Novel Tree based Efficient Data Aggregation and Dissemination Protocol for IoT enabled Wireless Sensor Networks

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Novel Tree based Efficient Data Aggregation and Dissemination Protocol for IoT enabled Wireless Sensor Networks

Virendrakumar A. Dhotre¹, Dr.Avnish Raj Verma², Somnath B. Thigale³, Prakash R Gadekar⁴, Dr.Aparna A Junnarkar⁵, Swapna V. Tikore⁶

1Ph.D Research Student MUIT, Lukhnow, India

2 Ph.D Guide MUIT, Lukhnow, India

3 Ph.D Research Students MUIT, Lukhnow, India

4 Ph.D Research Students MUIT, Lukhnow, India

5 Professors, PES Modern College, Pune, India 6 KPC,Pandharpur, India

Abstract

In recent past, the use of Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs) is significantly increasing in wide range of real time applications like weather monitoring, farm monitoring, security monitoring, military etc. The sensor networks consist of tiny and small energy constrained device called sensor node. As the sensor nodes are energy constrained, short network lifetime is key research problem. The IoT enabled sensor networks may fail early due to energy expiration of sensor nodes. To improve the energy efficiency of such network, various solutions proposed by researchers such as clustering, energy efficient self- organization and deployment of sensor nodes, data aggregation, data dissemination, efficient MAC protocol etc. Data aggregation and dissemination is vital phase of IoT enabled WSNs on which the energy consumption is mainly depends. Thus the efficient design of data aggregation and dissemination improves the energy efficiency of WSNs. In this paper, we proposed the Efficient Data aggregation an dissemination using Optimized Tree based clustering (EDOT) for IoT enabled WSNs. As the name indicates the aim of EDOT is to improve the process of data aggregation and transmission. The main properties of EDOT protocol is build routing tree with less number of messages, increased number of overlapping paths, improved aggregation rate, reliable data aggregation, and reliable data dissemination according to shortest path tree (SPT) and reliable data fusion solution. The main aim of the EDOT is build energy efficient and reliable tree with shortest paths which connect all the source sensor nodes to the intended sink to improve data aggregation and dissemination performance. The simulation results claimed that EDOT improves the performance compared to state-of-art protocols.

Keyword: Energy efficiency, Data aggregation, Dissemination, Build tree Cluster formation

I. Introduction

The Internet of Things (IoT) is the emerging technology which bridges the gap between physical world and cyber. In general, the IoT is network that consists of wide variety of objects like actuators, sensor nodes, mobile nodes, RFID tags etc. Thus the performance of network has the critical impact on the service performance of IoT. IoT has been utilized as a part of various applications, for example, smart home, smart network, smart horticulture, smart city, and so forth. The exponential development of IoT devices experiences embracing different benchmarks and advancements. Alternate major issue in IoT devices is interoperability among specialized devices and administrations. The specialized devices ought to be adaptable in embracing the circumstance in conveying data with less human contribution [1]. The human free and human-driven are two sorts of an unavoidable worldview in view of human association.

Since from last decade, the energy efficiency in IoT gained the significant research interests. Therefore this leads to the new challenges for the energy efficient routing protocols in IoT applications [2]. The key necessity of IoT empowered WSNs is that they ought to work steadily and economically for a more drawn out period with no requirement for human mediation. Gadgets in such IoT systems will ordinarily work dependent on battery power sources, and subsequently, vitality proficiency is normally of most extreme significance in

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gadget the executives [3] [4]. Thinking about the WSN area, vitality productivity for battery worked sensor hubs and lifetime upgrade have been research issues since from long time [5] [6], where Medium Access Control (MAC) layer conventions center around changing the obligation cycle for sensor hubs, and steering layer conventions are intended for information accumulation and many-to-one transmission. Likewise, since IoT gadgets working in the IoT arrange worldview are additionally battery worked, battery utilization ought to be remembered during IoT organize organization. The acknowledgment of cost decreases to accomplish green systems administration is the exploration goal of this paper. Numerous vitality productive plans for WSN have been proposed in the ongoing past, for example, chain of command [6] and careful [7] [8] ones, anyway there are numerous gaps identified with effective information total and spread strategies

