In this paper, the energy conservation in the ununiform clustered network field is proposed. The fundamental reason behind the methodology is that in the process of CH election, nodes Competition Radius (CR) task is based on not just the space between nodes and their Residual Energy (RE), which is utilized in Energy-Aware Distributed Unequal Clustering (EADUC) protocol but also a third-degree factor,
Wireless Sensor Networks (WSNs) are unique with more source restrictions like energy, the power to process, accumulation and communication range. Among the criteria, sensor energy is the critical resource limitation of the WSNs. Many research types have been studied to fix this issue [
In most cases, the Base Station (BS) is located faraway from the detecting area. In those networks, BS regularly collects the information. To get consistent monitoring networks, it is very fruitful to cluster them with classified topology. It was shown that the clustering of the network provides more life for the network than the one with transmitting information straight away. It was seen that the network shelf life becomes better twice or thrice with clustering [
Moreover, clustering aids’ collection of information at CH by reducing the count of data packets being transmitted, aids in minimizing the consumption of energy in sensor nodes [
The Multi-Hop (MH) communication works well in handling the downfalls in signal propagation because the radio loses energy not just while transmitting but also while receiving messages; transmitting straight away is even helpful. Yet, the occurrence of restriction is present here. It is fair to utilize it up to some distance only [
Losing the sensing coverage and dividing the network happen, finally influencing the functioning of the network. In the literature [
The surrounding information is taken into account as the clustering parameter to improve the network’s life span. The proposed method, ECSAC, self-controls the energy usage by the nodes in the network for equal distribution and un-equal delivery. Besides, extra attention is given to energy consumption in the communication process by applying a timeslot-based backtracking algorithm for increasing the network’s lifetime. Ultimately, the suggested algorithms minimize the transceiver’s energy expenditure consumption by fixing the issues mentioned above. Our methodology reduces the clustering overhead and node communication consumption to extend the network’s lifetime. The output of our suggested method is analyzed against the classical techniques under various performance metrics.
Zhu et al. [
A low-complexity distributed clustering scheme was proposed by [
The Energy-Driven Unequal Clustering protocol (EDUC) methods [
HEED and EEDC, a couple of energy-efficient distributed CAs, were put forth in [
The network used in this model has ‘
Moreover, we presume that the information detected by the nodes is strongly related. Once the cluster has been formed, DG gives the election of Cluster Head in WSN = (Sn, Ed). Here, Sn means that ‘N’ wireless SNs are along with the CH. Not including the Node acting as sink Sns., every SN Sni regularly produces size k. In Ed, (Sni,Snj) is the pair of guided edges, where Sni,Snj as the child and the parent node. The edge’s direction Sni→Snj indicates that in which way the information is being forwarded. The edges represent the information that performs the transmission of a spanning tree with the Sink Node (SN) as an origin. It assumes that backing of various radio frequencies for simultaneous interactions sans intrusion is performed by manufacturing, technical, health care band, etc. Allotting RF channels for a bunch of synchronous communications is NP-Hard. Therefore, the suggested method presumes that the network is present with the highest S RF channels. The following
The transmitter used energy to run and transmit the radio electronics circuit, but the energy of the recipient was in the component of the radio electronics alone. Furthermore, the free space φ
The transmitter then spends energy for receiving n-bit of data based on the following
In this suggested methodology, the TDMA method categories into a group of small timeslots of uniform distance end to end. Every cluster node is allotted a timeslot that regularly forwards a combined data packet to the CH in the allocated time.
As per
Researchers have detailed the accumulation methodologies for a couple of strong cases of compressing without the information (raw-data forwarding) in addition to compressing complete data (aggregated forwarding). They are compressing without information forwards data packets created by every Node separately. On the other hand, in the other type, the data obtained from every Node of the sub-tree is combined into a uniform size packet by the root node. Later, just a combined packet from the sub-tree’s root node is transmitted. Once a robust geological correlation is obtained by the data, the combined forwarding is used or gathering brief data like the highest, lowest or mean is the aim. In our research work, we suggest a novel combination method in which the root node of every sub-tree joins the sensing information obtained from every Node in the sub-tree with its data.
