This work aims to present a joint resource allocation method for a fog-assisted network wherein IoT wireless devices simultaneously offload their tasks to a serving fog node. The main contribution is to formulate joint minimization of service latency and energy consumption objectives subject to both radio and computing constraints. Moreover, unlike previous works that set a fixed value to the circuit power dissipated to operate a wireless device, practical models are considered. To derive the Pareto boundary between two conflicting objectives we consider, Tchebyshev theorem is used for each wireless device. The interactions among IoT devices are represented through a cooperative Nash bargaining framework, with the unique Nash equilibrium (NE) being computed via a block coordinate descent method. Numerical results obtained using realistic models are presented to corroborate the effectiveness of the proposed algorithm.

This paper proposes a novel tensor decomposition method, cooperative parallel factor (Co-PARAFAC), that is devised to achieve higher accuracy with lower computational complexity and memory requirements than the conventional PARAFAC. The rationale relies on dividing a given tensor, even with a large size, into smaller disjoint sub-tensors, which are independently and parallelly decomposed using the conventional PARAFAC. The intermediate results are then properly merged to obtain the decomposition of the original tensor. As case study, we apply Co-PARAFAC to estimate the uplink channels of RIS-assisted wireless communications. Simulation results corroborate the efficiency of the Co-PARAFAC in achieving significantly lower computational complexity and higher channel estimation accuracy than the conventional PARAFAC. Broadly, the proposed algorithm is advantageous in various fields requiring efficient and high accurate tensor decomposition.

Fog computing allows for energy-efficient and low-latency offloading of computationally intensive tasks from wireless devices to nearby servers. The integration of this technology in drone communications enables the managing of challenging tasks such as the ones found in remote areas with complex civil protection environments, such as disaster areas and emergency zones. In this paper, we propose a joint resource allocation scheme that optimizes both radio and computational resources for fog-assisted drone communication networks. Each drone decides whether to execute its task locally on its edge node or offload it to a fog node deployed on the base station (BS). Our scalable solution effectively minimizes service latency and energy consumption jointly, while taking into account physical- and application-layer constraints. Specifically, we allocate the CPU frequency capacity of both the local edge node and the remote fog node, as well as link bandwidth. Wireless channels to access the BS are limited, so only the most beneficial drones offload their tasks, while others use their local edge nodes. We formulate the power dissipation of various electronic circuits in the network using practical models. To develop the bi-objective minimization for each drone, we apply the Tchebysheff theorem, which derives the Pareto boundary between the two objectives (service latency and energy consumption). The competition among drones is modeled using the non-cooperative game framework, and the existence and uniqueness of the Nash equilibrium (NE) are proven. NE is computed using an algorithm based on subgradient projection. Numerical results concerning both theoretical aspects and a practical case study are presented to corroborate the efficiency of the proposed solution.

Reconfigurable intelligent surfaces have emerged as a promising technology for future wireless networks. Given that a large number of reflecting elements is typically used and that the surface has no signal processing capabilities, a major challenge is to cope with the overhead that is required to estimate the channel state information and to report the optimized phase shifts to the surface. This issue has not been addressed by previous works, which do not explicitly consider the overhead during the resource allocation phase. This work aims at filling this gap, by developing an overhead-aware resource allocation framework for wireless networks where reconfigurable intelligent surfaces are used to improve the communication performance. An overhead model is proposed and incorporated in the expressions of the system rate and energy efficiency, which are then optimized with respect to the phase shifts of the reconfigurable intelligent surface, the transmit and receive filters, the power and bandwidth used for the communication and feedback phases. The bi-objective maximization of the rate and energy efficiency is investigated, too. The proposed framework characterizes the trade-off between optimized radio resource allocation policies and the related overhead in networks with reconfigurable intelligent surfaces.

This work aims at developing an energy-efficient power control algorithm for a swarm of drones that simultaneously transmit data to the serving base station while moving around a given coverage area. A stochastic mobility model based on random walk is developed, which guarantees boundedness and continuity of the movement, and used in a non-cooperative stochastic differential game, with utility functions defined by an Hamilton-Jacobi-Bellman (HJB) equation for each drone. We analyze the existence and uniqueness of the equilibrium point, and develop a distributed algorithm whose convergence to such equilibrium point is guaranteed. Numerical results are used to validate the performance of the proposed solution.

