Journal articles: 'Approximation algorithms; resource allocation; optimization'

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Du, Ning, Changqing Zhou, and Xiyuan Ma. "A Novel Subchannel and Power Allocation Algorithm in V2V Communication." Wireless Communications and Mobile Computing 2021 (October 26, 2021): 1–15. http://dx.doi.org/10.1155/2021/5530612.

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This paper investigates resource allocation of latency constrained vehicle-to-vehicle (V2V) communication. When a subchannel of a vehicle-to-infrastructure (V2I) link can be reused by multiple V2V links, a nonlinear mixed integer optimization problem with the goal of maximizing the spectral efficiency of the system is derived under the constraints of minimum transmission rate of V2I links and transmission latency of V2V links. The subchannel allocation problem is solved by means of two-sided exchange matching theory, optimal transmission power of V2I and V2V links is solved based on the poly-block approximation (PBA) algorithm, and the system spectrum efficiency is maximized through loop iteration. In order to reduce the computational complexity of power allocation problem, a power allocation algorithm based on iterative convex optimization (ICO) is proposed. The convergence of the resource allocation algorithm is also proved. The simulation results show that the proposed algorithms can guarantee transmission latency requirements of V2V links and improve the system sum rate and access ratio of V2V links. Compared with two traditional algorithms, latency of poly-block approximation combined with many to one matching (PBAMTO) is reduced by 30.41% and 20.43%, respectively.

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Du, Ning, Kaishi Sun, Changqing Zhou, and Xiyuan Ma. "A Novel Access Control and Energy-Saving Resource Allocation Scheme for D2D Communication in 5G Networks." Complexity 2020 (January 8, 2020): 1–11. http://dx.doi.org/10.1155/2020/3696015.

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This paper investigates access link control and resource allocation for the device-to-device (D2D) communication in the fifth generation (5G) cellular networks. The optimization objective of this problem is to maximize the number of admitted D2D links and minimize the total power consumption of D2D links under the condition of meeting the minimum transmission rate requirements of D2D links and common cellular links. This problem is a two-stage nondeterministic polynomial (NP) problem, the solving process of which is very complex. So, we transform it into a one-stage optimization problem. According to the monotonicity of objective function and constraint conditions, a monotone optimization problem is established, which is solved by reverse polyblock approximation algorithm. In order to reduce the complexity of this algorithm, a solution algorithm based on iterative convex optimization is proposed. Simulation results show that both algorithms can maximize the number of admitted D2D links and minimize the total power consumption of D2D links. The proposed two algorithms are better than the energy efficiency optimization algorithm.

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Hameed, Iqra, Pham-Viet Tuan, and Insoo Koo. "Exploiting a Deep Neural Network for Efficient Transmit Power Minimization in a Wireless Powered Communication Network." Applied Sciences 10, no. 13 (July 3, 2020): 4622. http://dx.doi.org/10.3390/app10134622.

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In this paper, we propose a learning-based solution for resource allocation in a wireless powered communication network (WPCN). We provide a study and analysis of a deep neural network (DNN) which can reasonably effectively approximate the iterative optimization algorithm for resource allocation in the WPCN. In this scheme, the deep neural network provides an optimized solution for transmitting power with different channel coefficients. The proposed deep neural network accepts the channel coefficient as an input and outputs minimized power for this channel in the WPCN. The DNN learns the relationship between input and output and gives a fairly accurate approximation for the transmit power optimization iterative algorithm. We exploit the sequential parametric convex approximation (SPCA) iterative algorithm to solve the optimization problem for transmit power in the WPCN. The proposed approach ensures the quality of service (QoS) of the WPCN by managing user throughput and by keeping harvested energy levels above a defined threshold. Through numerical results and simulations, it is verified that the proposed scheme can best approximate the SPCA iterative algorithms with low computational time consumption.

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Li, Huanyu, Hui Li, and Youling Zhou. "Optimization Algorithms for Joint Power and Sub-Channel Allocation for NOMA-Based Maritime Communications." Entropy 23, no. 11 (November 1, 2021): 1454. http://dx.doi.org/10.3390/e23111454.

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This paper investigates resource optimization schemes in a marine communication scenario based on non-orthogonal multiple access (NOMA). According to the offshore environment of the South China Sea, we first establish a Longley–Rice-based channel model. Then, the weighted achievable rate (WAR) is considered as the optimization objective to weigh the information rate and user fairness effectively. Our work introduces an improved joint power and user allocation scheme (RBPUA) based on a single resource block. Taking RBPUA as a basic module, we propose three joint multi-subchannel power and marine user allocation algorithms. The gradient descent algorithm (GRAD) is used as the reference standard for WAR optimization. The multi-choice knapsack algorithm combined with dynamic programming (MCKP-DP) obtains a WAR optimization result almost equal to that of GRAD. These two NOMA-based solutions are able to improve WAR performance by 7.47% compared with OMA. Due to the high computational complexity of the MCKP-DP, we further propose a DP-based fully polynomial-time approximation algorithm (DP-FPTA). The simulation results show that DP-FPTA can reduce the complexity by 84.3% while achieving an approximate optimized performance of 99.55%. This advantage of realizing the trade-off between performance optimization and complexity meets the requirements of practical low-latency systems.

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Yu, Bencheng, Zihui Ren, and Shoufeng Tang. "Robust Secure Resource Allocation for RIS-Aided SWIPT Communication Systems." Sensors 22, no. 21 (October 28, 2022): 8274. http://dx.doi.org/10.3390/s22218274.

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Aiming at the influence of channel uncertainty, user information leakage and harvested energy improvement, this paper proposes a robust resource allocation algorithm for reconfigurable intelligent reflector (RIS) multiple-input single-output systems based on imperfect channel state information. First, considering the legal user minimum secret rate constraint, the base station maximum transmit power constraint and the RIS phase shift constraint with the bounded channel uncertainty, a joint optimization of the base station active beam, energy beam and RIS phase shift is established. A multivariate coupled nonlinear resource allocation problem for matrices is addressed. Then, using S-procedure and alternating optimization methods, the original non-convex problem is transformed into a deterministic convex optimization problem and an alternating optimization algorithm based on continuous convex approximation is proposed. The simulation results show that the proposed algorithm has better fairness harvested energy compared with the traditional robust algorithm.

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Han, Qinghua, Minghai Pan, Weijun Long, Zhiheng Liang, and Chenggang Shan. "Joint Adaptive Sampling Interval and Power Allocation for Maneuvering Target Tracking in a Multiple Opportunistic Array Radar System." Sensors 20, no. 4 (February 12, 2020): 981. http://dx.doi.org/10.3390/s20040981.

