The study presents a dynamic framework for detecting, recognizing, and categorizing cyber threats in network traffic. It uses a transformer-based encoder to identify malicious behavior and detects attacks with a 100% recall rate. The system then uses a Gaussian Mixture Model to identify unseen attack types. This allows the framework to refine its recognition abilities over time, sustaining strong detection performance. Despite the inclusion of unknown attack types, the system maintains robustness, achieving 95.6% in classification accuracy and recall metrics. The findings validate the framework’s ability to adapt to the evolving threat environment and deliver reliable detection and threat characterization results. The goal is to create a scalable, real-time intrusion detection system that can continuously adapt to network security threats.

The exponential growth of internet-based services is enhancing human-machine interactions as we transition to nextgeneration networks. However, the threat landscape is also changing rapidly in tandem with this transformation, with increasingly complex cyberattacks that can pass through current security mechanisms because of their unidentified features. Identifying such novel threats is essential for an effective response, but existing AI-based intrusion detection systems methods often lack reliability when facing these unseen threats. Although transformer-based models have shown promise in threat detection and massive data volume management, they are prone to overconfident predictions on unfamiliar inputs, limiting their robustness. To address this, we propose a BERT-based intrusion detection approach trained with logit normalization, which enhances the model reliability by mitigating overconfidence in the model prediction, leading to better seen-unseen separation boundary. We evaluate our method on four benchmark datasets namely CICIDS2017, CICIoT2023, Edge-IIoTset and NSL-KDD, under intra- and cross-dataset conditions. Results show a substantial improvement in detecting unseen attacks, with accuracy rising from 3.9 % to almost 100 % while maintaining at least 95 % detection for seen threats. This work results in a robust and unified model capable of detecting seen attacks and identifying unseen ones.

The fifth-generation (5G) offers advanced services, supporting applications such as intelligent transportation, con-nected healthcare, and smart cities within the Internet of Things (IoT). However, these advancements introduce significant security challenges, with increasingly sophisticated cyber-attacks. This paper proposes a robust intrusion detection system (IDS) using federated learning and large language models (LLMs). The core of our IDS is based on BERT, a transformer model adapted to identify malicious network flows. We modified this transformer to optimize performance on edge devices with limited resources. Experiments were conducted in both centralized and federated learning contexts. In the centralized setup, the model achieved an inference accuracy of 97.79 %. In a federated learning context, the model was trained across multiple devices using both IID (Independent and Identically Distributed) and non-IID data, based on various scenarios, ensuring data privacy and compliance with regulations. We also leveraged linear quantization to com-press the model for deployment on edge devices. This reduction resulted in a slight decrease of 0.02 % in accuracy for a model size reduction of 28.74 %. The results underscore the viability of LLMs for deployment in IoT ecosystems, highlighting their ability to operate on devices with constrained computational and storage resources.

In this work, the adjustment of nodes’ sending rate in IPv6 over low-power wireless personal area networks (6LoWPAN) is investigated. 6LoWPAN enables low-power equipment connecting to the Internet via Internet of Things (IoT) network. In such network, nodes are competing to share the bandwidth, in order to deliver their sensed data as fast as possible, to a central node (access point, cloud server, aggregator, etc.). With the lack of an optimal sharing policy, such competitive behavior however may affect directly networks’ quality of service and degrade their performance in terms of nodes’ throughput (sending rate), latency of the network, and nodes’ energy consumption. To overcome this, we propose a new noncooperative game-based scheme, called DeepGame, where each IoT device is acted as a player, asking for a high data throughput. DeepGame enables to adjust nodes’ throughput based on four main criteria: 1) nodes’ preferences concerning the data rate; 2) nodes’ priorities in the IoT network; 3) the quality of nodes data; and 4) nodes’ remaining energy. Moreover, a multiagent deep reinforcement learning model is built in federated way, on top of our game model in order to enable nodes (agents) learning the optimal action at each step of the game, and hence reaching the Nash equilibrium (NE) state. We use the Cooja emulator on top of Contiki OS to implement our game-based model. We evaluate and validate the DeepGame scheme on top of two different medium access techniques, carrier-sense multiple access with collision avoidance and time-division medium access (TDMA). Numerical results, with a good confidence interval, illustrate the efficiency of our scheme when leveraging the TDMA access technique in not only quickly converging to NE situation but also improving the performances of the IoT network including, nodes’ energy consumption, nodes’ throughput, and network overhead, when compared to other schemes.

