Block Assignment Research Articles

Proactive blockchain deployment mechanism in resource-constrained rate-splitting multiple access IoT networks

In this paper, we propose an innovative blockchain deployment mechanism tailored for resource-constrained rate-splitting multiple access (RSMA) Internet of Things (IoT) networks. To address the storage limitations and security concerns inherent in IoT environments, our approach includes several advanced techniques. First, we utilize a Block Allocation Strategy Contract (BASC) to manage storage efficiently. Second, we employ a deep reinforcement learning (DRL) model to dynamically perform block assignment, ensuring optimal storage utilization. To accommodate the resource constraints of IoT devices, we adopt the Smart Byzantine Fault Tolerance (SBFT) consensus mechanism, which offers low latency and energy efficiency. Our framework demonstrates superior performance in storage optimization and reduced running time compared to existing methods, making it well-suited for large-scale IoT networks. Through extensive simulations, we validate the effectiveness of our proposed solution in enhancing security and operational efficiency in RSMA IoT networks.

Proportional-fair uplink resource allocation with statistical QoS provisioning for RAN slicing

This paper investigates the uplink resource allocation issue for radio access network (RAN) slicing with a focus on three primary fifth generation (5G) use cases, namely ultra-reliable low-latency communications (uRLLC), enhanced mobile broadband (eMBB) and massive machine-type communications (mMTC). Most of the existing resource allocation schemes for RAN slicing were designed to address the use cases in downlink communications. However, these schemes are not applicable to the uplink RAN slicing, even though the primary use cases also entail stringent requirements in the uplink. To this end, we aim to develop a new, fairness-aware uplink resource allocation scheme for RAN slicing, catering to the three 5G use cases. Firstly, we formulate a new uplink resource allocation optimization problem that maximizes a sum logarithmic utility for ensuring proportional-fair effective capacity distribution and resource allocation among RAN slices, subject to the distinct quality of service (QoS) requirements of the uRLLC, eMBB and mMTC slices. Then, an efficient, hierarchical resource allocation framework is then proposed to solve the problem deterministically, whereby resource block assignment is first performed at the base station via a demand-oriented greedy algorithm, followed by power allocation using a bisection method at individual user equipment. Results show that the proposed scheme outperforms the baseline schemes with fairness and QoS provisioning performance gains of up to 44.3% and 19.17% respectively.

Tangent Pelican search optimization for block assignment in blockchain based IoT

Tangent Pelican search optimization for block assignment in blockchain based IoT

A matheuristic algorithm for block assignment problem in long-term production planning in the shipbuilding industry

This study introduced the block assignment problem in the long-term production planning of a very large real-world shipbuilder and formulated it as a mixed integer linear programming model. For the problem, blocks are assigned to assembly shops for processing; some blocks must be assigned as a group to a region, a collection of assembly shops, because of their mutual dependency. Each assembly shop has its target work level. The objective is to minimize the sum of workload deviations of the assembly shops during a specific planning period. Because the mathematical model could not be solved within a reasonable time, a two-stage matheuristic algorithm was proposed. The algorithm determines the region of blocks in the first stage and the assembly shop for each block in the second stage. Computational experiments were performed using a real-world instance provided by the shipbuilder, including instances of different sizes generated from real-world data. The experiment results demonstrated that the algorithm yielded remarkable reductions of 10.5% and 35.3% in assignment costs and factory workload violations compared with manual planning, respectively. The proposed decomposition-based algorithm also demonstrated superior effectiveness in solving the problem than the full mathematical model. The proposed algorithm is currently embedded in the planning system of the shipbuilder.

Gender bias in funding evaluation: A randomized experiment

Abstract Gender differences in research funding exist, but bias evidence is elusive and findings are contradictory. Bias has multiple dimensions, but in evaluation processes, bias would be the outcome of the reviewers’ assessment. Evidence in observational approaches is often based either on outcome distributions or on modeling bias as the residual. Causal claims are usually mixed with simple statistical associations. In this paper we use an experimental design to measure the effects of a cause: the effect of the gender of the principal investigator (PI) on the score of a research funding application (treatment). We embedded a hypothetical research application description in a field experiment. The subjects were the reviewers selected by a funding agency, and the experiment was implemented simultaneously with the funding call’s peer review assessment. We manipulated the application item that described the gender of the PI, with two designations: female PI and male PI. Treatment was randomly allocated with block assignment, and the response rate was 100% of the population, avoiding problems of biased estimates in pooled data. Contrary to some research, we find no evidence that male or female PIs received significantly different scores, nor any evidence of same-gender preferences of reviewers regarding the applicants’ gender.