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In this paper, a joint adaptive sampling interval and power allocation (JASIPA) scheme based on chance-constraint programming (CCP) is proposed for maneuvering target tracking (MTT) in a multiple opportunistic array radar (OAR) system. In order to conveniently predict the maneuvering target state of the next sampling instant, the best-fitting Gaussian (BFG) approximation is introduced and used to replace the multimodal prior target probability density function (PDF) at each time step. Since the mean and covariance of the BFG approximation can be computed by a recursive formula, we can utilize an existing Riccati-like recursion to accomplish effective resource allocation. The prior Cramér-Rao lower boundary (prior CRLB-like) is compared with the upper boundary of the desired tracking error range to determine the adaptive sampling interval, and the Bayesian CRLB-like (BCRLB-like) gives a criterion used for measuring power allocation. In addition, considering the randomness of target radar cross section (RCS), we adopt the CCP to package the deterministic resource management model, which minimizes the total transmitted power by effective resource allocation. Lastly, the stochastic simulation is embedded into a genetic algorithm (GA) to produce a hybrid intelligent optimization algorithm (HIOA) to solve the CCP optimization problem. Simulation results show that the global performance of the radar system can be improved effectively by the resource allocation scheme.

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Yang, Xiaoxia, Zhengqiang Wang, Xiaoyu Wan, and Zifu Fan. "Secure Energy-Efficient Resource Allocation Algorithm of Massive MIMO System with SWIPT." Electronics 9, no. 1 (December 25, 2019): 26. http://dx.doi.org/10.3390/electronics9010026.

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In this paper, we consider the resource allocation problem to maximize the minimum (max–min) user’s secure energy efficiency (SEE) in downlink massive multiple-input multiple-output (MIMO) systems with simultaneous wireless information and power transfer (SWIPT). First, transmission power and power splitting ratio are designed to achieve the max–min user’s SEE subject to harvested energy threshold, the constraints of transmission power, and power splitting ratio. Secondly, the optimization problem is non-convex and very difficult to tackle. In order to solve the optimization problem, we converted to a series of parameter optimization subproblems by fractional programming. Then, we employ the first-order Taylor expansion and successive convex approximation (SCA) method to solve parameter optimization problems. Next, a secure energy-efficient resource allocation (SERA) algorithm with the bisection method is proposed to find the max–min SEE of the system. Finally, simulation results show the effectiveness and superiority of the SERA algorithm.

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Yu, Guanding, Xin Ding, and Shengli Liu. "Joint Resource Management and Trajectory Optimization for UAV-Enabled Maritime Network." Sensors 22, no. 24 (December 13, 2022): 9763. http://dx.doi.org/10.3390/s22249763.

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Due to the lack of places to employ communication infrastructures, there are many coverage blind zones in maritime communication networks. Benefiting from the high flexibility and maneuverability, unmanned aerial vehicles (UAVs) have been proposed as a promising method to provide broadband maritime coverage for these blind zones. In this paper, a multi-UAV-enabled maritime communication model is proposed, where UAVs are deployed to provide the transmission service for maritime users. To improve the performance of the maritime communication systems, an optimization problem is formulated to maximize the minimum average throughput among all users by jointly optimizing the user association, power allocation, and UAV trajectory. To derive the solutions with a low computational complexity, we decompose this problem into three subproblems, namely user association optimization, power allocation optimization, and UAV trajectory optimization. Then, a joint iterative algorithm is developed to achieve the solutions based on the successive convex approximation and interior-point methods. Extensive simulation results validate the effectiveness of the proposed algorithm and demonstrate that UAVs can be used to enhance the maritime coverage.

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An, Qi, Yu Pan, Huizhu Han, and Hang Hu. "Secrecy Capacity Maximization of UAV-Enabled Relaying Systems with 3D Trajectory Design and Resource Allocation." Sensors 22, no. 12 (June 15, 2022): 4519. http://dx.doi.org/10.3390/s22124519.

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Unmanned aerial vehicles (UAVs) have attracted considerable attention, thanks to their high flexibility, on-demand deployment and the freedom in trajectory design. The communication channel quality can be effectively improved by using UAV to build a line-of-sight communication link between the transmitter and the receiver. Furthermore, there is increasing demand for communication security improvement, as the openness of a wireless channel brings serious threat. This paper formulates a secrecy capacity optimization problem of a UAV-enabled relay communication system in the presence of malicious eavesdroppers, in which the secrecy capacity is maximized by jointly optimizing the UAV relay’s location, power allocation, and bandwidth allocation under the communication quality and information causality constraints. A successive convex approximation–alternative iterative optimization (SCA-AIO) algorithm is proposed to solve this highly coupled nonconvex problem. Simulation results demonstrate the superiority of the proposed secrecy transmission strategy with optimal trajectory design and resource allocation compared with the benchmark schemes and reveal the impacts of communication resources on system performance.

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Bu, Yinglan, Jiaying Zong, Xinjiang Xia, Yang Liu, Fengyi Yang, and Dongming Wang. "Joint User Scheduling and Resource Allocation in Distributed MIMO Systems with Multi-Carriers." Electronics 11, no. 12 (June 9, 2022): 1836. http://dx.doi.org/10.3390/electronics11121836.

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Compared with the traditional collocated multi-input multi-output system (C-MIMO), distributed MIMO (D-MIMO) systems have the advantage of higher throughput and coverage, making them strong candidates for next-generation communication architecture. As a practical implementation of a D-MIMO cooperative network, the multi-TRP (multiple transmission/reception point) system becomes a hotspot in the research of advanced 5G. Different from previous research on a cooperative D-MIMO network with single narrowband transmission, this paper proposes a joint optimization scheme to address the user scheduling problem along with carrier allocation to maximize the total spectral efficiency (SE) in the downlink of coherent multi-TRP systems with multi-carriers. We establish a joint optimization model of user scheduling and resource allocation to maximize the system spectral efficiency under the constraints of power consumption and the backhaul capacity limits at each RAU (remote antenna unit), as well as the QoS (quality of service) requirement at each user. Since the optimization model is both non-covex and non-smooth, a joint optimization algorithm is proposed to solve this non-convex combinatorial optimization problem. We first smooth the mixed-integer problem by employing penalty functions, and after decoupling the coupled variables by introducing auxiliary variables, the original problem is transformed into a series of tractable convex optimization problems by using successive convex approximation (SCA). Numerical results demonstrate that the proposed joint optimization algorithm for user scheduling and resource allocation can reliably converge and achieve a higher system SE than the general multi-TRP system without carrier allocation, and this advantage is more pronounced under a higher backhaul capacity or higher power consumption constraints.

