Optimal Energy-Delay Scheduling for Energy-Harvesting WSNs With Interference Channel via Negatively Correlated Search

Jiao, Dongbin; Yang, Peng; Fu, Liqun; Ke, Liangjun; Tang, Ke

doi:10.1109/jiot.2019.2954604

Cited by 16 publications

(9 citation statements)

References 31 publications

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“…The solution at each step is affected only by the battery and data buffer status at each time-slot. The maximum buffer capacity is C In this paper, first we compare the offline solution of the approximated convex problem (16) with that of the original non-convex optimization problem (15) using the global solver Solving Constraint Integer Programs (SCIP). Second, we compare the performance of the EH-WSN considering OMA and NOMA multiple access techniques.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The devices are assumed to be able to harvest energy from radio-frequency signals. In [16], the authors considered optimal energy-delay scheduling for EH-WSNs with interference channels. The non-convex resource allocation problem was solved using negatively correlated search, while in their earlier work [17], the non-convex optimization problem was transformed into a convex optimization problem by convex approximation.…”

Section: A Related Work and Contributionsmentioning

confidence: 99%

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Optimal Power Management in Energy-Harvesting NOMA-Enabled WSNs

Barhumi¹,

Al-Tous

2022

IEEE Internet Things J.

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Optimal resource allocation is crucial for successful deployment of energy harvesting wireless sensor networks (EH-WSN) such as Internet-of-Things (IoT) devices. Non-orthogonal multiple-access (NOMA) can significantly improve the network throughput compared to orthogonal multiple-access (OMA). This paper considers optimal power management and data scheduling in multi-hop EH-WSN using NOMA. The EH-WSN consists of M sensor nodes aiming to transmit their data to a sink node. Assuming network connectivity, the multi-hop EH-WSN is represented by a directed graph. The resource allocation problem is formulated to efficiently utilize the available harvested energy to send the available data to the sink node with minimum cost. The resource allocation problem given the system dynamics is non-convex due to the non-convex constraints. Assuming high signal-to-interference and noise ratio (SINR), the non-convex constraints are lower bounded by convex constraints. With the aid of variable transformation, the constrained non-convex problem is approximated with a convex problem. The convex problem is solved using finite horizon dynamic programming considering offline and online operations. The offline problem is formulated assuming non-causal information of the harvested energy and data arrival. Model predictive control (MPC) framework is used to obtain the solution of the online operation of the EH-WSN. A distributed MPC (DMPC) is proposed to overcome the computational complexity of solving the centralized MPC problem, assuming each sensor node is allowed to exchange information with its neighboring nodes. In the simulations, we use energy efficiency and average data transmitted to compare the performance of the EH-WSN using NOMA and OMA. Simulation results confirm that NOMA in multi-hop EH-WSN results in higher throughput compared to OMA.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: A Related Work and Contributionsmentioning

confidence: 99%

Optimal Power Management in Energy-Harvesting NOMA-Enabled WSNs

Barhumi¹,

Al-Tous

2022

IEEE Internet Things J.

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“…Nowadays, WSN is one of the promising technologies for real-world applications [ 2 , 3 ]. To improve their performance and lifetime, various computational intelligence techniques have been used recently for the design of WSNs [ 4 ], such as particle swarm optimization (PSO) [ 5 ], genetic algorithms (GAs), ant colony optimization (ACO) [ 6 ], machine learning algorithms [ 7 ], lion optimization [ 8 ], negatively correlated search [ 9 ], and Fibonacci tree optimization algorithm [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved Multioperator-Based Constrained Differential Evolution for Optimal Power Allocation in WSNs

Gong

2021

Sensors

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Optimal power allocation (OPA), which can be transformed into an optimization problem with constraints, plays a key role in wireless sensor networks (WSNs). In this paper, inspired by ant colony optimization, an improved multioperator-based constrained adaptive differential evolution (namely, IMO-CADE) is proposed for the OPA. The proposed IMO-CADE can be featured as follows: (i) to adaptively select the proper operator among different operators, the feedback of operators and the status of individuals are considered simultaneously to assign the selection probability; (ii) the constrained reward assignment is used to measure the feedback of operators; (iii) the parameter adaptation is used for the parameters of differential evolution. To extensively evaluate the performance of IMO-CADE, it is used to solve the OPA for both the independent and correlated observations with different numbers of sensor nodes. Compared with other advanced methods, simulation results clearly indicate that IMO-CADE yields the best performance on the whole. Therefore, IMO-CADE can be an efficient alternative for the OPA of WSNs, especially for WSNs with a large number of sensor nodes.

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“…2 in [1] for illustration). Although the basic idea of NCS has attracted increasing research interests [4][5][6][7] and has shown very promising performance in various real-world problems [7][8][9][10][11][12], the original instantiation of NCS [1] was mostly devised by intuition, lacking the mathematical explanations of why the negatively correlated search processes can lead to a parallel exploration and the guidance of how to optimally obtain the negatively correlated search processes. In this paper, a mathematically principled NCS framework is proposed to address this issue.…”

Section: Introductionmentioning

confidence: 99%

Parallel exploration via negatively correlated search

Yang

Tang

et al. 2021

Front. Comput. Sci.

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Effective exploration is key to a successful search process. The recently proposed negatively correlated search (NCS) tries to achieve this by coordinated parallel exploration, where a set of search processes are driven to be negatively correlated so that different promising areas of the search space can be visited simultaneously. Despite successful applications of NCS, the negatively correlated search behaviors were mostly devised by intuition, while deeper (e.g., mathematical) understanding is missing. In this paper, a more principled NCS, namely NCNES, is presented, showing that the parallel exploration is equivalent to a process of seeking probabilistic models that both lead to solutions of high quality and are distant from previous obtained probabilistic models. Reinforcement learning, for which exploration is of particular importance, are considered for empirical assessment. The proposed NCNES is applied to directly train a deep convolution network with 1.7 million connection weights for playing Atari games. Empirical results show that the significant advantages of NCNES, especially on games with uncertain and delayed rewards, can be highly owed to the effective parallel exploration ability.

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Optimal Energy-Delay Scheduling for Energy-Harvesting WSNs With Interference Channel via Negatively Correlated Search

Cited by 16 publications

References 31 publications

Optimal Power Management in Energy-Harvesting NOMA-Enabled WSNs

Optimal Power Management in Energy-Harvesting NOMA-Enabled WSNs

An Improved Multioperator-Based Constrained Differential Evolution for Optimal Power Allocation in WSNs

Parallel exploration via negatively correlated search

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