The term Industrial Internet of Things (IIoT) was created to describe a specific area of the Internet of Things (IoT) that integrates information and communication technologies (ICTs) like cloud/edge computing, wireless sensor/actuator networks, and connected objects to enable and accelerate the development of Industry 4.0. IIoT applications (e.g., smart manufacturing, remote control of industrial machinery, and critical system monitoring) have various levels of criticality and Quality-of-Service (QoS) requirements. However, the characteristics of data collected by interconnected devices complicate the task of guaranteeing the QoS requirements in terms of latency and reliability in addition to the huge amount of energy consumption. As a potential solution, edge computing offers additional powerful resources in the proximity of the IIoT devices. Hence, the required QoS can be achieved by offloading computation-intensive tasks to edge servers. Moreover, the offloading process needs to be optimized to take full advantage. Unlikely, conventional optimization methods are very complex to be applied in the IIoT context. To overcome this issue, we propose a computation offloading approach based on deep reinforcement learning (DRL) to minimize long-term energy consumption and maximize the number of tasks completed before their tolerant deadlines. We introduce a system with multiple agents to deal with the increasing dimension of the action space, where each IIoT device is represented by its own DRL model. The goal of the model is to maximize a flexible and long-term reward. In addition, the DRL models are trained in the cloud and make decisions online in the edge servers, allowing quick decision making by avoiding iterative online optimization procedures. The performance of the proposed approach is evaluated through simulation. The proposal shows promising results compared to other approaches.

The emergence of smart grids to replace conventional power grids is mainly characterized by the integration of renewable energy sources (RESs), with wind and solar being the most dominant due to their affordability and ease of installation. However, their intermittent nature can impact the reliability of the power system. Battery energy storage systems (BESSs) have proven to be an effective solution to address this intermittency by storing excess energy for later use. In addition, there is growing interest in creating residential communities and sharing a single BESS among neighbors. However, the subsequent charging and discharging of the shared BESS can result in significant energy losses within the community. Direct energy exchange between neighbors could be a solution to this problem since it reduces the use of the BESS, but energy losses may also occur during the exchange due to the distance traveled by the energy to be delivered. In this context, we propose in this paper ANDORRA, an energy trading framework for residential communities that optimizes hourly exchange operations to minimize energy losses. We propose a mathematical formulation of the trading problem and solve it using two distinct methods: Lagrange multipliers and particle swarm optimization (PSO). Our framework is tested on real data from a residential community, and the results show that the Lagrange multipliers method outperforms the PSO algorithm by reducing energy losses by 40.95%, energy consumption by 92.41%, and execution time by 96%.

With the expansion of connected devices used for industrial purposes, the Industrial Internet of Things (IIoT) has emerged as a specific branch of the Internet of Things (IoT) for Industry 4.0. Its applications in industrial domains include monitoring, smart manufacturing, and virtual and augmented reality. However, the huge amount of data generated by IIoT devices with limited computing resources makes it challenging to guarantee the different required quality of service (QoS) of the applications while minimizing the computation cost in terms of energy consumption. especially in terms of latency. In this paper, we opt for the offloading of dependent computation-intensive tasks to edge and cloud servers with more powerful computation capacities. Our proposed model aims to minimize the energy consumption of each IIoT device while respecting the maximal tolerant deadline of task completion. We propose to cluster the IIoT devices that have dependent tasks together to better handle this dependency. Moreover, we propose a distributed cooperative game that allows each device to decide whether it is beneficial for it and for its cluster, in terms of task completion and energy consumption, to offload its task or execute it locally. We prove that the Nash Equilibrium exists by proving that our game is a weighted potential game. Finally, we propose a practical distributed offloading algorithm to implement the cooperative game. The performance evaluation results show that the proposal optimizes energy consumption whilst increasing the number of tasks completed on time.

The term ‘’Industrial Internet of Things’’ (IIoT) was created to describe a specific area of the Internet of Things (IoT) that integrates Information and Communication Technologies (ICT) like cloud/edge computing, wireless sensor/actuator networks, connected objects, etc. to enable and accelerate the development of industry 4.0. Industry standards for IoT device connectivity and operation set it apart. A few of the properties of Industrial IoT include its high levels of resilience, communication availability, enormous data collecting, security, accuracy, automation, and interoperability. IIoT is also used for complicated task delegation, data-driven decision-making, and remote control of equipment. The IIoT applications also have various levels of criticality and quality of service requirements. Hence, it is very challenging to respect the specificity of each application and guarantee the required QoS. In this paper, we offload computationally demanding tasks to edge and cloud servers with stronger computational capabilities. Our suggested model tries to maximize the number of tasks completed before their tolerant deadlines while minimizing each IIoT device’s energy consumption. Furthermore, we introduce a distributed cooperative game that enables each device to choose whether to offload its tasks or locally execute them based on task completion and energy consumption. We demonstrate the existence of the Nash equilibrium by demonstrating that the proposed game is a weighted potential game. We also propose a practical implementation of the game using a distributed mechanism. Finally, we evaluate the proposal using simulation to show the benefits of computation offloading in terms of latency and energy consumption.