In our methodology, the parent node is assigned by a routing protocol to the extent that the following limitation is fulfilled. Let every Node make a
As per
The nodes’ distance from BS is first calculated once they are placed. To accomplish this, an indication is transmitted by BS, and the entire nodes could hear it. Every Node consolidates the distance of sits with BStn by the obtained indicator’s power. Every round contains a cluster establishment stage and a firm-state stage, where data diffusion happens. To have a clustering topology, the establishment stage is categorized into three sub-stages: fellow node spec gathering phase, CH selection phase and cluster creation stage. In the information forwarding stage, cluster members gather information within the surroundings and transmit the collected information to the CHs that receive and combine the data from their fellow clusters and then forward the combined information to the next-hop nodes according to the routing tree designed in this model.
State | Explanation |
---|---|
Contender | Contender Node |
Head | Head Node |
Simple | Simple Node |
Message | Explanation |
---|---|
Node_ Information | Tuple(Node_ID, Node_Energy) |
Head_ Information | Tuple(Node_ID) |
Link_ Information | Tuple(Node_ID, Head_ID) |
Schedule_ Information | Tuple(Schedule, Order) |
Path_ Information | Tuple(Node_ID, Node_Energy) |
The data transmission, which is a firm-state phase, must be longer than the establishment phase for preserving the algorithm’s overhead and extending the network lifetime. The state message of every Node is mentioned in
The BS transmits a signal at a particular power range. Every Node can work out its position corresponding to the BS according to the strength of the signal received. There are three sub-stages in cluster setup phases: fellow node spec gathering stage, where duration is
where
where
For any node
where maxd and minds are the max and min space between the nodes in the network and the BS,
The ECSAC system is according to the improved EADUC methodology, but contrary to the EASAC scheme, for producing unequal clusters, it uses a different CR rule. Only the distance between nodes and BS are taken into account in the original EADUC protocol for the expression of CR and the RE of nodes. Therefore, the suggested scheme addresses, apart from the latter two considerations, the amount of neighbors and calculates the radii rivalry to compensate the costs involved in the aggregation. The strategic range is the distance from the BS, CH’s remaining strength, and adjacent nodes’ count. The suggested scheme nodes with comparatively more remaining power, a total distance of nodes from the BS, few neighbouring nodes, etc., should have a more comprehensive broader range of competition and the formula given in
In heterogeneous networks, nodes have different initial energy. Here, every Node has equal energy consumption, and the nodes with minimal initial energy fall off early, thereby decreasing the lifetime of the network. To have the full benefits of the high-energy nodes, the high-energy nodes must accept more tasks. Hence, taking into account the gap amid nodes, the BS and the RE of nodes, we derive the formula of
where
In the intra-cluster communication process, all the nodes present within the cluster sense their local environment and collect it. Each Node then forwards the gathered information to their corresponding CHs. The CH sums up the received message and transmits the packet to the SN with the help of inter-cluster communication
The CHs aggregate the data that had been obtained from its nodes. The aggregated data are then forwarded to the SN. The management of inter-cluster communication is essential as it alone utilizes the central part of any sensor network’s aggregate RE level.
In this model, a backtrack-based channel allocation is employed to ensure better energy management among the CHs. The scheduling algorithm presented here discovers maximum possible ways with the help of backtracking—the algorithm CATUB receives an allocation solution with less energy consumption. A pseudo-code CATUB is depicted by Algorithm 1. The algorithm inputs are G = (V, E), the directed routing graph and S, the maximum No. of channels available. For each sensor node, the algorithms produce a communication Allocate [T][C]. Here, the Node assigned with an ith timeslot and the jth channel is represented by the values in Allocate [i][j]. To gather the sensing information from entire nodes at the sink (root) node atone time, Child node timeslots should be allocated before their origin node timeslot. Therefore, the algorithms begin by assigning the schedule in a bottom-up style from the leaf nodes towards the SN.
CATUB preserves the lowest energy solution in Allocate [T] [C] available post recursive function calls. Backtracking tree schedule’s present situation is hoarded in PrsnSolu [T][C]. For memorization, the plan of the present example’s cost metric is hoarded by the algorithm in NrSolu. Allocate [T] [C]’s minimum energy cost is hoarded in Nrmin.
The values
In every resolution, the algorithm gets a schedule and channel provision to the entire nodes before the algorithm attained the SN. It is done when the current answer has a small cost metric compared to the former top solution (Nrsolu < Nrmin) and the algorithm stores both cost Nrsolu and scheduling outcome PrsnSolu[T][C] into Nrmin
1. A directed graph, Dg = (Sn, Ed), where Sn is the vertices that represent the representing SN and Ed represent the edges of the directed graph.
