AU2021101571A4 - An efficient coverage management for smart iot application - Google Patents

Abstract

The present disclosure seeks to provide a multi-objective optimization strategy for enhancing quality of service in IoT-enabled WSN applications. The Adaptive Coverage and Connectivity (ACC) scheme is proposed to attain the effective IWSN model. This scheme provides an efficient WSN with appropriate coverage, connectivity and energy management. There are two underlying methods implemented in the present disclosure namely: Adaptive Coverage (AC) and Energy-Efficient Connectivity (EEC). The AC method is responsible for sensing coverage, whereas the network connectivity and energy consumption are managed by the EEC method. The primary features addressed in this work are prolonging the coverage rate, maintaining network connectivity, minimizing energy consumption and time complexity. 20 100v depyWinsemw ort s esrviomy rIFeeI -- IE oswerenet _-U-- -lrorimentregeirl sMeeiasetog / /102 netword nmethcd toowc o .~ MIEver apera tes to tNentirs -em rg area3toncre a _*-teN_ tvor klIetime-ofWS,1N 106 Figure1I 60% a b Figure 2

Description

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AN EFFICIENT COVERAGE MANAGEMENT FOR SMART IOT APPLICATION FIELD OF THE INVENTION

The present disclosure relates to a multi-objective optimization strategy for enhancing quality of service in IoT-enabled WSN applications.

BACKGROUND OF THE INVENTION

In today's world, the Internet of Things (IoT) has become more relevant owing to the growth in smart grid, smart city and smart home applications. Network sustainability is considered as a significant characteristic for IoT based applications. Wireless Sensor Network (WSN) offers such network sustainability where WSN is acted as the subnets in the IoT model. However, the multi-objectives like coverage, connectivity and energy consumption are required to improve the quality of service in IoT based WSN (IWSN) model. A hybrid multi-objective evolutionary algorithm (Hybrid-MOEA/D-I and Hybrid MOEA/D-II) was proposed in order to reduce energy consumption and improve the sensing coverage. In the first hybrid algorithm, a genetic algorithm is incorporated with differential evolution to attain an improved pareto solution. A particle swarm optimization algorithm is utilized in the second hybrid algorithm. These two hybrid algorithms aimed to choose optimal node as the cluster head and thus more target objects are covered in the sensing area. It yields the battery energy of the entire network is conserved and balanced. Although this hybrid algorithm reduces energy consumption, it creates a more complex network structure and needs higher run time. Another scheme called Mobile Sink based Coverage Optimization and Link-stability Estimation Routing (MSCOLER) has been proposed to handle the sensing coverage and energy consumption of WSN. The coverage hole problem is generally due to node failure and communication failure. An idle listening and overhearing are the foremost reason for reducing the battery energy of the node. The MSCOLER scheme provides an efficient solution to solve these two problems by patrolling the redundant nodes and accomplish the routing strategy with link quality. The major issue behind the MSCOLER scheme is the requirement of node mobility which particularly impacts the topology and time complexity.

Moreover, the frequent change of topology affects the connectivity of the network and it reduces the uniform packet transmission throughout the entire network. Partial Coverage with Learning Automata (PCLA) scheme has been proposed to address the coverage and connectivity issues by implementing the sleep scheduling approaches. Its motive is to reduce the number of nodes to activate for covering a specified portion in the field of interest. The PCLA scheme added the advantage of learning automata algorithm to appropriately schedule nodes into an active or idle state in order to prolong the lifetime of the network. Subsequently, each sensor node implements the PCLA scheme which first generates a backbone by choosing a number of sensors and leveraging the sensing coverage grid of the network. After that, these sensors utilize their neighbour node's information to meet the coverage and connectivity necessities. The PCLA scheme can ensure both coverage and connectivity necessities but failed to attain lower energy consumption and time-complexity. Improved Genetic Algorithm (IGA) scheme has been proposed to offer k-coverage to all target objects and m-connectivity to every node in the network. The new fitness value of IGA is determined with the aid of three parameters namely: coverage, connectivity and number of deployed nodes. Afterwards, the crossover and mutation operations can be carried out consistently to create valid chromosomes. This avoids the need for the validity checking phase, thus it reduces the time complexity of the IGA scheme. The major constraint with the IGA scheme is a scarcity of reliability, which means the number of iterations for IGA is not sub-linear to the number of nodes. Based on the above analysis, a limited number of existing works address simultaneous consideration of coverage, connectivity and energy consumption for an IWSN model. In particular, network connectivity is not properly maintained in most of the previous work. Many existing works have been proposed as the combined objective of coverage and connectivity whereas existing work fails to focus in combination with the energy consumption of an IWSN model. Therefore, in order to overcome the aforementioned drawbacks, there exists a need to develop a novel multi-objective optimization strategy for enhancing quality of service in IoT enabled WSN applications. SUMMARY OF THE INVENTION

