To Explore the Energy of Wireless Sensor Networks to Solve Bottleneck Problem Using Swarm Routing Approach: A Review

To Explore the Energy of Wireless Sensor Networks to Solve Bottleneck Problem Using Swarm Routing Approach: A Review

 

Taruna1, Dr. Rashid Hussain 2

Ph.D. Scholar 1, Associate Professor 2

Suresh Gyan Vihar University, Mahal Jagatpura, Jaipur 1, 2

Abstract: The Sensor network is related to spatially dispersed sensing network for organizing data gathered in a central location from data through Wireless Sensor after tracking and recording physical and environmental conditions. Wireless sensor networks (WS Networks) consist of many, even thousands of self-guided sensors that are wirelessly embedded into an atmosphere for interacting with each other and whose task is to find, capture and transmit environment-related information for monitoring Centre. Continuous movement of sensor nodes and their insufficient battery capacity creates issues with the routing for these network types. So, it seems important to provide a reliable and secure protocol in WSN.

Key Word: WSN Network, Swarm Routing Approach, Bottleneck Problem, Network Efficiency

 

  1. INTRODUCTION

 

The wireless sensor network is made up of randomly dispersed autonomous sensor nodes, monitoring capable, inaccessible, and open areas such as forest fire, perception of the desert, monitoring air emissions, perception of landslides and monitoring of water quality. Generally, each sensor network node is fitted with a radio transceiver with an internal or external antenna, storage device, microcontroller and each node have its own battery, and power is restricted to reduce each node’s power consumption. Arduous challenge we have seen growing progress in the usage of Wireless Sensor Networks (WSN) in different applications in recent years, such as forest control, emergency prevention, space exploration and battlefield surveillance. The sensor nodes have minimal battery capacity. Sensor nodes are commonly located in faulty places which may make charging or removing batteries next to impossible. Hence optimizing the energy consumption rate of each sensor node in each network topology in WSN becomes essentialif the network life is desired. In the measurement field, the sensor node is deployed to collect simple knowledge from it. The sensor’s location and network topology remainundetermined until deployment. The deployed sensors therefore need to self-organize and get rid of the obstacles in a distributed way. Failure of sensor nodes below a defined point in wireless sensor networks can cause the network to drop below the desired “efficiency” standard, and we know what areas of the network need to be fixed. Within a given network topology the weakest group of nodes is called the bottleneck field. The sensor nodes easily release power in the bottleneck region, and trigger power problems in the WSN. Hence the bottleneck region must be established, and other sensor nodes have to be added to prolong the network’s existence.

 

  1. REVIEW OF LITERATURE

 

Muhammad, Z. &Saxena (2018), an analysis of the use of optimization methods for wireless energy effective sensor networks. They study the studies in this paper on the application of heuristics and met heuristic algorithms to achieve the energy efficient. WSN Investigating an updated mathematical model for lifetime transition in a network of wireless sensors. This document uses nodes in sleep mode as relays when transferring data from the transmitter to the receiver to improve the network’s energy efficiency. Basic engineering properties in the mathematics involved are used in modeling to select as much sleep node as possible for data transmission. Such an approach however requires knowing only the contract between the sender and the receiver’s geographical location.

 

Liping, L. V. (2017), share their views on Improving Wireless Sensor Networks’ energy quality routing. They have now released updated RE-AEDG improved delay and control models for more output enhancements. Results from simulation show the feasibility of our RE-AEDG approach with respect to chosen performance matrices.

They also introduced the RE-AEDG protocol to UWS Networks in this article. The RE-AEDG makes use of the mutual routing system to improve data efficiency. The implemented cooperative routing therefore results in high node energy consumption. Mobile sink is used for data collection to solve this problem. In fact, two improved models of RE-AEDG are introduced to further reduce the costs to be charged, solving the issues of heavy energy consumption and high E2E latency. Results from the simulation reveal that RE-AEDG has increased data durability over AEDG and U-LVRP. Optimized models of RE-AEDG do better in terms of energy consumption and message duration than the chosen current techniques did.