2. Set of maximum available channels Cmax.
3. The least found energy solution available recently, Nrmin.
4. A 2D array Allocate [T][C] that could store the results. The Allocate[
5. Nrsolu, the current energy consumption level.
6. Prsnsolu[T][C] stores the scheduling parameter for the current energy consumption level Nrsolu.
7. ReadyQ holds the list of nodes that are available to be scheduled in the next timeslot.
8. IdleQ holds the record of nodes in idle mode waiting in the present time slot.
9. Timeslot, the implementation of present timeslot.
1. Allocate [T][C], which specifies the transmission-schedule for all the nodes in Sn.
1. Nrmin = ∞
2. Nrsolu= 0
3. ReadyQ = Enqueue (n ∈ Sn|n.Child → 0)
4. IdleQ = ∅
5. Timeslot = 0
Allocate_Slot (Timeslot, C, Nrsolu, Nrmin, Prsnsolu[ ][C], Allocate[ ][C], ReadyQ, IdleQ)
1. Valueret = 0
2. If ReadyQ = = ∅
3. If Nrsolu < Nrmin
4. Nrmin= Nrsolu
5. Allocate = Prsnsolu
6. Valueret = 1
7. Else Valueret = 0
8. End If
9.Else If Nrsolu ≥ Nrmin
10. Valueret = 0
11. Else
12. While Prsnsolu[timeslot] = Choose_Allocate_node(ReadyQ) ≠ SINK
13. For n = ∀Prsnsolu[timeslot]
14. ReadyQ =Dequeue(n)
15. IdleQ = Dequeue(n)
16. Nrsolu+= n.NrTrx + n.Parent.NrRex
17. IdleQ = Enqueue(n.Parent)
18. ReadyQ = Enqueue(n.Parent)
19. End For
20. For i = ∀IdleQ
21. For n = ∀Prsnsolu[Timeslot]
22. If n.Parent = i
23. Break
24. End If
25. End For
26. Nrsolu += Nridl
27. End For
28. Valueret = Valueret|Allocate_slot(Timeslot + 1, Nrsolu,Nrmin, Prsnsolu[ ][C], Allocate[ ], ReadyQ, IdleQ.
29. For i = ∀IdleQ
30. For n = ∀Prsnsolu[Timeslot]
31. If n.Parent = i
32. Break
33. End If
34. End For
35. Nrsolu - = Nridle
36. End For
37. For n = ∀Prsnsolu[Timeslot]
38. ReadyQ =Dequeue(n.Parent)
39. IdleQ = Dequeue(n.Parent)
40. Nrsolu - =n.NrTrx + n.Parent. NrRex
41. IdleQ = Enqueue(n)
42. ReadyQ = Enqueue(n)
43. For End
44. End While
45. Return Valueret
46. End If
and Schedule [T] [S], correspondingly. It explains that the present clarification offers a minimum consumption compared to former schemes (Nrsolu < Nrmin). CATUB at first lines up every branching node into the (s ∈S|s.children== 0) into queue ReadyQ that accumulates the nodes, which are ready for getting planned with the setting: (1) the leaf nodes (without child nodes) and (2) priorly scheduled nodes with child nodes. By inviting the Recursive function (RF) from the timeslot 0, the CATUB algorithm begins. RF’s input has two queues: ReadyQ and IdleQ. At first, IdleQ begins as a queue without any nodes. As the branch node is without children, they start quickly in their broadcasting schedule. In every timeslot, the algorithm looks into every possible subset of nodes fulfilling the application limitations. C gives a large-sized subset. In the timeslot, for every queued subset, the algorithm recurrently performs the other nodes that are not in the queue for the following timeslots. Finally, the algorithm comes back to the minimum energy schedule Allocate [T] [C].
As a sample, in the network of
Nodes Sn5, Sn18, Sn1, Sn23, Sn11, Sn10, Sn17, Sn20, Sn21 and Sn22 are the latest queue ReadyQ. To queue IdleQ, the parents of the scheduled nodes, Sn17, Sn22, Sn23, Sn11, are too restructured. When the latest consumption is greater than the former least consumption (1 is the initial minimum cost) value in the T1 timeslot, CATUB performs backtracking to timeslot T0, and the remaining subsets are discovered. When lesser is the latest cost than the legal best cost, the execution of CATUB in the timeslot T1 is done in a similar manner to the one in the timeslot T0. The other timeslots also once again go through a similar process.