The migration towards Smart Grids (SGs) presents a significant opportunity for the energy industry to modernize. SGs are characterized by the exchange of both information and energy between consumers and suppliers in a bidirectional manner. Nevertheless, the growing energy demands in residential areas is emerging as a complicated issue to address. In order to tackle this problem, we propose PRAHA, a price-based demand-side management (DSM) framework for load scheduling in smart homes (SHs) that minimizes electricity payments while taking into consideration consumer’s comfort. Our framework considers distributed energy resources (DERs) and selling back energy to the grid as well. We propose a mathematical formulation of our scheduling problem and an improved particle swarm optimization (PSO) algorithm to solve it. Moreover, we use a machine learning (ML) algorithm that we proposed in a previous work to extract the preferences of the consumer from the consumption patterns of his smart appliances. The effectiveness of our proposal is demonstrated by testing it on real data collected from a SH and comparing it with our previous work. Simulation results show that our framework outperforms an existing one by reducing 8.88% of the electricity payments and 17.77% of the peak-to-average ratio (PAR).

The advent of smart grid technologies provides new tools and services to optimally manage the electricity grids. One of the most interesting services that emerged with the development of Information and Communication Technologies (ICTs) is energy demand management. This service permits us to face the issues caused by the ever-increasing energy demand such as grid congestion during peak hours, increasing energy generation costs, and even blackouts. In this paper, we investigate the problem of consumer-side optimization of residential energy demand. Our main aim is to better distribute the energy consumption over a day to avoid or reduce the demand during peak hours. Hence, we propose a fog computing-based model for energy demand scheduling using energy consumption cost as an incentive. In this model, the fog nodes schedule the appliances’ operations in order to reduce the individual and global energy bills whilst respecting consumers’ preferences. The proposed approach performs a multi-agent system-based cooperative scheduling game with minimal interactions between the nodes. Moreover, we present a fog nodes’ assignment scheme to decide which node will handle which appliances’ schedules. The nodes’ assignment strategy aims to optimize the use of fog nodes’ resources whilst reducing the scheduling process latency. The performance evaluation shows that the use of fog computing can achieve interesting results in terms of the reduction of energy consumption cost. For instance, the energy consumption during peak hour decreases by more than 25% from 670 kWh to 500 kWh when the scheduling game is performed. As a consequence, the energy consumption cost decreases by 7% from 806 € to 750 € .

Energy efficiency and occupant thermal comfort are considered as high-interest topics in a smart building. Internet of Things (IoT) technology enables smart building management and operation to improve building energy efficiency and occupant thermal comfort. In this paper, we use IoT-generated data to derive accurate thermal comfort and electricity load forecast model for smart building control. Due to privacy concerns and high accuracy targets, we take advantage of the use of edge computing and federated learning. Federated learning is a decentralized machine learning scheme that permits data volume increase and data diversity in a training model while preserving privacy. Different households contribute to the training process without revealing privacy. A deep neural network is used to model the relationship between the environmental variables, controllable building operations, and thermal comfort. The Long-Short Term Memory is used as well to forecast the energy load using previous observations of the household electrical load and real-time environmental variables. Finally, the derived thermal comfort model is used to control the smart building environment by searching for the optimal cooling set-point, which results in the desired comfort while decreasing the total energy consumed. Results demonstrate the performance of federated learning settings in terms of accuracy and prediction. The control proposition results as well in less energy consumption within a comfort zone.

The emerging Internet of things (IoT) environment permits smart building management in a way to enhance occupants' thermal comfort, that is directly related to the healthcare of the occupants, while maintaining energy efficiency. Predicted Mean Vote (PMV) model is acknowledged as the most utilized model for estimating occupants' thermal comfort in air-conditioned spaces. However, few works deal with assessment of occupants' thermal comfort in real time. In this paper, an accurate thermal comfort model for smart building control in real time is derived from an IoT generated building data. Auto-regressive with exogenous variables (ARX) model is used to determine the relation between thermal comfort and different personal, outdoor and indoor attributes. As thermal comfort model may derive from different attributes, a sensitivity analysis using Morris method is used to figure out the best model input parameters that results in lower error and cost. After that, an ARX model using the chosen parameters is trained. Our experiments show that Fanger's parameters are the best combination of parameters used to estimate the PMV index. They also demonstrate the efficiency of ARX model in terms of prediction accuracy and complexity. Besides, results show that the ARX model improves the mean absolute error and complexity of the thermal prediction when compared to other machine learning models.

