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International audience ; Game theory is a branch of mathematics aimed at modeling and studying the interactions between several decision-makers (called players) who can have conflicting or common interests. A "game" is essentially a situation in which the benefit or cost reaped by each player gets from an interactive situation does not only depend on its own decisions but also on that taken by the other players. Therefore, in a game, the actions and objectives of the players are tightly coupled. Until very recently, game theory has been used quite marginally in signal processing, noticeable examples being some applications in robust detection and estimation [1], [2] as well as watermarking [3] (in which the watermarking problem is seen as a game between the data embedder and the attacker). However, the real catalyzer of the application of game theory to signal processing has been the blooming of all issues related to networking in general, and distributed networks, in particular. The primary goal of this survey is to provide an all-inclusive, holistic reference on the use of game theory in signal processing application domains. However, we note that, the extensive form, which is used to investigate simple, dynamic situations, will not be discussed in this survey (e.g., see [24] and references therein for more details). The main reason is that, in general, the extensive form is often mathematically less convenient, especially for typical signal processing problems, as advocated by the current state of the signal processing literature, that shows that the dominant model is the strategic form. Defining the corresponding model and providing important results related to the strategic form is the purpose of Section III, which shows how some solution concepts that are inherent to the strategic form can be related to algorithmic aspects. Section IV discusses the coalition form. Whereas issues related to the strategic form concentrate on the strategic choices of the individual and what strategies it chooses to reach its objective, the coalition form is typically concerned about options available to subsets of players (cooperative groups or coalitions), what cooperative coalitions can form, and how the coalition utility is divided among its members. In practice, for a given signal processing problem, the structure of the problem at hand and the practical constraints associated with it will say if the strategic or coalition form is the most suited one. For example, it may occur that both forms are acceptable in terms of information assumptions while complexity issues will lead to selecting the strategic form instead of the coalition one.

International audience ; Game theory is a branch of mathematics aimed at modeling and studying the interactions between several decision-makers (called players) who can have conflicting or common interests. A "game" is essentially a situation in which the benefit or cost reaped by each player gets from an interactive situation does not only depend on its own decisions but also on that taken by the other players. Therefore, in a game, the actions and objectives of the players are tightly coupled. Until very recently, game theory has been used quite marginally in signal processing, noticeable examples being some applications in robust detection and estimation [1], [2] as well as watermarking [3] (in which the watermarking problem is seen as a game between the data embedder and the attacker). However, the real catalyzer of the application of game theory to signal processing has been the blooming of all issues related to networking in general, and distributed networks, in particular. The primary goal of this survey is to provide an all-inclusive, holistic reference on the use of game theory in signal processing application domains. However, we note that, the extensive form, which is used to investigate simple, dynamic situations, will not be discussed in this survey (e.g., see [24] and references therein for more details). The main reason is that, in general, the extensive form is often mathematically less convenient, especially for typical signal processing problems, as advocated by the current state of the signal processing literature, that shows that the dominant model is the strategic form. Defining the corresponding model and providing important results related to the strategic form is the purpose of Section III, which shows how some solution concepts that are inherent to the strategic form can be related to algorithmic aspects. Section IV discusses the coalition form. Whereas issues related to the strategic form concentrate on the strategic choices of the individual and what strategies it chooses to reach its ...

International audience ; Autonomous agents such as unmanned aerial vehicles (UAVs) have a great potential for deployment in next generation wireless networks. While current literature has been mainly focused on the use of UAVs for connectivity enhancement and routing in military ad hoc networks, this paper proposes a novel usage model for UAVs in wireless communication networks. In the proposed model, a number of UAVs are required to collect data from a number of randomly located tasks and transmit this data wirelessly to a common receiver (such as the central command). Each task represents a queue of packets that require collection and transmission to the central receiver. The problem is modeled as a hedonic coalition formation game between the UAVs and the tasks that interact in order to form disjoint coalitions. Each formed coalition is modeled as a polling system consisting of a number of UAVs, designated as collectors, which act as a single server that moves between the different tasks present in the coalition, collects and transmits the packets to a common receiver. Within each coalition, some UAVs can also take the role as a relay for improving the packet success rate of the transmission. The proposed coalition formation algorithm allows the tasks and the UAVs to take local selfish decisions to join or leave a coalition, based on the achieved benefit, in terms of effective throughput, and the cost in terms of delay. Simulation results show how the proposed algorithm allows the UAVs and tasks to self-organize into independent coalitions, while improving the performance, in terms of average player (UAV or task) payoff, of at least 61:96% relatively to a scheme that allocates nearby tasks equally among UAVs.

