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| Year | Author and reference details | Metric used for evaluating performance | Bullet points | Future research directions |
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| 2012 | Meng et al. [130] | Number of nodes available and throughput | (i) A new clustering routing protocol is presented for EH-WSNs which is known as an adaptive energy harvesting-aware clustering routing protocol.
(ii) In the proposed framework, energy state of the node is considered for selecting the cluster head.
(iii) The parameters in the proposed framework can be adjusted as per the deployed environment. | (i) Future research directions may consider the work towards some more advanced energy-efficient schemes with more metrics for cluster head elections. |
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| 2013 | Xiao et al. [131] | Throughput, number of dead nodes, and data failure rate | (i) A new function has been proposed which is known as the energy potential function for assessing the capabilities of sensor nodes in terms of harvested energy.
(ii) Next, for EH-WSNs, a new protocol known as energy potential LEACH has been presented which is the extended version of the LEACH. | (i) Future research directions may consider the work towards applying the proposed framework in the practical environment with consideration of two constraints related to sensor nodes such as the capacity of the battery and limited computing capabilities. |
| Zhang et al. [132] | Number of rounds until the first node dies | (i) A new framework has been proposed for enhancing the lifespan of the network.
(ii) In the proposed framework, the relay nodes for cluster heads are actually the energy harvesting sensor nodes. | (i) Future research directions may consider the work towards proposing a new distributed EH clustering scheme.
(ii) The next work may be related to exploring energy harvesting nodes with different configurations.
(iii) The next work may be related to using the testbed for evaluating the proposed framework. |
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| 2014 | Mostafa and Hassan [133] | Number of alive nodes | (i) A new clustering algorithm has been presented for EH-WSNs in which three factors are considered primarily such as sensor node centrality, amount of energy harvested, and the total neighbors.
(ii) Next, fuzzy petri nets are used in the proposed algorithm for selecting the cluster heads. | (i) Future research directions may consider the work towards using type-2 fuzzy petri nets and other advanced versions such as extended TOPSIS for selecting the cluster heads. |
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| 2015 | Peng et al. [134] | Accumulated cluster failure time and total amount of information bits | (i) A new framework has been proposed for clustering in EH-WSNs and is known as the distributive energy-neutral clustering protocol.
(ii) The proposed framework utilizes the novel cluster head group mechanism.
(iii) Next, for efficiently handling the high traffic load, a cluster is allowed to use multiple cluster heads. | (i) Future research directions may consider the work towards using hybrid advanced metaheuristic algorithms for finding the solution to the optimization problem in terms of the optimal number of clusters. |
| Yukun et al. [135] | Total number of cluster heads, alive nodes, success ratio of data transmission, and residual energy | (i) A new clustering routing framework has been presented for solar energy-harvested WSNs.
(ii) The proposed framework utilized the energy threshold factor for reviving the sensor nodes. | (i) Future research directions may consider the work towards using other energy harvesting environments since the proposed framework dedicated to solar energy harvesting. |
| Li and Liu [136] | Normalized average throughput, awake nodes, and residual energy | (i) A new clustering routing framework has been presented for EH-WSNs.
(ii) The proposed framework utilized discrete particle swarm optimization.
(iii) Next, in the proposed framework, the modified discrete particle swarm optimization algorithm has been utilized for determining the optimal topology. | (i) Future research directions may consider the work towards optimization of the sensor nodes number in each cluster.
(ii) Next, the future work may consider two performance metrics, namely, throughput and alive nodes for comparing the proposed framework with the distributed framework. |
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| 2016 | Li and Liu [137] | Normalized average throughput, awake nodes, and residual energy | (i) A new distributed clustering routing framework is presented for EH-WSNs.
(ii) In the proposed framework, the cluster head electing mechanism is based on the current residual energy profile of the sensor nodes and the amount of harvested energy.
(iii) Also, the proposed framework utilized the model for predicting solar energy based on a neural network. | (i) Future research directions may consider the work towards dynamic tuning of various coefficients that are used in the framework for overall improving the performance.
(ii) The next future work may consider other different prediction models for solar energy for improved accuracy. |
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| 2017 | Bozorgi et al. [138] | Alive nodes and average energy of alive nodes | (i) A new hybrid framework has been proposed consisting of both static and dynamic clustering mechanisms.
(ii) The distributed-centralized approach with multihop routing has been used in the proposed framework.
(iii) The proposed framework considers the three primary factors for the clustering mechanism such as the current level of energy, the number of neighbors, and the amount of harvested energy. | (i) Future research directions may consider the work towards some more advanced energy-efficient schemes with more metrics for cluster head elections in the clustering mechanism. |
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| 2019 | Afsar and Younis [139] | Live nodes, throughput, consumed energy, and maximum hop count | (i) A new cross-layer design framework has been presented with capabilities such as energy scavenging and transfer for EH-WSNs.
(ii) The proposed framework adopted a distributed two-tier routing topology.
(iii) The proposed framework divided the network into virtual tracks further the width is based on the density of node and distance to the base station. | (i) Future research directions may consider the work towards some more advanced energy-efficient schemes with more metrics for cluster head elections in the clustering mechanism. |
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| 2020 | Ge et al. [140] | Packets received by the base station, packets received by the cluster head, and failure node ratio | (i) A new uneven clustering framework has been presented for EH-WSNs.
(ii) In the proposed framework, the energy prediction model is based on a long short-term memory.
(iii) Also, the proposed framework considers the energy consumption of the node with supplement models for clustering mechanism. | (i) Future research directions may consider the work towards enhancing the rate of dynamic data transmission by automatically adjusting the network parameters and measuring the performance with the help of more simulations. |
| Sah and Amgoth [141] | Network lifetime, alive nodes, and total commutative packets arrived at base station | (i) A new clustering framework has been presented for solar EH-WSNs.
(ii) The proposed framework utilized the hierarchical clustering approach. | (i) Future research directions may consider the work towards using advanced efficient harvesting devices for EH-WSNs. |
| Kumar and Chaparala [142] | Energy consumption, throughput, delay, delivery ratio, drop ratio, and network lifetime | (i) A new clustering framework has been presented utilizing a hybrid optimization approach consisting of two optimization algorithms such as bacterial foraging and fruit fly optimization algorithm for cluster head selection in the EH-WSNs. | (i) Future research directions may consider the work towards using other advanced hybrid models of optimization algorithms for selecting the cluster heads. |
| Rathore et al. [143] | Energy consumption, delay, delivery ratio, and throughput | (i) A new clustering framework is presented utilizing a hybrid optimization approach consisting of whale and grey wolf optimizer.
(ii) In the proposed framework, the exploitation and exploration abilities are significantly improved with the help of the hybrid approach. | (i) Future research directions may consider the work towards using other advanced hybrid models of optimization algorithms for the clustering mechanism. |
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