Medical institutions are increasingly adopting IoT platforms to share data, communicate rapidly and improve healthcare treatment abilities. However, this trend is also raising the risk of potential data manipulation attacks. In decentralized networks, defense mechanisms against external entities have been widely enabled while protection against insider attackers is still the weakest link of the chain. Most of the platforms are based on the assumption that all the insider nodes are trustworthy. However, these nodes are exploiting of this assumption to lead manipulation attacks and violate data integrity and reliability without being detected. To address this problem, we propose a secure decentralized management system able to detect insider malicious nodes. Our proposal is based on a three layer architecture: storage layer, blockchain based network layer and IoT devices layer. In this paper, we mainly focus on the network layer where we propose to integrate a decentralized trust based authorization module. This latter allows updating dynamically the nodes access rights by observing and evaluating their behavior. To this aim, we combine probabilistic modelling and stochastic modelling to classify and predict the nodes behavior. Conducted performance evaluation and security analysis show that our proposition provides efficient detection of malicious nodes compared to other trust based management approaches.

The proliferation of cloud-based applications in smart systems has made the cloud data center a vital and critical part for ensuring a connected world. Due to their energy-hungry servers and huge power facilities, cloud data centers tend to consume a lot of power. In fact, the data centers use about 1.4% of all the power generated on the planet. On one hand, this has a negative impact on the power grid and may cause blackouts. On the other hand, it has a negative impact on the environment and it is mainly involved in global warming. Thus, one of the most important challenges in cloud data centers is power consumption minimization. In this paper, we propose a microgrid-cloud based architecture and study the grid power dispatching problem to cloud data centers. At first, we model the power quantity demand between the data centers and smart microgrids as a non-cooperative game, due to their non-cooperative power demands behavior. Then, we try to allocate the optimal quantity of power to each data center according to its green power consumption, network bandwidth usage and its network equipment power usage. Second, we formulate the game payoff function as a non-linear optimization problem and solve it using Lagrange multipliers and KarushKuhnTucker (KKT) conditions. Finally, we compare the performance of our approach with two power allocation algorithms and showed that our game is more effective and reduces power load with a rate of 40.5%. Furthermore, our scheme incites data centers to use green energy and significantly reduces dioxide carbon emission.

International audience

Fog computing has becoming an attractive solution to face the low responsiveness existing in cloud-based networks. With the rapid emerging of Internet of Things (IoT), more and more terminal nodes are offloading their tasks to nearby fog nodes, located at the network edge, in order to reduce the processing delay. However, this tasks offloading requires an efficient scheduling mechanism that considers both user preferences and fog-cloud requirements. Existing research works for task scheduling in fog-cloud computing networks have mainly focused on reducing task delay and the overall energy consumption, without considering user preferences regarding the fog nodes' constraints. In this work, we present a ranking based task scheduling method that aggregates both user preferences and fog nodes features using linguistic and fuzzy quantified proposition to rank fog nodes from the most to the least satisfactory one. Moreover, we used two parameters called least satisfactory proportion (lsp) and greatest satisfactory proportion (gsp) in order to distinguish the similarities. Experimental results show that our approach satisfies the user preferences, and provides a compromising solution between the average user satisfaction, execution delay and energy consumption.

Occupants' thermal comfort assessment is becoming a crucial research topic since it aims not only at improving indoor thermal comfort but also to save energy in both commercial and residential buildings. Hence, it makes buildings more sustainable. Predicted Mean Vote (PMV) model is considered as the most recognized in thermal comfort standards and was widely used to estimate thermal sensation of occupants. However, few works are dealing with the assessment and control of occupants' thermal comfort in real time and most of them do not provide mechanisms to improve occupants' comfort in case of detecting indoor thermal discomfort. In this paper, we propose ThermCont a novel machine learning based tool to predict and control occupants' thermal comfort through the PMV model, in real time. Our tool uses multiple linear regression algorithm and is based on findings from a one-year longitudinal case study of occupants' thermal comfort in office building. Moreover, we also propose a new genetic algorithm based scheme to optimize parameters values of thermal comfort, when observing occupants' thermal discomfort, and hence to improve the indoor thermal comfort. The experimental results show the efficiency of ThermCont in terms of prediction accuracy and time complexity when compared to other machine learning algorithms, in addition to its ability to control and improve occupants' thermal comfort in real time.

The demand-side management is considered as an interesting functionality offered by the recent smart grid. This functionality allows the control and scheduling of consumer energy demand, which helps avoiding the problems of offer-demand gap and consumption peaks. The cloud is considered as a powerful tool that ensures scheduling appliances' energy demand in centralized manner. However, the distance between the end users and the cloud might be a problem for latency. In addition, the more and more increasing number of connected objects, generating a huge amount of data that must be transmitted over the communication network, worsens the situation. Fortunately, the novel paradigm of fog computing came to mitigate these issues. In this paper, we propose a cloud-fog computing architecture for the energy demand scheduling. We propose to use this architecture to improve the consumption distribution over the day to reduce the total energy cost for smart buildings. Our work includes two parts: a distributed game-based approach for demand scheduling, and a model for the selection of fog nodes that will perform this distributed approach. The simulation results of the two proposals show that the integration of the fog architecture helps to considerably reduce the energy scheduling delay while determining the optimal demand schedule.