There are various methods for information interchanges in IoT applications, and essentially dependent on the kind of information correspondences, for example, mixed media information transmission [6] however its need the new vitality proficient steering conventions to expand the conventions life and execution. An in- depth study of literature revealed that lot many system architectures, communication protocols and data aggregation algorithms are available addressing the need of energy efficiency. In a non-deterministic environment, wireless sensors are deployed randomly and they form an ad hoc network. Nonetheless, various algorithms supporting the hierarchical clustering for efficient coverage and connectivity and information processing are available in literature and are being discussed in the upcoming sections. However, very few researchers have thought of employing mobile agents to improve the efficiency of these highly useful tiny motes. For WSNs, one the key problem is guaranteed sensed data transmission to the sink node especially in presence of failed sensor nodes. This problem becomes sever for the data aggregation and dissemination methods. In this way for any information collection strategy, the key necessities, for example, assemble steering tree with less number of messages, expanded number of covering courses, dependable information transmission, and higher information total rate.

Since from most recent few decades, various information conglomeration and dispersal calculations proposed for the WSNs enhancement. The calculations are basically three classes, for example, tree based calculations, bunch based calculations and structure-less calculations. The grouping based and tree based are outstanding answers for accomplish the vitality effective and solid information total in WSNs. Anyway the two arrangements experienced the couple of impediments. In this paper, we expect to introduce the new steering answer for productive information conglomeration and scattering considering the properties and advantages of tree based and group based strategies. We proposed EDOT directing convention for vitality proficient information total and spread in IoT empowered WSNs. The EDOT depends on productive tree based bunching approach in which first tree work from sink hub to all the source sensors, after that group development performed on that tree to choose the facilitator and hand-off hubs for the information collection and scattering, at long last the courses set up and information transmission stages performed. In area II, the survey of various vitality productive conventions for IoT empowered WSNs exhibited. In area III, the model for EDOT introduced. In area IV, the reenactment results and discourses displayed. In area V, end and future work depicted.

II. Related Works

This section presents the review of various works reported on data aggregation and dissemination in WSNs and also the recent methods reported for the energy efficiency in IoT enabled WSNs described. The data aggregation methods presented under three categories such as tree based, cluster based, and structure less.

In [9], an alternate methodology is proposed in the Center at Nearest (CNS) calculation. In this calculation, each hub that identifies an occasion sends its data to a particular hub, called aggregator, by utilizing a most brief way. For this situation, the aggregator is the nearest hub to the sink (in jumps) that identifies an occasion.

In [10], the Directed Diffusion calculation is one of the soonest answers for likewise proposed trait based steering. In these cases, information can be astutely totaled when they meet at any transitional hub. In [11], in view of Directed Diffusion, the Greedy Incremental Tree (GIT) approach was proposed. The GIT calculation builds up a vitality productive way and ravenously connects different sources onto the built up way. In [12]

and [13], information accumulation is actualized in a genuine world testbed and the Tiny AGgregation Service (TAG) structure is presented. Label utilizes a most limited way tree, and proposes enhancements, for example, snooping-based and theory testing-based advancements, dynamic parent exchanging, and the utilization of a youngster reserve to assess information misfortune.

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In [14], creator proposed an information conglomeration strategy for remote sensor systems utilizing counterfeit neural systems. The information combination tree is built up to decrease the bundles stream and can refresh the leaf hubs powerfully. In [15] creator presented a joint plan of information collection with the directing innovation, and exhibited a network based steering and aggregator determination plan to accomplish low vitality dissemination and low inertness without relinquishing quality. By researching information combination with correspondence imperative between the combination focus and every sensor, in [16] creator displayed an information combination instrument for target following in remote sensor systems dependent on quantized developments and Kalman sifting. By including some defer time, every one of the information gathered by transfer hub can be combined at one time in order to decrease the vitality utilization. Planning to guarantee the information quality, in [17] creator proposed different measurements for QoS (nature of administration) during the time spent information total, including lifetime, information deferral, and retransmission rate. Likewise, the methodology is examined to guarantee above QoS measurements in subtleties.

Likewise to the tree-based methodologies, bunch based plans additionally comprise of a various leveled association of the system. Be that as it may, in these methodologies, hubs are isolated into groups. In [18], the outstanding Low-Energy Adaptive Clustering Hierarchy (LEACH) convention planned in which the grouped structures are misused to perform information accumulation. After the LEACH numerous other grouping conventions presented for the vitality effective information collection. In beneath we portrayed the ongoing vitality effective calculations for IoT empowered WSNs.

