# An Efficient Energy Management Routing and Scalable Topology in Wireless Sensor Network Using Virtual Backbone

Authors: Alanazi Rayan; Ahmed I. Taloba; Nadir O. Hamed; Heba Y. Zahran; Emad E. Mahmoud
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

The wireless sensor network (WSN) approach is one of the fastest growing approaches in the world of communications and engineering. The primary objective of a WSN is to discover the important information about the environment, depending on the nature of the applications under which it is implemented, and to communicate this information to a single base station (BS) so that appropriate measures can be taken. These sensor nodes communicate via a variety of protocols. The difficulty with the traditional system is that while collecting the observed data, each node transmits its felt information directly to a base station, which quickly exhausts its power. This study suggests a Backbone Energy-Efficient Sleeping (BEES) management strategy with two appealing features: (i) the capacity of backbone is scalable by basic parameters, and (ii) the backbone nodes were distributed equally, implying that the backbone on its own is energy efficient during routine activities. Reliable connections are expected to obtain QoS and routing protocols of such backbone nodes in wireless multihop systems. As a result, present localized routing in virtualized backbone schedule cannot ensure energy-efficient paths. An energy-efficient routing scheme for Virtual Back Bone Nodes (VBS) increases life of node and switches off its radio while in sleep state to spend less power. BEES’ performance is evaluated by comparing it to two different topology management techniques. The results show that BEES performs better algorithms. It ensures optimal routing with minimal node power consumption but also implements the essential communication range for backbone networks.


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## Body

## 1. Introduction

The primary goal in implementing a wireless sensor network (WSN) is to detect or predict the state of a physically dynamic event from each individual, geographically separated sensor of the network. Each sensor performs a local computation and passes the data or predictions to a synthesis station, which integrates the inputs to create an evaluation of the parameter or process, into a central distributed estimation scheme [1]. Distributed estimate using wireless sensor nodes provides several intriguing issues, including the apparent restrictions of transmission capacity, which contribute to quantization error in addition to a disturbance in the transmitted signal. Another significant limitation is the poor battery power of the detecting nodes themselves [2]. Then, a WSN estimate paradigm that provides adequate efficiency while may be energy efficient is sought to extend the WSN’s lifespan.Because sensors are frequently available sources, their operating period is strictly limited by storage capacity. Energy-saving procedures must be developed to extend the lifespan of the network [3]. Several energy-efficient solutions for WSNs have indeed planned recently, utilizing numerous levels of the networking protocol load. This study will focus on topological management strategies. Topology management is based on the perception in a realistic WSN with adequate network size; just a limited number of connected devices must be engaged at any given moment to perform the required packet transmission functions [4]. By putting those unnecessary networks to sleep, you may dramatically cut energy use. The Backbone Energy-Efficient Sleeping (BEES) mechanism is proposed in this study as a unique topology management system [5]. BEES have two appealing qualities. Initially, it is typically scalable, with reasonable backbone size restrictions. Furthermore, BEES helps conserve not only by putting numerous detectors to sleep but also by lowering the routing energy consumption of the backbone nodes.A subset of network nodes is mentioned to as the backbone, and a backbone network is to be connected. Backbone is mostly utilized to improve routing procedures, which boosts high throughput, reduces overall electricity usage, and extends node lifespan [6]. The radio is the most energy-intensive computational operation. Focus on network dynamic loads by building backbone nodes that are engaged whenever the stimulation transmitting procedure occurs and go to sleep by turning off their radios. As a result, it does not affect the quality of communications. Creating a single backbone node, on the other hand, does not provide the greatest node lifespan; so, it is desirable to establish numerous discontinuous Connected Dominated Sets (CDS) that perform effectively and can respond to changes in the network topology [7]. Connected Dominated Sets (CDS) function effectively and can respond to changes in the topology of the network. CDS is divided into several categories: (1) Unit Disk Graph (UDG) and (2) Disk Graphs with Bidirectional Links (DGB). UDG and DGB are both NP-hard. Non-CDS nodes are put into a sleep mode to preserve network energy. Backbone nodes employ the predictable strategy to keep the backbone node small while performing a great amount of computation. As a result, this technique ensures CDS in a network connection [8]. Previously, localized routing consumed more energy than would be optimal. In this work, look at how to maximize the life of a virtual backbone by adopting energy-efficient sequencing. The following are the contributions made:
(i)
The network is separated into zones or regions, for each backbone node having a transmission range and being confined to finding neighboring backbone nodes within their broadcasting radius to locate technology which is linked(ii)
A strategy known as the restricted back bone neighbor routine is developed, which ensures effective routing while consuming the least amount of energy. In this case, the backbone nodes choose a neighboring backbone node within the transmitting radius and build the Connected Dominated Set (CDS) of backbones from the sink node [9]. If no node is located within the communication radius, the backbone node expands its transmission radius to identify a next backbone network and establishes a linked dominated collection of backbone networks(iii)
A sink node is located in the center and is always active(iv)
Backbone nodes can be given a lifetime based on the duty ratio assigned to a specific zone or regionThus, the backbone minimizes network latency, improves bandwidth efficiency, reduces total consumption of energy in the network, and finally covers node life in the WSN.