Three conditions were selected for imitations:
In the methodology put forth in our study, the count of alive nodes, the avg. of network energy and network stability, First Node Death (FND), Half Node Death (HND), destruction of 10% and 20% of nodes (PND) and Last Node Death (LND) in the entire imitation duration were all assessed. The reproduction was performed in 50 periods.
Few are explained below:
The factors responsible for replication are shown in
The simulations are repeated many times for finding round No. in a significant slot and many slots in the data transmission stage. Replication factors, like the nodes’ positions, are seen as the same for every Node so that the outcomes are reliable and trustworthy. This simulation has got a significant part to play in the suggested method. Here, the simulation could be done more accurately by reducing round count, leading to increased overhead, energy consumption and reduced network lifetime. The project’s critical aim is to raise the life span of the network by removing almost all of the regulation messages and decreasing the overhead, thereby resulting in reduced energy consumption of nodes. As per the simulation results, it is observed that every information forwarding stage has seven parts of the vital slot that has six rounds, one cluster head rotation, and one adjustment route. In general, in the information forwarding stage, information was forwarded in 42 rounds, and our objective was to eliminate most of the network overhead.
One of the essential parameters in clustering is finding the radius. Thus, in
For finding the RMRnrg parameter in
Parameter | Value |
---|---|
Maximum packet size (Max(p)) | 255 Kbps |
Data rate | 76.8 Kbps |
TDMA timeslot length | 33 ms |
Sensing data size (Ds) | 6 bytes |
Header information size (h) | 16 bytes |
Ack packet size | 21 bytes |
Channel switch time | 64 μs |
Synchronization margin time | 2 ms |
Carrier sensing time | 152 μs |
Interframe space | 487 μs |
Etx | 148.5 mJ/s |
Erx | 56.1 mJ/s |
EIdle | 52.8 mJ/s |
Esleep | 0.0033 mJ/s |
Modulation | Frequency Shift Keying |
Pout | +13 dBm |
Transmission distance | 250 m |
Etotal | 3000 mA |
Vout | 3.3 V |
In this proposed model, the right CH count is calculated to be equivalent to 5% of the network’s entire nodes. Every Node has a distance that has to be such that the No. of clubs in the network must be appropriate. In this case, the parameter’s value RMRnrg was estimated to be equivalent to 100 m, Which implies that CH is around 5.
The ECSAC, EADUC and HUCL protocol’s mean energy consumption is calculated for the four situations included here.
Three is just more than scenario 2; like in scenario 3, the area near the BS vicinity is scarcely organized. Therefore, there is more possibility of a long-distance between the last transmit Node and BS than scenario 2. It is thickly arranged.
To increase the quality and throughput, additional information to the BS has to be transmitted, and here, it is essential to avoid the Node’s downfall. Hence, efficiency and performance can be enhanced by raising the steadiness of the network. In
Protocol | FND (100 node) | 10% PND (90 nodes) | 20% PND (80 nodes) | HND (50 nodes) | LND (0 nodes) | |||||
---|---|---|---|---|---|---|---|---|---|---|
Time | Packets | Time | Packets | Time | Packets | Time | Packets | Time | Packets | |
HUCL | 301.07 | 30103.7 | 577.17 | 56321.1 | 702.24 | 66650.1 | 811.69 | 73210.5 | 952.6 | 75727.3 |
EADUC | 433.62 | 43364.2 | 669.68 | 66151.8 | 728.31 | 71099.6 | 760.43 | 73001.5 | 791.34 | 73353.5 |
EASAC | 772.42 | 77225.5 | 970.09 | 94780.4 | 1010.13 | 97510.6 | 1060.4 | 99705.1 | 1152.03 | 100389.3 |
Protocol | FND (100 node) | 10% PND (90 nodes) | 20% PND (80 nodes) | HND (50 nodes) | LND (0 nodes) | |||||
---|---|---|---|---|---|---|---|---|---|---|
Time | Packets | Time | Packets | Time | Packets | Time | Packets | Time | Packets | |