Background: The evaluation of Vascular Age, according to the definition given using the cardiovascular risk score tables, is a method of estimating individual cardiovascular risk, which may represent a new therapeutic target for physicians. However, the association of Vascular Age with surrogate markers of atherosclerosis and vascular aging, able to identify vascular alterations at the sub-clinical, asymptomatic stages, has not been determined yet.

The development of strategies, technologies and tools to enhance user collaboration around disasters has become an emergent field, that has triggered the integration of different technologies in order to efficiently drive decision making in emergency management (EM). In this paper, we critically review the existing strategies and technologies for EM and outline a new approach based on the functional integration of the mining of social networks with the capabilities of sensor networks to collect data and the availability of a dedicated telecommunication system to exchange information securely and reliably. The focus is placed on different strategies for decision support and for alert and information dissemination. Effectiveness of the proposed approach is validated by discussing performances of its implementation in a system for the prevention and the management of natural disasters developed with a regional research project.

The 2010 earthquake in Haiti is often referred to as the turning point that changed the way social media can be used during disasters. The development of strategies, technologies and tools to enhance user collaboration around disasters has become an emergent field, and their integration with appropriate sensor networks presents itself as an effective solution to drive decision making in emergency management. In this paper, we present a review of existing disaster management systems and their underlying strategies and technologies, and identify the limitations of the tools in which they are implemented. We then propose an architecture for disaster management that integrates the mining of social networks and the use of sensor networks as two complementary technologies to overcome the limitations of the current emergency management tools.

In traditional power distribution models, consumers acquire power from the central distribution unit, while "micro-grids" in a smart power grid can also trade power between themselves. In this paper, we investigate the problem of power trading coordination among such micro-grids. Each micro-grid has a surplus or a deficit quantity of power to transfer or to acquire, respectively. A coalitional game theory based algorithm is devised to form a set of coalitions. The coordination among micro-grids determines the amount of power to transfer over each transmission line in order to serve all micro-grids in demand by the supplier micro-grids and the central distribution unit with the purpose of minimizing the amount of dissipated power during generation and transfer. We propose two dynamic learning processes: one to form a coalition structure and one to provide the formed coalitions with the highest power saving. Numerical results show that dissipated power in the proposed cooperative smart grid is only 10% of that in traditional power distribution networks.

In this paper, we investigate the power control problem in a cooperative network with multiple wireless transmitters, multiple amplify-and-forward relays, and one destination. The relay communication can be either full duplex or half-duplex, and all source nodes interfere with each other at every intermediate relay node, and all active nodes (transmitters and relay nodes) interfere with each other at the base station. A game-theory-based power control algorithm is devised to allocate the powers among all active nodes. The source nodes aim at maximizing their energy efficiency (in bits per Joule per Hertz), whereas the relays aim at maximizing the network sum rate. We show that the proposed game admits multiple pure/mixed-strategy Nash equilibrium points. A Q-learning-based algorithm is then formulated to let the active players converge to the best Nash equilibrium point that combines good performance in terms of both energy efficiency and overall data rate. Numerical results show that the full-duplex scheme outperforms half-duplex configuration, Nash bargaining solution, the max-min fairness, and the max-rate optimization schemes in terms of energy efficiency, and outperforms the half-duplex mode, Nash bargaining system, and the max-min fairness scheme in terms of network sum rate.

This paper studies a resource allocation problem for a cooperative network with multiple wireless transmitters, multiple full-duplex amplify-and-forward relays, and one destination. A game-theoretic model is used to devise a power control algorithm among all active nodes, wherein the sources aim at maximizing their energy efficiency, and the relays aim at maximizing the network sum-rate. To this end, we formulate a low-complexity Q-learning-based algorithm to let the active players converge to the best mixed-strategy Nash equilibrium point, that combines good performance in terms of energy efficiency and overall data rate. Numerical results show that the proposed scheme outperforms Nash bargaining, max-min fairness, and max-rate optimization schemes.