The present disclosure seeks to provide a multi-objective optimization strategy for enhancing quality of service in IoT-enabled WSN applications. The Adaptive Coverage and

Connectivity (ACC) scheme is proposed to attain the effective IWSN model. This scheme provides an efficient WSN with appropriate coverage, connectivity and energy management. There are two underlying methods implemented in the present disclosure namely: Adaptive Coverage (AC) and Energy-Efficient Connectivity (EEC). The AC method is responsible for sensing coverage, whereas the network connectivity and energy consumption are managed by the EEC method. The primary features addressed in this work are prolonging the coverage rate, maintaining network connectivity, minimizing energy consumption and time complexity.

In an embodiment, a multi-objective optimization method 100 for optimizing quality of service in IoT-enabled wireless sensor network applications, the method comprises the following steps: At step 102, deploying sensor nodes randomly in sensing field to supervise environment regularly, wherein all sensor nodes remain stationary in the sensing field; At step 104, locating a sink node in the sensing field to monitor energy level of all sensor nodes periodically in the network; and At step 106, proposing Adaptive Coverage and Connectivity (ACC) scheme consisting of Adaptive Coverage (AC) method to optimize coverage rates to the entire sensing area to increase the network lifetime of WSN and Energy-Efficient Connectivity (EEC) method to optimize network connectivity among multiple sensor nodes that reduces the energy consumption and maximizes the network sustainability of wireless sensor network (WSN).

The various objectives of the present disclosure are as follows:

• An AC method providing an automatic switching strategy between dense and spare coverage modes. This process increases the coverage efficiency of the network. • A mathematical model of the AC method demonstrates the relationship between two coverage modes. • A new bio-inspired algorithm offers optimal connectivity by balancing the routing path among multiple sensor nodes. To further clarify advantages and features of the present disclosure, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.

BRIEF DESCRIPTION OF FIGURES

These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein: Figure 1 illustrates a multi-objective optimization strategy for enhancing quality of service in IoT-enabled WSN applications in accordance with an embodiment of the present disclosure. Figure 2 illustrates (a) Sensor node characteristics; and(b)Generic model of AC method in accordance with an embodiment of the present disclosure. Figure 3illustrates (a) Triangular deployment of dense coverage mode; (b) Basic element of the deployment pattern;(c)Triangular deployment of sparse coverage mode; and (d) Basic unit of the deployment pattern in accordance with an embodiment of the present disclosure. Figure 4 illustrates (a) WSN#1 environment; and (b) WSN#2 environment in accordance with an embodiment of the present disclosure. Figure 5 illustrates analysis of CR in WSN#1 (a) 50 nodes; (b) 300 nodes. Analysis of CR in WSN#2; (c) 50 nodes; and (d) 300 nodes in accordance with an embodiment of the present disclosure. Figure 6 illustrates analysis of AEC in (a) WSN#1; (b) WSN#2;Comparison of Network lifetime in (c) WSN#1;and (d) WSN#2in accordance with an embodiment of the present disclosure.

Figure 7 illustrates analysis of RR in (a) WSN#1; (b) WSN#2; and Comparison of CC in (c) WSN#1; and (d) WSN#2in accordance with an embodiment of the present disclosure. Figure 8 illustrates analysis of CE in (a) WSN#1; and (b) WSN#2 in accordance with an embodiment of the present disclosure.

Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have been necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present disclosure. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.

DETAILED DESCRIPTION

For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.

It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.

Reference throughout this specification to "an aspect", "another aspect" or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.

The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises...a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.

Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.

