|
| Year | Author and reference details | Metric used for evaluating performance | Bullet points | Future research directions |
|
| 2004 | Kansal et al. [60] | Latency factor | (i) An analytically tractable characterization model is proposed.
(ii) Also, a harvesting theory is proposed.
(iii) Further, a solar energy harvesting circuit is proposed.
(iv) Furthermore, environment-aware tasking methods are proposed. | (i) The future research directions may include the steps for finding the appropriate framework consisting of estimation for the source characterization parameters with multiple energy harvesting techniques. |
|
| 2006 | Hsu et al. [61] | Utilization of energy | (i) An adaptive duty cycling algorithm is presented.
(ii) In the proposed framework, the available energy is used for adjusting the duty cycle of sensor nodes. | (i) Future work may include extending the proposed methods to exploit different power scaling frameworks, namely, dynamic voltage scaling (DVS), submodule power switching (SPS), and the use of multiple low power modes (MLPM). |
|
| 2007 | Moser et al. [62] | Violation of deadline | (i) An energy-driven scheduling scenario is presented.
(ii) The energy variability characterization curves (EVCC) have been introduced in this article. | (i) Future work may include extension towards multihop networks.
(ii) Need for distributed energy management solutions. |
|
| 2010 | Lee et al. [63] | Throughput | (i) A duty cycling scheme is presented with awareness about the harvesting mechanism.
(ii) In the proposed framework, the sleep time is adjusted dynamically. | (i) Future work may include extending the works towards the multihop data delivery scheme.
(ii) Further, future research directions include efforts towards the in-depth analysis of the energy harvesting process for transmission strategies. |
| Li et al. [64] | Packet delivery ratio | (i) The scheduling problem is defined in terms of a partially observable Markov decision process.
(ii) This article also presents that the formulated scheduling problem can be portioned as Markov decision process (MDP). | (i) The simulations are based on a single group consisting of the source node, relay, and destination node. Future work may include an extension towards considering multiple groups.
(ii) Future work may also include some more performance evaluation metrics. |
|
| 2011 | Ghor et al. [65] | Energy storage capacity & deadline miss rate | (i) An online scheduling mechanism is presented which is known as the earliest deadline with an energy guarantee (EDeg).
(ii) Further online scheduling mechanism characterizes various objectives such as the source of energy, energy storage, energy consumption, and total time. | (i) Future work may include the extension towards expanding the applicability of the scheduling framework by incorporating various techniques, namely, voltage scaling, frequency scaling, and dynamic power management, and then measuring the effectiveness of the proposed scheme. |
| Audet et al. [66] | Violation rate of energy & battery charge level | (i) In this article, two scheduling algorithms have been presented, namely, smooth to average method (STAM) and smooth to full utilization (STFU).
(ii) Further, these two scheduling algorithms are energy-aware and reduce the task violation likelihood. | (i) The future work may include an extension towards evaluating the proposed scheme by incorporating the data acquisition application. |
|
| 2012 | Györke and Pataki [67] | Energy consumption | (i) This article suggests that a scheduling system with prediction capability should be used in dynamically determining the sensors’ measurement timing.
(ii) This framework must have previous information about factors such as environment, sensors, experiments, and also about future prediction. | (i) The future research work may include the work towards performing the real-life testing of our assumptions.
(ii) Further, our future research directions will refine the scheduling framework to generate more accurate outputs with fewer energy consumptions. |
| Kooti et al. [68] | Violation count for QoS | (i) This article proposes an efficient energy management framework for enhancing QoS.
(ii) The framework consists of dual steps, an offline step, and an online step.
(iii) Next, prediction with frame-based energy harvesting is used for determining the appropriate QoS level in an offline step. | (i) This article utilized the window-based QoS model in which deadline misses are handled by specific distribution patterns.
(ii) Future work may include considering random/general distribution patterns for the window-based QoS model. |
| Renner and Turau [69] | Root mean square error (RMSE) | (i) This article proposes the framework for replacing the static scheme for enhancing the effectiveness of existing forecasting algorithms. | (i) The future work may include an extension towards improving prediction quality.
(ii) The future work will focus on the global information consisting of cloud cover or snow warnings that can be used for enhancing the accuracy in long-term prediction. |
|
| 2013 | Akgün and Aykın [70] | Life span of network | (i) This article proposes a framework for enhancing the life span of the network.
