LITERATURE REVIEW - papanisaicharan/Scalable-energy-efficient-scheme-on-mobile-nodes GitHub Wiki

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LPWAN technologies, present in the data link layer provides more range, low bit-rate and power Machine to Machine communications. LoRa was primarily aiming to communicate to long distance nodes by using less amount of transmission energy. Narrow Band IoT is an LTE-based system designed primarily for low power & cost, wide-area (LPWA) technology that enables IoT devices to be in robust cellular connectivity, so optimizing their energy-expenditure. Cluster-based solutions is the best approach to extend the battery lifetime of resource-constrained and energy-limited wireless devices Two types of hierarchical clustering approaches have been identified: dynamic and static [1].

• In dynamic clustering, clusters size and number are changed periodically.
• In static clustering, during initialization phase clusters are formed which is unchanged during entire life time of network.

LEACH(Low Energy Adaptive Clustering Hierarchy) [3]is one of the most important protocols for energy-efficient homogenous WSNs [1]. Leach defines a distributed and dynamic cluster forming algorithm. This algorithm rotates the role of cluster-head, which consumes the highest energy, among all the network nodes and hence provides an energy load balancing and prolongs the network lifetime [1]. LEACH-C, a centralized version of LEACH, is introduced by [3]in which the process of creating clusters is run at the base station to find k optimal clusters. Here the cluster heads are selected only when their energy is greater than the residual energy of others [1].

Smaragdakis [4]proposed a Stable Election Protocol (SEP) that considers two types of sensor nodes having two different levels of the initial energy. The cluster head here is elected based on weighted probability according to the node initial energy. In this protocol the cluster heads formed are of higher energy when compared to the normal nodes.

Z-SEP (Zonal-Stable Election Protocol) is proposed by [5] faisal. In this protocol he considered both static and dynamic clustering. The working area is partitioned into three rectangular zones: a middle zone containing the normal nodes that directly communicate with the base station at the center, and two head zones containing the advanced nodes that communicate with the base station through forming clusters using techniques as in SEP [1]. However, in the case of largescale networks such as IoT, the way of fixed partitioning and direct communication with the base station may lead to undesirable effects. Huang et al. [6] proposed an energy-efficient deployment scheme for green IoT, introducing a hierarchical framework that uses relay nodes as an intermediate layer depending on the communication radius [1]. The number of relay nodes increases as the transmission range of sensor nodes decreases and vis versa. The protocol mainly focused on the energy saving and network lifetime indicate its flexibility and thus applicable for the IoT-based systems.

Recently, integrating multiple techniques has been applied for better energy savings as well as keeping a desired system performance in terms of other major parameters (such as security, data accuracy, reliability, etc.), depending on the nature of applications [1]. Daniello proposed a new quality-aware data management framework that comprises the use of virtual sensors and a data estimation technique using rule mining. The virtualized quality-aware sensor network provides different users with sensor data having different levels of quality according to their individual requirements [1]. Here the association rule mining is used to estimate the missing values in case of low perception quality. However, due to the lack of space, we have not included further protocols like those proposed by [1], [7], [8], [9].As the number of nodes increases cluster formations also increases thereby increasing the complexities and overhead. In the large scale networks, the zone-based static clustering schemes gain the advantages which do not consider many important factors at the time of zone formation such as the total number of nodes [1]. Further, they do not consider higher levels of node heterogeneity and larger numbers of election parameters or utilize efficient schemes for relay node placement. Hence, these are not flexible enough and become ineffective in the case of IoT-based systems. To address such challenges and give a better solution, with the requirements of green IoT as our major objectives. Here in this report we will be applying SEES protocol on mobile nodes and evaluate this performance with sees protocol on static nodes [1].