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Huang, Jiaying, Yawen Shi, and Fahui Wu. "Tradeoff of Computation Bits and Computing Speed in an Edge Computing System for Sensor Networks." Journal of Sensors 2021 (September 15, 2021): 1–12. http://dx.doi.org/10.1155/2021/5775953.

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Unmanned aerial vehicle (UAV) enabled mobile-edge computing (MEC) has been recognized as a promising approach for providing enhanced coverage and computation capability to Internet of Things (IoT), especially in the scenario with limited or without infrastructure. In this paper, we consider the UAV assisted partial computation offloading mode MEC system, where ground sensor users are served by a moving UAV equipped with computing server. Computation bits (CB) and computation efficiency (CE) are two vital metrics describe the computation performance of system. To reveal the CB-CE tradeoff, an optimization problem is formulated to maximize the weighted sum of the above two metrics, by optimizing the UAV trajectory jointly with communication resource, as well as the computation resource. As the formulated problem is non-convex, it is difficult to be optimally solved in general. To tackle this issue, we decouple it into two sub-problems: UAV trajectory optimization and resource allocation optimization. We propose an iterative algorithm to solve the two sub-problems by Dinkelbach’s method, Lagrange duality and successive convex approximation technique. Extensive simulation results demonstrate that our proposed resource allocation optimization scheme can achieve better computational performance than the other schemes. Moreover, the proposed alternative algorithm can converge with a few iterations.

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Yang, Jie, Jiajia Zhu, and Ziyu Pan. "A Fairness Index Based on Rate Variance for Downlink Non-Orthogonal Multiple Access System." Future Internet 14, no. 9 (August 31, 2022): 261. http://dx.doi.org/10.3390/fi14090261.

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Aiming at the resource allocation problem of a non-orthogonal multiple access (NOMA) system, a fairness index based on sample variance of users’ transmission rates is proposed, which has a fixed range and high sensitivity. Based on the proposed fairness index, the fairness-constrained power allocation problem in NOMA system is studied; the problem is decoupled into the intra cluster power allocation problem and the inter cluster power allocation problem. The nonconvex optimization problem is solved by the continuous convex approximation (SCA) method, and an intra and inter cluster power iterative allocation algorithm with fairness constrained is proposed to maximize the total throughput. Simulation results show that the proposed algorithm can take into account intra cluster, inter cluster, and system fairness, and maximize the system throughput on the premise of fairness.

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Zhang, Jingmin, Xiaokui Yue, Xuan Li, Haofei Zhang, Tao Ni, and Wensheng Lin. "Secrecy-Oriented Optimization of Sparse Code Multiple Access for Simultaneous Wireless Information and Power Transfer in 6G Aerial Access Networks." Wireless Communications and Mobile Computing 2021 (April 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/9922043.

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This article focuses on the simultaneous wireless information and power transfer (SWIPT) systems, which provide both the power supply and the communications for Internet-of-Things (IoT) devices in the sixth-generation (6G) network. Due to the extremely stringent requirements on reliability, speed, and security in the 6G network, aerial access networks (AANs) are deployed to extend the coverage of wireless communications and guarantee robustness. Moreover, sparse code multiple access (SCMA) is implemented on the SWIPT system to further promote the spectrum efficiency. To improve the speed and security of SWIPT systems in 6G AANs, we have developed an optimization algorithm of SCMA to maximize the secrecy sum rate (SSR). Specifically, a power-splitting (PS) strategy is applied by each user to coordinate its energy harvesting and information decoding. Hence, the SSR maximization problems in the SCMA system are formulated in terms of the PS and resource allocation, under the constraints on the minimum rates and minimum harvested energy of individual users. Then, a successive convex approximation method is introduced to transform the nonconvex problems to the convex ones, which are then solved by an iterative algorithm. In addition, we investigate the SSR performance of the SCMA system supported by our optimization methods, when the impacts from different perspectives are considered. Our studies and simulation results show that the SCMA system supported by our proposed optimization algorithms significantly outperforms the legacy system with uniform power allocation and fixed PS.

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Zhou, Fanqin, Lei Feng, Peng Yu, Wenjing Li, and Luoming Meng. "Utility Maximization for Load Optimization in Cellular/WLAN Interworking Network Based on Generalized Benders Decomposition." Mobile Information Systems 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/6929575.

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Load steering is widely accepted as a key SON function in cellular/WLAN interworking network. To investigate load optimizing from a perspective of system utilization maximization more than just offloading to improve APs’ usage, a utility maximization (UTMAX) optimization model and an ASRAO algorithm based on generalized Benders Decomposition are proposed in this paper. UTMAX is to maximize the sum of logarithmic utility functions of user data rate by jointly optimizing user association and resource allocation. To maintain the flexibility of resource allocation, a parameter β is added to the utility function, where smaller β means more resources can be allocated to edge users. As a result, it reflects a tradeoff between improvements in user throughput fairness and system total throughput. UTMAX turns out to be a mixed integer nonlinear programming, which is intractable intuitively. So ASRAO is proposed to solve it optimally and effectively, and an optional phase for expediting ASRAO is proposed by using relaxation and approximation techniques, which reduces nearly 10% iterations and time needed by normal ASRAO from simulation results. The results also show UTMAX’s good effects on improving WLAN usage and edge user throughput.

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Xue, Yishi, Bo Xu, Wenchao Xia, Jun Zhang, and Hongbo Zhu. "Backhaul-Aware Resource Allocation and Optimum Placement for UAV-Assisted Wireless Communication Network." Electronics 9, no. 9 (August 28, 2020): 1397. http://dx.doi.org/10.3390/electronics9091397.

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Driven by its agile maneuverability and deployment, the unmanned aerial vehicle (UAV) becomes a potential enabler of the terrestrial networks. In this paper, we consider downlink communications in a UAV-assisted wireless communication network, where a multi-antenna UAV assists the ground base station (GBS) to forward signals to multiple user equipments (UEs). The UAV is associated with the GBS through in-band wireless backhaul, which shares the spectrum resource with the access links between UEs and the UAV. The optimization problem is formulated to maximize the downlink ergodic sum-rate by jointly optimizing UAV placement, spectrum resource allocation and transmit power matrix of the UAV. The deterministic equivalents of UE’s achievable rate and backhaul capacity are first derived by utilizing large-dimensional random matrix theory, in which, only the slowly varying large-scale channel state information is required. An approximation problem of the joint optimization problem is then introduced based on the deterministic equivalents. Finally, an algorithm is proposed to obtain the optimal solution of the approximate problem. Simulation results are provided to validate the accuracy of the deterministic equivalents, and the effectiveness of the proposed method.