## 2. Related Works

The BeeSensor network, which is inspired by bees and is energy-aware, adaptable, and effective, is detailed in [10]. This work makes an important impact by presenting a three-stage protocol strategy plan: first, taking ideas from biological institutions to enhance a sparse, distributed, and simple routing mechanism; then properly designing basic procedures to perform the procedure to gain a diagnostic insight into the behavior; and finally, optimizing the protocol based on the analysis in phase 2. Then, put it through its paces in a sensing simulation environment. The findings of experimentations demonstrate the effectiveness of this three-phase procedure design, which enabled BeeSensor to achieve its strategic objectives while using the least level of collaboration and power processing—two major sources of the energy consumption in a sensor network—when especially in comparison to other SI-based WSN routing algorithms.Algorithms that take advantage of mobile nodes to enhance system lifetime are explained in [11]. These methods are classified into three types: those that use mobile sinks, those that use mobile sensing redeployment, and those that use mobile relays. Energy is conserved by adopting shorter multihop data delivery channels with mobile sinks, and the collection of sensors situated near a source changes all the time, balancing energy usage across the network. The early installation of mobile sensors can be enhanced by sensor displacement to optimize energy usage and lengthen lifetime of network. Mobile nodes could be employed as a relay, taking over the functions of colocated static devices or carrying information to the base station to decrease the price of long-distance transmission.Energy efficiency in wireless sensor networks (WSNs) has long been a hot topic that has been researched in [12]. The mechanism of Sleep Scheduling (SS) is an effectual way to control the power of each node and can extend the lifespan of the overall infrastructure. SDN-ECCKN, a Software-defined Networking- (SDN-) based Sleep Schedule technique, is suggested in this study to regulate the network’s resources. When implementing a method, use EC-CKN as the basic algorithm. Every computation in the planned SDN-ECCKN method is performed in controllers instead of the sensor, so there is no transmission among each pair of nodes, which have been the major aspects of the existing EC-CKN method. The SDN-ECCKN results demonstrate its benefits in power management, including such network longevity; the findings were presented nodes and the percentage of single nodes within the network.The main structural goals of routing algorithms for WSN help in lowering final latency and energy effectiveness without overlooking additional design concerns [13]. This assesses routing algorithms that are response time, low energy consumption, and time sensitivity. TEEN (Threshold-sensitivity Energy-Efficient sensor protocols), a responsive network protocol well adapted for time important data electrochemical sensors, is very efficient and effective in energy usage and response time. APTEEN (Adaptive Periodic Threshold-sensitive Energy-Efficient sensor Network protocol), a hybrid model procedure, provides an overall image of the system at regular intervals while using almost no power. SPEED is a unitary, extremely efficient, and scalable sensing network protocol that delivers end-to-end fuzzy real-time communication by preserving a specified delivery performance all transverse systems via a novel combination of control method and nondeterministic geographical routing. RAP is an actual communication system for a large-scale sensor system that utilizes Velocity-Monotonic Scheduling (VMS) to dramatically minimize end-to-end latency. RPAR is an acronym for Real-Time Power Adaptive Routing Algorithm, which promotes energy-efficient actual communication by continuously modifying transmit energy and decisions of routing. Then, it goes over benefits and drawbacks of each routing system.The energy-efficient design of the linked backbone and unassuming routing stateless over a backbone was demonstrated to attain some goals [14]. This presents an ns2 computational model correlation of data transmission in the WSNs whenever (a) each sensor paths that information of the sensor directly to a drain flat collaborative network and (b) a backbone is placed on the top of both the horizontal routing protocols and only just a few nodes are already in charge of data delivery to the sink’s hierarchical organization, here referred to as VBES: Virtual Backbone for Saving Energy. Multiple objects are permitted to roam network of the static sensors. Simulations were run on the networks of density increases, with up to three targets defined and reporting to up to three sinks. The findings show that accessing through such backbone enhances the lifetime of the network by up to seven moments over a plain network organization, under which system capacity is described as (a) the time till the first base station passes away due to depletion of energy, (b) the time mandatory for an assumed proportion of nodes in the network to pass away, and (c) the period needed for the network to just be disengaged.


## 3. Proposed Methodology

### 3.1. Constructing Protocol of BEES

Consider an individual sink, interested in establishing a backbone in a discD of radius R around this specific sink [5]. This can see that D only covers a small portion of this transmission range. The backbone devices should be arranged so that at least one of them is within communication range of every sensor in D. To verify that such backbone has appropriate connectivity characteristics, tend to associate the subnets of backbone devices. The following are the main elements in backbone development protocol:
(i)
Phase of tilingThe discD around the sink is tiled with a series of matching regular geometric shapes, giving the area from around sinks the appearance of a honeycomb.
(ii)
Phase selection of backboneThe sensor that is nearest to the center of each hexagon is chosen.(iii)
Phase of clusteringBackbone sensors alert other detectors to their presence and the hexagonal they symbolize. The other sensors use RSS measurements to establish which clusters (hexagon) they belong to.The disc is tilted.D begins with the sinks: a first hexagon’s center aligns with the sink. The diameter of the tiling hexagons’ enclosed circle is tx/3, where tx is the high propagation distance of a detector [15]. This suggests coordinates for correctly identifying the numerous hexagons as in tiling seen above. The hexagon in column c of row r in sector s, in particular, is individually recognized to use the tuple s,r,c. Although there are numerous routing techniques for hexagonal connections, the coordinate system appears being more suitable for the construction process.


