improve therefore one user experience and achieve as well a good performances in terms of authentication time. This system aims to identify users according to different methodologies, likelihood, last-sate, random and weighting. The system already finds its application in both cloud and mobile computing. While giving a case study with three-modal biometrics, a simulation of different algorithms performed for the four methodologies is done to test the system performances and usefulness.