In [19], author proposed Ring Routing protocol to diminish the vitality utilization by WSN nodes. They designed protocol as creative, scattered and vitality proficient portable sink routing protocol which is expected for time delicate application, which intend to decrease these overheads while saving energy of versatile sinks.

The Ring Routing is various levelled versatile sink protocol which depends on virtual ring synthesis which can be effortlessly reconfigurable and in addition available. Further in [20], vitality effective directing answer for the IoT empowered applications proposed dependent on the bunching approach.

In [21], creator exhibited the vitality imperative issue of gadgets in IoT applications as a streamlining issue.

To save the vitality of gadget hubs, the steering convention first totals gadgets into groups dependent on various highlights, for example, remove from base station, information/message length and information detected from the earth in the present age. At that point, a bunch head is chosen for each group and a coordinated non-cyclic diagram (DAG) is created with all the bunch heads as hubs. Edges speak to correspondence goal from transmitter to beneficiary and the edge loads are figured utilizing a detailed condition. The base cost way to the base station is registered to take into account effective ongoing directing.

Rest booking is likewise alternatively used to further lift arrange vitality effectiveness. The proposed directing convention has been mimicked and beats existing steering conventions as far as measurements, for example, number of dynamic hubs, vitality elements and system inclusion.

In [22], author investigated that how IoT has pick up fame as the quantity of smart devices being utilized as a part of everyday human life having system lifetime as a requirement. In giving availability between nodes, going of routing data assumes a conspicuous part. They distinguished that most extreme energy of smart devices is used in routing the information (or) control bundles. Their goal was to address the holes in improving the system utilization, which thus augment the system lifetime. In these ways, so far the writing survey made on versatility, energy proficiency, Quality of Service (QoS), arrange lifetime, node sending with Wireless Sensor Networks (WSN) viewpoint.

In [23], creator proposed productive cross breed vitality mindful grouping correspondence convention for green IoT system registering; Hy-IoT, yet likewise gives a genuine IoT organize design for looking proposed convention contrasted with usually existed conventions. Effective bunch head determination supports the utilization of the hubs vitality substance and thusly builds the system life time and furthermore the bundles transmission rate to the base station. Hy-IoT utilized different weighted race probabilities for choosing a Cluster-head in context of heterogeneity dimension of the locale.

In [24], creator revived Internet of Things (IoT) gadgets is particularly used in different fields, for example, regular checking, associations, fast home and so on. Under such occurrence, a group head is chosen among the diverse IoT gadgets of WSN based IoT system to keep up the time tested system with proficient information transmission. To achieve the productive group head choice, they utilized Fuzzy C-Means (FCM) bunching count.

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In [25], creator proposed IoT related issues mostly on vitality proficiency. The development of these contraptions in a conveying inciting framework that makes the Internet of Things (IoT) captivating, wherein sensors and actuators blend reliably with nature around us, and the information is shared across over stages to develop a regular working picture.

In [26], proposed the different directing answers for buy in techniques that incorporate substance and setting based steering for IoT empowered WSNs. They planned the Energy-Efficient Content-Based Routing (EECBR) convention for the IoT that limits the vitality utilization in WSNs.

In [27] [28], creators talked about the requirement for green IoT and the different software and equipment based advancements required to empower its acknowledgment. Vitality proficient between hub correspondence and improved steering systems have been recognized as the issues that should be routed to encourage enormous scale appropriation of green IoT.

In [29], the distinctive methodology proposed wherein an evaluated open detecting structure is proposed for open information conveyance accumulated from cloud and heterogeneous assets. The work is information driven, centered around free market activity chain of open information from cell phones.

In [30], creator proposed information gathering in cell gadgets utilizing gadget to gadget interchanges in an IoT and Smart City setting. This outcomes in progressively productive asset use and limits vitality utilization.

They utilize one gadget that totals information from a few encompassing gadgets and after that sends the information to cell station, rather than every gadget sending information independently. In [31], a diagram of utilizing customary WSN conventions for accomplishing gadget to gadget correspondence in IoT has been exhibited.

In [32], the latest work proposed for the information total in WSNs utilizing the fluffy c-implies bunching approach. They structured similitude mindful information collection utilizing a fluffy c-implies approach. The fluffy c means used to play out the bunching to sort out sensors into groups dependent on information closeness. They utilized help degree work for the anomaly discovery. Anyway the assessments of vitality effectiveness and QoS investigation were absent.