(ii) In this framework, the scheduling scheme based on TDMA has been modified, and in this modified scheduling scheme, members will request a time slot based on their energy prediction.
(iii) Next, the cluster heads will be responsible for assigning particular slots to members. | (i) The future work may include the extension towards simulating the system in other simulation platforms such as NS3 and then assessing the system performance.
(ii) The future research direction also considers other energy sources such as vibration or pressure and comparing our framework with MAC protocols other than LEACH. |
| Sommer et al. [71] | State of charge for battery | (i) This article presents a framework for estimating the state of charge for a battery of sensor node by considering the two points (1) the current flow in the battery and (2) the battery voltage is used as a measure of absolute state of charge.
(ii) Further, this article shows that appropriate prediction of the state of charge can be used for adaptively scheduling for operations having energy-neutral characteristics for multiple applications of sensor nodes. | (i) The future research direction may include efforts towards improving the prediction efficiency by incorporating some advanced methods such as recurrent neural networks or other alternatives. |
| Li et al. [72] | Consumption of energy and time | (i) This article proposes a scheme for the scheduling of the dynamic reconfiguration for a sensor node.
(ii) Further, this scheduling framework considers various factors such as statistical information on tasks and available energy under an energy harvesting environment. | (i) The future work will explore other communication mechanisms that will be more energy-efficient in an energy harvesting environment. |
|
| 2014 | Chetto [73] | Energy and time constraints | (i) This article proposes a semionline scheduling algorithm that is based on the earliest deadline first methodology.
(ii) Further, this framework depends on two specific factors such as energy demand and slack energy. | (i) The future work may include an extension towards incorporating fixed priority environments in the proposed framework.
(ii) Next, extension towards supporting dynamic voltage and frequency selection technology.
(iii) Furthermore, the extension towards considering the smallest harvester etc. |
| Castagnetti [74] | Packet reception ratio & energy efficiency | (i) This article proposes the power management framework for EH-WSNs.
(ii) Next, the proposed framework utilized two factors, namely, optimization of duty cycle and power control in transmission. | (i) The future work may include extension towards considering various adaptation methodologies, namely, adaptive modulation and coding for improving the performance and energy efficiency. |
| Li et al. [75] | Total number of executed tasks | (i) This article proposes the task scheduling scheme utilizing prediction data, and further, this framework is based on weather forecasts in EH-WSNs. | (i) The future work may include extension towards exploring the advantages of weather forecasting data for longer scheduling. |
|
| 2015 | Liu et al. [76] | Energy utilization & deadline miss rate (DMR) | (i) In this article, an intratask scheduling scheme is presented for the storage-less and converter-less channels.
(ii) Next, this framework utilizes neural network training. | (i) The future work may include extension towards utilizing the learning automata in place of the neural network and then assessing the system performance. |
| Zhang et al. [77] | Total number of ready tasks & deadline miss rate (DMR) | (i) This article proposes the scheduling algorithm utilizing the neural network for determining the appropriate scheduling pattern, the optimal size of the capacitor, and finally queue of tasks for improving the deadline miss rate. | (i) The future work may include extension towards utilizing other alternatives in place of ANN such as learning automata for further enhancing the deadline miss rate. |
|
| 2016 | Ali [78] | Lifetime of battery & consumption of power | (i) This article proposes the event-driven duty cycling framework for reducing the power consumption of the roadside unit which are having self-powered capabilities. | (i) The future work may include extension towards modifying the proposed framework by including few more factors, such as the weather conditions, for enhancing the performance of the distributed power management scheme. |
| Gomez et al. [79] | Efficiency of system | (i) This article proposes an efficient energy management unit to maintain power supply especially in those circumstances in which the harvested power is not sufficient for smooth system operation. | (i) In the proposed work, optimal-sized buffer is used and the future work may include the variable-sized buffer and then measuring the system efficiency. |
| Zhang et al. [80] | Efficiency in energy utilization & deadline miss ratio (DMR) | (i) This article proposes a new scheduling framework.
