I. INTRODUCTION:

Today, energy saving methods has become a hot topic in the Internet of Things (IoT) space. By embedding restricted transceivers on multiple devices, IoT creates new ways of interacting between people and objects, and between objects themselves as shown in Fig.1. IoT nodes are made up of a sensor or processor sensor. Nodes in the system should be wise to synchronize dynamic location updates and find the network route. Currently any standard regarding the IoT protocol has not yet emerged. It is very important to get an efficient IoT protocol.

The Wireless Sensor and Ad hoc network consist of a group of mobile sites that are dynamically distributed without fixed infrastructure. Each node in the network can act as a host and router, and participate in the network in the same way and is free to travel in any direction [1]. Various types of tunnels have been exploited in order to communicate effectively with other areas. Node mobility, bandwidth, power and physical security are all taken into description for the routing protocol [2] [3]. In collaboration with IoT and WSN, great similarities can be found in node systems and distributed ecosystems. So it is possible to use wireless sensor networks and Ad Hoc network in the IoT environment. And it is very important to analyze the effectiveness of existing Ad Hoc protocols for the use of the IoT environment [4] [5].

Fig. 1: Evolution of IoT

As a layer of IoT sensors, WSN contains a large number of low sensors, used in broad applications with different needs and [6]. However, these sensors are powered by limited batteries and operate for a long time, and it is affordable and expensive to replace them when sensor nodes are used. Therefore, energy efficiency remains a key issue in WSNs [7] [8]. Also, it is necessary to design an energy efficiency protocol to save significantly energy.

Fig. 2: IoT Devices World Wide

In this paper, the following section discusses the work related to the prediction of WSN-based protocols used by WSN. It also introduces the proposed mechanism and describes simulation results obtained using a network simulator and describes the conclusion and future work.

II. RELATED WORK:

In previous studies, it is observed that energy efficiency plays a very important role in the construction of route contracts. Here, this research work talks more about the research work done specifically for WSN-assisted IoT networks.

In [9], studied a test machine for existing road systems such as AODV, DRS and OLSR and compared their performance in other IoT-enabled environments and studied the process of existing protocols in order to use the appropriate IoT protocol. In another work [10], propose a new efficient centroid based routing protocol (EECRP) for IoT enabled by WSN to improve network performance. The proposed EECRP incorporates three key components, a new set of algorithms that enable local automation, a new series of cluster synchronization algorithms and cluster cluster rotation (CH) based on the centroid's ability to distribute the power equally across all sensors, and a new way to reduce the power consumption of remote communications.

In [11], he is introducing the Multicast Routing Protocol (ESMR) for the Multicast Routing (ESMR) of the IoT network. According to ESMR, nodes are divided into two types: one is a network area and the other is a non-network area. Existing network locations are called network locations. The non-network area uses various measurements, to measure the weight of the network path. The highest-weighted node will be selected as the sink. The non-network location then sends the request to the input point for network access. The proposed [12], DEEC-VD method not only uses a set of overlapping groups with the help of a vector but also uses the Dijkstra Algorithm to find the shortest path between the active group heads (CHs) to provide maximum power. DEEC-VD provides a link between cluster header and using the Dijkstra Algorithm, the minimum distance calculated to connect the functional group heads that creates short-term results for a more efficient system operation.

The [13] paper for the routing capacity protocol known for the use of heterogenible IoT is established in the presence of continuous energy sources. We propose a new EHARA route algorithm, which is also enhanced by integrating a new parameter called 'extra backoff'. The proposed algorithm improves sensor node time and service quality (QoS) under variable traffic load and power availability conditions. In [14] they proposed a model called MEGA that focuses on extending network lifetime and optimization strategies. The proposed method combines models such as Local Search and S sleep-Wake mechanism to develop a better route algorithm.[15], leverages hybrid multipath capability and service quality (QoS) - restarting the state-of-the-art routing protocol version 2 (MEQSA-OLSRv2), which is developed to address the challenges presented by limited power sources, node mobility, and vulnerability of traffic during data transfer in the MANET-WSN converistic scenario of the IoT network The proposed protocol uses the node rate depending on the number of node rank metric (MCNR). This MCNR combines multiple energy-related parameters with QoS into a complete metric to dramatically reduce the complexity of multiple assumptions and avoid over-regulation caused by different distributions of multiple parameters.[16].