### 3.2. Backbone Scheduling

This illustrates that backbone scheduling is used to identify efficient routing focused on localized effective backbone routing. The whole modules are explained further below.(i)
Schedule Transition Graph (STG)The STG method is depicted in the picture below; it is a centralized approximation approach. The time scale is horizontal and is counted in rounds. In each cycle, the possible configurations of the backbone networks are enumerated vertically [16]. The quantity of backbones in each round is equivalent to the total of conceivable states. There’s also a one-to-one correspondence among condition and backbone. Energy is consumed in 1 session, which symbolizes the time that elapses during every round when energy is consumed. Whenever a node’s condition is depleted, node movement is not permitted as shown in Figure 1.Figure 1
Scheduling of STG.Bis a collection of backbone connection rounds that are used by the backbone routers. All connection is a networking linked subgraph, as well as all nodes become one step away from each point in Bi.
(ii)
Energy-efficient localized routingThis section goes into the specifics of the routing idea, which is focused on localized routing. It is a greedy way of technique routing. In greedy networking, the router chooses the next-hop neighbor depending on the difference to an objective [17]. As a result, such sequencing may not even be thermally efficient. The transmitting connection must have greater energy efficiency to be more eco-friendly. To utilize less energy, the overall distance traveled should be modest. As a result, a new notion known as a limited zone is established to limit advancing directions.
(i)
The backbone nodes with such communication to forwarding locate the neighbor backbone node between several neighbors existing inside the limited area. The greatest neighbor has the most energy efficiency or a stable connection(ii)
When there are no backbone nodes in the closed area, it expands its type of transmission range to select the optimal node(iii)
If there are no nodes in the region or zone, the standard greedy routing is used. Figure2 depicts the data flow of this process(iii)
Critical transmission radiusFigure 2
Flow diagram of data.In this research, during routing data to a base station, the destination node can delete packets as they arrive at the destination since they are unable to discover a stronger neighbor node. As a result, to discover the better neighbor backbone node and assure productive and effective routing, each backbone network must have a sufficient size broadcasting range.The critical transmission range is denoted as(1)ρxDn=βXlnR.nnπ.ρx is a method of generalized routing.Dn is a number of nodes in the network.βX is a proportion of the center of access point u to the radius r of the transmitting area from which the approximate backbone node u selects its neighbor backbone network w.


### 3.3. Backbone Establishment

(i)
Election of HBNDuring the initialization step, all devices have the same amount of energy. HBNs were selected avariciously and in a distributed manner. If a network identifies there is no HBN inside its VND, it declares its desire to become an HBN by transmitting a HEAD communication to the networks through its VND. Any node network that gets the HEAD message will be temporarily labeled as an AFN. If not enough HBNs can be connected to the system, the election system concludes.(ii)
Discovery of ABNsIf the selected HBNs are successful in constructing a linked backbone, the procedure of backbone development is completed. Otherwise, complementary connections are required to connect the individual HBNs. Using the ABN discovery technique, investigate the interconnectivity of HBNs. The process is initiated by the data center sending out a CHECK notification. Any node that receives the CHECK message should assist in spreading it. The message of CHECK has four critical parameters: LAST ID, LAST TYPE, CURRENT ID, and CURRENT TYPE [3]. Before broadcasting the signal, every node must improve the model. The CURRENT ID and CURRENT TYPE values relate to the ID and type of the input nodes. If the base station can obtain CHECK communications from HBNs, it replaces the variables LAST TYPE and LAST ID again with ID as well as category of last HBN which transmits a message to it; alternatively, unless it can obtain CHECK communications from AFNs, it substitutes the LAST TYPE and LAST ID with both the ID as well as a category during the last AFN which creates the impression to the base station. When an HBN could only accept CHECK signals from AFNs, it knows that it has been disconnected from the Main Backbone Component (MBC) that links it to the cloud server.


### 3.4. Backbone Maintenance

BEES rotate backbone networks to stability transmitting tasks through all the sensors. The updating procedures for HBNs and ABNs vary, as stated below.(i)
Replacement of HBNDuring the initialization phase, all devices have the same amount of energy. HBNs are chosen in a decentralized manner following the -MHP. If a node discovers that there is no HBN in its−ND, it proclaims its intention of becoming an HBN by transmitting a HEAD message to its −ND neighbors. Any node that gets the HEAD packet will be temporarily labeled as an AFN. So that no more HBNs can be connected to the system, the electoral system concludes.
(ii)
ABN assignment and withdrawalBecause ABNs are simply nodes that link HBNs, they would be removed whenever the HBNs they belong to are updated. On the other hand, once the HBN is replaced, the current HBN must ensure that the replacement does not disrupt backbone connection. It compares the list of its nearby HBNs to a table of a previous HBN. The connectivity is kept if its list includes all of the elements in the former HBN’s. Instead, the new HBN must transmit a discover message for each disconnected HBN in order to find a route to that HBN. If it does not receive a response from the unplugged HBN, new ABNs are required. In its two-hop neighborhood, the incoming HBN will discover some AFNs which will link it to the separated HBN [4]. Whether there are two-hop pathways, this should select the AFNs with the highest power also as ABN. Now, if three-hop pathways are available, it will choose the pairing of AFNs with the highest total power as ABNs. Because there is no such step to ensure, the replacement HBN must designate the outgoing HBN also as ABN. It is significant to mention that such a circumstance occurs with a very small chance, and when it does, the departing HBN would only be in responsible of relaying activities from current HBN to the severed HBN.