Referring to Figure 1 illustrates a flowchart of the multi-objective optimization method for optimizing quality of service in IoT-enabled wireless sensor network applications in accordance with an embodiment of the present disclosure. In an embodiment, a multi objective optimization method 100 for optimizing quality of service in IoT-enabled wireless sensor network applications, the method comprises the following steps: At step 102, deploying sensor nodes randomly in sensing field to supervise environment regularly, wherein all sensor nodes remain stationary in the sensing field; At step 104, locating a sink node in the sensing field to monitor energy level of all sensor nodes periodically in the network; and At step 106, proposing Adaptive Coverage and Connectivity (ACC) scheme consisting of Adaptive Coverage (AC) method to optimize coverage rates to the entire sensing area to increase the network lifetime of WSN and Energy-Efficient Connectivity (EEC) method to optimize network connectivity among multiple sensor nodes that reduces the energy consumption and maximizes the network sustainability of wireless sensor network (WSN).

Figure 2 illustrates (a) Sensor node characteristics; and (b)Generic model of AC method in accordance with an embodiment of the present disclosure. Each sensor node is considered as an autonomous wireless node with a non-rechargeable battery. It has the capability to change its operating state between active and idle. If a node is active, it gathers the sensed data and is considered as a covered area. At the same time, if a node at idle state, it saves its energy. Node characteristics are formulated as follows. Nodes are positioned on a time-dependent two-dimensional location through coordinates (x, y). Every node has a time dependent energy level, a stationary wireless emission range and a velocity. The communication area is assumed as a circular of radius CR. Besides, this communication area is the local eyesight of the location from a node perspective. The following hypothesis is considered for the network model such as, Sensor nodes are deployed randomly to supervise the environment regularly; All the sensor nodes remain stationary (not moving) in the sensing field; Only one sink node is located in the sensing field; Initially, the energy level of all sensor nodes is equal; The energy level of a node is decremented at each round based on their activities; There is no restriction for a sink node with respect to energy, processing power and memory; Nodes within the range of communication are capable to exchange the current status about their state, energy and location; The location of the sink node is known to all sensor nodes and vice versa; The sink node periodically monitors the energy level of all sensor nodes in the network. The sensing environment is considered as a squared area.

The area coverage can be categorized as full coverage and partial coverage. The full coverage refers to every point of sensing area is covered by at least one node and there arise overlapping among sensor nodes whereas some points are not covered in partial coverage. In the proposed work, dense coverage mode and sparse coverage mode are considered as full coverage and partial coverage respectively. Since dense coverage is stringent and costly, it should be utilized only if required.

Most of the IWSN applications may operate satisfactorily with sparse coverage. For instance, IWSN with sparse coverage is adequate for weather forecast applications. Instead of identifying the humidity at each location in the sensing field, the evaluation of a few fractions of the region might prove adequate for the humidity profile of the entire sensing field. Moreover, the sparse coverage mode is capable to guarantee a longer lifespan to an IWSN although it employs comfortable constraints on it. Figure 2b states a generic model for adaptive coverage. The figure depicts an area of interest fragmented into four regions (X1, X2, Yl, Y2) to acquire different levels of sensing coverage. For example, Y2 has a 90% coverage necessity being a crucial sensing area. Instead, supervising 50% of X2 is sufficient. Thus, IWSN applications may need network configurations with different modes of coverage.

The nodes will regularly operate in dense mode cause more battery energy whereas the node kept a large amount of energy in sparse mode. The proposed AC method figures out the information and pays more consideration to both modes. Based on inter-node distance, the AC method automatically switched to any one of the modes. This category of switching technique can extend the battery energy of sensor nodes for a prolonged time.

Figure 3 illustrates (a) Triangular deployment of dense coverage mode; (b) Basic element of the deployment pattern;(c)Triangular deployment of sparse coverage mode; and (d) Basic unit of the deployment pattern in accordance with an embodiment of the present disclosure. The network model consists of sensor nodes which are uniformly distributed over a sensing field (y) for detecting abnormal events. These events can occur randomly at any location in the sensing area. In this work, the sensing events are indicated as space points. A sensor node can sense the abnormal events within a certain distance are called Sensing Radius (SR). These sensing events can communicate with neighbor sensors within its communication range is called Communication Radius (CR).

A sensor network is demonstrated by the coverage graph G = (V, E), where Vrepresents a set of randomly distributed sensor nodes and it is given by

V= {n,n2,.....,n,,) (1)

E denotes the set of the transmission links between sensor nodes. The distance between a node (n) and a space point or target object (p) is indicated by d,(n,p).

Let (n, ny) and (p, py) be the coordinates of the node and the space point respectively. The coverage function of a space point with respect to a particular node 'ni' is given by

cf(de(ni,p)) =1 dt(ni,p) SR (2) to otherwise.