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Zhang, Yijia, and Ruiying Liu. "Robust Power Optimization for Downlink Cloud Radio Access Networks with Physical Layer Security." Entropy 22, no. 2 (February 17, 2020): 223. http://dx.doi.org/10.3390/e22020223.

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Since the cloud radio access network (C-RAN) transmits information signals by jointly transmission, the multiple copies of information signals might be eavesdropped on. Therefore, this paper studies the resource allocation algorithm for secure energy optimization in a downlink C-RAN, via jointly designing base station (BS) mode, beamforming and artificial noise (AN) given imperfect channel state information (CSI) of information receivers (IRs) and eavesdrop receivers (ERs). The considered resource allocation design problem is formulated as a nonlinear programming problem of power minimization under the quality of service (QoS) for each IR, the power constraint for each BS, and the physical layer security (PLS) constraints for each ER. To solve this non-trivial problem, we first adopt smooth ℓ 0 -norm approximation and propose a general iterative difference of convex (IDC) algorithm with provable convergence for a difference of convex programming problem. Then, a three-stage algorithm is proposed to solve the original problem, which firstly apply the iterative difference of convex programming with semi-definite relaxation (SDR) technique to provide a roughly (approximately) sparse solution, and then improve the sparsity of the solutions using a deflation based post processing method. The effectiveness of the proposed algorithm is validated with extensive simulations for power minimization in secure downlink C-RANs.

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Amjad, Maliha, Ashfaq Ahmed, Muhammad Naeem, Muhammad Awais, Waleed Ejaz, and Alagan Anpalagan. "Resource Management in Energy Harvesting Cooperative IoT Network under QoS Constraints." Sensors 18, no. 10 (October 20, 2018): 3560. http://dx.doi.org/10.3390/s18103560.

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Cooperative communication with RF energy harvesting relays has emerged as a promising technique to improve the reliability, coverage, longevity and capacity of future IoT networks. An efficient relay assignment with proper power allocation and splitting is required to satisfy the network’s QoS requirements. This work considers the resource management problem in decode and forward relay based cooperative IoT network. A realistic mathematical model is proposed for joint user admission, relay assignment, power allocation and splitting ratio selection problem. The optimization problem is a mixed integer non-linear problem (MINLP) whose objective is to maximize the overall sum rate (bps) while satisfying the practical network constraints. Further, an outer approximation algorithm is adopted which provides epsilon-optimal solution to the problem with guaranteed convergence and reasonable complexity. Simulations of the proposed solution are carried out for various network scenarios. The simulation results demonstrate that cooperative communication with diversity achieves a better admission of IoT users and increases not only their individual data rates but also the overall sum rate of an IoT network.

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Cao, Peng, Yi Liu, and Chao Yang. "Robust Resource Allocation and Trajectory Planning of UAV-Aided Mobile Edge Computing in Post-Disaster Areas." Applied Sciences 12, no. 4 (February 21, 2022): 2226. http://dx.doi.org/10.3390/app12042226.

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When natural disasters strike, users in the disaster area may be isolated and unable to transmit disaster information to the outside due to the damage of communication facilities. Unmanned aerial vehicles can be exploited as mobile edge servers to provide emergency service for ground users due to its mobility and flexibility. In this paper, a robust UAV-aided wireless-powered mobile edge computing (MEC) system in post disaster areas is proposed, where the UAV provides charging and computing service for users in the disaster area. Considering the estimation error of users’ locations, our target is to maximize the energy acquisition of each user by jointly optimizing the computing offloading process and the UAV trajectory. Due to the strongly coupled connectionbetween optimization variables and the non-convex nature for trajectory optimization, the problem is difficult to solve. Furthermore, the semi-infinity of the users’ possible location makes the problem even more intractable. To tackle these difficulties, we ignore the estimation error of users’ location firstly, and propose an iterative algorithm by using Lagrange dual method and successive convex approximation (SCA) technology. Then, we propose a cutting-set method to deal with the uncertainty of users’ location. In this method, we degrade the influence of location uncertainty by alternating between optimization step and pessimization step. Finally, simulation results show that the proposed robust algorithm can effectively improve the user energy acquisition.

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Huang, Jinming, Sijie Xu, Jun Zhang, and Yi Wu. "Resource Allocation and 3D Deployment of UAVs-Assisted MEC Network with Air-Ground Cooperation." Sensors 22, no. 7 (March 28, 2022): 2590. http://dx.doi.org/10.3390/s22072590.

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Equipping an unmanned aerial vehicle (UAV) with a mobile edge computing (MEC) server is an interesting technique for assisting terminal devices (TDs) to complete their delay sensitive computing tasks. In this paper, we investigate a UAV-assisted MEC network with air–ground cooperation, where both UAV and ground access point (GAP) have a direct link with TDs and undertake computing tasks cooperatively. We set out to minimize the maximum delay among TDs by optimizing the resource allocation of the system and by three-dimensional (3D) deployment of UAVs. Specifically, we propose an iterative algorithm by jointly optimizing UAV–TD association, UAV horizontal location, UAV vertical location, bandwidth allocation, and task split ratio. However, the overall optimization problem will be a mixed-integer nonlinear programming (MINLP) problem, which is hard to deal with. Thus, we adopt successive convex approximation (SCA) and block coordinate descent (BCD) methods to obtain a solution. The simulation results have shown that our proposed algorithm is efficient and has a great performance compared to other benchmark schemes.

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Pi, Weichao, and Jianming Zhou. "Multi-UAV Enabled Data Collection with Efficient Joint Adaptive Interference Management and Trajectory Design." Electronics 10, no. 5 (February 26, 2021): 547. http://dx.doi.org/10.3390/electronics10050547.