With the recent development of delay-sensitive Internet of Things (IoT) applications, the energy consumption has drawn a significant attention. With the growing popularity of the Fog computing, it is expected to be an effective solution to meet not only low latency, but also decreasing the energy consumed by the system. This paper studies the energy efficiency and quality of service (QoS) issues in IoT-Fog-Cloud systems by proposing a joint optimization of resource allocation and workload dispatching over a fog-cloud system. The joint communication and computing optimization problem is formulated by a Nash Equilibrium problem (NEP), which allows the trade-off between consumed energy by the system and QoS. To break the curse of large-scale systems, we propose a Reinforcement Learning-based algorithm that allows users to learn the optimal policy without having a priori knowledge of the dynamic statistics of the system. Finally, we conduct simulation experiments based and comparison two benchmarks. Evaluations and comparisons demonstrate the efficiency of our proposal.

Fog computing is a distributed architecture which extends cloud computing resources to the end user. Furthermore, fog computing is deployed increasingly to fulfill the requirements of the Internet of Object (IoT) technology. This solution has been proposed to overcome the shortcomings of the cloud. Motivated by recent advances in fog computing, we aim to improve the performances of fog-cloud systems. A novel system design is proposed in which the energy consumption and the delay are considered. Different queue models are applied to our system, where the energy and delay costs are modeled. A coalition game between fog nodes is formulated to minimize the cost, while examining the energy-delay trade-off. To this end, we propose three energy-efficient solutions with the objective of minimizing the total cost of the execution of IoT applications in a fog-cloud system. Our simulation shows the effectiveness of the proposed solutions compared to works from the literature.

One of the most interesting challenges in the modern grids is the consumer demand management and optimization. However, the Information and Communication Technologies (ICTs) can offer promising solutions for this challenge. In this paper, we introduce a distributed multi-level solution for the energy consumption optimization. The proposed solution offers an automated demand control mechanism, using a Wireless Sensor Actuator Network (WSAN), to reduce the energy consumption of each consumer. Moreover, we propose a scheduling scheme that minimizes the total cost of a building energy demand based on a cooperative game model to achieve the collective goal. We introduce also a mechanism that improves the consumer satisfaction when the available energy is insufficient. The performance evaluation shows that the proposed approach reduces the energy demand, the total consumption cost, and the peak to average ratio.

The advent of smart grids offers us the opportunity to better manage the electricity grids. One of the most interesting challenges in the modern grids is the consumer demand management. Indeed, the development in Information and Communication Technologies (ICTs) encourages the development of demand-side management systems. In this paper, we propose a distributed energy demand scheduling approach that uses minimal interactions between consumers to optimize the energy demand. We formulate the consumption scheduling as a constrained optimization problem and use game theory to solve this problem. On one hand, the proposed approach aims to reduce the total energy cost of a building's consumers. This imposes the cooperation between all the consumers to achieve the collective goal. On the other hand, the privacy of each user must be protected, which means that our distributed approach must operate with a minimal information exchange. The performance evaluation shows that the proposed approach reduces the total energy cost, each consumer's individual cost, as well as the peak to average ratio.

Indoor thermal comfort monitoring is becoming a crucial research topic to improve not only the occupants' comfort but also the energy consumption, and thus the building sustainability. Existing works focus on real time thermal comfort assessment of people that are performing some activities and able to answer a questionnaire. However, few works deal with thermal comfort for people with physical disabilities which may have different thermal requirements from those without physical disability, due to the disability itself. Furthermore, the remote and constant monitoring amenities are not established yet, properly. To overcome this, Internet of Things (IoT) can be used, which would introduce more flexibility to monitor residential building of these population from anywhere. As a first step, we aim to provide remote availability of thermal comfort information from A.P.E.I buildings of Troyes city11A.P.E.I stands for Association des Parents d'Enfants Inadapts, is an association of parents of in-adapted children and people with physical disabilities., located in east of France, in order to enable remote monitoring and assessment of thermal comfort in these residential buildings. To do so, a complete IoT architecture is proposed. This architecture is based on sensor devices and permits to collect data, to be transferred and processed in the Cloud infrastructure for an adequate decision-making. Moreover, we optimize sensors deployment in addition to the data collection process while ensuring high data collection accuracy. Numerical results show the efficiency and the reliability of our schemes.