Proposes multiuser protocol and multi-hop hierarchical -based routing protocol (EAMMH-RP) includes multi-hop communication when equally distributed load capacity across all sensor nodes in cluster configurations, -algorithms for adapting and rotating graduates and a new way to reduce long-distance communication power. Multi-hop where it uses transmission nodes when transmitting information to a base station. Another research project [17], is a novel protocol for regional novel operation (referred to ER-SR). In the ER-SR, a power region algorithm is analyzed to select the high-power areas remaining in the network as the source of power transmission mode. Subsequently, the source control nodes calculate the optimal route path for each virtual node, enabling the nodes to partly participate in the route process and estimate the energy consumption of the sensor nodes.

III. PROPOSED MODEL:

This section describes the detailed design and pseudo code of EEM. Fig. 3 and Fig. 4 show the Pseudo code of EOT. An Energy Optimization techniques (EOT) is proposed to decrease the energy consumption and increase the lifetime of networks. This mechanism uses the caching of RREQ and RSEND packets and keeps more than one route in routing table for each destination. The EEM mechanism estimates the nodes density of the network and calculates their energy levels EL. Whenever a source node S want to communicate with the destination node D, it check the energy level EL of neighbor nodes Ni, if the EL is found to be more than a Threshold TH level of energy, it selects those neighboring nodes Ni and update the routing table RT.

When the node reaches the TH it goes into sleep mode after performing following functions; If the EL of the Ni is greater than TH, then cache updating is performed on the node and new route is established through that node. Otherwise, the node with maximum energy is chosen for cache updating then a new route is established Whenever a neighbor node energy level is found to be less than 50 joules, it skips the source node to send the data and stop communication among them. Otherwise, the source node seek the routing table, and select those neighboring nodes which has highest energy level and start sending data towards destination node.

This mechanism uses short control message (low overhead) and faster routing mechanism. This method would enhance the route selection procedure of routing protocols. It initiates the link lifetime and node’s residual energy to augment the route discovery process that allows the routes that assures the link lifetime and energy requirements.

Fig. 3: EOT Methodology

Algorithm Energy Efficient Method (EOT)
1: All nodes follow EOT
2: EOT estimates density of nodes
3: Calculate EL of nodes
4: When S wants to communicate with D
5:	EOT checks EL of Ni
6:	If(EL> TH)
7:	Select Ni and update RT
8:	else
9:	Decrease TR of Ni
10: end if
11: for all Ni
12:	if (EL < TH)
13:	Abandon S to stop communication
14:	else
15:	S select Ni with Max_ EL as a forwarding node
16: end if

Fig 4: Description of Symbol Definition

Symbol	Description
EEM	Energy Efficient Method
EL	Energy Level
Ni	Neighbor Nodes
TH	Threshold Energy Level
RT	Routing Table of Node
TR	Transmission Range
RX	Receiving Power
TX	Transmission Power
S	Source Node
D	Destination Node

IV. SIMULATION SETUP:

The Energy Efficient Method (EOT) are used and their performance is simulated in a typical IOT scenarios The Energy Efficient Method (EOT) is implemented in network simulator 2 (v2.35). The average energy consumption of EEM mechanism along with AODV, DSDV, DSR and OLSR protocols has been considered. The simulation result obtained according to varying number of nodes, no of connections, speed, source sending rate and pause time. Average Energy Consumption is the total energy consumption of mobile nodes divided by total number of nodes, the equation is given by:

Table 1: Simulation Parameter

Parameters	Value
Topology Area	500 x 500 m
Number of Nodes	5-10-15-20-25
Mobility Model	Random Waypoint
MAC Layer	IEEE 802.11 DCF
Propagation Model	Two way Ground
Transmission range (m)	250
Simulation Time (s)	200
Pause time (s)	0-40-80-120-160
Traffic type	CBR
Packet Size (bytes)	512
Initial Energy (Joules)	100
Rate (packet/sec)	1-2-3-4-5
Max. Connection	5-10-15-20-25
Initial Energy	100 Joule
Idle Power	712e-6
Receiving Power	0.3
Transmission Power	0.6
Sleep Power	144e-9