The binary value 1 indicates that the node can sense the point p. Further, a space point might be covered by multiple nodes at the same moment. The Coverage Function (CF) of a space point with respect to the set of nodes is computed by

CF (p) = Z' cf (dt (ni, p)) (3)

If CF(p) = q, then it implies that the space point p is q-covered. Moreover, the coverage of the sensing area with respect to the whole network can be calculated by using Equation (4) which is called the network coverage. Let NCF be the network coverage function and it is stated as the minimum value of CF(p) among all the probable values of p in the whole network, i.e.,

NCF = minv p y CF(p) (4)

The sensing area [34] of a particular node ni is

SA(n) = x 2 (y Sy)2 !SR (5)

where(x,y) c y. The neighbor set of sensor node ni is

NSA(ni) = V(X, - x +)2 + (y, - y) 2 SR (6)

wherenj c V and ni # n

In a deterministic environment to cover a sensing plane, an appropriate deployment pattern is equilateral triangle lattice in which nodes are deployed on the vertices of triangles. Nodes in the triangular lattice pattern offer optimal dense coverage where the side length of the triangle

outline is V/SR. The side length represents the highest inter-node distance (d) where it guarantees 100% coverage.

AC = (Dense coverage if dt <VSR, (7) Sparse coverage if d ! SR

If the spacing between sensor nodes is increased, then the overlap region becomes zero at d, = 2 S. Hence, there is no overlap region is formed at dt 2 2 S. Therefore, for analysis of

sparse area coverage, the area of interest isdt /SR. Further, this whole area is divided into

two sub-areas, i.e., /SR dt : 2 SR anddt 2SR. Consider a small sensing area in which two sensor nodes n and nj are located. The distance between two nodes ni and nare specified by

dt = dt (ni, nj) (8)

Let A 1;be the common sensing region of two sensor nodes and it is formulated as

Sj 2 arccos _ _-1(d (9)

In Figure 3a, the total coverage ratioof the sensing area is denoted as C'which equals to the coverage ratio of the triangular outline in Figure 3b.

7 (dt\ 3dt (d\ SR -3 arccos 2 o + 1 ±2R)2SRFk2SRY_(0

C v(10) 4 t

The term P is introduced to make the relationship between C, and dtin the triangular mesh and it is given by

#3 fl dt (11)

( 2SR

By putting the P value in Equation (10) and it becomes

C = 1 - 3 arccos(#) + 3# J1 -(#)2] (12)

In order to meet desired C-coverage, the spacing between triangle vertices is adjusted and the spacing is represented asd. For instance, when C, = 0.906, the value of d, is 2 SR. Let do, be the overlap point among adjacent nodes and C, is computed with respect to do, i.e.

< 90.6% dt > do, C,= 90.6% dt = dop (13) > 90.6% dt < do,

To determine the communication radius of the nodes, the following processes are followed. Primarily, the sensor nodes are positioned and the triangular pattern in Figure 3d can be repeated to acquire the node configuration exposed in Figure 3c. The Cfor y is equal to

in A XYZ C, = Area of circularregion Area ofA XYZ

2 C, = (15)

To find dthe Equation (15) becomes

d(=.SR (16)

To sustain good connectivity in any monitoring area, CR must be > d, i.e.

CR ( V ) - SR (17)

To formulate a connected sensor network, the sensor nodes require to fix their minimum CRequal to 2 S. Eventually, CRneed not be fixed greater than 2 SR as it provides no additional

benefit and leads to more energy loss in the nodes. The condition CR"' > 2SR is one of the essential relationships to ensure network connectivity which guarantees the required coverage. The following equation provides the minimum communication radius for the several ranges ofC(.

f(C,)SR C,(<90.6% cmin 2SR C, = 90.6% (18) e (V3SR, 2SR) Ce (90.6%, 100%)

where, f(C,) = . The mathematical model of the AC method clearly provides an

appropriate evaluation of total coverage ratio and minimum communication radius of the sensing area. As a result, the aforesaid model guarantees the coverage rate as well as coverage efficiency of IWSN.

In order to provide better global connectivity, the whole sensing area is divided into a group of zones. In each zone, one sensor node is chosen as Zone Head (ZH) and the remaining nodes are considered as Member Nodes (MNs). The major role of ZH is to collect all sensing information from MNs and it will finally forward the collected data to the central sink. However, the connectivity inside the zone and among the multiple zones are the major constrains in WSN. To overcome these constrains, the Energy-Efficient Connectivity (EEC) method is proposed where Bio-inspired Zone Formation (BZF) algorithm is implemented to attain global connectivity. In BZF, the optimum ZH selection can be carried out by using the battery energy of sensor nodes. This optimal ZH selection automatically increases the global connectivity inside and between the zones.

