Wireless Mote Middleware for Flexible Resource Allocation Validation

Since the existing terrestrial fifth generation (5G) network has limited coverage, it is difficult to meet the growing demand for seamless network connection. Meanwhile, current network resource allocation methods mainly research on how to improve system performance only from the perspective of resource utilization, but rarely take users’ specific needs for network content into consideration. This brings severe challenges to efficient network service and flexible resource allocation. Therefore, we construct the content service-oriented resource allocation model for space–air–ground integrated sixth generation networks (SAGIN 6G), and formulate the three-sided matching issue among the space–air–ground integrated network equipment (SAGINE), content sources, and users. In this model, users request to establish connection with SAGIN which forwards users’ request to content service provider (CSP). CSP manages the creation of content data, and finally returns the requested content to users through SAGIN. For the content service-oriented resource allocation in SAGIN, finding the optimal stable three-sided matching with the largest cardinality is an NP-complete problem. Therefore, to efficiently solve the above issue, we design some reasonable restrictions and convert it to a restricted three-sided matching problem with size and cycle preferences. We further develop the content-oriented resource allocation algorithm (COR2A) and the user-oriented resource allocation algorithm (UOR2A) in a distributed manner. Extensive simulations verify our approach outperforms traditional benchmark resource allocation schemes in terms of system throughput, CSP revenue, and user experience.

Visual working memory (VWM) resources have been shown to be flexibly distributed according to item priority. This flexible allocation of resources may depend on attentional control, an executive function known to decline with age. In this study, we sought to determine how age differences in attentional control affect VWM performance when attention is flexibly allocated amongst targets of varying priority. Participants performed a delayed-recall task wherein item priority was varied. Error was modelled using a three-component mixture model to probe different aspects of performance (precision, guess-rate, and non-target errors). The flexible resource model offered a good fit to the data from both age groups, but older adults showed consistently lower precision and higher guess rates. Importantly, when demands on flexible resource allocation were highest, older adults showed more non-target errors, often swapping in the item that had a higher priority at encoding. Taken together, these results suggest that the ability to flexibly allocate attention in VWM is largely maintained with age, but older adults are less precise overall and sometimes swap in salient, but no longer relevant, items possibly due to their lessened ability to inhibit previously attended information.

We study the problem of admission control and resource (subcarriers, power and bit-loading) allocation in a heterogeneous OFDMA wireless network serving both QoS-constrained high-priority users and best-effort users. By clustering subcarriers, efficient algorithms for cluster and power allocation have been proposed. Our strategy maximizes the total network utility of the best-effort users while satisfying the QoS request for maximum number of high-priority users. We approximate best-effort user utility function as a piece-wise linear function and propose a linear programming based cluster allocation algorithm. The feasibility of the resource allocation problem depends on the number of HP users in the network. Since a large number of demanding HP users would render the resource allocation problem infeasible, a joint admission control and resource allocation scheme could be an efficient way of tackling both problems with less overhead. By incorporating admission control, we propose an efficient and optimal joint admission control and resource allocation algorithm.

In this paper, we investigate a fifth generation cellular vehicle-to-everything based platoon system and propose low-complexity dynamic manager selection and resource allocation algorithms in the finite block length regime, to meet the ultra reliable and low latency communication requirements of intra-platoon safety-related data transmission. A closed-form expression for the platoon manager index is derived, based on which a low-complexity dynamic platoon manager selection algorithm is proposed, enabling a significant communication performance enhancement over the conventional head vehicle platoon manager algorithm. This is also the first work to investigate the impact of finite block length on platoon communication. The optimal finite block length coding rate under automatic repeat request retransmission is obtained. A subchannel multiplexing criterion is proposed and closed-form expressions for the optimal transmission powers of the platoon manager and its subchannel multiplexing partner are derived. Based on the theoretical findings, a joint resource allocation and coding rate optimization algorithm is proposed, achieving a near-optimal performance in terms of groupcast latency with a significant complexity reduction over exhaustive search.