HUCL | 346.2 | 34619.3 | 663.7 | 64769.3 | 807.6 | 76647.6 | 933.4 | 84192.1 | 1095.5 | 87086.4 |
EADUC | 498.7 | 49868.8 | 770.1 | 76074.6 | 837.6 | 81764.5 | 874.5 | 83951.7 | 910.0 | 84356.5 |
EASAC | 888.3 | 88809.3 | 1115.6 | 108997.5 | 1161.6 | 112137.2 | 1219.5 | 114660.9 | 1324.8 | 115447.7 |
Protocol | FND (100 node) | 10% PND (90 nodes) | 20% PND (80 nodes) | HND (50 nodes) | LND (0 nodes) | |||||
---|---|---|---|---|---|---|---|---|---|---|
Time | Packets | Time | Packets | Time | Packets | Time | Packets | Time | Packets | |
HUCL | 421.5 | 42145.2 | 808.0 | 78849.5 | 983.1 | 93310.1 | 1136.4 | 102494.7 | 1333.6 | 106018.2 |
EADUC | 607.1 | 60709.9 | 937.6 | 92612.5 | 1019.6 | 99539.4 | 1064.6 | 102202.1 | 1107.9 | 102694.9 |
EASAC | 1081.4 | 108115.7 | 1358.1 | 132692.6 | 1414.2 | 136514.8 | 1484.6 | 139587.1 | 1612.8 | 140545.0 |
Protocol | FND (100 node) | 10% PND (90 nodes) | 20% PND (80 nodes) | HND (50 nodes) | LND (0 nodes) | |||||
---|---|---|---|---|---|---|---|---|---|---|
Time | Packets | Time | Packets | Time | Packets | Time | Packets | Time | Packets | |
HUCL | 240.9 | 24083.0 | 461.7 | 45056.9 | 561.8 | 53320.1 | 649.4 | 58568.4 | 762.1 | 60581.8 |
EADUC | 346.9 | 34691.4 | 535.7 | 52921.4 | 582.6 | 56879.7 | 608.3 | 58401.2 | 633.1 | 58682.8 |
EASAC | 617.9 | 61780.4 | 776.1 | 75824.3 | 808.1 | 78008.5 | 848.3 | 79764.1 | 921.6 | 80311.4 |
It was stressed that these nodes without perfect energy levels could not be a CH as they require much energy. Hence, it leads to the division of energy consumption uniformly amid the nodes at simulation time. The protocol put forth assistance in bringing down the energy spent in the network by removing abnormal regulating information and minimizing overhead; hence, it leads to a stretched network lifetime.
Protocol | Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 |
---|---|---|---|---|
HUCL | 24.86 | 27.36 | 22.12 | 23.62 |
EADUC | 23.54 | 26.28 | 20.91 | 22.36 |
EASAC | 33.77 | 39.24 | 29.85 | 32.08 |
Post the completion of the forwarding of the collected information packet and every SN changes to the eternity sleep mode until the expected information collection duration. The energy spent in sleep mode is insignificant and not taken into account. The outcome shows that the CATUB algorithm employed in the ECSAC model spends minimum energy. The energy consumption raises proportionately with the network size. The results of the competing models and the proposed model’s energy consumption during the transmission phase are indicated in the following
When it comes to our methodology, the rate at which energy is spent is polynomial. The outcomes also show that the energy spent by CATUB is the least compared to other algorithms as it finds the best method amid various probable methodologies with effective backtracking. Meantime, the job done by HUCL is at par with CATUB considering tiny networks; CATUB outdoes for the more extensive networks, resulting in the prevention of loss of energy and its conservation around 24% in a data collection period. As per the outcomes, we agree that in the suggested methodology, the energy consumption was minimized by minimizing the network overhead.
WSNs come with certain restrictions like energy sources for energy consumption as they are directly connected to the transmission. Fine-tuning energy consumption in routing protocols is an essential process for increasing the lifetime of the network. This work has been expanded to improve the life of the WSN by EADUC. This research also used the non-uniform approach to clustering. Based on unequal competitive ranges, the shaped clusters are of uneven scale. There are smaller clusters nearer to the BS compared to the ones away from the BS. By using several factors, the nodes are assigned an uneven radius of competitiveness. It increases energy spent within the CH nodes. The outcome indicates that ECSAC can efficiently augment the network’s steadiness and lifetime compared to ECDC, HUCL and EADUC.