The proposed BZF algorithm offers a multi layered hierarchical network structure. Multi layered hierarchical networks consist of network, zones, and nodes where the collection of different distributed nodes is termed as zones and a combination of different zones is called network. A zone is a group of cells in which cells with similar genetic values are assembled into the same zone. The genes (g) of its corresponding cells to form the genome of the zone (GZ) which is expressed in the following equations,

GZ = {gi (value), g2 (value),..., g(value)} (19) gi = f{OGV, CSV, ZA, DV} (20)

wheregiis the function of the Own Genetic Value (OGV), Common Shared Value (CSV) in the zone, the Zone Address identification (ZA) and Difference Value (DV). The purpose of having OGV and CSV is to identify the neighbour gene information and ZA provides information about the corresponding zone address where the gene is currently connected. The DV offers the difference between a particular cell OGV and CSV value. The gene mentioned in the BZF algorithm denotes the sensor nodes in the WSN environment. The zone is constructed by the sink on the underlying of a centralized approach. The sink implements the BZF algorithm to form the N number of zones with the aid of genetic information. The genetic information of all genes incorporate their distance information (Euclidean distance between the gene's position and the center of the deployed area). Moreover, this algorithm will select the ZH and Associate ZH (AZH) where AZH acted as next ZH in the zone once the current ZH failed. Afterwards, sink broadcast the ZH and AZH information to all MNs which helps the MNs to aware of the current ZH as well as next ZH in the subsequent rounds. The major intention of having AZH is to reduce the ZH selection rounds that improve the energy efficiency of MNs and the entire network. For a typical WSN with K sensors, the entire network is classified into N zones. However forming each zone, the Average Number of Sensor Nodes (ANSN) inside each zone is determined by K ANSN = (21) N

The eight procedure steps are considered in the proposed BZF algorithm for selecting the ZH in the network. This optimal ZH selection yields the proper connectivity among the sensor nodes. As a result, the constant routing path can be sustained throughout the entire network.

Figure 4 illustrates (a) WSN#1 environment, (b) WSN#2 environment in accordance with an embodiment of the present disclosure.The simulation experiment was implemented in the MATLAB environment. It was conducted in the sensing area of 100 m x 100 m in which the sensor nodes are randomly deployed. All sensors know their geographical location of the sensing region where they have been deployed. The sensing and connectivity range of each node will be in the range of 10 m and 20 m respectively. Each node begins its operation with the initial battery energy of 2J. The control and data packet sizes are considered as 20 bytes and 50 bytes respectively. Each simulation experiment has been executed ten times and thus the average value was computed. All the experiments were implemented in a stationary field, i.e., the sink and sensors were fixed. The communication and sensing ranges of all sensor nodes are assumed to be the same. There are two types of networks examined in the proposed work such as a smaller network (50 nodes) and a larger network (300 nodes).

The efficiency of the proposed and existing methodologies is assessed through carrying out two different simulation experiments namely: WSN#1 and WSN#2. In WSN#1, the sink was located at (50, 50) whereas, the sink was located at (100, 50) in WSN#2. In Figure 4(a-b), the red and blue nodes represent the randomly deployed nodes and target objects respectively.

The sink node is denoted by the yellow triangle. Finally, the green circle intends the coverage area of nodes with a radius of SR. In most of the existing works, simulation experiments are executed in WSN#1 setup only. To overcome this, the proposed work performed in a new simulation setup (WSN#2) for analyzing the performance of IWSN. The key role of analyzing WSN#2 setup is to test the performance of proposed as well as existing schemes under different sink position.