 

Islam, S. M. M., Reza, M. A. R., &Kiber, M. A. (2013).)Wireless sensor consists of lightweight, low-cost sensor nodes with restricted communication range and limited bandwidth for processing, storage, and energy resources. The key function of such a network is to gather information from a node and to forward it for further transmission to a base station. WSN has numerous problems, such as optimum sensor distribution, node position, base station positioning, target node range, energy-conscious clustering, and data aggregation. For the least number of sensors in the wireless network, the planned study has the potential to achieve optimum converge problem solving. The findings show that the PSO approach is effective and reliable for efficient sensor delivery coverage issue and is considered in WSN to provide nearly the optimal solutions. Future emphasis on achieving 100 percent coverage with minimal number of sensors can be provided in future. Studying the coverage of 100 percent using different optimum search strategies often poses some important challenges

 

Schiøler, H., Hansen, M. B., &Schwefel, H. P. (2006)share their opinions on the network of Wireless Sensors. The first phase to enabling the Intelligent Guardian Angel is the creation of sensor systems for pedestrians, bicycles and cars, their vehicles respectively. Such sensors will create networks and share information ad-hoc. The input is analyzed according to the aspect of the system (pedestrian or vehicle), and the sensor device sends warnings about a potentially unsafe situation to the car driver or the pedestrian.

 

 

III. SOLVE BOTTLE NECK PROBLEM USING SWARM ROUTING APPROACH

 

This research endeavors to address, solving the Bottle Neck problem in Wireless Sensor Networks (WSN) employing Clusteringand Swarm Routing Approach between Network Nodes to reduce node transfers. We also used several targets based on different criteria to solve the solution for optimum. Parameters such as temperature, minimum clustering, distance to the routing route and energy efficiency were taken into consideration to solve multi-objective problems as described above. An energy-efficient network ensures that the network would have limited transfer of energy between nodes, and hence reduced node temperature. It increases the resilience of nodes in the network. In this thesis, the key emphasis is on using new techniques such as clustering and the Multiple Objective Particle Swarm Optimization (PSO) algorithm to solve the Bottle Neck problem in WSN.

The scatter sensor is in a wider field, and is used primarily to map, classify, and monitor the location’s physical or environmental conditions. Such physical environments include primarily temperature, sound, wind, etc. It is defined as a collection of nodes placed at random in the field of the sensor. Via wireless channels these nodes are connected to each other enabling data transfer between nodes. Sensor node battery activity requires algorithms which save electricity. This is needed mostly due to many factors that are not human interference, remote areas that are unavailable, no recharging facilities, etc. When there is inadequate energy in the node, it cannot transmit data across the network.

 

  1. PROPOSED ALGORITHM FOR SOLVE BOTTLE NECK PROBLEM

 

Algorithm of the Bottle Neck Problem – PSO implementation:

 

Input: All parameters (no. of nodes and initial energy of each node)

Output: Optimal no. of clusters in the network.

Initialize the no. of nodes, initial energy in the nodes, repository limit.

(E0=initial energy value, Node Num=no. of nodes, Rep Limit=maximum limit)

Now we initialize no of steps and no. of iterations in each seed.

(iteration=integer value, steps=no. of step)

We initialize rate of movement of energy or transmission rate of nodes from values obtained from archive vector set.

For each iteration find the energy of transmission after round taking into account routing distance and rate of transmission.

 

Energy (ii, Priority Energy(jj)) =E (Priority Energy(jj))- ( (ETX+EDA)*(4000) + Emp*4000*( distance*distance*distance*distance ));

Select clusters based on optimality values, based on condition.

Temp = CurFitness>= LBest;

Result = (sum (Temp,2) == ObjNum);    or,

 

Temp = CurFitness<= LBest;

Result = (-1).*(sum (Temp,2) == ObjNum) + Result;

Find dead nodes (which do not have energy left) in them.  Eliminate them and put in separate clusters.

NewX= (Energy<=0 )*(-XBound/2) + ~(Energy<=0).*NewX

 

For each particle in the swarm:

  • Select leader from the archive and obtain global best.

NewV = Weight * V + C1 * (rand (1, ParticleSize (2)). * (LBestP – X)) +

C2 * (rand (1, ParticleSize (2)). * (GBestP – X));

 

  • Update clusters as in step 5.
  • Update order of nodes, from PSO (non-dominated pairs).

[CurFitness Energy] = MultObjFitness (GBestAC, GBestVal, X,Y,R,E,next,sender,ETX,EDA,Emp,PlotSize,do,Efs,2,SenderIndex);

  • Update energy values of nodes from step 4.

Update the archive of non-dominated solutions

Repeat for each seed(updation), goto step no. 3.

Now plot graph in MATLAB for final energy values, optimality figures and objective plots.

Print the Optimal No. of Clusters.