- The proposed EDOT in this paper is contrast from above work which depends on blend of both tree based and grouping based methodology for the solid and proficient information conglomeration and dispersal in WSNs. The key commitments of this paper are:

- Efficient tree arrangements as per the SPT approach after the WSN organization structure the sink hub to all the sensor hubs in system.

- After the tree arrangement, when any occasion distinguished in system, the group is conformed to that occasion, at that point select the one facilitator hub and others are worked together hubs of that bunch.

The facilitator hub is chosen dependent on weight calculation esteem.

- Once the choice of colleague and facilitator, the connection arrangement dependent on short way and weight esteems performed from organizer hub to sink hub. The accumulated information sent from facilitator hub to sink through proficient and solid connection.

- The broad execution assessments for little to enormous systems.

III. Methodology

This section presents the methodology of EDOT protocol. As described above, the EDOT model proposed for efficient process of data aggregation and dissemination using the hybrid approach. The Methodology is based on tree construction and clusters formation for WSNs communications. The routing method EDOT is consisting of three key steps:

- Build Tree: In initial step, development of tree from the source sensor hubs to the sink hub. The procedure of bounce tree building started by the sink hub which will be additionally utilized by the facilitator hubs to play out the information sending.

- Clustering: In second step, when the any occasion recognized in the system, the procedure of bunch development started in which the pioneer (Coordinator) hub and colleague hubs chose dependent on its leftover vitality, separate from the sink, and number of rounds.

- Routing: In third step, the calculation of setting up the correspondence joins for dependable information total and transmission utilizing the technique of information combination and most brief way tree (STP) and update tree as needs be.

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Figures 1, 2, and 3 are represents the algorithms for above three steps such as Tree building, clustering, and routing respectively. Table 1 show the notations used in the figures.

Table 1: Table of notations Notation Significance

HCM Hop Configuration Message HTT Hop To Tree

FS First Sending

Ru Set of nodes that received HCM

F False message

u/w Sensor node

S Set of nodes detected event

L Node elected as the leader from the set S NL Set of neighbours of node L

W Weight metrics computed to become the leader.

HC Hop count

NH Next hop

CCM Cluster Configuration Message

Figure 1: Tree building

The IoT empowered WSN framework model comprise of four parts, for example, organizer hub (the hub distinguish occasions, gather and total all the accumulated information sent by teammate hub, at long last sent information towards the sink hub), associate hub (the identify occasions, send gathered information towards the facilitator hub), hand-off (the hub advances information towards the sink), and sink (the hub got information from facilitator and partner hubs). As the figures are plain as day, in initial step tree building (figure 1 and algorithm 1) started by the sink hub through the HCM message broadcasting to the all neighbor sensor hubs. Utilizing the HTT esteems the one by one tree building performed and set up by associating all

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the sensor hubs in system dependent on the most brief way approach. After the tree arrangement, the subsequent stage is to play out the bunching and determination of pioneer hub for the information collection.

Algorithm 1: Tree building

1. Node sink needs a broadcast of HCM messages with the value of HC =1;

2. For each u ∈ Ru do // for each node u from the set Ru 3. if HTT (u) >HTT(HCM) and

4. FS (u) then // if the u is the first sending node

5. NH (u) ← IDHCM ; // Store the current node u as next hope 6. HTT(u) ← HTT(HCM) + 1;

7. Node u update the value of the ID field in the message HCM 8. IDHCM← IDu ;

9. HCHCM← HCu ; // Assign the current hope count value at node u to hope count value of HCM message.

10. Node u sends a broadcast message of HCM with the new values;

11. FS (u)← false

12. Generate the tree and end 13. else

14. Node u discards the received message HCM ; 15. end

16. End

Figure 2: Cluster formation and selection of leader

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Figure 2 show the procedure of bunch arrangement and facilitator and teammate determination. The result of this algorithm to choose one hub as the organizer, and the rest of the sensor hubs under a similar occasion will consider as the teammates. The point of facilitator (only pioneer) is to gather and total the information from the every single chosen partner and scatter it to the sink hub. The oddity of such methodology separated from the productive information conglomeration at single hub (organizer) for comparative occasion is that the choice of facilitator and colleague is performed by the weight factor W. This weight is registered by estimating the remaining vitality, number of rounds; remove from the sink and so on. This weight grid guarantees that, there is constantly ideal and dependable information total with least vitality utilization.