Internet of Things (IoT) is an emerging paradigm that aims at making objects in the world to be connected through Internet. IPv6 over Low-power Wireless Personal Area Networks (6LoWPAN) is considered as one of the common protocol stack suite for IoT applications. The 6LoWPAN network is implemented on the top of IEEE 802.15.4 standard in order to alleviate the challenges of connecting resource constrained objects to the Internet. In such a network, nodes are competing to send their sensed data as high as possible in a selfish way. However, high network data traffic degrades network performance and quality of service aspects, e.g., data sending rate, network latency and reliability and energy consumption. In this paper, we formulate the sending rate adjustments as a non-cooperative game where each node is modeled as a player in the game and demands high data sending rate in a selfish way. The basic idea of our scheme is to adjust the data sending rate according to the preferences of nodes to send high data rate, the quality of data in terms of similarity and nodes priorities in the targeted IoT application. We then prove the existence and uniqueness of Nash equilibrium before computing the optimal sending rate using Lagrange multipliers and KarushKuhnTucker (KKT) conditions. We called our game-based scheme ThingsGame. We validate and evaluate ThingsGame scheme in the IoT operating system Contiki OS using Cooja simulator. Simulation results show that ThingsGame improves significantly network performance in terms of overall throughput, energy consumption, number of lost packets, as compared to the Selfish way scheme.

Spectrum sensing is a cognitive radio technique which distributes the unused spectrum hole of primary users (PU) to secondary users (SU). This technique can remarkably improve the efficiency of spectrum resources. However, spectrum waste of SUs is caused by the inconsistency of information exchanged between sender and receiver, notably the false alarm. In this paper, a false-alarm based energy control scheme is proposed to convert more energy into higher transmission rate of SUs at a high false alarm probability; otherwise, when more sensing cost is spent on SUs, the sensing process turns to be accurate enough and SUs stay quiet to avoid collision if a PU is detected occupied. An activation function is proposed to dynamically control the ON/OFF state of SUs according to the false alarm probability. Simulation results show that by using our energy activation scheme, SUs tend to contribute more, and in return obtain more gains at high false alarm risk. In this way, energy can be utilized more efficiently.

The emergence of smart systems based on Internet of Things (IoT) and new technologies has led to use more cloud computing services. This incentivizes to build more geographically distributed data centers. However, the data centers consume a tremendous amount of electricity which significantly increases load on power grid. There are broad concerns about the impact that this huge consumption may cause to the power grid. Moreover, the data centers are competing to get the maximum of power from the smart grid in a selfish way, which also has a negative impact on both the smart grid and the other data centers. In this paper, we model the power allocation problem between the smart grid and cloud data centers as a non-cooperative game. The basic idea of our approach is to determine the optimal quantity of power that will be assigned to each data center, in order to have a fair power allocation. To do so, we consider the data center priority in terms of number of active servers, state of energy charge and number of running critical applications. Moreover, we prove the existence and uniqueness of Nash equilibrium, and compute the optimal quantity of power using Lagrange multipliers and KarushKuhnTucker (KKT) conditions. Simulation results confirm the effectiveness of the proposed approach, and show that our scheme can reduce the load on the power grid up to 80%.

This paper introduces a study of the fog computing suitability assessment as a solution for the increasing demand of the IoT devices. In particular, we focus on the energy consumption and the Quality of Service (QoS) as two important metrics of the performance of the fog. Therefore, we present a modeling of these two metrics in the fog. Then, we express the problem as constrained optimization and solve it efficiently using Evolutionary Algorithms (EA). Our approach stands out as an energy-efficient solution.

We formulate and study a multi-user multi-armed bandit (MAB) problem that exploits the temporal-spatial reuse of primary user (PU) channels so that secondary users (SUs) who do not interfere with each other can make use of the same PU channel. We first propose a centralized channel allocation policy that has logarithmic regret, but requires a central processor to solve a NP-complete optimization problem at exponentially increasing time intervals. To avoid the high computation complexity at the central processor and the need for SU synchronization, we propose a heuristic distributed policy that incorporates channel access rank learning in a local procedure at each SU at the cost of a higher regret. We compare the performance of our proposed policies with other distributed policies recently proposed for opportunistic spectrum access. Simulations suggest that our proposed policies significantly outperform the benchmark algorithms when spectrum temporal-spatial reuse is allowed.

