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Today, a directional wireless multimedia sensor network is a popular environment for solving the task assignment problem. Achieving long‐term frontal monitoring of moving objects is a crucial challenge for scholars in this field. Utilizing directional sensors equipped with energy harvesting is an effective technique to enhance network performance. In this way, the energy of nodes is no longer limited to batteries and can result in better frontal monitoring. In this method, each sensor categorizes tasks based on its own energy, allowing the determination of task execution nodes through bidding. The present study proposes a distributed algorithm for directional task assignment, EN‐DADA, based on energy harvesting. The task was first classified to determine the candidate node set that could execute the task, and then the task assignment was determined according to the monitoring income of each node in the candidate node set. The comparative analysis confirmed that the proposed method had advantages in terms of task revenue and network lifetime when using the same energy harvesting model. [ABSTRACT FROM AUTHOR]

To address the issue that Lagrangian dual-function-based algorithms cannot guarantee convergence and global optimality for decentralised multi-area security constrained unit commitment (M-SCUC) problems, a novel decomposition and coordination method using mixed-integer linear programming (MILP) value functions is proposed. In the proposed solution method, first, each regional system operator sets the tie-line power injections as variational parameters in its regional SCUC model, and utilises a finite algorithm to generate an MILP value function, which returns the optimal generation cost for any given interchange plan. Then, with the value functions available from all system operators, theoretically, a coordinator is able to devise a globally optimal interchange plan. After the problem is solved, considering that power exchanges may alter the financial position of each area considerably from what it would have been via scheduling independently, the authors then propose a fair savings allocation method using the value functions derived above and the Shapley value in cooperative game theory. Numerical experiments on a two-area 12-bus system and a three-area 457-bus system were carried out. The validity of the value-function-based method was verified for the decentralised M-SCUC problem. The outcome of savings allocation was compared with that of the locational marginal cost-based method. [ABSTRACT FROM AUTHOR]

Solving a dynamic traffic assignment problem in a transportation network is a computational challenge. This study first reviews the different algorithms in the literature used to numerically calculate the user equilibrium (UE) related to dynamic network loading. Most of them are based on iterative methods to solve a fixed‐point problem. Two elements must be computed: the path set and the optimal path flow distribution between all origin–destination pairs. In a generic framework, these two steps are referred to as the outer and the inner loops, respectively. The goal of this study is to assess the computational performance of the inner loop methods that calculate the path flow distribution for different network settings (mainly network size and demand levels). Several improvements are also proposed to speed up convergence: four new swapping algorithms and two new methods for the step size initialization used in each descent iteration. All these extensions significantly reduce the number of iterations to obtain a good convergence rate and drastically speed up the overall simulations. The results show that the performance of different components of the solution algorithm is sensitive to the network size and saturation. Finally, the best algorithms and settings are identified for all network sizes with particular attention being given to the largest scale. [ABSTRACT FROM AUTHOR]

Fundamental phasor estimation is one of the most important jobs to be done by the digital protective relay (DPR). However, fault signals given to DPR consist of decaying DC (DDC) components, harmonics, and noise which can lead to inaccuracy in phasor estimation. Hence, for accurate results, digital filtering algorithms (DFAs) are mandatory to wipe out all these unwanted components. In modern DPR, discrete Fourier transform (DFT) is extensively used DFA for fundamental phasor estimation. However, the accuracy of DFT is affected by the presence of DDC. Hence, to tackle this problem, a new real-time fast DFA based on the mathematical morphology (MM) technique is put forward. First, the DDC components are filtered out using the morphological median filter (MMF) and are subtracted from the original fault signal. Afterwards, the fundamental phasor is extracted from the residual DDC-free signal using the DFT. Several computer-simulated, EMTP-generated, and realfield fault signals are used to examine the efficacy of the proposed DFA. In addition, the proposed DFA is compared with other existing techniques. The obtained results prove that the suggested DFA has vigorous performance in the presence of multiple DDC components, noise, and severe harmonic conditions with a low computational burden and high accuracy. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is emerging as a promising multiple access technology for the fifth generation cellular networks to address the fast growing mobile data traffic. It applies superposition coding in transmitters, allowing simultaneous allocation of the same frequency resource to multiple intra-cell users. Successive interference cancellation is used at the receivers to cancel intra-cell interference. User pairing and power allocation (UPPA) is a key design aspect of NOMA. Existing UPPA algorithms are mainly based on exhaustive search method with extensive computation complexity, which can severely affect the NOMA performance. A fast proportional fairness (PF) scheduling based UPPA algorithm is proposed to address the problem. The novel idea is to form user pairs around the users with the highest PF metrics with pre-configured fixed power allocation. Systemlevel simulation results show that the proposed algorithm is significantly faster (seven times faster for the scenario with 20 users) with a negligible throughput loss than the existing exhaustive search algorithm. [ABSTRACT FROM AUTHOR]