International audience ; Autonomous agents such as unmanned aerial vehicles (UAVs) have a great potential for deployment in next generation wireless networks. While current literature has been mainly focused on the use of UAVs for connectivity enhancement and routing in military ad hoc networks, this paper proposes a novel usage model for UAVs in wireless communication networks. In the proposed model, a number of UAVs are required to collect data from a number of randomly located tasks and transmit this data wirelessly to a common receiver (such as the central command). Each task represents a queue of packets that require collection and transmission to the central receiver. The problem is modeled as a hedonic coalition formation game between the UAVs and the tasks that interact in order to form disjoint coalitions. Each formed coalition is modeled as a polling system consisting of a number of UAVs, designated as collectors, which act as a single server that moves between the different tasks present in the coalition, collects and transmits the packets to a common receiver. Within each coalition, some UAVs can also take the role as a relay for improving the packet success rate of the transmission. The proposed coalition formation algorithm allows the tasks and the UAVs to take local selfish decisions to join or leave a coalition, based on the achieved benefit, in terms of effective throughput, and the cost in terms of delay. Simulation results show how the proposed algorithm allows the UAVs and tasks to self-organize into independent coalitions, while improving the performance, in terms of average player (UAV or task) payoff, of at least 61:96% relatively to a scheme that allocates nearby tasks equally among UAVs.

The field of emergency and crisis management continuously strives to enhance collaboration, communication, and coordination among public safety organizations. Successful integration is challenging due to current policies and regulations. Moreover, policymakers must predict future needs. Regardless of the challenges, development and growth of a national public safety communication system is no longer a hopeless cause and is anticipated to mitigate challenges by enhancing security, dependability and fault tolerance, cost effectiveness, interoperability, spectral efficiency, and advanced capabilities. Although this national public safety communication system is in the process of being implemented by various local, state, and federal agencies, such adoption is voluntary and attributes to a disconnect between policies and stakeholders. This study reviews the evolution of public safety communication system and discusses benefits and challenges of a national system, the policies and regulations affecting wireless communication technologies and spectrum sharing, and the influence of evolving technologies.

The challenge of meeting increasing water, energy, and food needs is linked not only to growing demands globally, but also to the growing interdependency between these interconnected resource systems. Pressures on these systems will emerge to become hotspots with different characteristics, and will require a fresh look at the challenges existing both within each of the resource systems and at their respective interfaces. Proposing solutions to address different resource hotspots must be multi-faceted and need to acknowledge the multiple dimensions of the biophysical water, energy, and food systems, and the players connected with them. This commentary first explores the multiple dimensions of water, energy, and food systems as these relate to government, business, and society. It then identifies contemporary critical questions at the interface of these stressed resource systems. A 3-Filter framework is then introduced for vetting the feasibility of proposed resource allocation scenarios and to account for the bio-physical resource interactions and trade-offs, the stakeholder interactions and trade-offs, and to address governance and financing schemes for carrying forward the implementation of those scenarios. ; Published version

Emerging cross-device artificial intelligence (AI) applications require a transition from conventional centralized learning systems toward large-scale distributed AI systems that can collaboratively perform complex learning tasks. In this regard, democratized learning (Dem-AI) lays out a holistic philosophy with underlying principles for building large-scale distributed and democratized machine learning systems. The outlined principles are meant to study a generalization in distributed learning systems that go beyond existing mechanisms such as federated learning (FL). Moreover, such learning systems rely on hierarchical self-organization of well-connected distributed learning agents who have limited and highly personalized data and can evolve and regulate themselves based on the underlying duality of specialized and generalized processes. Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this article. The approach consists of a self-organizing hierarchical structuring mechanism based on agglomerative clustering, hierarchical generalization, and corresponding learning mechanism. Subsequently, hierarchical generalized learning problems in recursive forms are formulated and shown to be approximately solved using the solutions of distributed personalized learning problems and hierarchical update mechanisms. To that end, a distributed learning algorithm, namely DemLearn, is proposed. Extensive experiments on benchmark MNIST, Fashion-MNIST, FE-MNIST, and CIFAR-10 datasets show that the proposed algorithm demonstrates better results in the generalization performance of learning models in agents compared to the conventional FL algorithms. The detailed analysis provides useful observations to further handle both the generalization and specialization performance of the learning models in Dem-AI systems. ; National Research Foundation of Korea (NRF) - Korean Government (MSIT) [2020R1A4A1018607]; Institute of Information & Communications Technology Planning & Evaluation (IITP) - ...