Algorithm 2 exhibits the procedure of figure 2. As saw in algorithm 2, when an occasion happens, a group dependent on the hubs which identify it will be shaped. The key procedure of the bunch development began with the race of the pioneer hub (called Coordinator) for the group and rest different hubs turns into the partners in this stage is by methods for CCM. CCM is likewise a four-tuple: < Type, ID, HTT, State>, where ID is the identifier of the hub, HTT and State fields store the HTT and State estimation of the hub with the identifier ID independently.

Algorithm 2: Cluster formation and Leader selection Input

S: set of nodes that detected the event L: Selected leader from the set of nodes S 1. For each L ∈ S do

2. roleL← coordinator;

3. Announcement of event detection;

4. For each w ∈ NL do

5. IF HTT(L) >HTT(w) && W (L) > W (w) 6. roleL = Collaborator

7. Node u retransmits the MCC message received from node w ; 8. ELSE IF W(L) = W(w) && HTT (L) > HTT (w) then

9. roleL = Collaborator

10. Node L retransmits the CCM message received from node w;

11. Else

17. Node L discards the CCM message received from w;

18. End 19. End 20. End 21. End For 22. End For

Figure 3, shows the last advance of EDOT algorithm in which the productive and short course execution performed and update the tree as needs be after the grouping and choice of organizer and partner hubs. While choosing the hand-off hubs we consider the SPT approach as well as consider the weight esteem. The hubs with most astounding weight worth are chosen as the transfer hub for information sending reason. Steering arrangement of jump tree updates and course fix affirmation component portrayed in algorithm 3.

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Figure 3: Route formation and maintenance Algorithm 3: Routing

Inputs:

C: coordinator node

I: intermediate next hop node Sink: node that received the data

1. C broadcasts RREQs to all its neighbours NB 2. While (!Sink)

3. For each I ∈ NB 4. If (W(I) ==max) 5. HTT = 0;

6. RoleI← Relay; // next hop

7. Node L sends the message to its NH(L);

8. Node L broadcasts the message HCM with the value of HTT=1;

9. Nodes that receive the HCM message sent by node L, update hop tree of algorithm 1;

10. End if 11. End for 12. End While

13. While (data transmit/ retransmit) 12. if sons (L) >1 then

13. Aggregates all data and sends it to the NH(L);

14. if RoleI == Relay 15. Apply route repair

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16. End 17. Else

18. Send data to NH(L);

19. End 20. End While

As saw in algorithm, the facilitator hub course solicitation message (RREQ) to its next jump hub so as to build up the way towards the sink hub. At the point when next jump get the RREQ message, it further transmit same message to its next bounce hub and all the while refreshing the jump tree. Such procedure rehashed until the sink hub distinguished. The determination of next jump is relies upon its weight score. The weight score is processed utilizing the two key parameters, for example, lingering vitality and geological separation between current hubs to sink. The W(I) is figured as:

𝑊(𝐼) =(𝑅𝐸(𝐼)+ 𝐷(𝐼))

2 … (1)

Where the 𝑅𝐸(𝐼) is the remaining energy of node I and 𝐷(𝐼) is the distance from node I to sink. The 𝑅𝐸(𝐼) is computed as:

𝑅𝐸(𝐼) = 𝐼𝐸(𝐼) − 𝐶𝐸(𝐼) … (2)

Where 𝐼𝐸(𝐼) is initial energy of node I and 𝐶𝐸(𝐼) is consumed energy of node I. Similarly, we compute the 𝐷(𝐼) as:

𝐷(𝐼) =𝑑𝑖𝑠𝑡 (𝑆𝑖𝑛𝑘,𝐼)

1000 … (3).

IV. Simulation Results

In this area we present the reenactment results gotten by NS2. We investiged the exhibition of EDOT convention with two ongoing information collection and scattering conventions for WSNs, for example, Hy- IoT [13] and SDAF [22] as far as normal throughput, bundle dropped, normal vitality utilization, and directing overhead. We structured IoT empowered WSNs fluctuating from 30 hubs to 150 static sensor hubs arbitrarily conveyed with one Sink in system. The systems are reenacted for 100 seconds with MAC convention 802.11.

Table 2 demonstrates the system reenactment parameters for little WSNs. The figures 4 to 7 exhibit the presentation assessments of steering techniques.