In this paper, we study the energy efficiency of the future 5G networks. These networks are known to be heterogeneous, containing different types of Base Stations (BSs) each one characterized by its own network coverage and delivered capacity. The aim of our work is to study and improve the energy efficiency of these networks. This is achieved by adjusting network density (i.e., number of active BSs) according to the traffic load while keeping ongoing users covered and provided with their required capacity. We first express the optimization problem and then we prove that it is NP-complete. To solve this problem, we propose a graph based heuristic to find an optimized network configuration in a polynomial time. The proposed algorithm considers both the covered area and the delivered capacity of each type of BS to guarantee users needed capacity. The strength of our method is its adaptation over multiple types of BSs and that by using the energy models of these BSs. The experiments show that the proposed method can achieve high energy efficiency by reaching 70% of energy saving during low traffic load periods within a polynomial time.

By integrating the Information and Communication Technologies (ICTs), the smart grid supports bidirectional information flows between the energy user and the utility grid. Bidirectional flows allow energy users not only to consume energy, but also to generate energy and to share it with the utility grid or with other consumers. Many approaches have been developed in the literature to provide an efficient energy management in the smart grid. However, these approaches have some common drawbacks such as: the lack of integration of storage system and the high frequency of charging and discharging of the storage system. The contribution of this paper is twofold. Firstly, it discusses the effects of battery charging and discharging on the energy cost and the battery life. Secondly, a novel algorithm aiming to help the storage system to fairly meet daily consumers' demands will be presented. Simulation results show that our proposal minimizes consumers' bill, reduces charging and discharging of energy storage system and distributes fairly the stored energy to all consumers.

We formulate and study a multi-user multi-armed bandit problem that exploits the temporal–spatial opportunistic spectrum access (OSA) of primary user (PU) channels, so that secondary users (SUs) who do not interfere with each other can make use of the same PU channel. We first propose a centralized channel allocation policy that has logarithmic regret, but requires a central processor to solve an NP-complete optimization problem at exponentially increasing time intervals. To overcome the high computation complexity at the central processor, we also propose heuristic distributed policies that, however, have linear regrets. Our first distributed policy utilizes a distributed graph coloring and consensus algorithm to determine SUs’ channel access ranks, while our second distributed policy incorporates channel access rank learning in a local procedure at each SU at the cost of a higher regret. We compare the performance of our proposed policies with other distributed policies recently proposed for temporal (but not spatial) OSA. We show that all these policies have linear regrets in our temporal–spatial OSA framework. Simulations suggest that our proposed policies have significantly smaller regrets than the other policies when spectrum temporal–spatial reuse is allowed.

In this paper, we address the problem of energy saving in communication networks; we give more interest to heterogenous networks seeing their major role in the future wireless communication, namely, the future cellular networks (5G) [1]. The specificity of heterogenous networks, is the way that MNs (mobile nodes) switch between different communication technologies, this is known by vertical handovers. The aim of this work is to make the used protocols for vertical handover collaborative, to reach more energy efficiency for the entire network. We based our study on MIH (Media Independent Handover) protocol [2], some modifications are made into this later, in order to create a green collaborative MIH protocol. The proposed enhancements on MIH allow us to get more information about network state; these information are used as statistics to adapt network density according to traffic load. This is achieved by switching off the less efficient PoAs (Points of Attachement) within the network. Our experimentations are performed using NS2.29 [3] with Nist (National Institute of Standards and Technology) add-on which implement MIH process (802.21). The results of the proposed contribution are compared to the conventional MIH protocol and showed a significant improvement in terms of energy saving while maintaining reliability.

In this paper, we study the energy consumption in heterogeneous networks (HetNets). HetNets are known to be one of the main characteristics of the future 5G networks. In this work we aim to minimize the global energy consumed within these networks by adapting the network density to the operating traffic load. We formulate this problem by expressing the energy gain (i.e., the energy saved) which should be maximized for an optimal network configuration, and then we show that finding an optimal solution for this problem is NP-Complete; thus we propose a feasible solution based on estimating the network state using queuing networks. Given that state, we proceed to adapt the network infrastructure by taking into account users required capacity. We study in our experimentations the performance of the proposed method according to different criteria; the results show significant improvements in terms of energy consumption while preserving as much as possible users' requirements in terms of capacity.

Spectrum management task is difficult to achieve in a dynamic and distributed network where cognitive radio users may only make local decision and have to react to the environment changes. In this paper, we describe a novel spectrum management approach for a cognitive radio ad-hoc network. Our proposal is based on multi-agent auctions and provides a decentralized control. First, we implement in a realistic way the auction based protocol for spectrum allocation and handoff decision. We make a comparison between two types of spectrum allocations, which are uni-band and multi-band assignments. Second, we introduce a mechanism that takes into consideration the frequency used by the cognitive radio user during the handoff in order to reduce the number of spectrum handoffs. Then, we integrate a learning module in the cognitive radio users' behaviors to accelerate their spectrum band allocation. Simulation results prove that our algorithm ensures high spectrum utilization rate and confirms the contributions of our protocol in terms of frequency handoff rate and number of users' attempts before spectrum access.