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This paper studies interference in a data collection scenario in which multiple unmanned aerial vehicles (UAVs) are dispatched to wirelessly collect data from a set of distributed sensors. To improve the communication throughput and minimize the completion time, we design a joint resource allocation and trajectory optimization framework that not only is compatible with the traditional time-division scheme and interference coordination scheme but also combines their advantages. First, we analyse a basic quasi-stationary scenario with two UAVs and four devices, in which the two UAVs hover at optimal displacements to execute the data collection mission, and it is proven that the proposed optimal resource allocation and trajectory solution is adaptively adjustable according to the severity of the interference and that the common throughput of the network is non-decreasing. Second, for the general mobile case, we design an efficient algorithm to jointly address resource allocation and trajectory optimization, in which we first apply the block coordinate descent method to decompose the original non-convex problem into three non-convex sub-problems and then employ a dedicated genetic algorithm, a penalty function and the sequential convex approximation (SCA) technique to efficiently solve the individual sub-problems and obtain a satisfactory locally optimal solution with an adaptive initialization scheme. Subsequently, numerical experiments are presented to demonstrate that the completion time of the data collection task with our proposed method is at least 25% shorter than those with several baseline dynamic orthogonal schemes when 4 UAVs are deployed. Finally, we provide a practical application principle concerning the maximum suitable number of UAVs to avoid the inherent deficiencies of the proposed algorithm.

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Setiawan, F., A. Sadiyoko, and C. Setiardjo. "Application of Pigeon Inspired Optimization for Multidimensional Knapsack Problem." International Journal of Industrial Engineering and Engineering Management 2, no. 1 (June 15, 2020): 45–56. http://dx.doi.org/10.24002/ijieem.v2i1.3841.

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The multidimensional knapsack problem (MKP) is a generalization of the classical knapsack problem, a problem for allocating a resource by selecting a subset of objects that seek for the highest profit while satisfying the capacity of knapsack constraint. The MKP have many practical applications in different areas and classified as a NP-hard problem. An exact method like branch and bound and dynamic programming can solve the problem, but its time computation increases exponentially with the size of the problem. Whereas some approximation method has been developed to produce a near-optimal solution within reasonable computational times. In this paper a pigeon inspired optimization (PIO) is proposed for solving MKP. PIO is one of the metaheuristic algorithms that is classified in population-based swarm intelligent that is developed based on the behavior of the pigeon to find its home although it had gone far away from it home. In this paper, PIO implementation to solve MKP is applied to two different characteristic cases in total 10 cases. The result of the implementation of the two-best combination of parameter values for 10 cases compared to particle swarm optimization, intelligent water drop algorithm and the genetic algorithm gives satisfactory results.

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Chou, Frederick N. F., Nguyen Thi Thuy Linh, and Chia-Wen Wu. "Optimizing the Management Strategies of a Multi-Purpose Multi-Reservoir System in Vietnam." Water 12, no. 4 (March 26, 2020): 938. http://dx.doi.org/10.3390/w12040938.

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Resource shortages are having an increasingly severe impact as global trends like rapid population growth, urbanization, economic development, and climate change unfold. Moreover, rising living standards across many regions are also affecting water and energy resources. This entails an urgent requirement to improve water resources management. An important improvement is to transfer water between the different uses of the reservoir system. A compromise between the needs of hydropower generation and the water supply can be negotiated for the reservoir system to reduce the severity of water shortages. The Be River basin in Vietnam was selected as a case study to investigate. The combination of the generalized water allocation simulation model (GWASIM) and the bounded optimization by quadratic approximation (BOBYQA) algorithm was applied to optimize hydropower generation in various water shortage scenarios. The results present optimized hydropower generation policies for cascade reservoirs that would significantly improve the present operating policy in terms of both the water supply and hydropower generation. Moreover, multiple scenarios will provide flexibility to the reservoir operator by giving the relationship between water and energy. Given water supply conditions, the operator will be able to choose among several optimal solutions to ensure greater water resource efficiency in the Be River basin.

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Changyuan Xu, Changyuan Xu, Cheng Zhan Changyuan Xu, Jingrui Liao Cheng Zhan, and Bin Zeng Jingrui Liao. "UAV-Enabled Mobile Edge Computing with Binary Computation Offloading and Energy Constraints." 網際網路技術學刊 23, no. 5 (September 2022): 947–54. http://dx.doi.org/10.53106/160792642022092305003.

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Mobile edge computing (MEC) has been considered to provide computation services near the edge of mobile networks, while the unmanned aerial vehicle (UAV) is becoming an important integrated component to extend service coverage. In this paper, we consider a UAV-enabled MEC with binary computation offloading and energy constraints, where an energy-limited UAV is employed as an aerial edge server and each task of devices is either executing locally or offloading to the aerial edge server as a whole. To provide fairness among different ground devices, we aim to maximize the minimum computation throughput among all devices via the joint design of computing mode selection and UAV trajectory as well as resource allocation. The optimization problem is formulated as a mixed-integer non-linear problem consisting of binary variables, which is difficult to tackle. By employing deductive penalty function to penalize the effect of non-binary solution, we develop an efficient iterative algorithm to obtain a suboptimal solution via leveraging the penalty successive convex approximation (P-SCA) method and difference of two convex (D.C.) optimization framework, where the algorithm is guaranteed to converge. Extensive simulations are conducted and the results with different system parameters show that the proposed joint design algorithm can improve the computation throughput by about 40% compared to other benchmark schemes.

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Singh, A., A. Krause, C. Guestrin, and W. J. Kaiser. "Efficient Informative Sensing using Multiple Robots." Journal of Artificial Intelligence Research 34 (April 27, 2009): 707–55. http://dx.doi.org/10.1613/jair.2674.

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The need for efficient monitoring of spatio-temporal dynamics in large environmental applications, such as the water quality monitoring in rivers and lakes, motivates the use of robotic sensors in order to achieve sufficient spatial coverage. Typically, these robots have bounded resources, such as limited battery or limited amounts of time to obtain measurements. Thus, careful coordination of their paths is required in order to maximize the amount of information collected, while respecting the resource constraints. In this paper, we present an efficient approach for near-optimally solving the NP-hard optimization problem of planning such informative paths. In particular, we first develop eSIP (efficient Single-robot Informative Path planning), an approximation algorithm for optimizing the path of a single robot. Hereby, we use a Gaussian Process to model the underlying phenomenon, and use the mutual information between the visited locations and remainder of the space to quantify the amount of information collected. We prove that the mutual information collected using paths obtained by using eSIP is close to the information obtained by an optimal solution. We then provide a general technique, sequential allocation, which can be used to extend any single robot planning algorithm, such as eSIP, for the multi-robot problem. This procedure approximately generalizes any guarantees for the single-robot problem to the multi-robot case. We extensively evaluate the effectiveness of our approach on several experiments performed in-field for two important environmental sensing applications, lake and river monitoring, and simulation experiments performed using several real world sensor network data sets.