## 3.1. Constructing Protocol of BEES

Consider an individual sink, interested in establishing a backbone in a discD of radius R around this specific sink [5]. This can see that D only covers a small portion of this transmission range. The backbone devices should be arranged so that at least one of them is within communication range of every sensor in D. To verify that such backbone has appropriate connectivity characteristics, tend to associate the subnets of backbone devices. The following are the main elements in backbone development protocol:
(i)
Phase of tilingThe discD around the sink is tiled with a series of matching regular geometric shapes, giving the area from around sinks the appearance of a honeycomb.
(ii)
Phase selection of backboneThe sensor that is nearest to the center of each hexagon is chosen.(iii)
Phase of clusteringBackbone sensors alert other detectors to their presence and the hexagonal they symbolize. The other sensors use RSS measurements to establish which clusters (hexagon) they belong to.The disc is tilted.D begins with the sinks: a first hexagon’s center aligns with the sink. The diameter of the tiling hexagons’ enclosed circle is tx/3, where tx is the high propagation distance of a detector [15]. This suggests coordinates for correctly identifying the numerous hexagons as in tiling seen above. The hexagon in column c of row r in sector s, in particular, is individually recognized to use the tuple s,r,c. Although there are numerous routing techniques for hexagonal connections, the coordinate system appears being more suitable for the construction process.


## 3.2. Backbone Scheduling

This illustrates that backbone scheduling is used to identify efficient routing focused on localized effective backbone routing. The whole modules are explained further below.(i)
Schedule Transition Graph (STG)The STG method is depicted in the picture below; it is a centralized approximation approach. The time scale is horizontal and is counted in rounds. In each cycle, the possible configurations of the backbone networks are enumerated vertically [16]. The quantity of backbones in each round is equivalent to the total of conceivable states. There’s also a one-to-one correspondence among condition and backbone. Energy is consumed in 1 session, which symbolizes the time that elapses during every round when energy is consumed. Whenever a node’s condition is depleted, node movement is not permitted as shown in Figure 1.Figure 1
Scheduling of STG.Bis a collection of backbone connection rounds that are used by the backbone routers. All connection is a networking linked subgraph, as well as all nodes become one step away from each point in Bi.
(ii)
Energy-efficient localized routingThis section goes into the specifics of the routing idea, which is focused on localized routing. It is a greedy way of technique routing. In greedy networking, the router chooses the next-hop neighbor depending on the difference to an objective [17]. As a result, such sequencing may not even be thermally efficient. The transmitting connection must have greater energy efficiency to be more eco-friendly. To utilize less energy, the overall distance traveled should be modest. As a result, a new notion known as a limited zone is established to limit advancing directions.
(i)
The backbone nodes with such communication to forwarding locate the neighbor backbone node between several neighbors existing inside the limited area. The greatest neighbor has the most energy efficiency or a stable connection(ii)
When there are no backbone nodes in the closed area, it expands its type of transmission range to select the optimal node(iii)
If there are no nodes in the region or zone, the standard greedy routing is used. Figure2 depicts the data flow of this process(iii)
Critical transmission radiusFigure 2
Flow diagram of data.In this research, during routing data to a base station, the destination node can delete packets as they arrive at the destination since they are unable to discover a stronger neighbor node. As a result, to discover the better neighbor backbone node and assure productive and effective routing, each backbone network must have a sufficient size broadcasting range.The critical transmission range is denoted as(1)ρxDn=βXlnR.nnπ.ρx is a method of generalized routing.Dn is a number of nodes in the network.βX is a proportion of the center of access point u to the radius r of the transmitting area from which the approximate backbone node u selects its neighbor backbone network w.


## 3.3. Backbone Establishment

(i)
Election of HBNDuring the initialization step, all devices have the same amount of energy. HBNs were selected avariciously and in a distributed manner. If a network identifies there is no HBN inside its VND, it declares its desire to become an HBN by transmitting a HEAD communication to the networks through its VND. Any node network that gets the HEAD message will be temporarily labeled as an AFN. If not enough HBNs can be connected to the system, the election system concludes.(ii)
Discovery of ABNsIf the selected HBNs are successful in constructing a linked backbone, the procedure of backbone development is completed. Otherwise, complementary connections are required to connect the individual HBNs. Using the ABN discovery technique, investigate the interconnectivity of HBNs. The process is initiated by the data center sending out a CHECK notification. Any node that receives the CHECK message should assist in spreading it. The message of CHECK has four critical parameters: LAST ID, LAST TYPE, CURRENT ID, and CURRENT TYPE [3]. Before broadcasting the signal, every node must improve the model. The CURRENT ID and CURRENT TYPE values relate to the ID and type of the input nodes. If the base station can obtain CHECK communications from HBNs, it replaces the variables LAST TYPE and LAST ID again with ID as well as category of last HBN which transmits a message to it; alternatively, unless it can obtain CHECK communications from AFNs, it substitutes the LAST TYPE and LAST ID with both the ID as well as a category during the last AFN which creates the impression to the base station. When an HBN could only accept CHECK signals from AFNs, it knows that it has been disconnected from the Main Backbone Component (MBC) that links it to the cloud server.