Gradual request for radio frequencies makes the traditional static spectrum allocation unable to cover all users' needs in terms of spectrum resources. Accordingly, it is extremely necessary to introduce dynamic and more efficient spectrum assignment approaches. Cognitive radio was conceived as an innovative technology to enhance spectrum utilization by allowing dynamic and opportunistic access. Previous works in cognitive radio context have more focused on spectrum sensing and sharing concepts than spectrum handoff. However, nodes' mobility still arises as a major issue. Thus, we present in this paper a new approach for handoff management including a spectrum sharing solution and a spectrum handoff decision mechanism. Our proposal relies on multi-agent negotiation to enable cognitive radio terminals switching towards the best available spectrum band, while respecting users' applications requirements and environmental conditions. Simulations results prove that our approach gives four main contributions. First, it performs efficient spectrum sharing allowing spectrum' use to reach up to 90%. Second, it ensures smooth and prompt spectrum handoff. Third, our proposal minimizes the handoff blocking rate which is mandatory to avoid service interruption during users' mobility. Finally, it guarantees a high utility for cognitive radio users.

One of the most promising frameworks to handle mobility management in wireless heterogeneous networks is the Media Independent Handover (MIH) standard. Among the services MIH is providing to achieve efficient mobility management, Information Services allow data exchanging between heterogeneous network nodes. In this paper, we propose an implementation of this Media Independent Information Services (MIIS) on Network Simulator 2 (NS2). With this implementation, new mobility and handoff scenarios can be realized. As an example, we study the case where a mobile node gathers relevant data of nearby networks through only one interface while keeping the others disconnected in order to save battery power. Results show that our implementation is fully functional and demonstrate how useful can be MIIS for mobility management.

Dynamic spectrum sharing is a promising technique to optimize the spectrum utilization. It allows the cognitive radio (CR) nodes to access the available spectrum dynamically, without being restricted to static usage. In this paper, we propose multiagent system (MAS) based solutions to achieve licensed and unlicensed dynamic spectrum sharing. Firstly, we present a cooperative approach where the CR nodes embarked with agents are capable of performing spectrum sharing by exchanging a series of messages with the neighboring licensed devices. While analyzing the performance of this proposal under ad-hoc wireless conditions, we show that it achieves good performance in term of spectrum access, without incurring greater communication cost. Then, we focus on enabling unlicensed spectrum sharing between the CR users. Our proposed solutions can achieve good performance while maintaining fair spectrum distribution.

Recent static spectrum allocations have created an opportunity to develop novel solutions that can efficiently share the available spectrum both in licensed and unlicensed bands. Considering the relative rarity of solutions for unlicensed spectrum access, in this paper, we propose a scheme, where the cognitive radio (CR) devices (equipped with agents) interact with their neighbors to form several coalitions over the unlicensed bands. These types of coalitions can provide a less-conflicted spectrum access as the agents mutually agree for spectrum sharing. Further, we present a continuous time Markov chain (CTMC) with queuing to model the user interactions with the movement of spectrum access process from one state to another and derive the important performance metric as the blocking probability. Numerical results are presented to observe the impact of forming multiple coalitions on the blocking probability.

In this application paper, we investigate the introduction of the autonomic technology into usual centralized management systems. First, we present the global architecture of the proposed hybrid management system. Then, we describe the core entity of this architecture which is the autonomic agent. Finally, we develop a demonstrator and we define a relevant testing scenario in order to assess the feasibility and the effectiveness of our proposals. This work achieves the first steps towards an autonomic management system.

One of the major concerns in wireless sensor networks is improving the network lifetime. In this paper, we propose an event reporting scheme and we show that based on it the average amount of energy required to report an event to the sink decreases when the network sensor density increases. Moreover, we prove that the speed of increasing the network lifetime goes up faster than that of the network density. Based on this result, we derive the minimal number of sensor nodes required to supervise a given area during a given period. Finally, we prove that these nodes must be deployed at the same time and not over different deployment phases.

One of the main concerns in wireless sensor networks is improving the network lifetime. The basic idea underlying current studies is the partial utilization of the sensor network resources. Nevertheless, most of the existing works focus on the connection upholding issue, while neglecting the coverage problem. Therefore, we investigate, in this paper, the relationship between the network lifetime and the coverage problem. We show that controlling the density function, relying on efficient sensor nodes placement, can improve significantly network lifetime. In this regard, we propose an efficient placement algorithm addressing the case where the monitored area density is equal to two. Afterwards, we extend the algorithm dealing with the case where the terrain density is higher than two. Finally, we gauge the efficiency of our proposal through analytical models and simulations.