Joint RMSSA scheme and link load balancing for static SD-SCNs from model building to algorithm design

The spatial channel network (SCN) is a new optical network architecture that can support traffic requests from 100 Gb/s to 10 Tb/s and beyond. Software-defined networking (SDN) has superb management and control capabilities, making it easier to obtain network topology and link load information with it than with traditional networks. Combining the advantages of both, we propose a multi-plane SDN-based spatial channel network (SD-SCN) architecture and focus on solving the routing, modulation format, spatial lane, and spectrum block assignment (RMSSA) problem in static SD-SCNs. In this paper, we build a multi-plane network model of an SD-SCN, design the functional modules of each plane, and give the operation principle of the model. Furthermore, we formulate the RMSSA problem in the static SD-SCN model and propose a heuristic spatial lane and spectrum block minimization and load balancing (MLB2) algorithm to solve the problem. Also, we design an improved ant colony optimization (IACO) algorithm (the heuristic function and iteration criterion are optimized) to implement the routing path assignment function of the MLB2 algorithm. The simulation results show that the MLB2 algorithm can carry traffic requests with minimum network resources, and when the total traffic rate is high, the link load can be balanced by invoking the IACO algorithm to increase the network throughput. The MLB2 algorithm performs better in small-scale networks than in large-scale networks.

Simulating a sterilization processing department to evaluate block schedules and tray configurations

AbstractDiscrete event simulation is a well‐established tool for examining the effect of different operating room (OR) block schedules on various performance metrics within the OR suite and adjacent units. However, one unit that has rarely been studied is the sterilization processing department (SPD), which cleans and assembles reusable OR instruments. As part of a larger research study, we developed a series of OR block assignment models that sought to reduce the workload of the SPD and developed a tray optimization model to reduce the number of instruments on increasingly bloated instrument trays. While initial numerical experiments were promising, a comprehensive simulation model of the OR and SPD was needed to more thoroughly examine how potential changes to the block schedule and/or more efficient tray configurations could improve SPD processing times. In this article, we incorporate the SPD into an existing simulation model of an OR suite, which is the first of its kind, and examine the effect that different block schedules and tray configurations have on SPD processing times. Simulation results confirm earlier numerical computations. Furthermore, simulation results suggest that more efficient instrument tray configurations are a much better and more viable method for improving SPD processing time than reconfiguring block schedules.

Motion vector steganography algorithm of sports training video integrating with artificial bee colony algorithm and human-centered AI for web applications

AbstractIn multimedia correspondence, steganography schemes are commonly applied. To reduce storage capacity, multimedia files, including images, are always compressed. Most steganographic video schemes are, therefore, not compression tolerant. In the frame sequences, the video includes extra hidden space. Artificial intelligence (AI) creates a digital world of real-time information for athletes, sponsors, and broadcasters. AI is reshaping business, and although it has already produced a significant impact on other sectors, the sports industry is the newest and most receptive one. Human-centered AI for web applications has substantially influenced audience participation, strategic plan execution, and other aspects of the sports industry that have traditionally relied heavily on statistics. Thus, this study presents the motion vector steganography of sports training video integrating with the artificial bee colony algorithm (MVS-ABC). The motion vector stenography detects the hidden information from the motion vectors in the sports training video bitstreams. Artificial bee colony (ABC) algorithm optimizes the block assignment to inject a hidden message into a host video, in which the block assignment is considered a combinatorial optimization problem. The experimental analysis evaluates the data embedding performance using steganographic technology compared with existing embedding technologies, using the ABC algorithm compared with other genetic algorithms. The findings show that the proposed model can give the highest performance in terms of embedding capacity and the least error rate of video steganography compared with the existing models.

Resource Allocation in an Open RAN System Using Network Slicing

The next radio access network (RAN) generation, open RAN (O-RAN), aims to enable more flexibility and openness, including efficient service slicing, and to lower the operational costs in 5G and beyond wireless networks. Nevertheless, strictly satisfying quality-of-service requirements while establishing priorities and promoting balance between the significantly heterogeneous services remains a key research problem. In this paper, we use network slicing to study the service-aware baseband resource allocation and virtual network function (VNF) activation in O-RAN systems. The limited fronthaul capacity and end-to-end delay constraints are simultaneously considered. Optimizing baseband resources includes O-RAN radio unit (O-RU), physical resource block (PRB) assignment, and power allocation. The main problem is a mixed-integer non-linear programming problem that is non-trivial to solve. Consequently, we break it down into two different steps and propose an iterative algorithm that finds a near-optimal solution. In the first step, we reformulate and simplify the problem to find the power allocation, PRB assignment, and the number of VNFs. In the second step, the O-RU association is resolved. The proposed method is validated via simulations, which achieve a higher data rate and lower end-to-end delay than existing methods.