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Farias, Vivek F., and Benjamin Van Roy. "Approximation algorithms for dynamic resource allocation." Operations Research Letters 34, no. 2 (March 2006): 180–90. http://dx.doi.org/10.1016/j.orl.2005.02.006.

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Liao, K., and H. Shen. "LP-Based Approximation Algorithms for Reliable Resource Allocation." Computer Journal 57, no. 1 (January 11, 2013): 154–64. http://dx.doi.org/10.1093/comjnl/bxs164.

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Liao, Kewen, Hong Shen, and Longkun Guo. "Improved approximation algorithms for constrained fault-tolerant resource allocation." Theoretical Computer Science 590 (July 2015): 118–28. http://dx.doi.org/10.1016/j.tcs.2015.02.029.

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Devanur, Nikhil R., Kamal Jain, Balasubramanian Sivan, and Christopher A. Wilkens. "Near Optimal Online Algorithms and Fast Approximation Algorithms for Resource Allocation Problems." Journal of the ACM 66, no. 1 (January 12, 2019): 1–41. http://dx.doi.org/10.1145/3284177.

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Bibi, Nazia, Zeeshan Anwar, and Ali Ahsan. "Comparison of Search-Based Software Engineering Algorithms for Resource Allocation Optimization." Journal of Intelligent Systems 25, no. 4 (October 1, 2016): 629–42. http://dx.doi.org/10.1515/jisys-2015-0016.

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AbstractA project manager balances the resource allocation using resource leveling algorithms after assigning resources to project activities. However, resource leveling does not ensure optimized allocation of resources. Furthermore, the duration and cost of a project may increase after leveling resources. The objectives of resource allocation optimization used in our research are to (i) increase resource utilization, (ii) decrease project cost, and (iii) decrease project duration. We implemented three search-based software engineering algorithms, i.e. multiobjective genetic algorithm, multiobjective particle swarm algorithm (MOPSO), and elicit nondominated sorting evolutionary strategy. Twelve experiments to optimize the resource allocation are performed on a published case study. The experimental results are analyzed and compared in the form of Pareto fronts, average Pareto fronts, percent increase in resource utilization, percent decrease in project cost, and percent decrease in project duration. The experimental results show that MOPSO is the best technique for resource optimization because after optimization with MOPSO, resource utilization is increased and the project cost and duration are reduced.

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Hegazy, Tarek. "Optimization of Resource Allocation and Leveling Using Genetic Algorithms." Journal of Construction Engineering and Management 125, no. 3 (June 1999): 167–75. http://dx.doi.org/10.1061/(asce)0733-9364(1999)125:3(167).

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Li, Xujie, Lingjie Zhou, Xing Chen, Ailin Qi, Chenming Li, and Yanli Xu. "Resource Allocation Schemes Based on Intelligent Optimization Algorithms for D2D Communications Underlaying Cellular Networks." Mobile Information Systems 2018 (December 5, 2018): 1–10. http://dx.doi.org/10.1155/2018/7852407.

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In this paper, the resource allocation problem for device-to-device (D2D) communications underlaying cellular networks is formulated and analyzed. In our scenario, we consider that the number of D2D user equipment (DUE) pairs is far larger than that of cellular user equipments (CUEs). Meanwhile, the resource blocks are divided into two types: resource blocks for CUEs and the ones for CUEs and DUEs. Firstly, the system model is presented, and the resource allocation problem is formulated. Then, a resource allocation scheme based on the genetic algorithm is proposed. To overcome the problem that the dedicated resource is not fully shared in the genetic algorithm, an improved harmony search algorithm is proposed for resource allocation. Finally, the analysis and simulation results show that the performances of the proposed genetic algorithm and the improved harmony search algorithm outperform than that of the random algorithm and are very close to that of the exhaustive algorithm. This result can provide an effective optimization for resource allocation of D2D communications.

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L, Nithyanandan, and Susila J. "Resource Allocation Algorithms for QoS Optimization in Mobile Wimax Networks." International Journal of Wireless & Mobile Networks 5, no. 3 (June 30, 2013): 77–91. http://dx.doi.org/10.5121/ijwmn.2013.5306.

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Mohseni, Maryam, S. Alireza Banani, Andrew W. Eckford, and Raviraj S. Adve. "Scheduling for VoLTE: Resource Allocation Optimization and Low-Complexity Algorithms." IEEE Transactions on Wireless Communications 18, no. 3 (March 2019): 1534–47. http://dx.doi.org/10.1109/twc.2019.2892128.

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Tuzlukov, V. P. "Approximation of Capacity in MIMO Systems." Doklady BGUIR 20, no. 2 (April 5, 2022): 53–61. http://dx.doi.org/10.35596/1729-7648-2022-20-2-53-61.

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This paper introduces functional approximations to the MIMO capacity over flat Rayleigh fading channels, which allow for analytical solutions to network resource optimization problems. This approximation allows to solve the problem of resource allocation optimization in radio networks and in other systems used to transfer information. The precision of the suggested approximations is assessed and is shown to provide a very close match to the exact capacity expression.

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P S, Swapna, Sakuntala S Pillai, and Sreeni K. G. "Resource allocation algorithm for symmetrical services in OFDMA systems." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 2 (May 1, 2020): 867. http://dx.doi.org/10.11591/ijeecs.v18.i2.pp867-874.

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The widespread acceptance of symmetrical services has urged for performance betterment techniques in wireless communication systems. In this paper, we propose an algorithm for resource allocation in MIMO-OFDMA system for applications that demand similar quality in uplink and downlink direction. The problem is formulated as multiobjective optimization problem with objectives to maximize the bidirectional data rates for individual users and to minimize the difference between the uplink and downlink data rate for each user. Fairness has been considered as a constraint in the optimization problem. The power allocation for each subcarrier in the OFDMA system is carried out using Linear Programming (LP) techniques, while the subcarrier allocation problem has been undertaken using an innovative multiobjective optimization technique that employs the concept of non-dominance in evolutionary algorithms. The results are extremely encouraging as they outperform the algorithms reported in literature using linear programming techniques or evolutionary algorithms solely.

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Shen, Zhentao. "Slice resource allocation based on Comprehensive Utility." Journal of Physics: Conference Series 2384, no. 1 (December 1, 2022): 012037. http://dx.doi.org/10.1088/1742-6596/2384/1/012037.