Multiple-input multiple-output (MIMO) wireless communication systems in combination with orthogonal frequency division multiple access and space division multiple access are flexible, spectrally efficient and of high capacity. These systems can allocate frequency, time, and space resources adaptively among mobile stations. Optimal resource allocation (RA) for maximizing the sum rate is usually too complex for practical applications, hence suboptimal algorithms are required. The performance of RA algorithms are degraded due to imperfections in channel state information at transmitter (CSIT). Thus, techniques that make the RA algorithm, robust against CSIT errors are favorable. In this paper, two novel suboptimal grouping algorithms are introduced that are shown to achieve better sum rate-complexity and fairness-complexity performances. A novel index is proposed to measure the efficiency of RA algorithms, which takes into account sum rate and complexity simultaneously. Moreover, the effect of erroneous CSIT on sum rate is investigated and it is shown that performance of the proposed RA algorithms with erroneous CSIT are superior to the existing schemes. Finally, fairness performance of the proposed RA algorithms are evaluated, which are shown to be the same if not superior to other existing algorithms.

The fifth generation (5G) mobile communication system is regard as a key enabler in promoting the deployment of Internet of Things (IoT), which is accompanied by the increasing service demands such like high data rate, enormous connection, and low latency. To meet these demands, network slicing has been envisioned as an efficient technology to customize infrastructures and allocate resources for 5G IoT services. However, due to various application backgrounds and ubiquitous social interactions of IoT services, the heterogeneous and social-driven resource requirement of users should be carefully assessed in resource allocation for the sliced 5G wireless network. In this paper, a novel nature-inspired wireless resource allocation scheme with slice characteristic perception is proposed, which comprehensively analyzes the properties of slices and converts them into a network profit model of resource utilization. Specifically, personalized service preferences and evolutionary interest relationships of users are exploited to model the complex and dynamic network environment with cellular automaton, and a biologically inspired allocation strategy of virtual wireless resource is proposed on the requirements of continuously updated user groups. Simulation results show that the proposed scheme achieves favorable resource utilization and low computational complexity, which favors the dynamic IoT slicing architecture and improves the efficiency and flexibility of resource allocation.

In this letter, we investigate the adaptive resource allocation with Lyapunov optimization for time-varying wireless networks. The transmit power minimization is characterized by a stochastic optimization model. A dynamic resource allocation (DRA) algorithm is developed to accommodate the wireless network dynamics, i.e., time-varying wireless channels and as well the queuing dynamics. We investigate the tracking error between the DRA algorithm output and the target optimal resource allocation solution. Based on these results, we further develop an adaptive-compensation resource allocation (ACRA) algorithm, which iterates only once when the network state changes for saving the huge iteration overheads. Finally, we determine a sufficient condition that the ACRA algorithm asymptotically tracks the moving equilibrium point with no tracking errors. Simulation results validate the theoretical analysis of our proposed scheme.

In 5G networks, the deployment of network slices enabled by Software-Defined Networking (SDN) is becoming a critical component for delivering tailored services to meet diverse application needs. However, this introduces challenges in network management, particularly in efficiently allocating resources to ensure that each network slice meets its specific Quality of Service (QoS) and availability requirements. Simultaneously, it must optimize overall network performance and network operator’s profit, which is linked to the Quality of Experience (QoE) of the end-users. Existing works offer either an availability-based solution or a QoE-aware solution to this problem, but not both. This paper addresses the end-to-end network slice resource allocation problem by simultaneously considering QoS and availability requirements in slice placement, while employing a QoE-aware strategy for resource allocation. We propose a framework that optimizes the network operator profit i.e. the highest QoE with the least resource usage, and can be flexibly configured to model realistic scenarios. Arbitrary network slice requirements can be defined using slice-specific QoS/QoE mapping, resource requirements, end-to-end latency and availability. For solving the formulated problem a Mixed Integer Nonlinear Programming (MINLP) formulation and efficient heuristic methods are proposed. Our solution accounts for the non-linear QoS/QoE relationship, utilizes redundantly placed Service Function Chains (SFCs) to increase availability, and supports the sharing of Virtual Network Functions (VNFs) among SFCs to optimize resource usage. Through extensive simulations on realistic network topologies and slice requests, we demonstrate the framework’s effectiveness in offering flexible and efficient network slice placement and resource allocation, utilizing a baseline heuristic from related studies. The results indicate that while the exact method delivers an optimal solution, heuristic approaches are suitable for time-sensitive tasks, such as dynamic slice configuration.