The solution works well if no. of iterations for each step and no. of steps is increased allowing the seeding of the archive and generating dominated pairs

 

  1. IMPLEMENTATION AND APPROACH

 

The swarm intelligence algorithm influences the key behaviors of flying to achieve the resources realized in the LEACH protocol. To shape separate clusters, these protocols incorporate swarm intelligence algorithms and help classify each CH while constructing a WSN in the same conditions as real-time networks. Community dependent algorithms can generate ideal outcomes. By applying multiple specialized paths detecting techniques, you may prolong the existence of your network. A recent and forthcoming approach that operates much like swarm-based optimization technology is the Synthetic Bee Colony (ABC) algorithm, thus utilizing WSN for community tracking.

 

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Fig 5.1Initialization of PSO

The most inevitable obstacle facing sensor networks is to obtain the best way to transmit data to the BS. Generally, WSN nodes operate in a multi-mode mode. To guide data to its target, many algorithms have been implemented. Some of WSN’s well-known routing technologies hope to use ants as a roaming agent while exploring ways to find the best route for WSN to submit info. In this inquiry, depending on their functions and capacities, we will evaluate different nodes and follow a framework for guiding data along the best path. New photos have been developed in recent years by applying routing-based protocols to group movements to reflect the best route on the designated path to deliver the retrieved data. The algorithm that manipulates the nodes not only requires more energy savers, but also must be fast enough to capture and send data. This technology is not suitable for managing applications that require timely transmission of data, but Ali Colony Optimization (ACO), which aims to easily describe the behavior of ants in order to find food sources, is a valuable solution for various route detection.

To upgrade the network execution, the cluster mechanism is connected to the hierarchical network with the lowest basic energy consumption. Clustering is a method used in a wide network environment with wide demand to aggregate the amount with nodes. A node is known as the cluster header (CH) among the other nodes that are considered cluster members per cluster. In a cluster, randomly deployed nodes will join to run jobs. Here, the signal to the node is announced on the node

 

  1. STEPS TO SOLVE BOTTLENECK DETECTION ALGORITHM RELATING TO WIRELESS SENSOR NETWORKS
  • Initially, all sensors in the network area are essentially immobile and have limited energy.
  • All sensor nodes regularly perform sensing tasks and transmit information directly to the receiver.
  • All sensor nodes in the network use the multi-hop routing method to transmit data to the nearest relay node.
  • The sink node can interact with all sensor nodes in the network.
  • All nodes are assigned a unique number based on their position in the network.
  • The bottleneck detection algorithm can identify the weakest area in the network and requires other sensors to be placed in the weaker area and repaired to extend the service life of the network.
  • The smallest bottleneck node is the node with the least energy and is about to disappear from the network.
  • A bottleneck node is a node that has no energy to send data and is therefore considered dead.
  • The smallest bottleneck and bottleneck nodes can quickly release

VII. CONCLUSION AND FINDINGS

 

The Sensor network relates to a spatially dispersed sensing network for tracking and recording physical and environmental conditions and for organizing data gathered in a central location from data through Wireless Sensor. Wireless sensor networks (WS Networks) consist of dozens, hundreds or even thousands of self-guided sensors that are wirelessly embedded into an atmosphere for interacting with each other and whose task is to find, capture and transmit environment-related information for monitoring. The size of the sensor node can range from the size of a shoebox to the size of a grain of salt, while the true microscopic measurements of the “spots” are still to be determined in operation. Constraints on the size and expense of the device contract contribute to constraints on associated services, such as equipment, electricity, processing speed and bandwidth for communication. Wireless Sensor Network (WSN) is a network that comprises of a cumulative number of sensor nodes located in an application setting for tracking specific entities in a target region, such as temperature sensing system , water level, pressure control and health care, and numerous military applications.

This research suggests an approach for solving the Bottle Neck problem in Wireless Sensor Networks (WSN) Using Swarm Routing Approach between Network Nodes and Clustering to reduce node transfers. We also used several targets based on different criteria to solve the solution for optimum. Parameters such as temperature, minimum clustering, distance to the routing route and energy efficiency were taken into consideration to solve multi-objective problems as described above.

 

 

VIII. FUTURE SCOPE

A novel idea for the future is that the distance of node is closest to the source so as to minimize the energy overhead of the overall network, provided the nodes sensors posses a strong sensing capacity so as they are able to sense different parameters which are even at some distance, i.e. a network design where nodes proximity to the source and the target both are optimum.

 

 

 

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