Table 2: Network design parameters for small WSNs Number of sensor nodes 30-150

Simulation Time 100 second

Aggregation methods Hy-IoT, SDAF, EDOT

MAC IEEE 802.11

Propagation Model Two-Ray Ground

Antenna Omni Antenna

CBR Connections 5

Network size 500 x 500

Packet size 512

Initial energy (SNs & MNs) 20 nJ/bit

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Figure 4: Average throughput performance analysis

Figure 5: Number of packets dropped performance analysis

As observed in figures 4 and 5, the average throughput and number of packets dropped performance is superior for the EDOT method as compared both recent solutions due to the efficient process of tree building, aggregation, and route formation in EDOT. This also leads the minimized routing overhead and energy consumption performance for EDOT.

0 20 40 60 80 100 120 140

30 60 90 120 150

Throughput

Varying IoT Sensor Nodes

HYIOT SDAF EDOT

0 50 100 150 200 250 300 350

30 60 90 120 150

Number of dropped packets

Varying IoT Sensor Nodes

HYIOT SDAF EDOT

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Figure 6: Routing overhead performance analysis

Figure 7: Average energy consumption performance analysis

The data aggregation process is effectively reduces the number of messages during the communications and hence the less efforts required to transmit data towards the sink node. The weight values also responsible to select the incredible route establishment which is consider the key factors residual energy (the node with highest remaining energy selected) and distance from sink node (the node minimum distance from the sink selected). Above all results evaluated with the small networks, however the some real time applications like smart city management includes the large number of sensor nodes ranging from 200 to 700 for environment monitoring. Thus it is must to verify the performance of EDOT with large number of sensor nodes. Table 3 shows the network simulation parameters for large WSNs.

Table 3: Network design parameters for large WSNs Number of sensor nodes 30-150

Simulation Time 100 second

Aggregation methods Hy-IoT, SDAF, EDOT

0 0.5 1 1.5 2 2.5 3

30 60 90 120 150

OVerhead

Varying Sensor Nodes

HYIOT SDAF EDOT

0 1 2 3 4 5 6

30 60 90 120 150

Energy

Varying Sensor Nodes

HYIOT SDAF EDOT

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MAC IEEE 802.11

Propagation Model Two-Ray Ground

Antenna Omni Antenna

CBR Connections 5

Network size 500 x 500

Packet size 512

Initial energy (SNs & MNs) 20 nJ/bit

Figure 8: Performance analysis of average energy consumed

For large networks, we mainly interested in two factors energy consumption and network throughput. Figure 8 demonstrates that as the number of senor nodes increases, the network consumption increase. The EDOT shows the significant reduction in energy consumption compared to both existing methods as it selects the coordinator as well as next hop node based on higher residual energy and minimum distance parameters. As the distance minimized, the number of packets transmission rate is also increased for EDOT protocol (figure

9).

0 1 2 3 4 5 6 7 8 9 10

200 300 400 500 600 700

Avg. Energy consumed (nj)

Number of Sensor Nodes

HYIOT SDAF EDOT

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Figure 9: Successful packets received performance analysis

Figure 10: Performance analysis of average throughput

Figure 10 demonstrate the throughput performance. The performance of throughput of proposed method EDOT is outperforming the existing routing protocol. HY-IoT and SDAF are recent approaches which are based on energy efficient communications in WSN. EDOT protocol is enhanced by using hybrid approach for tree building, clustering, and route formation which improve the communication approach by selecting optimal path for data gathering in WSN.

V. Conclusion and Future Work

In this paper, we initiated our research work with the current challenges of IoT enabled WSN applications. We presented the recent methods review, novel EDOT algorithm which is hybrid (based on tree formation and cluster formation) for effective, reliable, and efficient data aggregation and dissemination process in WSN communications. The EDOT described in three key phases in this paper. The complete phases and its algorithms described for EDOT in three phases such as tree building phase, clustering and leader selection

0 500 1000 1500 2000 2500

200 300 400 500 600 700

Packets

Number of Sensor Nodes

HYIOT SDAF EDOT

0 100 200 300 400 500 600

200 300 400 500 600 700

Avg. Throughput (Kbps)

Number of Sensor Nodes

HYIOT SDAF EDOT

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phase, route formation and data transmission phase. For coordinator selection and route formation, we computed the weight score of each node to become the strong candidate. The performance of EDOT is compared with HY-IOT and SDAF protocols. The simulation results are shows that EDOT improves the performance in terms of throughout, overhead, energy consumption, and packet dropped. For the future work, it will be interesting to apply the compression method to further reduce the number of messages and hence the energy consumption. Security is another aspect to work in future.

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