In a shared virtual computing environment, dynamic load changes as well as different quality requirements of applications in their lifetime give rise to dynamic and various capacity demands, which results in lower resource utilization and application quality using the existing static resource allocation. Furthermore, the total required capacities of all the hosted applications in current enterprise data centers, for example, Google, may surpass the capacities of the platform. In this paper, we argue that the existing techniques by turning on or off servers with the help of virtual machine (VM) migration is not enough. Instead, finding an optimized dynamic resource allocation method to solve the problem of on-demand resource provision for VMs is the key to improve the efficiency of data centers. However, the existing dynamic resource allocation methods only focus on either the local optimization within a server or central global optimization, limiting the efficiency of data centers. We propose a two-tiered on-demand resource allocation mechanism consisting of the local and global resource allocation with feedback to provide on-demand capacities to the concurrent applications. We model the on-demand resource allocation using optimization theory. Based on the proposed dynamic resource allocation mechanism and model, we propose a set of on-demand resource allocation algorithms. Our algorithms preferentially ensure performance of critical applications named by the data center manager when resource competition arises according to the time-varying capacity demands and the quality of applications. Using Rainbow, a Xen-based prototype we implemented, we evaluate the VM-based shared platform as well as the two-tiered on-demand resource allocation mechanism and algorithms. The experimental results show that Rainbow without dynamic resource allocation (Rainbow-NDA) provides 26 to 324 percent improvements in the application performance, as well as 26 percent higher average CPU utilization than traditional service computing framework, in which applications use exclusive servers. The two-tiered on-demand resource allocation further improves performance by 9 to 16 percent for those critical applications, 75 percent of the maximum performance improvement, introducing up to 5 percent performance degradations to others, with 1 to 5 percent improvements in the resource utilization in comparison with Rainbow-NDA.

This system consists of STM32F407 single chip microcomputer, STM32F103 single chip microcomputer, openmv vision module and k210 vision module are composed. Taking stm32f407 single chip microcomputer as the control core, combined with STM32F103 single chip microcomputer, openmv and k210, the device is designed to be used for drug transportation in wards and isolation wards with large workload in the hospital. The device uses openmv to identify the red solid line on the transportation ground of the hospital, Through the dynamic priority scheduling algorithm in PD algorithm, the priority of tasks is dynamically allocated according to the resource requirements of tasks, so as to have greater flexibility in resource allocation and scheduling, realize flexible tracking function and dynamic adjustment process, and make the car drive along the red. K210 visual module is used to reflect the appearance difference through the given training photos, which is as same as the identification number as possible, which is convenient to improve the identification rate. After more identification times, the identification number and its position information can be accurately identified, and the identified number and its position information can be transmitted to stm32f407 single chip microcomputer through serial port communication (considering the need to identify four numbers at the same time, two k210 are installed on the front of each vehicle), stm32f407 single chip microcomputer processes the identification information of k210 and tells STM32F103 single chip microcomputer the movement state of the car at this time through serial port communication. Finally, STM32F103 single chip microcomputer controls the car to move accordingly. The data interaction method of the two cars adopts wireless communication. The system has the advantages of short recognition time, digital recognition accuracy of up to 90% and good stability. It can better complete the drug delivery work required by hospital medical staff, and the work efficiency is higher than manual work. At the same time, the machine itself can also cut off the direct contact between medical staff and patients, which can greatly reduce the number of contact between medical staff and patients, Reduce the prevalence of cross infection and medical staff. The device adopts a multi-level priority scheduling algorithm to assign a priority to each process. During each process scheduling, the scheduler always schedules the task with the highest priority to execute, so as to make the car movement more flexible and more active. It is convenient to deal with some emergencies by using interrupts, which is more reasonable and suitable for real hospital medical work.