V. RESULT ANALYSIS:

Fig. 5 represents the average power consumption of the route protocols according to the connection number 5,10,15,20 and 25. The value of the connection is set according to the source of the two destinations unintentionally in the direction of the ad networks. The graph shows that the average power consumption of the AODV protocol is high compared to other policies. The average power consumption of the OLSR and DSDV protocol is very similar across the entire network. Also the DSR protocol and EER mechanism showed similar performance but the energy consumption rate of the EER method is much smaller than the DSR protocol. The EER protocol showed 4.03 percent improvement over the DSR protocol.

A Graph in Fig. 6 represents the average power consumption of route protocols in terms of the numbers 5,10,15,20 and 25. The number of sites set according to the source is a pair that travels irregularly in any direction of ad networks. The average power consumption of the AODV, OLSR and DSDV protocol is very similar across the number of nodes. Also the DSR protocol and EER mechanism showed similar performance but the energy consumption rate of the EER method is much smaller than the DSR protocol. The EER protocol showed a 3.63 percent improvement over the DSR protocol.

Fig. 5: Number of Connection

Fig. 6: Number of Nodes

Fig.7 represents the average power consumption of these route protocols in terms of rest periods 0, 40, 80, 120 and 160. The downtime is set according to the source by the destination pair without directing in any direction of the ad networks. The average power consumption of the AODV, OLSR and DSDV protocols is the same in all temporary suspension conditions. Also the DSR protocol and EER mechanism showed similar performance but the energy consumption rate of the EER method is much smaller than the DSR protocol. The EER protocol showed 4.73 percent improvement over the DSR protocol.

Fig. 8 represents the average power consumption of the route protocols depending on the transmission rate of source 1, 2, 3, 4 and 5. The amount of source shipping is set according to the source in pairs that travels indiscriminately in any direction of ad hoc networks. The average power consumption of the AODV, OLSR and DSDV protocol is very similar for all source transmission measurements. Also the DSR protocol and EER mechanism showed similar performance but the energy consumption rate of the EER method is much smaller than the DSR protocol. The EER protocol showed 0.73 percent improvement over the DSR protocol.

Fig. 7: Pause Time

Fig. 8: Source Sending Rate

Fig. 9 Speed

Fig. 9 represents the average power consumption of these route protocols depending on the speeds 0, 5, 10, 15, 20 and 25. The rate of source shipping is set according to the source by the two travelers who are unusually close to any direction of ad hoc networks. The average power consumption of the AODV, OLSR and DSDV protocols is the same in all speed conditions because the surfaces are not moving in any direction at different speeds. Also the DSR protocol and EER mechanism showed similar performance but the energy consumption rate of the EER method is much smaller than the DSR protocol. The EER protocol showed 9.16 percent improvement over the DSR protocol. cases. The EER machine compares with all terms and shows a 4.03% improvement in the number of connected items, an improvement of 3.63% in consideration of the number of locations, a 4.73% improvement in recovery time, 0.72% improvement in source shipping rate. , 9.16 Improved %% when the speed condition is compared with the DSR protocol.

V. CONCLUSION:

Active power consumption is a major problem in the IoT network. To increase the power consumption of the Wireless IoT network, this paper has introduced a power consumption approach that can be applied to the IoT network of large multi-hop accordingly. Functional evaluation of protocols has been discussed. Various mobility and traffic models are used to study their energy use. The power parameter is considered separately in the number of locations, the number of connections, the rate of source transmission, speed and downtime. The simulation results show that all test terms are the same in all

VI. REFERENCES:

1 A. A. Kumar, K. Ovsthus, and L. M. Kristensen, “An industrial perspective on wireless sensor networks_A survey of requirements, protocols, and challenges,'' IEEE Commun. Surveys Tuts., vol. 16, no. 3, pp. 1391_1412, 3rd Quart., 2014.