Routing, Modulation Format, Spatial Lane, and Spectrum Block Assignment in Static Spatial Channel Networks

Spatial channel networks (SCNs) and related key technologies have been proposed to increase the capacity and flexibility of optical networks. We define the network resource allocation problem in a static SCN as the routing, modulation format (MF), spatial lane, and spectrum block assignment (RMSSA) problem and try to solve it. In this paper, we derive the relationship between the traffic bit rate, the transmission distance of optical channels, and MFs in SCNs, and obtain the adoption method of MFs. In addition, we introduce conversion nodes (CNs) into SCNs to perform a modulation format conversion (MFC) for more efficient use of network resources. Moreover, the RMSSA problem in static SCNs is modeled, and heuristic spatial lane and spectrum block minimization based on simulated annealing (LBMSA) algorithm is proposed to solve the RMSSA problem. Simulation results show that when the throughput of SCNs is small, the LBMSA algorithm can carry traffic requests with the least amount of network resources and maximize the network resource utilization. When the network throughput is high, the LBMSA algorithm is more inclined to carry all requests rather than efficient transmission. We also show that network resource utilization can be improved with the LBMSA algorithm by setting CNs to perform the MFC.

Relay-Assisted Uplink Transmission Design of URLLC Packets

With the emergence and development of mission-critical applications, the design of uplink URLLC transmission has become one of the issues of concern. In this article, we consider the ultrareliable uplink transmission design between multiple robots and a central controller in a smart factory with stringent delay requirements while multiple relays are deployed for cooperative transmission. Subject to the throughput and reliability requirements, a relay-assisted two-phase transmission protocol is proposed aiming at total transmit power minimization by jointly optimizing the decoding error probability, relay selection, resource block (RB) assignment, and transmit power allocation. To support the mission-critical applications in Internet of Things (IoT) with diverse throughput targets and make full use of RBs, we investigate the optimization problem under the single-RB allocation and multi-RB allocation schemes. The problems in these two cases have an analogous structure and can be solved with a unified framework. In the solution framework, we present the optimal design solved by the nonconvex penalty (NCP)- and quadratic penalty (QP)-based successive convex approximation (SCA) algorithms and the corresponding low-complexity design with near-optimal settings of decoding error probabilities. Numerical results demonstrate the effectiveness of the proposed penalty-based iterative algorithms and confirm that the near-optimal design only causes minor power performance loss compared to the optimal design. The power performance gap under these two allocation schemes and the impact of system parameters are revealed and analyzed.

Optimal Deployment Mechanism of Blockchain in Resource-Constrained IoT Systems

The recently emerging blockchain technology provides a promising tool to enable endogenous security in Internet-of-Things (IoT) systems. However, when applying the legacy blockchain technology to the existing IoT systems, some technical bottlenecks due to resource constraints, such as storage resource, should be carefully addressed. In this article, we propose an optimal blockchain deployment mechanism for wireless IoT systems, where IoT devices have limited resources and the wireless links connection IoT devices are vulnerable, to improve the storage efficiency of massive blockchain data and realize the organic integration of blockchain technology and the communication process. We propose to maintain a complete blockchain by a set of proximity IoT nodes in a collaborative way on the premise of ensuring that each node can check every transaction. Through dynamically adjusting optimal block assignment, the tradeoff between the length of the blockchain to be stored and the security level provided can be well made. Moreover, a chaotic-based genetic algorithm is developed to obtain the near-optimal block assignment solution efficiently. Simulation results show that our proposed blockchain-based mechanism can effectively address the security issue in wireless IoT systems.