Figure 5 illustrates Analysis of CR in WSN#1 (a) 50 nodes, (b) 300 nodes. Analysis of CR in WSN#2 (c) 50 nodes, (d) 300 nodes in accordance with an embodiment of the present disclosure. Coverage Rate (CR) is a significant metric for computing the coverage consistency of IWSN and is the region covered by nodes in an area of interest. This CR metric is likely to be a maximum (almost 100%) range as long as possible. Typically, the CR for coverage control schemes begins in an extreme of almost 100% range however a scheme is considered more effective if it maintains the CR value as maximum for a prolonged time. Figure 5(a-b) depicts the attained experiment results of the CR value in WSN#1 environment. ACC remains maximum values in CR for a prolonged time. In particular, the ACC scheme maintains the higher CR value as a maximum of 16th round for larger networks whereas the existing schemes like DS and GA schemes maintain until 12th round only. It clearly shows the better network sustainability of the proposed ACC scheme. This is due to optimal network coverage and connectivity methodologies are incorporated in the ACC scheme. In the proposed scheme, the sensing area is fully covered which is guaranteed by the AC method and necessary information is conveyed to the sink node with the aid of the EEC method. On the contrary, only partial coverage is provided by DS and GA schemes which leads to attaining lesser CR values than the ACC scheme. The lower CR values in existing schemes clearly indicate that they are not adopted for IoT based applications where those applications require coverage reliability and periodical sensing information. The obtained results of CR value for the WSN#2 environment are shown in Figure 5(c-d). The results clearly evident that the proposed scheme maintains higher CR values compared to the existing schemes. At the same time, the CR values of WSN#1 environment is higher than WSN#2 due to center position of sink that covers all sensing area in the network. This center position ensures the sensing target objects are properly sensed and report to the sink node. Figure 6 illustrates Analysis of AEC in (a) WSN#1, (b) WSN#2. Comparison of Network lifetime in (c) WSN#1, (d) WSN#2 in accordance with an embodiment of the present disclosure. The values attained for Average Energy Consumption (AEC) is shown in Figure 6(a-b). Here, it can be noticed that the AEC of the proposed scheme is lesser than DS and GA schemes. The minimum value of AEC is 0.016 J which is obtained by proposed ACC at WSN#1 environment.

Even if considering both WSN environments, the ACC scheme avoids the larger energy utilization at one sensor node by switching its sensing coverage modes. The network structure of ACC is isomorphic and it does not require any additional topology, thus it is commonly used for energy constraint environments. But in the case of DS and GA schemes, the battery energy of sensor nodes is quickly drained out due to the non-static approach of nodes. As a result, the mobility strategy leads to higher AEC in the network. Hence, the AEC clearly reveals that the proposed ACC scheme consumes lesser energy for coverage and connectivity process.

Typically, different metrics are offered to represent the network lifetime of IWSN. In this work, First Node Die (FND) has been employed to compare and assess the network lifetime. The FND values are exposed in Figure 6(c-d) reveals that the network lifetime of proposed ACC outperforms DS and GA schemes. The motivation behind the improved results in ACC is sensor nodes operated in dual mode configuration and finest ZH selection strategy. This increases the coverage and connectivity consistency among multiple MNs and ZH, which in turn leads to enlarging the network lifetime of IWSN. Furthermore, the network lifetime is prolonged by evading large energy utilization at one sensor node while remaining sensor nodes involved in consuming battery energy.

In contrast, the FND of the GA scheme is shortened with an increase in connectivity constraints. Like the GA-based scheme, the DS concentrated on coverage problem but they failed to solve the connectivity issues in IWSN. These issues will increase the rapid death of the first node in the network.

Figure 7 illustrates Analysis of RR in (a) WSN#1, (b) WSN#2. Comparison of CC in (c) WSN#1, (d) WSN#2in accordance with an embodiment of the present disclosure. Figure 7(a-b) shows the comparison of RR for different environments. While considering the RR value of the larger network, the proposed ACC attains 0.36 in WSN#1 environment whereas the RR value of DS and GA are 0.75 and 0.85 respectively. The intention of RR reduction can be obtained through the working principle employed by two underlying methodologies of ACC.

Alternatively, the mobile sink idea is implemented to improve the sensing coverage in the DS scheme. But, the location of the sink changed frequently and thus it requires a larger number of control packets exchanged for intimation of the current sink position. The RR value of GA is similar to DS by allowing sensor nodes to be dynamic. This mobility strategy of both DS and GA generates more redundant data in the network. Therefore, these two schemes cannot be preferred for a large scale environment.

The other metric used was CC [34] and it is computed by

CC = NNcoverage- connectivity (22) NNcoverage

whereNNcoverage-connectivity intends the number of nodes in the coverage

connectivity set and NNcoverage denotes the number of nodes in the coverage set. As stated

by the CC definition, if a scheme requires a lesser number of sensor nodes for connecting, then CC value is low. It can be noticed from Figure 7(c-d), the CC value of the proposed ACC is lower than the DS and GA schemes. The major reason behind this CC reduction interprets the appropriate connectivity path among multiple MNs and ZHs in the proposed

ACC scheme. The underlying EEC method in the ACC scheme selects the optimal node as ZH where the role of the ZH node can be maintained for a longer time. It also requires only a minimum number of nodes for connection strategy that automatically reduces the CC value.

