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Spectrum allocation has gained a lot of attention in cognitive wireless networks and research as one of the key problems for enhancing spectrum quality in the communication processes in the contemporary communication-dependent wireless environment. By effectively managing the restricted spectrum resources, adjusting to dynamic network conditions, lowering interference, and increasing energy efficiency, the study on dynamic spectrum allocation in wireless sensor networks seeks to improve packet transmission. In a variety of applications, this improves network performance, dependability, and quality of service. This is the reason we apply the dynamic source allocation to the sensor nodes in the network visualization for the packet transmission performance study in the 5G spectrum region. The primary goal of the study conducted for this article is to improve packet delivery ratios and data packet throughputs from the source to the destinations. Compared to previous research, this work has achieved 100% of its aims. In this context, certain DRL concerns are also addressed. The spectrum is allotted such that, in the event of phishing or malicious nodal assaults on the cluster groups of the wireless sensor nodal points in the WSN, efficient packet transmission will occur, beginning at the source and terminating at the sink. The simulation results demonstrate the efficacy of the approach described in the research paper and its application to data transmission.HighlightsParticularly in 5G, dynamic spectrum allocation improves communication quality in cognitive wireless networks.Effective packet transport in susceptible locations is ensured by defense against malicious assaults.Research achieves 100% of its objectives, greatly increasing throughput and packet delivery.

The effective use of limited resources for controlling spreading processes on networks is of prime significance in diverse contexts, ranging from the identification of “influential spreaders” for maximizing information dissemination and targeted interventions in regulatory networks, to the development of mitigation policies for infectious diseases and financial contagion in economic systems. Solutions for these optimization tasks that are based purely on topological arguments are not fully satisfactory; in realistic settings, the problem is often characterized by heterogeneous interactions and requires interventions in a dynamic fashion over a finite time window via a restricted set of controllable nodes. The optimal distribution of available resources hence results from an interplay between network topology and spreading dynamics. We show how these problems can be addressed as particular instances of a universal analytical framework based on a scalable dynamic message-passing approach and demonstrate the efficacy of the method on a variety of real-world examples.

Platform businesses are typically resource-intensive and must scale up their business quickly in the early stage to compete successfully against fast-emerging rivals. We study a critical question faced by such firms in the novel context of multicategory two-sided platforms: how to optimally make investment decisions across two sides, multiple categories, and different time periods to achieve fast and sustainable growth. We first develop a two-category two-period theoretical model and propose optimal resource allocation strategies that account for heterogeneous within-category direct and indirect network effects and cross-category interdependence. We find that the proposed strategy shares the spirit of the allocation rules for multiproduct nonplatform firms and single-product platform firms, yet it does not amount to a simple combination of the existing rules. Interestingly, the business model that platforms adopt crucially determines the optimal strategy. Platforms that charge by user should adopt a “reinforcing” rule for both within- and cross-category allocations by allocating more resources toward the stronger growth driver. Platforms that charge by transaction should also adopt the reinforcing rule for within-category allocation, but follow a “compensatory” rule for cross-category and intertemporal allocations by allocating more resources toward the weaker growth driver. We use data from the daily deals industry to empirically identify the network effects, propose alternative allocation strategies stemming from our theoretical findings, and use simulations to show the benefits of these strategies. For instance, we show that reallocating 10% of the average observed investment from Fitness to Beauty can increase profits by up to 15.5% for some cities. This paper was accepted by Matthew Shum, marketing.

Unmanned aerial vehicles (UAVs) used as aerial base stations (ABS) can provide economical, on-demand wireless access. This research investigates dynamic resource allocation in multi-UAV-enabled communication systems with the aim of maximizing long-term rewards. More specifically, without exchanging information with other UAVs, every UAV chooses its communicating users, power levels, and sub-channels to establish communication with a ground user. In the proposed work, the dynamic scheme-based resource allocation is investigated of communication networks made possible by many UAVs to achieve the highest possible performance level over time. Specifically, each UAV selects its connected users, battery power, and communication channel independently, without exchanging information across multiple UAVs. This allows each UAV to connect with ground users. To model the unpredictability of the environment, we present the problem of long-term allocation of system resources as a stochastic game to maximize the anticipated reward. Each UAV in this game plays the role of a learnable agent, and the system solution for resource allocation matches the actions made by the UAV. Afterward, we built a framework called reward-based multi-agent learning (RMAL), in which each agent uses learning to identify its best strategies based on local observations. RMAL is an acronym for ″reward-based multi-agent learning″. We specifically offer an agent-independent strategy where each agent decides algorithms separately but cooperates on a common Q-learning-based framework. The performance of the suggested RMAL-based resource allocation method may be enhanced by employing the right development and exploration parameters, according to the simulation findings. Secondly, the proposed RMAL algorithm provides acceptable performance over full information exchange between UAVs. Doing so achieves a satisfactory compromise between the increase in performance and the additional burden of information transmission.

We consider a class of online resource-allocation problems in which there are n types of resources with limited initial inventory and n demand classes. The resources are flexible in that each type of resource can serve more than one demand class. In this paper, we focus on a special class of structures with limited flexibility, the long-chain design, which was proposed by Jordan and Graves [Jordan WC, Graves SC (1995) Principles on the benefits of manufacturing process flexibility. Management Sci. 41(4):577–594.] and has been an important concept in the design of sparse flexible processes. We study the long-chain design in an online stochastic environment in which the requests are drawn repeatedly and independently from a nonstationary probability distribution over the different demand classes. Also, the decision on how to address each request must be made immediately upon its arrival. We show the effectiveness of the long-chain design in mitigating supply–demand mismatch under a simple myopic online allocation policy. In particular, we provide an upper bound on the expected total number of lost sales that is irrespective of how large the market size is. This paper was accepted by Yinyu Ye, optimization.

