FEDRESOURCE: Federated Learning Based Resource Allocation in Modern Wireless Networks

Deep reinforcement learning can effectively deal with resource allocation (RA) in wireless networks. However, more complex networks can have slower learning speeds, and a lack of network adaptability requires new policies to be learned for newly introduced systems. To address these issues, a novel federated learning-based resource allocation (FEDRESOURCE) has been proposed in this paper which efficiently performs RA in wireless networks. The proposed FEDRESOURCE technique uses federated learning (FL) which is a ML technique that shares the DRL-based RA model between distributed systems and a cloud server to describe a policy. The regularized local loss that occurs in the network will be reduced by using a butterfly optimization technique, which increases the convergence of the FL algorithm. The suggested FL framework speeds up policy learning and allows for adoption by employing deep learning and the optimization technique. Experiments were conducted using a Python-based simulator and detailed numerical results for the wireless RA sub-problems. The theoretical results of the novel FEDRESOURCE algorithm have been validated in terms of transmission power, convergence of algorithm, throughput, and cost. The proposed FEDRESOURCE technique achieves maximum transmit power up to 27%, 55%, and 68% energy efficiency compared to Scheduling policy, Asynchronous FL framework, and Heterogeneous computation schemes respectively. The proposed FEDRESOURCE technique can increase discrimination accuracy by 1.7%, 1.2%, and 0.78% compared to the scheduling policy framework, Asynchronous FL framework, and Heterogeneous computation schemes respectively

A survey of task allocation techniques in MAS

Multi-agent systems and especially unmanned vehicles, are a crucial part of the solution to a lot of real world problems, making essential the improvement of task allocation techniques. In this review, we present the main techniques used for task allocation algorithms, categorising them based on the techniques used, focusing mainly on recent works. We also analyse these methods, focusing mainly on their complexity, optimality and scalability. We also refer to common communication schemes used in task allocation methods, as well as to the role of uncertainty in task allocation. Finally, we compare them based on the above criteria, trying to find gaps in the literature and to propose the most promising ones

Max-SAT-based synthesis of optimal and Nash equilibrium strategies for multi-agent systems

We present techniques for verifying strategic abilities of multi-agent systems via SAT-based and Max-SAT-based bounded model checking. In our approach we focus on systems of agents that pursue goals with regard to the allocation of shared resources. One of the problems to be solved is to determine whether a coalition of agents has a joint strategy that guarantees the achievement of all resource goals, irrespective of how the opposing agents in the system act. Our approach does not only decide whether such a winning strategy exists, but also synthesises the strategy. Winning strategies are particularly useful in the presence of an opposition because they guarantee that each agent of the coalition will achieve its individual goal, no matter how the opposition behaves. However, for the grand coalition consisting of all agents in the system, following a winning strategy may involve an inefficient use of resources. A winning strategy will only ensure that each agent will reach its goal at some time. But in practical resource allocation problems it may be of additional importance that once-off resource goals will be achieved as early as possible or that repetitive goals will be achieved as frequent as possible. We present an extended technique that synthesises strategies that are collectively optimal with regard to such quantitative performance criteria. A collectively optimal strategy allows to optimise the overall system performance but it may favour certain agents over others. In competitive scenarios a Nash equilibrium strategy may be a more adequate solution. It guarantees that no agent can improve its individual performance by unilaterally deviating from the strategy. We developed an algorithm that initially generates a collectively optimal strategy and then iteratively alternates this strategy until the strategy becomes a Nash equilibrium or a cycle of non-equilibrium strategies is detected. Our approach is based on a propositional logic encoding of strategy synthesis problems. We reduce the synthesis of winning strategies to the Boolean satisfiability problem and the synthesis of optimal and Nash equilibrium strategies to the maximum satisfiability problem. Hence, efficient SAT- and Max-SAT solvers can be employed to solve the encoded strategy synthesis problemshttp://www.elsevier.com/locate/scicoam2024Computer ScienceSDG-09: Industry, innovation and infrastructur

Using foresight futures and systems thinking to evaluate digitally enhanced advanced service concepts for a rolling stock company (ROSCO)

Purpose: This paper reports on a study in conjunction with a UK-based rolling stock leasing company (ROSCO). The aim was to generate and evaluate future operational concepts for digitally enhanced advanced services from the point of view of a ROSCO – one of many stakeholders (or actors) within a future wider mobility ecosystem.Design/Methodology/Approach: The research design followed the Generic Foresight Process Framework (Voros 2003). Desk-based research and horizon scanning analysis revealed technologies, mobility and transport trends, and other predictions towards 2060. A workshop was developed and participants were presented with a series of future scenarios and design fictions for end-to-end intermodal mobility and passenger carbon quotas. A future Mobility Servitization Systems Architecture was developed.Findings: Five future megatrends were identified; Decarbonisation, changing traveller needs, digitisation, mobility ecosystems and new business models in digital ecosystems. The ‘what-if’ activities revealed insights into alternate futures; revealing system of systems (SoS) actors, the role of a ROSCO, integrations, assumptions and operational constraints.Originality/Value: This research contributes to engineering and design methods for digitally enhanced advanced services, particularly for corporate strategic foresight in a dominant design industry. The Mobility Servitization Systems Architecture was seen to be a powerful model for ecosystem understanding.</div