## 3.4. Backbone Maintenance

BEES rotate backbone networks to stability transmitting tasks through all the sensors. The updating procedures for HBNs and ABNs vary, as stated below.(i)
Replacement of HBNDuring the initialization phase, all devices have the same amount of energy. HBNs are chosen in a decentralized manner following the -MHP. If a node discovers that there is no HBN in its−ND, it proclaims its intention of becoming an HBN by transmitting a HEAD message to its −ND neighbors. Any node that gets the HEAD packet will be temporarily labeled as an AFN. So that no more HBNs can be connected to the system, the electoral system concludes.
(ii)
ABN assignment and withdrawalBecause ABNs are simply nodes that link HBNs, they would be removed whenever the HBNs they belong to are updated. On the other hand, once the HBN is replaced, the current HBN must ensure that the replacement does not disrupt backbone connection. It compares the list of its nearby HBNs to a table of a previous HBN. The connectivity is kept if its list includes all of the elements in the former HBN’s. Instead, the new HBN must transmit a discover message for each disconnected HBN in order to find a route to that HBN. If it does not receive a response from the unplugged HBN, new ABNs are required. In its two-hop neighborhood, the incoming HBN will discover some AFNs which will link it to the separated HBN [4]. Whether there are two-hop pathways, this should select the AFNs with the highest power also as ABN. Now, if three-hop pathways are available, it will choose the pairing of AFNs with the highest total power as ABNs. Because there is no such step to ensure, the replacement HBN must designate the outgoing HBN also as ABN. It is significant to mention that such a circumstance occurs with a very small chance, and when it does, the departing HBN would only be in responsible of relaying activities from current HBN to the severed HBN.


## 4. Analysis of Backbone

In this section, look at the theoretical and experimental elements of BEES’ backbone properties. Three crucial aspects are taken into account: HBN connection, distribution of backbone size, and backbone node.


### 4.1. Connectivity of HBN

The connectivity risk of HBNs is based on the likelihood that HBNs will form a linked backbone in the absence of ABNs. BEES require HBN access for such following purposes. As previously stated, BEES make use of the -MHP principle for routing packets, which makes it easily extensible and cost-efficient. However, the presence of ABNs is a violation of the -MHP notion. The benefits of BEES might well be lost if the ratio of ABNs within the backbone becomes too high [18]. Furthermore, the procedures of ABN assignment and reassignment are far more complex than those of HBN voting and replacements. The presence of ABNs raises the existing system for backbone installation and configuration and also the computational cost. Because ABNs appear only if HBNs fail to form a linked backbone, connectedness of the HBNs affects the percentage of ABNs in a backbone. As a result, the connection of HBNs is closely tied to the actual quality of BEES.The connection of BEES’ HBNs is investigated using simulated results. This takes a square field of100×100m2 with such a network size of ρ=0.1/m2. The variable values are transformed from 0 to 1. Each is subjected to δ,104 different random experiments [19], the results of which are used to compute the HBN connection. Node transmission power distances are set to R =15 m and R=30 m, correspondingly. Figure 3 depicts the results. It was found that there is a significant threshold of where the HBNs are coupled with high probability. For example, if 0.5 when R=15 m or 0.8 when R=30 m, the connection chance of HBNs is more than 94%.Figure 3
Connectivity of HBNs.


### 4.2. Size of Backbone

However, the backbone in BEES is made up of two elements: HBNs and ABNs, the number of ABNs can be quite modest if it is set to a suitable value. As a result, one can determine the backbone size simply by calculating the number of HBNs. To that end, consider the work on the group concentration of the DMAC method, which generated and empirically validated the highest accuracy.(2)PCN=11+EN/2.PCNis the chance that chosen node randomly is a cluster head, i.e., the HBN in this case; and ED is the anticipated node degrees PCN=nHBN/N in the issue, where nHBN is the amount of HBNs and N is the total number of detectors. Furthermore, the estimated access to smart is provided by ED=ρπδV2 in the Poisson point process model. As a result, the approximate size of the backbone is
(3)nBN≈nHBN=N1+ρπδV2/2.Thus, in simple terms, the frequency of the backbone networks is observed to be(4)λBEES=ρ1+ρπδV2/2.Equation (4) is validated through simulation. Figure 4 depicts the results. The lead to improve in (4) matches the outcome extremely well. This means that given characteristic, the backbone diameter in BEES may be accurately calculated by (4). Traditionally, it may control the backbone diameter using the option.Figure 4
Node density of backbone.


### 4.3. Distribution of Backbone Node

Another backbone element that interests us is the location of backbone nodes, but it has an intrinsic effect on routing energy usage. This investigates a characteristic from two perspectives: intuitive experience and link speed metrics. In addition to BEES, the findings of two other current sleeping organizations’ management, GAF and Naps, are offered for comparison [20]. The arrangement of backbone networks and sleeping networks provides us with a visual representation of the backbone architecture in such three techniques. For the sake of clarity, only plot a 5×5m2 zone. In comparison, BEES generates a backbone with more uniformly dispersed nodes than the GAF and Naps.As previously stated, the minimum backbone connection length in BEES is limited by theδ−MHP notion. However, there is no such restriction in several other sleeping management methods. Study the link structure measures within three components to see this in action [21]. The backbone link distance distribution in GAF and Naps is not constrained, as seen in Figure 5. There are connections with distances ranging from 0 to R, indicating that backbone units are not dispersed uniformly. The backbone link in BEES, on the other hand, is inadequate to a smaller array between δR and R. This link length of limitation decreases the variability of backbone network connections in BEES, allowing it to have a uniform distribution backbone node network.Figure 5
Distance link of statistics.