However, the dynamic characteristic of the sink in the DS scheme causes poor network connectivity and it leads to higher CC value. Meanwhile, the GA scheme does not guarantee the connectivity of the network by introducing the mobility among sensor nodes that makes the GR to have a more complex network structure as well as greater CC. Thus, the proposed ACC has better performance than MS and GA schemes for smaller and larger networks from the perspective of CC which is due to simpler network structure and lower CC value of ACC.

Figure 8 illustrates Analysis of CE in (a) WSN#1, (b) WSN#2 in accordance with an embodiment of the present disclosure. Coverage Efficiency (CE) is another important factor for computing the selection efficiency of the proposed scheme. The key concern about the superiority of node selection rather than considering the number of selected sensor nodes. Moreover, it demonstrates the rate of coverage per maximum of probable coverage that can be attained by triggering all the active nodes. Experiment results in Figure 8(a-b) exhibit the proposed ACC scheme chooses the finest nodes effectively longer than DS and GA schemes.

The higher value of CE in proposed ACC can be maintained until the 28th round for larger networks whereas DS and GA maintain till 20th round and 12th round respectively. This is due to the fact of the minimum number of sensor nodes is selected in larger networks to attain the maximum coverage rate. Another rationale behind this better coverage performance is due to the efficient coverage function that leads the proposed ACC scheme to guarantee desired coverage and connectivity in WSN.

The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.

Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.

Claims (10)

WE CLAIM

1. A multi-objective optimization method for optimizing quality of service in IoT enabled wireless sensor network applications, the method comprises: deploying sensor nodes randomly in sensing field to supervise environment regularly, wherein all sensor nodes remain stationary in the sensing field; locating a sink node in the sensing field to monitor energy level of all sensor nodes periodically in the network; and proposing Adaptive Coverage and Connectivity (ACC) scheme consisting of Adaptive Coverage (AC) method to optimize coverage rates to the entire sensing area to increase the network lifetime of WSN and Energy-Efficient Connectivity (EEC) method to optimize network connectivity among multiple sensor nodes that reduces the energy consumption and maximizes the network sustainability of wireless sensor network (WSN).

2. The method as claimed in claim 1, wherein the sensor node is capable of changing its operating state between active and idle, wherein if the sensor node is active, it gathers the sensed data and is considered as a covered area, whereas if the sensor node at idle state, it saves its energy.

3. The method as claimed in claim 2, wherein every node has a time-dependent energy level, a stationary wireless emission range and a velocity.

4. The method as claimed in claim 3, wherein in the AC method, the coverage area is categorized into dense coverage mode for full coverage and sparse coverage mode for partial coverage.

5. The method as claimed in claim 4, wherein instead of identifying the humidity at each location in the sensing field, the humidity evaluation of the dense coverage mode of the coverage area provides adequate humidity profile of the entire sensing field and thereby reduces the energy consumption.

6. The method as claimed in claim 2, wherein nodes within the range of communication are capable to exchange the current status about their state, energy and location.

7. The method as claimed in claim 1, wherein the Energy-Efficient Connectivity (EEC) method is equipped with a novel Bio-inspired Zone Formation (BZF) algorithm to attain global connectivity,

8. The method as claimed in claim 7, wherein in BZF, the optimum ZH selection is performed by using the battery energy of sensor nodes to increases the global connectivity inside and between the zones.

9. The method as claimed in claim 7, wherein the BZF offers a multilayered hierarchical network structure consist of network, zones, and nodes where the collection of different distributed nodes is termed as zones and a combination of different zones is called network.

10. The method as claimed in claim 7, wherein steps for BZF comprises: collecting genetic information of all genes and obtaining genome of the zone (GZ)by means of collected distance information of genes; computing Average Number of Sensor Nodes (ANSN) inside each zone; splitting the GZ into a different number of sub-GZ choosing first and second lowest distance values as Zone Head (ZH) and AZH nodes; performing sink broadcast of selected ZH and AZH information to all MNs for each GZ; and performing intra-zone routing from each Member Nodes (MNs) to corresponding ZH.