Resource Allocation for URLLC Service in In-Band Full-Duplex-Based V2I Networks

This paper investigates the first resource management problem for vehicular-to-infrastructure (V2I) networks under the in-band full-duplex (IBFD) backhauling scheme with guaranteed ultra-reliable and low-latency (URLLC) service. The considered networks suffers from interference caused by three node transmission in IBFD scheme and the mobility of vehicular user equipments (VUEs). The resource allocation problem is formulated to jointly optimize VUE association, resource block assignment (RA) and power allocation (PA), while satisfying the reliability and latency constraints at the same time. The formulated problem is a mixed integer non-linear problem with non-convex objective function and constraints. Finding globally optimal solution for this type of problem is still an open problem. To develop tractable solutions, the original problem is firstly simplified using derived equivalent expression for the objective function. The proposed method then decomposed it into RA and PA sub-problems. In each iteration, the PA sub-problem is solved for a set of given PA solution; while the solution for PA sub-problem is searched under determined RA results. The RA and PA sub-problems are solved iteratively until the converging condition is achieved. Theoretical analysis proves that the proposed method achieves Nash-stable equilibrium and local optimality for RA and PA sub-problems respectively. Simulation results verify the effectiveness of the derived performance analysis and demonstrate that the proposed algorithm outperforms the state-of-the-art algorithms in the literatures.

Hybrid Energy Resource Allocation for Simultaneous Wireless Information and Power Transfer for Green Communications

In this paper, we consider a simultaneous wireless information and hybrid power transfer system for cellular green networks. The small cells (SCs) use hybrid power of on-grid power and green energy harvested from environments, whilst the user equipments (UEs) are enabled by a power-splitting receiver. A quality-of-service (QoS) constrained problem is designed to maximize energy efficiency (EE) considering joint strategies of hybrid power allocation, resource block assignment, and power splitting ratio adjustment. We propose a joint wireless charging and hybrid power based resource allocation (J-WHA) algorithm by transforming the non-concave EE problem into a solvable one based on proved concavity property and Karush-Kuhn-Tucker (KKT) conditions. Moreover, a separated wireless charging and hybrid power based resource allocation (S-WHA) algorithm is proposed to obtain sub-optimal solutions with lower computational complexity. Simulation results demonstrate the convergence of both proposed algorithms in relation to transmission power utilization, wireless charging amount, and system EE. It is shown that the proposed J-WHA and S-WHA algorithms outperform existing schemes in terms of EE performance.

Optimized artificial neural network assisted trade-off between transmission and delay in LTE networks

The quality of data transmitted via LTE channels is often affected with the trade-off between the delay and network throughput. It often affects the performance of video transmitted with data rate distortion. In this paper, Lagrange dual multiplier with Artificial Neural Network (ANN) is used to solve the NP-hard problem and incorporates the Lagrange approach to achieve an optimum solution for higher network rating by reducing resource allocation. The way to find the optimal radio resource block (RRB) assignment strategy is iterated many times. Lagrange Dual Multiplier will also be used and implemented in this solution and this will help to achieve a closed form solution for any RRB assignment and further simulations to assess the efficiency of the process proposed. The experimental verification shows the efficacy of the model over existing state-of-art mechanism in terms of transmission errors and delay.

Robust resource allocation for NOMA-assisted heterogeneous networks

As a promising technology to improve spectrum efficiency and transmission coverage, Heterogeneous Network (HetNet) has attracted the attention of many scholars in recent years. Additionally, with the introduction of the Non-Orthogonal Multiple Access (NOMA) technology, the NOMA-assisted HetNet cannot only improve the system capacity but also allow more users to utilize the same frequency band resource, which makes the NOMA-assisted HetNet a hot topic. However, traditional resource allocation schemes assume that base stations can exactly estimate direct link gains and cross-tier link gains, which is impractical for practical HetNets due to the impact of channel delays and random perturbation. To further improve energy utilization and system robustness, in this paper, we investigate a robust resource allocation problem to maximize the total Energy Efficiency (EE) of Small-Cell Users (SCUs) in NOMA-assisted HetNets under imperfect channel state information. By considering bounded channel uncertainties, the robust resource optimization problem is formulated as a mixed-integer and nonlinear programming problem under the constraints of the cross-tier interference power of macrocell users, the maximum transmit power of small base station, the Resource Block (RB) assignment, and the quality of service requirement of each SCU. The original problem is converted into an equivalent convex optimization problem by using Dinkelbach's method and the successive convex approximation method. A robust Dinkelbach-based iteration algorithm is designed by jointly optimizing the transmit power and the RB allocation. Simulation results verify that the proposed algorithm has better EE and robustness than the existing algorithms.