### 4.4. Backbone Node Network Lifetime

In this section, the outcome of the suggested methodology is described [22]. The results are exhibited on the ns2 simulator; the notion of VBS and localized routing delivers the best results in locating stable backbone network connections that are region constrained.


#### 4.4.1. Model of Energy

When a node enters sleep mode, it needs energy whenever there is a message transfer and switches off its transmitter [23]. Energy is mostly consumed unless a communication exchange is required to meet the quality of service.


#### 4.4.2. Effective Routing

This demonstrates the efficient routing established by CDS by constructing stable links among the backbone nodes in this study [24–27].


## 4.1. Connectivity of HBN

The connectivity risk of HBNs is based on the likelihood that HBNs will form a linked backbone in the absence of ABNs. BEES require HBN access for such following purposes. As previously stated, BEES make use of the -MHP principle for routing packets, which makes it easily extensible and cost-efficient. However, the presence of ABNs is a violation of the -MHP notion. The benefits of BEES might well be lost if the ratio of ABNs within the backbone becomes too high [18]. Furthermore, the procedures of ABN assignment and reassignment are far more complex than those of HBN voting and replacements. The presence of ABNs raises the existing system for backbone installation and configuration and also the computational cost. Because ABNs appear only if HBNs fail to form a linked backbone, connectedness of the HBNs affects the percentage of ABNs in a backbone. As a result, the connection of HBNs is closely tied to the actual quality of BEES.The connection of BEES’ HBNs is investigated using simulated results. This takes a square field of100×100m2 with such a network size of ρ=0.1/m2. The variable values are transformed from 0 to 1. Each is subjected to δ,104 different random experiments [19], the results of which are used to compute the HBN connection. Node transmission power distances are set to R =15 m and R=30 m, correspondingly. Figure 3 depicts the results. It was found that there is a significant threshold of where the HBNs are coupled with high probability. For example, if 0.5 when R=15 m or 0.8 when R=30 m, the connection chance of HBNs is more than 94%.Figure 3
Connectivity of HBNs.


## 4.2. Size of Backbone

However, the backbone in BEES is made up of two elements: HBNs and ABNs, the number of ABNs can be quite modest if it is set to a suitable value. As a result, one can determine the backbone size simply by calculating the number of HBNs. To that end, consider the work on the group concentration of the DMAC method, which generated and empirically validated the highest accuracy.(2)PCN=11+EN/2.PCNis the chance that chosen node randomly is a cluster head, i.e., the HBN in this case; and ED is the anticipated node degrees PCN=nHBN/N in the issue, where nHBN is the amount of HBNs and N is the total number of detectors. Furthermore, the estimated access to smart is provided by ED=ρπδV2 in the Poisson point process model. As a result, the approximate size of the backbone is
(3)nBN≈nHBN=N1+ρπδV2/2.Thus, in simple terms, the frequency of the backbone networks is observed to be(4)λBEES=ρ1+ρπδV2/2.Equation (4) is validated through simulation. Figure 4 depicts the results. The lead to improve in (4) matches the outcome extremely well. This means that given characteristic, the backbone diameter in BEES may be accurately calculated by (4). Traditionally, it may control the backbone diameter using the option.Figure 4
Node density of backbone.


## 4.3. Distribution of Backbone Node

Another backbone element that interests us is the location of backbone nodes, but it has an intrinsic effect on routing energy usage. This investigates a characteristic from two perspectives: intuitive experience and link speed metrics. In addition to BEES, the findings of two other current sleeping organizations’ management, GAF and Naps, are offered for comparison [20]. The arrangement of backbone networks and sleeping networks provides us with a visual representation of the backbone architecture in such three techniques. For the sake of clarity, only plot a 5×5m2 zone. In comparison, BEES generates a backbone with more uniformly dispersed nodes than the GAF and Naps.As previously stated, the minimum backbone connection length in BEES is limited by theδ−MHP notion. However, there is no such restriction in several other sleeping management methods. Study the link structure measures within three components to see this in action [21]. The backbone link distance distribution in GAF and Naps is not constrained, as seen in Figure 5. There are connections with distances ranging from 0 to R, indicating that backbone units are not dispersed uniformly. The backbone link in BEES, on the other hand, is inadequate to a smaller array between δR and R. This link length of limitation decreases the variability of backbone network connections in BEES, allowing it to have a uniform distribution backbone node network.Figure 5
Distance link of statistics.


## 4.4. Backbone Node Network Lifetime

In this section, the outcome of the suggested methodology is described [22]. The results are exhibited on the ns2 simulator; the notion of VBS and localized routing delivers the best results in locating stable backbone network connections that are region constrained.


### 4.4.1. Model of Energy

When a node enters sleep mode, it needs energy whenever there is a message transfer and switches off its transmitter [23]. Energy is mostly consumed unless a communication exchange is required to meet the quality of service.