The severe shortage of liquidity and high inflation in the construction industry of developing countries have made traditional financial models inefficient and the need for alternative solutions has become more apparent. To bridge this gap, this exploratory study qualitatively investigates the nature, causes, and consequences of project-based bartering (PBB) as an emerging financing strategy in Iran’s hyperinflationary construction market. To achieve this objective, a novel thematic analysis method, Multi-Case Reflexive Codebook Thematic Analysis (MC-RCTA), was employed to analyse data derived from 17 in-depth interviews with key stakeholders across four construction projects. The findings show that project-based barter goes beyond a mere payment method and acts as a comprehensive asset management system that improves cash flow, leads to flexibility in resource allocation, reduces risks, and improves the project’s executive performance. From an applied and policy perspective, this study provides a new framework for the development of regulations and optimisation of financing methods for construction projects in unsustainable economies and provides a path for the survival and success of such projects.

Outsourcing data processing and storage to the cloud is a persistent trend in the last years. Cloud computing offers many advantages like flexibility in resource allocation, cost reduction and high availability. However, when sensitive information is handed to a third party, security questions are raised since the cloud provider and his employees are not fully trusted. Standard security mechanisms like transport encryption and regular audits alone cannot solve the issue of insider attacks. Additional cryptographic techniques are required. In this paper, we build upon an existing proxy for secure database outsourcing. We address potential side-channels and weaknesses, which are later analyzed and mitigated. Furthermore, we take a look at trusted execution environments (TEEs) like Intel Software Guard Extensions (SGX) and show how they can be applied to allow for secure execution in the secure database outsourcing case.

It is proposed that resource allocation algorithm considers rate proportion among the multicast groups in multicast orthogonal frequency division multiplexing (OFDM) downlink systems in this paper. However, it is minor significance to enforce the rate proportion strictly in actual mobile communication system. To solve this problem, the resource allocation algorithm is proposed to satisfy the rate proportion approximately in this text, including sub carrier allocation algorithm and power allocation algorithm. The former is achieved by modifying the existing unicast sub carrier assignment algorithm, the latter is Water-filling algorithm, which is capable of maximize the capacity when sub carrier assignment is determined. Simulation results show that compared to conventional algorithms, the proposed resource allocation algorithm has a good performance in capacity as well as rate proportion, which is a realistic method to solve the resource allocation in actual mobile communication system. In addition, we also discussed the capacity has a close relation with the number of multicast group.

With the rapid development of wireless networks, the growing number of mobile applications result in massive computation task to be processed. Satellite communication system, with the merits of wide coverage and flexible multiple-link capability, breaks the limitations of terrestrial network and delivers resilient as well as high-speed connectivity across the globe. In the traditional integrated satellite terrestrial network architecture, time and frequency resource allocation is usually not adapt to customized services. To solve the problem, in this paper we propose a double-edge satellite terrestrial network architecture and a more flexible slot resource allocation scheme based on service priority to improve the resource allocation efficiency and decrease service time delay. Performance of the proposed scheme is evaluated on the OPNET platform. Simulation results show that the proposed time slot allocation scheme in the double-edge satellite terrestrial outperforms the traditional one.

Pseudomonas putida is widely used in industrial applications, including the recombinant proteins production, because of its natural advantageous properties. In this study, the gene encoding FleQ, the primary regulator of flagellar synthesis, was deleted to construct a new non-motile P. putida KT2440-derived strain (ΔfleQ). The non-motile cells showed reduced biofilm formation and enhanced expression of a heterologous gene in nutrient-rich media compared with the wild-type (WT) strain, attributed to the reallocation of cellular resources from flagellar synthesis and cellular motility. Additionally, the ΔfleQ strain exhibited enhanced tolerance to chloramphenicol, indicating higher ribosome production, confirmed by a higher RNA/protein ratio relative to the WT. While the WT strain showed decreased growth and a three-fold increase in reporter gene activity in minimal media, the ΔfleQ strain maintained consistent reporter gene expression and exhibited a relatively higher growth rate. This suggests that the FleQ is involved in modulating proteome allocation based on nutrient quality. The removal of FleQ allows for more flexible resource allocation, creating a chassis strain with nutrient quality-independent gene expression capacity, which could be valuable in industrial applications where consistent output is essential.