One networking technology driving the creation of 5G networks is optical burst switching (OBS). High bandwidth low latency flexible resource allocation and cost effectiveness are just a few of its benefits. However, OBS does have some disadvantages. It requires precise network component synchronization and is susceptible to packet loss due to burst contention. This research approach uses a hybrid intelligent method that combines optimization and predictive functions to present a burst assembly strategy for OBS that is customized for 5G optical networks. This strategy seeks to reduce latency and improve overall network performance by dynamically generating bursts based on the networks current state. The best burst assembly parameters are found using an improved optimization technique and adaptive decision-making is supported by a forecasting learning model that predicts future network conditions. In comparison to previous solutions, the computational results show that this novel mechanism has significantly improved throughput and decreased average latency which ultimately lowers the likelihood of blocking in 5G network scenarios.

In recent years, companies have used the cloud computing paradigm to run various computing and storage workloads. The cloud offers faster and more profitable services. However, the issue of resource allocation is a significant challenge for cloud providers. The excessive consumption of resources has raised the need for better management of them. In addition, the resources required may exceed those available in the cloud as demand and capacity vary over time. Therefore, dynamic resource allocation techniques allow using the available capacity more efficiently. This paper provides a practical Dynamic Resource Allocation (DRA) study in a cloud computing environment. It illustrates the dynamic aspect of the cloud computing environment and how addressed in the literature. Also, it gives the taxonomies of approaches, scheduling types, and optimization metrics. This study helps scientists understand the dynamic aspect of resource allocation in the cloud, thereby improving its performance.

Different devices and applications in wireless networks share spectrum resources reasonably. However, there are still issues such as channel overlap and adjacent interference in spectrum allocation and utilization, making the process of data dynamic resource allocation more complex. Therefore, a new data dynamic resource allocation technique for wireless networks is proposed. An elastic optical wireless network is formed by combining elastic optical network and wireless network. A global constrained resource allocation optimization model is designed based on the threshold of the maximum frequency gap number occupied on the fiber core at the end of allocation. Then, by using the global optimization genetic algorithm, the optimal dynamic resource allocation results of the elastic optical wireless network are obtained. Experimental results show that the spectrum utilization obtained by this technology is higher when the number of cores is 12, and the spectrum utilization is significantly improved by employing the proposed method.

Dynamic resource allocation is the key objective of the paper motivated due to a large number of user’s service request and increasing network infrastructure complexity. Load balancing and Service Broker Policy are taken as two main key areas for the dynamic provision of resources to the cloud user in order to meet the QoS requirement. While provisioning the resources, the conventional approaches degrade due to QoS performance limits such as time delay, energy, etc. To overcome those problems, we proposed a new approach to provide dynamic provisioning of resources based on load balancing and service brokering. Initially, the Multi-agent Deep Reinforcement Learning-Dynamic Resource Allocation (MADRL-DRA) is used in the Local User Agent (LUA) to predict the environmental activities of user task and allocate the task to the Virtual Machine (VM) based on priority. Next, a Load balancing (LB) is performed in the VM, which increases the throughput and reduces the response time in the resource allocation task. Secondly, the Dynamic Optimal Load-Aware Service Broker (DOLASB) is used in the Global User Agent (GUA) for scheduling the task and provide the services to the users based on the available cloud brokers (CBs). In the global agent, cloud brokers are the mediators between users and providers. The optimization problem in Global Agent (GA) is formulated by the programming of mixed integers, and Bender decomposition algorithm. The result of our proposed method is better as compared with the conventional techniques in terms of Execution Time, Waiting Time, Energy Efficiency, Throughput, Resource Usage, and Makespan.

Purpose Volunteers play an increasingly important role in emergency response logistics. However, to make most use of their capabilities, they need to be dispatched to the emergency site in an effective manner and coordinated on-site. The purpose of this study is to present a requirements specification and initial design proposal for ICT-enabled dispatch of volunteers as first responders as part of emergency response digitalized co-production initiatives. Design/methodology/approach The study uses a case study approach inspired by action research and the theoretical lens of digitalized co-production. It includes a variety of methods for data collection, including interviews with volunteers, document analysis and participation in workshops. Findings The major themes identified are geofencing, dispatch coordination, dynamic resource allocation and communication and collaboration. First priority requirements include geofencing alert and positioning, a joint application programming interface, receipt of alert, receipt if arrival at incident site, withdrawal of resources, chat functionality and the ability to alert in descending order within the geofenced areas to avoid alarm fatigue. As to coordination and dynamic resource allocation, e.g. built-in alert restrictions, ability to pre-select profiles and to dispatch based on competence/training, capacity and equipment would enable a more optimized response. Originality/value While previous research on digital volunteerism mainly embraces spontaneous volunteers and social media, this study addresses long-time collaboration with professional response organizations – digitalized co-production – with a focus on the dispatch, coordination and task allocation of volunteers that are central to their integration with emergency response logistics.