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Abstract Due to the increasing demand for quality of service in power communication services quality.smart grids require network slicing technology to provide differentiated services. However, the existing algorithms lack in-depth research on the comprehensive service, particularly in power services and system accessibility. To address these issues, this paper proposes an integrated utility-based resource allocation scheme. Under the premise of meeting user needs, the priority of slices is comprehensively considered to optimize system utility and access rate. By comparing to local optimization algorithms, the results show that the new algorithm can maximize the overall user utility of the system and optimize the slice access rate simultaneously.

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Kosorukov, E. O., and M. G. Furugyan. "Some algorithms for resource allocation in multiprocessor systems." Moscow University Computational Mathematics and Cybernetics 33, no. 4 (December 2009): 202–5. http://dx.doi.org/10.3103/s0278641909040050.

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Wayer, Shahaf I., and Arie Reichman. "Resource Management in Satellite Communication Systems: Heuristic Schemes and Algorithms." Journal of Electrical and Computer Engineering 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/169026.

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The high cost of frequency bandwidth in satellite communication emphasizes the need for good algorithms to cope with the resource allocation problem. In systems using DVB-S2 links, the optimization of resource allocation may be related to the classical multi-knapsack problem. Resource management should be carried out according to the requests of subscribers, their priority levels, and assured bandwidths. A satisfaction measure is defined to estimate the allocation processes. Heuristic algorithms together with some innovative scaling schemes are presented and compared using Monte Carlo simulation based on a traffic model introduced here.

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Chekanin, Vladislav A., and Alexander V. Chekanin. "Object-Oriented Class Library for Resource Allocation Problems." Applied Mechanics and Materials 799-800 (October 2015): 1149–53. http://dx.doi.org/10.4028/www.scientific.net/amm.799-800.1149.

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The object-oriented class library designed for solving various optimization problems of resource allocation, including problems of cutting materials and any dimensional packing problems, is described in this paper. The class library enables obtaining of suboptimal solutions of NP-completed resource allocation problems using standard evolutionary and modified heuristic optimization algorithms. The developed class library can be used in creation of an applied software for a wide class of optimization problems, including problems of resource allocation in storage systems and logistics, problems of cutting materials on machine tools with numerical control, scheduling problems and a large set of other practical problems.

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Klein, Rachelle S., Hanan Luss, and Uriel G. Rothblum. "Relaxation-based algorithms for minimax optimization problems with resource allocation applications." Mathematical Programming 64, no. 1-3 (March 1994): 337–63. http://dx.doi.org/10.1007/bf01582580.

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41

Zhou, Qiao. "Research on Optimization Algorithm of Cloud Computing Resource Allocation for Internet of Things Engineering Based on Improved Ant Colony Algorithm." Mathematical Problems in Engineering 2022 (April 26, 2022): 1–6. http://dx.doi.org/10.1155/2022/5632117.

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Considering the inability to use genetic algorithms, increased total execution time of tasks, and low user satisfaction and resource utilization based on existing algorithms, an improved ant colony algorithm optimization method for cloud computing resource allocation based on the mobile Internet of Things project is designed in order to better complete the allocation of cloud computing resources. In the mobile Internet of Things engineering environment, the tasks are classified by the characteristics of cloud computing resource allocation, and then, the justice distributive principle of the Berger model is analyzed through modeling by the human metamodel. Based on this, the global search capability of genetic algorithm is introduced into the initial information allocation process so as to integrate the genetic algorithm and ant colony algorithm and then apply them in the cloud computing resource allocation process. As can be learned from the simulation results, the proposed method can comprehensively improve user satisfaction and resource utilization while shortening the total execution time of tasks.

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42

Zhou, Qiao. "Research on Optimization Algorithm of Cloud Computing Resource Allocation for Internet of Things Engineering Based on Improved Ant Colony Algorithm." Mathematical Problems in Engineering 2022 (April 26, 2022): 1–6. http://dx.doi.org/10.1155/2022/5632117.

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Considering the inability to use genetic algorithms, increased total execution time of tasks, and low user satisfaction and resource utilization based on existing algorithms, an improved ant colony algorithm optimization method for cloud computing resource allocation based on the mobile Internet of Things project is designed in order to better complete the allocation of cloud computing resources. In the mobile Internet of Things engineering environment, the tasks are classified by the characteristics of cloud computing resource allocation, and then, the justice distributive principle of the Berger model is analyzed through modeling by the human metamodel. Based on this, the global search capability of genetic algorithm is introduced into the initial information allocation process so as to integrate the genetic algorithm and ant colony algorithm and then apply them in the cloud computing resource allocation process. As can be learned from the simulation results, the proposed method can comprehensively improve user satisfaction and resource utilization while shortening the total execution time of tasks.

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43

Zhou, Qiao. "Research on Optimization Algorithm of Cloud Computing Resource Allocation for Internet of Things Engineering Based on Improved Ant Colony Algorithm." Mathematical Problems in Engineering 2022 (April 26, 2022): 1–6. http://dx.doi.org/10.1155/2022/5632117.

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Considering the inability to use genetic algorithms, increased total execution time of tasks, and low user satisfaction and resource utilization based on existing algorithms, an improved ant colony algorithm optimization method for cloud computing resource allocation based on the mobile Internet of Things project is designed in order to better complete the allocation of cloud computing resources. In the mobile Internet of Things engineering environment, the tasks are classified by the characteristics of cloud computing resource allocation, and then, the justice distributive principle of the Berger model is analyzed through modeling by the human metamodel. Based on this, the global search capability of genetic algorithm is introduced into the initial information allocation process so as to integrate the genetic algorithm and ant colony algorithm and then apply them in the cloud computing resource allocation process. As can be learned from the simulation results, the proposed method can comprehensively improve user satisfaction and resource utilization while shortening the total execution time of tasks.

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44

Manekar, A. S., and Dr Pradeepini Gera. "Optimize Task Scheduling and Resource Allocation Using Nature Inspired Algorithms in Cloud based BDA." Webology 18, Special Issue 01 (April 29, 2021): 127–36. http://dx.doi.org/10.14704/web/v18si01/web18049.