Community-based short-term integrated palliative and supportive care reduces symptom distress for older people with chronic noncancer conditions compared with usual care: A randomised controlled single-blind mixed method trial

BackgroundGlobally, a rising number of people live into advanced age and die with multimorbidity and frailty. Palliative care is advocated as a person-centred approach to reduce health-related suffering and promote quality of life. However, no evidence-based interventions exist to deliver community-based palliative care for this population. AimTo evaluate the impact of the short-term integrated palliative and supportive care intervention for older people living with chronic noncancer conditions and frailty on clinical and economic outcomes and perceptions of care. DesignSingle-blind trial with random block assignment to usual care or the intervention and usual care. The intervention comprised integrated person-centred palliative care delivered by multidisciplinary palliative care teams working with general practitioners and community nurses. Main outcome was change in five key palliative care symptoms from baseline to 12-weeks. Data analysis used intention to treat and complete cases to examine the mean difference in change scores and effect size between the trial arms. Economic evaluation used cost-effectiveness planes and qualitative interviews explored perceptions of the intervention. Setting/participantsFour National Health Service general practices in England with recruitment of patients aged ≥75 years, with moderate to severe frailty, chronic noncancer condition(s) and ≥2 symptoms or concerns, and family caregivers when available. Results50 patients were randomly assigned to receive usual care (n = 26, mean age 86.0 years) or the intervention and usual care (n = 24, mean age 85.3 years), and 26 caregivers (control n = 16, mean age 77.0 years; intervention n = 10, mean age 77.3 years). Participants lived at home (n = 48) or care home (n = 2). Complete case analysis (n = 48) on the main outcome showed reduced symptom distress between the intervention compared with usual care (mean difference -1.20, 95% confidence interval -2.37 to -0.027) and medium effect size (omega squared = 0.071). Symptom distress reduced with decreased costs from the intervention compared with usual care, demonstrating cost-effectiveness. Patient (n = 19) and caregiver (n = 9) interviews generated themes about the intervention of ‘Little things make a big difference’ with optimal management of symptoms and ‘Care beyond medicines’ of psychosocial support to accommodate decline and maintain independence. ConclusionsThis palliative and supportive care intervention is an effective and cost-effective approach to reduce symptom distress for older people severely affected by chronic noncancer conditions. It is a clinically effective way to integrate specialist palliative care with primary and community care for older people with chronic conditions. Further research is indicated to examine its implementation more widely for people at home and in care homes.Trial registration: Controlled-Trials.com ISRCTN 45837097Tweetable abstract: Specialist palliative care integrated with district nurses and GPs is cost-effective to reduce symptom distress for older people severely affected by chronic conditions.

Correction to: Yard block assignment, internal truck operations, and berth allocation in container terminals: introducing carbon-footprint minimisation objectives

Correction to: Yard block assignment, internal truck operations, and berth allocation in container terminals: introducing carbon-footprint minimisation objectives

Joint uplink and downlink resource allocation for device to device communications

With the ever-increasing number of intelligent devices, extensive research efforts have been spent towards developing efficient resource allocation in underlying device to device communications. Centralised and distributed approaches have been proposed to improve the network performances through uplink and downlink schemes. However, the full-duplex resource allocation remains challenging. Here, a joint uplink–downlink resource block assignment for device to device communications is proposed. The device's geometric distribution via the Poisson point process is formulated and the optimization problem as a mixed strategy non-cooperative game is modelled. The proposed resource allocation scheme allows many device to device communication pairs to share the same cellular resource block but does not restrict their number in advance; it is determined dynamically according to the channel's capacity and interference condition. Inversely, a device to device communication transmitter can simultaneously be allocated many resource blocks according to its bandwidth requirement and the number of its antennas. Furthermore, the optimal amount of data and power transmitted by each device is calculated. The efficiency of this algorithm is evaluated through computer simulations, and the results demonstrate good performances in the spectral and energy efficiency with relatively low complexity and convergence time.

Yard block assignment, internal truck operations, and berth allocation in container terminals: introducing carbon-footprint minimisation objectives

It is of increasing importance to carry out port terminal operations in an environmentally sustainable way. We approach the yard block assignment and internal transportation problem in a way that establishes an optimum trade-off between the time of internal truck moves and environmental objectives. The framework has a dynamic structure and aims to offer different berthing alternatives according to yard block occupancy rates. Earlier limitations of deterministic modelling are addressed. Probabilistic methods are considered in an attempt to bring our outcomes closer to real terminal circumstances. Discrete Monte Carlo simulation, integer-linear programming and multi-objective optimisation methods are integrated to address container terminal modelling complexity. The uncertainty arising from situations such as irregular vehicle queuing at the yard or quay crane stations is successfully addressed. The opinions of managers involved in decisions regarding the ‘time–environment dilemma’ are weighted and included in the optimisation model. In five scenarios, we show that internal truck time efficiency can improve by 27.8% to 42.8%, and CO2 emissions reduction by 30.1% to 70.3%.