In this contribution, we present a survey on the radio resource allocation techniques in orthogonal frequency division multiplexing (OFDM) and orthogonal frequency division multiple access (OFDMA) systems. This problem goes back to 1960s and that is related to properly and efficiently allocate the radio resources, namely subcarriers and power. We start by overviewing the main open issues in OFDM. Then, we describe the problem formulation in OFDMA, and we review the existing solutions to allocate the radio resources. The goal is to discuss the fundamental concepts and relevant features of different radio resource management criteria, including water-filling, max–min fairness, proportional fairness, cross-layer optimization, utility maximization, and game theory, also including a toy example with two terminals to compare the performance of the different schemes. We conclude the survey with a review of the state-of-the-art in resource allocation for next-generation wireless networks, including multicellular systems, cognitive radio, and relay-assisted communications, and we summarize advantages and common problems of the existing solutions available in the literature. The distinguishing feature of this contribution is a tutorial-style introduction to the fundamental problems in this area of research, intended for beginners on this topic.

This paper focuses on the capacity of peer-to-peer relay communications wherein the transmitter are assisted by an arbitrary number of parallel relays, i.e. there is no link and cooperation between the relays themselves. We detail the mathematical model of different relaying strategies including cutset and amplify and forward strategies. The cutset upper bound capacity is presented as a reference to compare another realistic strategy. We present its outer region capacity which is lower than that in the existing literature. We show that a multiple parallel relayed network achieves its maximum capacity by virtue of only one relay or by virtue of all relays together. Adding a relay may even decrease the overall capacity or may do not change it. We exemplify various outer region capacities of the addressed strategies with two different case studies. The results exhibit that in low signal-to-noise ratio (SNR) environments the cutset outperforms the amplify and forward strategy and this is contrary in high SNR environments.

Game theory is the study of decision making in an interactive environment. Coalitional games fulfill the promise of group efficient solutions to problems involving strategic actions. Formulation of optimal player behavior is a fundamental element in this theory. This paper comprises a self-instructive didactic means to study basics of coalitional games indicating how coalitional game theory tools can provide a framework to tackle different problems in communications and networking. We show that coalitional game approaches achieve an improved performance compare to non-cooperative game theoretical solutions.

This work investigates a fair adaptive resource management criterion (in terms of transmit powers and subchannel assignment) for the uplink of an orthogonal frequency division multiple access (OFDMA) network, populated by mobile users with constraints in terms of target data rates. The inherent optimization problem is tackled with the analytical tools of coalitional game theory, and a practical algorithm based on Markov modeling is introduced. The proposed scheme allows the mobile devices to fulfill their rate demands exactly with a minimum utilization of network resources. Simulation results show that the average number of operations of the proposed iterative algorithm are much lower than K · N, where N and K are the number of allocated subcarriers and of mobile terminals.

This work investigates a fair adaptive resource management criterion (in terms of transmit powers and subchannel assignment) for the uplink of an orthogonal frequency-division multiple access network, populated by mobile users with constraints in terms of target data rates. The inherent optimization problem is tackled with the analytical tools of coalitional game theory, and a practical algorithm based on Markov modeling is introduced. The proposed scheme allows the mobile devices to fulfill their rate demands exactly with a minimum utilization of network resources. Simulation results show that the average number of operations of the proposed iterative algorithm are much lower than K · N , where N and K are the number of allocated subcarriers and of mobile terminals.

This work investigates the problem of resource allocation (in terms of transmit powers and subchannel assignment) in the uplink channel of an orthogonal frequency division multiple access (OFDMA) network, populated by mobile users with constraints in terms of target transmit data rates. The optimization problem is tackled with the analytical tools of coalitional game theory, and a simple and practical algorithm based on Markov modeling is introduced. The proposed algorithm allows the mobile devices to fulfill their data rate demands with minimum utilization of the network resources. Simulation results are provided to validate the theoretical analysis for practical OFDMA network parameters.