### 4.4.2. Effective Routing

This demonstrates the efficient routing established by CDS by constructing stable links among the backbone nodes in this study [24–27].


## 4.4.1. Model of Energy

When a node enters sleep mode, it needs energy whenever there is a message transfer and switches off its transmitter [23]. Energy is mostly consumed unless a communication exchange is required to meet the quality of service.


## 4.4.2. Effective Routing

This demonstrates the efficient routing established by CDS by constructing stable links among the backbone nodes in this study [24–27].


## 5. Result and Discussion

### 5.1. Energy Consumption

Because energy is a major issue in WSNs, creating virtual nodes is a great way to reduce energy use while increasing throughput. It is reasonable to measure the amount of power required based on data transmission in an accurate manner [28]. Figure 6 depicts the energy usage of backbone nodes throughout transmitting data, and while in sleep mode, the antennas are turned off to save energy. The graph depicts the number of backbone nodes within a network as well as their energy usage. As a result, the proposed system’s energy usage is not more than that of the present scheme. The line graph represents the amount of energy utilized by the backbone nodes in the entire WSN.Figure 6
Consumption of energy.


### 5.2. Effective Routing

The overall length of a path identified by LEARN in the routing protocol is always within a continuous optimum. The suggested system is compared to existing localized routing methods and demonstrates that it can ensure energy-efficient pathways from the sender to the receiver. Modern systems are used to performance characteristics of LEARN routing [29]. This demonstrates that the LEARN localized routing method guarantees energy-efficient pathways in a random network with such a high degree of certainty. Figure 7 depicts the backbone nodes that have efficient routing. Throughput numerical simulations are shown as routing protocol.Figure 7
Routing.


### 5.3. Lifetime of Node

Backbone nodes extend network longevity by extending node lifetime. As energy arrangements, accurate reading energy, and unbalanced energies are employed, the communication range is originally fixed; however, the transmission diameter can later be detailed work on the crucial transmission. The obtained network lifespan is depicted in Figure8, by properly spreading the energy. The blue line represents the lifespan of backbone nodes within the network.Figure 8
Lifetime of node.


### 5.4. Delay

This model result reveals that there is less time in determining an effective path and assembling CDS from sensor nodes, as illustrated in Figure9.Figure 9
Delay.This section compares BEES’ effectiveness to two distinct topology processes that require GAF and Naps. Scalability and routing energy efficiency are two performance challenges that are being explored. In the system, all three of these techniques are sustainable. GAF, for example, regulates the number of backbone nodes by adjusting an edge distancer of a synthetic grid. λGAF=1/r2 is utilized to calculate the density of the backbone nodes. Naps control the size of the backbone by adjusting the neighbor threshold c. The frequency of the backbone nodes is determined by λNaps=c/πR2. In BEES, expand the backbone by simply changing the variable and then using it to estimate the density of backbone node. It is important to note that, while a sustainability of GAF in is restricted by r≤R/5 to ensure connectivity, see that this restriction can be loosened if the backbone is just mandatory to be linked with a maximum probability. This connection enables the cost of r to be greater than the R/5 in the experiment.This changes the control settings in the three algorithms to generate backbones with varying node densities. Figure10 depicts the backbone connection in such three techniques with varying backbone network sizes. According to Figure 10, the backbone scaling range in BEES is substantially wider than in the remaining two algorithms, providing a connectivity probability. When the interconnectivity probability criterion is set to 90%, the density of backbone node is constrained to λBEES≥0.0158 in BEES, λGAF≥0.023 in GAF, and λNaps≥0.024 in Naps. Because the field size is set at 100×100m2, the minimal overall amount of backbone connections in 158 BEES, 230 in GAF, and 240 in Naps.Figure 10
Scalability comparison between Naps, GAF, and BEES.Aside from scalability, evaluate the power consumption of the three backbone routing algorithms. This specifically fixes the placements of the transmitter and the receiver. They are supposed to be100 metres apart. With varying backbone numbers of units producing requirements, the regular end-to-end energy usage is detected. The energy routing model is as follows: Ehop=adα+b, where ad indicates the transmission power consumption, α is the route loss, d is the duration of hop, and b indicates the power consumption only at reception for collecting and on both ends for information processing. For short-range transmissions, use adα=740 nJ, b=570 nJ, and path loss α=4. Given that the hop frequency is normally 5~8 m, set a=740/54=1.18 nJ/m4. In addition, the SP-power networking algorithms are used to construct end-to-end connections.As demonstrated in Figure11, the end-to-end routing energy usage in BEES is smaller than that in the other two techniques with varying backbone density needs. When it remains constant, BEES consumes 85 percent of the energy consumed by GAF and Naps; when it is large, BEES consumes 87 percent of the energy consumed by GAF and Naps. In other terms, BEES uses around 15% less routing energy than GAF and Naps. Because routing energy accounts for the majority of the energy consumed by a backbone, the above detection suggests that a WSN lifetime throughout BEES is approximately 1/1−15%1−15%−1=18% percent extended than in GAF and Naps, assuming that routing activities are well rounded across all detectors in such strategies.Figure 11
Energy-efficient routing.


