# Efficient Next-Hop Selection in Multi-Hop Routing for IoT Enabled Wireless Sensor Networks
## 2 Related Work  
We explored the literature on energy-efficient multi-hop routing solutions that claim to enhance the lifetime of the network. Efficient energy consumption for IoT-based smart devices is a challenging task. Ideally, our focus is to identify the schemes that reduce the energy consumption in the data aggregation procedure from sensor node to BS. We consider the hierarchy-based energy-efficient routing mechanism. [PERSON] et al. presented a lightweight path and energy-aware algorithm. For energy awareness, it divides the energy of sensing devices into multiple energy levels to adjust the link weight of the nodes based on the distance and energy of nodes. Controller can also change the weight of links. Energy level \(g\) is calculated using (1) where \(e\) is a residual energy, \(E_{0}\) is initial energy value and energy levels of sensing nodes are denoted by \(G\). When the value of \(g\) changes, controller receives a \(P_{cc}\) message from sensing nodes to inform about energy level. \[g=\left\lceil\frac{e}{E_{0}}G\right\rceil \tag{1}\]  
Controller estimates the time for route adjustment where timer \(T_{timer}\) is given in (2) where \(T_{0}\) is an initial value of timer. \(N_{thresh}=(N_{thresh}>1)\) when \(P_{cc}\) value attains threshold. On receiving \(P_{cc}\), the value of \(T_{timer}\) decreases. \[T_{timer}=\frac{(i-1)(t_{i}-t_{1})+(N_{thres}-i)T_{o}}{N_{thres}-1} \tag{2}\]  
In [13], an improved distributed, multi-hop, adaptive, tree-based energy-balanced (DMATEB) solution is presented. It identifies the nearby node carrying high residual energy with less distance from the sensing node. It involves a multi-hop routing approach using clusters, which is helpful for better data collection and enhancing the network lifetime. [PERSON] et al. presented the Energy Aware Context Recognition Algorithm (EACRA) to avoid redundant data transmission on a periodic basis. It transmits data from sensing devices in case of change in context at a certain time that results in less message sharing by reducing energy consumption. A network performance analysis is performed in terms of different network topologies. Sensing devices operate only when the heating process enables them, otherwise they remain disabled [14]. In [15], the reliable and energy-efficient route selection (REERS) was presented to provide energy efficiency for large-scale deployments. In the IoT scenario, massive communication is involved, which demands efficient route selection for data aggregation in critical application scenarios. The REER considers both real-time and non- real time scenarios. An energy-aware routing protocol provides efficient data routing by utilizing low cost paths for real-time applications, achieving better latency and energy consumption. [PERSON] et al. presented the data aggregation from particular monitoring points, where the prime concern is the collection of data efficiently. The algorithm in the scheme constructs an appropriate path for aggregate data from collection regions and also aggregates data from those regions loaded with the data. The presented work contributes to enhancing the lifetime of the network, in this context, analyzing the path cost from one region to another region and then transmitting the load of each sensing node device. A mobile-based sink provides energy-efficient data collection because of mobility. Therefore, mobile devices connect with data sensing devices to aggregate data while following an appropriate path. It is based on three different phases: initialization, selection of aggregation region, and appropriate path construction [16]. In [17], the energy and dynamic spectrum issues in cluster-based routing were presented. The scheme conducts self-distribution-based clustering to attain minimum power consumption while creating several optimal clusters. It contains a greater number of channels for cooperative sensing.