(ii) Next, this framework consists of various key attributes such as power prediction, task priorities are defined by using an artificial neural network, and finally an algorithm for selecting the task. | (i) The future work may include extension towards exploring the scheduling framework with different energy sources such as thermal, wireless, and vibration and also some hybrid approaches. |
| Oueis et al. [81] | Harvested energy, battery residual energy, and battery level variation | (i) This article discusses the effect of photovoltaic energy harvesting on the duty cycle of sensor nodes in both scenarios outdoor and indoor. | (i) The future work may include extension towards evaluating and comparing several other performance metrics such as end-to-end delay and packet delivery ratio. |
| Housseyni et al. [82] | Success ratio for deadline | (i) This article proposes a new scheduling framework for a sporadic task model.
(ii) Next, an energy-efficient offline task assignment heuristic is generated by the proposed framework. | (i) The future work may include an extension towards exploring the proposed framework in which arrival times are characterized with probabilistic distributions. |
| Maeda et al. [83] | End-to-end delay, total number of packets sent to base station (sink) per cycle, and the fairness index for the node position | (i) This article proposes a task scheduling scheme in which data is periodically collected from the sensor nodes. | (i) In this article, line topology is used for simulating the sensor network and future work may include extension towards adopting other topology and then measuring the performance of the system. |
|
| 2017 | Sanchez et al. [84] | Final remaining energy of sensor node | (i) The energy management strategy is based on a hybrid dynamical system approach.
(ii) The combination of continuous physical processes results in the hybrid nature; on the other hand, change in the functioning modes results in the discrete concept. | (i) In the current work, the sensor nodes’ energy is modeled using a specific representation named as a hybrid dynamical systems representation; future work may include modeling the energy with other representations and then measuring the performance. |
|
| 2018 | Huang et al. [85] | Optimal expected rewards | (i) This article proposes a scheduling policy for EH-WSNs based on the threshold value.
(ii) In this framework, limited memory is needed for storing optimal threshold values at the sensor node for carrying out energy management activity. | (i) The future work may include an extension towards utilizing Markov channels for energy harvesting systems.
(ii) In this scenario, the system state will be represented by data packets, energy, and the state of the wireless channel. |
| Bengheni et al. [86] | Mean latency, throughput, and packet delivery ratio (PDR) | (i) This article proposes the enhanced energy management scheme in EH-WSNs.
(ii) Further, this framework utilizes receiver-initiated communication.
(iii) Next, a policy based on the energy threshold is used for controlling the active/sleep periods. | (i) Future work may include extension towards considering other performance metrics such as average energy consumption and network lifetime. |
| Anagnostou et al. [87] | Rate of execution and energy efficiency | (i) This article proposes a hardware scheduling framework that is power-aware.
(ii) Next, this framework also observes harvesting power.
(iii) Further, the available energy along with the software module is used to dynamically activating the other system modules. | (i) Future work may include extension towards using the dynamic schedulers and also supporting the interruptible tasks. |
| Galmés and Escolar [88] | Energy consumption | (i) This article discusses solar energy harvesting-based WSN for monitoring the environment.
(ii) In this article, an analytical approach is used for enforcing the duty cycle. | (i) Future work may include extension towards using other software (with corresponding hardware) platforms.
(ii) Next, geographic latitude and meteorological conditions of the deployment should be used for refining the energy harvesting model. |
| Cui [89] | Mean relative error, and root mean square error | (i) This article proposes the solar energy prediction method which is based on long short-term memory recurrent neural network.
(ii) Also, a predictive task scheduling framework is proposed based on the prediction of energy available for improving the overall performance of the WSN. | (i) Future work may include extension towards using other alternatives in prediction such as gated recurrent unit (GRU) and bidirectional LSTM (bi-LSTM) and then measuring the performance. |
|
| 2019 | Sommer et al. [90] | Average tracking error | (i) In this article, a scheduling framework is presented which is having awareness about energy and mobility for achieving long-term tracking.
(ii) This framework calculates the virtual energy budget for forecasting energy. | (i) The future work may include extension towards incorporating some more advanced algorithms or some hybrid approach consisting of several methods for efficiently predicting energy harvesting. |
|
| 2020 | Zhang et al. [91] | Common active time (CAT) | (i) This article discusses the stochastic duty cycling problem and studies it under three cases (offline, online, and correlated stochastic duty cycling) to maximize utilization efficiency. Also, an offline algorithm is designed for the offline case with optimal performance. | (i) The point-to-point model can be extended to the networked case, such as multihop networks, where each device may have more than one neighbor. In multihop networks, the different pairs of neighboring nodes can be assigned with different periods by the coloring technique. |
|