## 5.1. Energy Consumption

Because energy is a major issue in WSNs, creating virtual nodes is a great way to reduce energy use while increasing throughput. It is reasonable to measure the amount of power required based on data transmission in an accurate manner [28]. Figure 6 depicts the energy usage of backbone nodes throughout transmitting data, and while in sleep mode, the antennas are turned off to save energy. The graph depicts the number of backbone nodes within a network as well as their energy usage. As a result, the proposed system’s energy usage is not more than that of the present scheme. The line graph represents the amount of energy utilized by the backbone nodes in the entire WSN.Figure 6
Consumption of energy.


## 5.2. Effective Routing

The overall length of a path identified by LEARN in the routing protocol is always within a continuous optimum. The suggested system is compared to existing localized routing methods and demonstrates that it can ensure energy-efficient pathways from the sender to the receiver. Modern systems are used to performance characteristics of LEARN routing [29]. This demonstrates that the LEARN localized routing method guarantees energy-efficient pathways in a random network with such a high degree of certainty. Figure 7 depicts the backbone nodes that have efficient routing. Throughput numerical simulations are shown as routing protocol.Figure 7
Routing.


## 5.3. Lifetime of Node

Backbone nodes extend network longevity by extending node lifetime. As energy arrangements, accurate reading energy, and unbalanced energies are employed, the communication range is originally fixed; however, the transmission diameter can later be detailed work on the crucial transmission. The obtained network lifespan is depicted in Figure8, by properly spreading the energy. The blue line represents the lifespan of backbone nodes within the network.Figure 8
Lifetime of node.


## 5.4. Delay

This model result reveals that there is less time in determining an effective path and assembling CDS from sensor nodes, as illustrated in Figure9.Figure 9
Delay.This section compares BEES’ effectiveness to two distinct topology processes that require GAF and Naps. Scalability and routing energy efficiency are two performance challenges that are being explored. In the system, all three of these techniques are sustainable. GAF, for example, regulates the number of backbone nodes by adjusting an edge distancer of a synthetic grid. λGAF=1/r2 is utilized to calculate the density of the backbone nodes. Naps control the size of the backbone by adjusting the neighbor threshold c. The frequency of the backbone nodes is determined by λNaps=c/πR2. In BEES, expand the backbone by simply changing the variable and then using it to estimate the density of backbone node. It is important to note that, while a sustainability of GAF in is restricted by r≤R/5 to ensure connectivity, see that this restriction can be loosened if the backbone is just mandatory to be linked with a maximum probability. This connection enables the cost of r to be greater than the R/5 in the experiment.This changes the control settings in the three algorithms to generate backbones with varying node densities. Figure10 depicts the backbone connection in such three techniques with varying backbone network sizes. According to Figure 10, the backbone scaling range in BEES is substantially wider than in the remaining two algorithms, providing a connectivity probability. When the interconnectivity probability criterion is set to 90%, the density of backbone node is constrained to λBEES≥0.0158 in BEES, λGAF≥0.023 in GAF, and λNaps≥0.024 in Naps. Because the field size is set at 100×100m2, the minimal overall amount of backbone connections in 158 BEES, 230 in GAF, and 240 in Naps.Figure 10
Scalability comparison between Naps, GAF, and BEES.Aside from scalability, evaluate the power consumption of the three backbone routing algorithms. This specifically fixes the placements of the transmitter and the receiver. They are supposed to be100 metres apart. With varying backbone numbers of units producing requirements, the regular end-to-end energy usage is detected. The energy routing model is as follows: Ehop=adα+b, where ad indicates the transmission power consumption, α is the route loss, d is the duration of hop, and b indicates the power consumption only at reception for collecting and on both ends for information processing. For short-range transmissions, use adα=740 nJ, b=570 nJ, and path loss α=4. Given that the hop frequency is normally 5~8 m, set a=740/54=1.18 nJ/m4. In addition, the SP-power networking algorithms are used to construct end-to-end connections.As demonstrated in Figure11, the end-to-end routing energy usage in BEES is smaller than that in the other two techniques with varying backbone density needs. When it remains constant, BEES consumes 85 percent of the energy consumed by GAF and Naps; when it is large, BEES consumes 87 percent of the energy consumed by GAF and Naps. In other terms, BEES uses around 15% less routing energy than GAF and Naps. Because routing energy accounts for the majority of the energy consumed by a backbone, the above detection suggests that a WSN lifetime throughout BEES is approximately 1/1−15%1−15%−1=18% percent extended than in GAF and Naps, assuming that routing activities are well rounded across all detectors in such strategies.Figure 11
Energy-efficient routing.


## 6. Conclusion

This study offers BEES, a scalable topology management approach that consumes less power not only through putting abundant networks to sleep but also by lowering the routing energy usage of backbone networks. Virtual backbone planning is a new scheduling mechanism for WSNs. Power communication is required for WSN to operate for a longer amount of time due to battery resources; thus, network longevity through power-aware network organization is very desirable. To plan the node activities among sleep and active states, an efficient approach based on power-saving planning must be created. One approach is to construct a backbone node and turn off its radios being in sleep state, and the routing must be power saving by extending the lifetime of the network. This results in energy savings while routing jobs. This virtualized backbone scheduling has the potential to conserve and efficiency of wireless sensor networks. BEES offers a wider scaling spectrum and spends around 15% less transit power than existing topological management algorithms, according to simulation results.



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*Source: 9327318-2022-04-20.xml*