+Data driven schemes are classified into two main approaches~\cite{ref59,ref22}:
+
+%\begin{enumerate} [(I)]
+\subsubsection{Data Reduction Schemes}
+Data driven schemes deal with reducing the amount of data need to be transmitted to sink. They can be divided into stochastic approaches, time series forecasting, and algorithmic approaches. In stochastic approaches, the physical phenomena are transformed using stochastic characterization. The aggregation by these protocols requires high processing. Therefore, it is feasible to work on a powerful sensor nodes with a big battery. In time series forecasting, the old values of periodic sampling can be used to forecast a future value in the same series. In algorithmic approaches, sensed phenomena are demonstrated using heuristic or state transition model.
+
+\subsubsection{Energy Efficient Data Acquisition Schemes}
+They are concentrated on the energy consumption reduction in the sensing unit. These schemes are divided into adaptive sampling, hierarchical sampling, and model-based active sampling. In adaptive sampling, the amount of data that acquired from the transducer can be reduced by spatial or temporal correlation between data. These approaches are more efficient to be used in centralized fusion, but it consumes more energy due to requiring a high processing. While, the hierarchical sampling are more efficient when there are different types of sensors are installed on the nodes. These approaches are more energy efficient and application specific. The model-based approaches are similar to data prediction schemes. These approaches aim to decrease the data samples by using computed models and to conserve the energy by means of data acquisition.
+%\end{enumerate}
+
+\subsection{Battery Repletion}
+
+\indent In the last years, extensive researches have been focused on energy harvesting and wireless charging techniques. These solutions represent alternate energy sources to recharge wireless sensor batteries without human intervention~\cite{ref91,ref59}.
+
+\subsubsection{Energy Harvesting} In energy harvesting, several sources of environmental energy have been developed so as to enable the wireless sensors to acquire energy from the surrounding environment like solar, wind energy, vibration based energy harvesting, radio signals for scavenging RF power, thermoelectric generators, and shoe-mounted piezoelectric generator to power artificial organs~\cite{ref59}.
+
+\subsubsection{Wireless Charging}In wireless charging, the wireless power can be transmitted between the devices without requiring to the connection between the transmitter and the receiver. These techniques participated in increasing the availability of WSNs and prolonging the network lifetime. Wireless charging in WSNs can be performed in two ways: magnetic resonant coupling and electromagnetic radiation~\cite{ref22}.
+
+\subsection{Radio Optimization}
+
+\indent In wireless sensor node, the radio is the most energy-consuming unit for draining the battery power. Extensive researches have been focused on decreasing the power depletion due to wireless communication by means of optimizing the radio parameters such as coding and modulation schemes; transmission power and antenna
+direction; and cognitive radio and Cooperative communications schemes~\cite{ref22}.
+
+\subsection{Relay nodes and Sink Mobility}
+\indent The relay node placement and the mobility of the sink can be considered as energy-efficient strategies, which are used to minimize the consumption of the energy and to extend the lifetime of WSNs.
+%\begin{enumerate} [(I)]
+\subsubsection{Relay node placement}
+In WSN, some wireless sensor nodes in a certain region may die and this creates a hole in the WSN. This problem can be solved by placing the wireless sensor nodes in sensing field by using optimal distribution or by deploying a small number of relay wireless sensor nodes with powerful capabilities. The major goal of relay nodes is the communication with other wireless sensor nodes or relay nodes~\cite{ref52}. This solution can enhance the power balancing and avoiding the overloaded wireless sensor nodes in a particular region in WSN.
+
+\subsubsection{Sink Mobility}
+In the WSNs including a static sink, the wireless sensor nodes, which are near the sink drain their power more rapidly compared with other sensor nodes, and this leads to WSN disconnection and limited network lifetime~\cite{ref53}. Sending all the data in WSN to the sink maximizes the overload on the sensor nodes near to sink. In order to overcome this problem and prolong the network lifetime, it is necessary to use a mobile sink to move within the area of WSN so as to collect the sensory data from the static sensor nodes over a single hop communication. The mobile sink avoids the multi-hop communication and conserves the energy at the static sensor nodes near to the base station, extending the lifetime of WSN~\cite{ref54,ref55}.
+