2 J. Huang, Q. Duan, Y. Zhao, Z. Zheng, and W. Wang, "Multicast routing for multimedia communications in the Internet of Things," IEEE Internet of Things Journal, vol. 4, no. 1, pp. 215-224, 2017.

3 F. K. Shaikh, S. Zeadally, and E. Exposito, "Enabling technologies for green internet of things," IEEE Systems Journal, vol. 11, no. 2, pp. 983-994, 2017.

4 C. Floerkemeier, M. Langheinrich, E. Fleisch, F. Mattern, and S. E. Sarma, “The internet of things,” Electronics World, vol. 297, no. 6, pp. 949 – 955, 2017.

5 M. Az, Mart, N. Cristian, and B. Rubio, “State-of-the-art, challenges, and open issues in the integration of internet of things and cloud computing,” Journal of Network and Computer Applications, vol. 67, no. C, pp. 99–117, 2016.

3 S. Misra, M. Maheswaran, and S. Hashmi, “Securing the internet of things,” Computer Fraud and Security, vol. 2016, no. 4, pp. 15–20, 2016.

6 M. Conti and S. Giordano, "Multihop Ad Hoc Networking: The Evolutionary Path," Mobile Ad Hoc Networking: The Cutting Edge Directions, vol. 35, p. 3, 2013.

7 P. Bellavista, G. Cardone, A. Corradi, and L. Foschini, "Convergence of MANET and WSN in IoT urban scenarios," Sensors Journal, IEEE, vol. 13, no. 10, pp. 3558-3567, 2013.

8 C. A. Tokognon, B. Gao, G. Y. Tian, and Y. Yan, "Structural health monitoring framework based on Internet of Things: A survey," IEEE Internet of Things Journal, vol. 4, no. 3, pp. 619-635, 2017.

9 H.M. Xin, Kun Yang, “Routing Protocols Analysis for Internet of Things”, 2nd International Conference on Information Science and Control Engineering, 2015.

10 J. Shen, A. Wang, C. Wang, C. K. Patrick Hung, C.F Lai, “An Efficient Centroid-based Routing Protocol for Energy Management in WSN-Assisted IoT”, Journal Of Latex Class Files, vol. 14, no. 8, august 2015.

11 S.Nisha, S.P. Balakannan. “An Energy Efficient Self Organizing Multicast Routing Protocol for Internet of Things”, IEEE International Conference On Intelligent Techniques In Control, Optimization And Signal Processing,2017.

12 T. M. Behera, S. K. Mohapatra, P. Mukherjee, “DEEC-VD: A Hybrid Energy Utilization C”, International Conference on Information Technology, IEEE Acces, 2017.

13 Thien D. Nguyen, Jamil Y. Khan, and Duy T. Ngo, “An Effective Energy-Harvesting-Aware Routing Algorithm for WSN-based IoT Applications”, IEEE ICC 2017 Green Communications Systems and Networks Symposium, 2017.

14 A. S. Hampiholi, V. Kumar, “Efficient routing protocol in IoT using modified Genetic algorithm and its comparison with existing protocols”, IEEE Third International Conference on Circuits, Control, Communication and Computing,2018.

15 W. A. Jabbar, W. K.Saad,M. Ismail, “ MEQSA-OLSRv2: A Multicriteria-based Hybrid Multipath Protocol for Energy-Efficient and QoS-Aware Data Routing in MANET-WSN Convergence Scenarios of IoT”, DOI 10.1109/ACCESS.2018.2882853, IEEE Access.

16 M.Priyanga, S. S. Vimalraj,J. Lydia, “Energy Aware Multiuser and Multi-hop Hierarchical –Based Routing Protocol for Energy Management in WSN-Assisted IoT”, Proceedings of the International Conference on Communication and Electronics Systems (ICCES 2018), pp-701-705,2018.

17 C. Xu, Z. X Iong, G.Zhao, S. Yu, “An Energy-Efficient Region Source Routing Protocol for Lifetime Maximization in WSN”, DOI: 10.1109/Access.2019.2942321, IEEE Access.

Received on 22.05.2020 Accepted on 18.06.2020

Research J. Engineering and Tech. 2020;11(2):75-81.

DOI: 10.5958/2321-581X.2020.00014.8