Comparision of Different Routing Protocols for Energy Consumption in Wireless Sensor Networks

Categories: Energy Consumption

In paper Gino Alvarado,, (2017) was proposed LEACH and ONCH protocol for the best energy level model and energy consumption. LEACH protocol use stochastic algorithm to implement in this network. For energy consumption uses direct communication and multi-hop routing in the network. The energy life of the sensor, using a TDMA-based MAC protocol (LEACH) can obtain lower energy consumption and maintain the clusters in order to improve the life time of the sensor network. Simulation using MATLAB software for OHCH algorithm based on particular circular topology.

By using Circular MH topology the ONCH algorithm over LEACH is a little better than LEACH without ONCH because the energy consumption has improved and give more life to the sensor network.

In paper WalidAbushiba,, (2017) was proposed CH-LEACH, LEACH and DEEC for large scale integration and energy efficiency. The main aim of this work was to design and implement a protocol which enhance exiting protocols in order extend the Life Time of Network.

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Hence base station is not near all sensor nodes; therefore the node will use excessive energy to deliver data. Cluster-Head algorithms aimed to solve this problem. CH-LEACH protocol is to be used. To ensure Centralized k-Means algorithm works unsupervised, the following inputs. To design and implement a protocol which enhances exiting protocols in order extend the Life Time of Network. Using MATLAB R2015a, as simulation software to improve the performance with LEACH and DEEC compared to CH-LEACH. The Life time comparing to LEACH and DEEC protocols. It is clear from the figure that CH-LEACH protocol has performed well.

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The life time of the network in CH-Leach shows major extension compared to LEACH and DEEC protocols.

In paper Deepa Phanish (2017) was proposed EEHC and LEACH protocol for supporting energy-efficient strategies. Consider ‘full aggregation’, where a unit of data gathered from all the nodes within a cluster is aggregated at the cluster head, resulting in a single unit only. Establish a new framework for the analysis of multi-hop, multi-level hierarchical clusters with possibly different degrees of data aggregation at each level. Using MATLAB as a simulation software to simulate networks of one, two and three levels with varying number of nodes, cluster head election probabilities, and data-size ratios. Analysis of the data sizes on the network optimality could be performed for models with different network architectures. Additionally, formulating an appropriate cost model to study the optimality of networks where the data size forwarded by the cluster head depends on the size of its cluster.

In paper Mayssa Bouraouri,, (2015) was proposed SEP protocol for minimize energy consumptions to extended network lifetime. It can also increase network scalability. In these networks, a few nodes are elected as Cluster Heads (CHs) whose main goal is to aggregate data from member nodes and transmit it to the Base Stations (BS) in order to minimize transmission and thus, energy consumption. Stable Election Protocol (SEP) introduces heterogeneity in WSNs. In SEP, after the cluster heads has been elected each node transmits to the closest cluster head so as to split the communication cost with the sink. SEP does not require energy knowledge sharing but is based on assigning weighted election probabilities of each node to become a CH according to their respective energy. By using this approach, SEP ensures that the CH is randomly selected based on the fraction of energy of each node. They simulated the SEP protocol using TINYOS as a simulator for random networks with different number of nodes. Results show that the optimal number of CH evolves as 2n π. Energy consumption, packet delivery ratio and end-to-end delay are important metrics to evaluate the performance of a routing protocol WSNs.

In paper Chaima Bejaoui,, (2015) was proposed LEACH protocol for limited energy supply. They propose a new election mechanism with important features: it ensures that all nodes are in range of a cluster head while keeping the number of cluster heads low, it takes into account the residual energy of nodes, and it requires a small communication overhead. Using NS2 simulation software to simulate our proposition and LEACH protocol. Designed a protocol that reduces the number of CHs without producing isolated nodes. Simulation results showed that our protocol reduces the number of CHs (which reaches 17% of reduction for 100 nodes) compared with LEACH. Our protocol presents an optimal result (number of isolated nodes is null) for isolated nodes. The perspective of this work includes the improvement of the election phase to be adapted to wireless multimedia sensor networks.