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Task Scheduling and Resource allocation is a prominent research topic in cloud computing. There are several objectives associated with Optimize Task Scheduling and Resource allocation as cloud computing systems are more complex than the traditional distributed system. There are several challenges like resolving the task mapped to the node on which task to be executed. A simplified but near optimal proposed nature inspired algorithms are focus in this paper. In this paper basic idea about optimization, reliability and complexity is considered while design a solution for modern BDA (Big Data Application). Detailed analysis of experimental results, it is shown that the proposed algorithm has better optimization effect on the fair share policies which are presently available in most of the BDA. In this paper we focused on Dragonfly algorithm and Sea lion algorithms which are nature inspired algorithms. These algorithms are efficient for optimization purpose for solving task scheduling and resource allocation problem. Finally performance of the hybrid DA algorithm and Sea lion is compared with traditional techniques used for modern BDA using Hadoop MapReduce. Simulation results prove the efficacy of the suggested algorithms.

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Liang, Gaoyang, Long Xu, and Liang Chen. "Optimization of Enterprise Labor Resource Allocation Based on Quality Optimization Model." Complexity 2021 (April 9, 2021): 1–10. http://dx.doi.org/10.1155/2021/5551762.

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Companies take project-based management as their organizational strategy, and project quality assurance plays a vital role in improving customer satisfaction and enhancing corporate image. Starting from the perspective of optimizing project quality, this paper assigns different quality influencing factors to each project and each task of the project, divides the labor resources shared by multiple projects in the enterprise according to the skill level, and transforms the problem of project quality optimization. The problem of the highest skill level of labor resources allocated to all projects of the enterprise is designed, and algorithms are designed to achieve the optimization of project quality through the optimal allocation of labor resources. The various links in this article are closely related to form a comprehensive, scientific, and systematic research system for optimal human resource allocation, human resource management, and development. Finally, case analysis is used to confirm the usability of the model and provide a quantitative method and perspective for project-oriented companies to allocate workforce.

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Goldberg, Paul W., Alexandros Hollender, and Warut Suksompong. "Contiguous Cake Cutting: Hardness Results and Approximation Algorithms." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (April 3, 2020): 1990–97. http://dx.doi.org/10.1609/aaai.v34i02.5570.

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We study the fair allocation of a cake, which serves as a metaphor for a divisible resource, under the requirement that each agent should receive a contiguous piece of the cake. While it is known that no finite envy-free algorithm exists in this setting, we exhibit efficient algorithms that produce allocations with low envy among the agents. We then establish NP-hardness results for various decision problems on the existence of envy-free allocations, such as when we fix the ordering of the agents or constrain the positions of certain cuts. In addition, we consider a discretized setting where indivisible items lie on a line and show a number of hardness results strengthening those from prior work.

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47

Lei, Xiaoli. "Resource Sharing Algorithm of Ideological and Political Course Based on Random Forest." Mathematical Problems in Engineering 2022 (May 21, 2022): 1–8. http://dx.doi.org/10.1155/2022/8765166.

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Three aspects of the system’s online resource distribution and application are built around subject, object, and intermediary resources. The invention relates to a method for allocating resources based on the random forest algorithm. The resource allocation process entails the following steps: constructing a mathematical model of the resource allocation process, defining a mathematical model of the resource allocation process for the target object, and designing the cost function. The training data set for random forest is constructed using the classification concept. It is based on the mathematical model of resource allocation and cost function. Generation of random forests and prediction of target objects are based on historical data. Resource allocation steps are based on predictive structure. The invention provides a resource allocation method that satisfies task completion degree constraints and includes a resource allocation algorithm based on random forest with a high probability of finding an optimal solution. It also addresses the issue that intelligent optimization algorithms such as genetic algorithms are prone to fall into local optimum.

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Gupta, Punit, Ujjwal Goyal, and Vaishali Verma. "Cost-Aware Ant Colony Optimization for Resource Allocation in Cloud Infrastructure." Recent Advances in Computer Science and Communications 13, no. 3 (August 12, 2020): 326–35. http://dx.doi.org/10.2174/2213275912666190124101714.

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Background: Cloud Computing is a growing industry for secure and low cost pay per use resources. Efficient resource allocation is the challenging issue in cloud computing environment. Many task scheduling algorithms used to improve the performance of system. It includes ant colony, genetic algorithm & Round Robin improve the performance but these are not cost efficient at the same time. Objective: In early proven task scheduling algorithms network cost are not included but in this proposed ACO network overhead or cost is taken into consideration which thus improves the efficiency of the algorithm as compared to the previous algorithm. Proposed algorithm aims to improve in term of cost and execution time and reduces network cost. Methods: The proposed task scheduling algorithm in cloud uses ACO with network cost and execution cost as a fitness function. This work tries to improve the existing ACO that will give improved result in terms of performance and execution cost for cloud architecture. Our study includes a comparison between various other algorithms with our proposed ACO model. Results: Performance is measured using an optimization criteria tasks completion time and resource operational cost in the duration of execution. The network cost and user requests measures the performance of the proposed model. Conclusion: The simulation shows that the proposed cost and time aware technique outperforms using performance measurement parameters (average finish time, resource cost, network cost).

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Shi, Rui Peng. "Research on Cloud Computing Resources Provide Optimization Method." Advanced Materials Research 1049-1050 (October 2014): 1375–79. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1375.

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Cloud computing core issues of resource management research is to achieve efficient resource sharing and dynamic configuration. In this paper, the background of cloud computing technology to study how to optimize cloud computing data center resources optimal allocation problem. This paper compares and analyzes the application field of traditional algorithms and heuristic intelligent algorithm.

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Sangaiah, Arun Kumar, Ali Asghar Rahmani Hosseinabadi, Morteza Babazadeh Shareh, Seyed Yaser Bozorgi Rad, Atekeh Zolfagharian, and Naveen Chilamkurti. "IoT Resource Allocation and Optimization Based on Heuristic Algorithm." Sensors 20, no. 2 (January 18, 2020): 539. http://dx.doi.org/10.3390/s20020539.

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The Internet of Things (IoT) is a distributed system that connects everything via internet. IoT infrastructure contains multiple resources and gateways. In such a system, the problem of optimizing IoT resource allocation and scheduling (IRAS) is vital, because resource allocation (RA) and scheduling deals with the mapping between recourses and gateways and is also responsible for optimally allocating resources to available gateways. In the IoT environment, a gateway may face hundreds of resources to connect. Therefore, manual resource allocation and scheduling is not possible. In this paper, the whale optimization algorithm (WOA) is used to solve the RA problem in IoT with the aim of optimal RA and reducing the total communication cost between resources and gateways. The proposed algorithm has been compared to the other existing algorithms. Results indicate the proper performance of the proposed algorithm. Based on various benchmarks, the proposed method, in terms of “total communication cost”, is better than other ones.

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Journal articles on the topic 'Approximation algorithms; resource allocation; optimization'

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