In paper Sankardas Roy,, (2014) for in-network data aggregation (i.e., combining partial results at intermediate nodes during message routing) significantly reduces the amount of communication overhead and energy consumption. Present an algorithm to enable the base station to securely compute predicate count or sum even in the presence of such an attack. In network data aggregation can reduce the amount of communication and hence the energy consumed, especially in large WSNs. The main idea is to combine partial results at intermediate nodes during message routing. The Tiny Aggregation Service (TAG) to compute aggregates, such as Count and Average, using tree-based aggregation algorithms were proposed. Our goal is to enable BS to obtain the ‘true’ estimate of the aggregate (which BS would compute if there were no compromised Nodes) even in the presence of the attack. More formally, Goal (a) is to detect if ˆB, the synopsis received at BS is the same as the ‘true’ final synopsis B, and goal (b) is to compute B from ˆB and other received information. Without loss of generality, in the rest of the paper we limit our discussion to the context of Sum aggregate (if not otherwise specified). Several secure aggregation algorithms have been proposed assuming that the BS is the only aggregator node in the network. They presented an attack-resilient computation algorithm which would guarantee the successful computation of the aggregate even in the presence of the attack.

In paper Jinyang Guo,, (2014) was proposed for energy efficiency. There are several measures to achieve high signal-noise ratio with the same transmission power. These measures include reducing power use during both active and sleep modes, reducing inference among the same protocol device or with other protocols’ device, selecting the frequency according to transmission distance, and having different transfer modes according to the size of payload. In wireless sensor networks, it is very important to keep the on-board battery’s life because it is very hard to supply those sensors with direct power source. Energy harvesting is very important for implementing a wireless sensor networks because it can get energy from renewable energy thus

Extend the life of non-renewable battery. Radio Frequency (RF) energy is becoming attractive because it can convert and store easily, and it also can easily send from the central station. Generally, there are three components in RF energy harvesting, transducer, condition and store. There are many protocols that deal with how to harvest energy efficiently. Different wireless protocols adopt these measures according to the application of the payload and the environment conditions of applications. New technology in hardware will soon be integrated into software protocol to take advantage its energy efficiency property.

In paper Mina Malekza deh,, (2013) proposed AODV and DSR for measure energy consumption level of the sensor nodes. They use Ad Hoc On-demand Distance Vector (AODV) and Dynamic Source Routing (DSR). Compare AODV and DSDV through an analytical model. The results prove the correlation between routing energy consumption with topology change rates and traffic conditions. Simulations using NS2 software.

From the results it can be concluded that DSR shows better performance than AODV in WSN. It has been shown that DSR provides shorter average delay and fewer packets lost than AODV when the nodes are stationary or there is lower mobility. Considering that energy is a critical parameter in WSN, based on the results we can conclude that DSR performs better in the WSNs that there are lower mobility. Considering that energy is a critical parameter in WSN, based on the results we can conclude that DSR performs better in the WSNs that there are lower mobility. In contrast, when there are frequent mobility in the WSNs, AODV can be a better choice. Note that, the use of a proper routing algorithm based on the network requirements, can highly improve the network performance.

In paper M.S.Fareed,, (2012) proposed LEACH, DEEC, SEP, TEEN protocol is used for energy minimization, route path selection, and increased connectivity. More recent protocols like SEP and TEEN considered the reverse that is energy heterogeneity which is more applicable to case of WSNs. In DDR scheme a bi dimensional Markov chain model is used to study their

Cluster-forming behaviour and derive formulas for statistics of system like probability mass function of number of CHs, Average, standard deviation and coefficient of variation of Number of CHs. Through DDR scheme uncertainty problems are well handled. Since Markov chain is irreducible and all states are positive recurrent and stationary distribution exists. Through this model we can study uncertainty of number of CHs by observing the distribution and selection of CHs in DEEC. DDR clustering scheme is promising in providing energy efficient, load balancing, scalable and robust communication in WSNs. We perform simulations to compare performance of selected protocol for cluster formation and CHs selection. They use MATLAB software as a simulator to analyze performance of cluster base routing protocol. From the Simulation results it is found that DEEC selects optimal number of Cluster head selections to forward data to BS and to increase life time of network.

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Comparision of Different Routing Protocols for Energy Consumption in Wireless Sensor Networks. (2022, May 01). Retrieved from

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