Evolutionary Computing Based Protocols Pso Algorithm

Published Date: 02 Nov 2017

Optimization is the technique of finding the maximum and minimum parameters of a particular function or process. This technique is implemented in scientific applications like physics, chemistry, economics and engineering where the optimum goal is to maximize the effectiveness, efficiency and productivity etc.

Particle Swarm Optimization (PSO) is a method that is required to explore the search spaces for a provided problem so as to find the settings or the parameters required to optimize the a particular objective. Generally this technique arises from two distinct concepts: the idea of swarm intelligence based off the examination of swarming behavior by definite types of animals like birds and fish; and the specialized study of the evolutionary computation. The PSO algorithm executes by maintaining numerous candidate solutions in the search space simultaneously. In the duration each iteration of the protocol, each candidate solution is calculated by the objective function that is being optimized by estimating the fitness or threshold of that solution. The individual candidate solution can be considered as a particle "flying" through the fitness landscape and finding the maximum or minimum of the objective function. At inception, the PSO algorithm chooses the candidate solutions randomly within the search space. The search space is comprised of all the possible optimal solutions along the x-axis and the originating curve represents the objective function. And it must be noted that the PSO algorithm has no information of the underlying objective function, and thus it is not having a way to know if any of the candidate solutions are near to or far away from a local or global maximum. The PSO algorithm simply uses the objective function to evaluate its candidate solutions, and operates upon the resultant fitness values.

Each particle component maintains its location that is comprised of the candidate solution along with its calculated fitness parameter and its velocity of roaming. On the other hand, it acknowledges the best possible fitness value that it has obtained during the entire operation of the algorithm and is generally referred as individual best fitness value and the candidate solution. Ultimately, the Particle swarm optimization (PSO) algorithm keeps its fitness value maintained among all particles in that particular swarm and thus is referred as the global best fitness, and the candidature solution that successfully achieved that particular fitness or threshold is referred as the global best position or global best candidate solution.

The standard particular swarm optimization (PSO) algorithm generally comprised of the following steps, which are repeatedly continued till the fitness conditions are met:

1. Calculate the fitness value or fitness parameter for each particle.

2. Update the achieved individual and global best fitnesses parameters and its positions

3. Update the velocity and the location of the participating particles.

The fitness parameters are calculated by providing the candidate solution to the predefined objective function. Meanwhile the individual as well as global optimum fitnesses parameters and locations are rationalized by comparing the newly raised threshold or the fitness parameter against the previous fitness parameters. Similarly, the swarm or particle velocity and its location update step are responsible for the optimization capability of the PSO algorithm.

The velocity of each particle in the swarm is updated using the following equation:

The notation of the particle is presented by. Therefore indicates the velocity of particle at a particular time and represents the position of particle at a particular time.

The parameters,, and are user-assigned coefficients. And are random parameters that are regenerated for each velocity update.

The value is the individual optimum candidate solution for a swarm particleat time, and represents the swarm’s global most optimum candidate solution at a particular time.

PSO algorithm has exhibited a potential role of a very robust system architecture where the system is optimized so as to deliver the best optimum output. A number of systems have been designed for PSO based network QoS optimization and even these technology have provided a better results as compared to the existing systems. Then while there are certain limitations in PSO algorithm, few of the limitations of Particle swarm optimization are like premature convergence that results into a huge degradation in performance as well as sub-optimal solutions. In order to illuminated the possibility of premature convergence, diversity guided PSO was utilized whereas mutation is also implemented to the swarm particles.

1.2 Motivation:

The development of WSNs relies also on wireless networking technologies apart from hardware technologies. In 1997, the first standard for wireless local area networks (WLANs), the 802.11 protocol was introduced. By increasing the data rate and CSMA/CA mechanisms for medium access control (MAC), it got upgraded to 802.11b. Routing techniques in wireless networks are another important research direction for WSNs above the physical and MAC layers. Actually, the existing routing protocols for wireless ad hoc networks or wireless mobile networks are the early routing protocols in WSNs. Due to the high power consumption, these protocols, including DSR and AODV, are hardly applicable to WSN. Therefore scaling down the power consumption is another area where there are ample research opportunities. In the near future, the era of WSNs is highly anticipated.

A number of research works have been done for maximizing the QoS of wireless sensing network and especially the network lifetime. Network lifetime plays a vital role in assuring the quality of a network and in the case of wireless sensor network, it becomes very critical. A number of system architecture and protocols have approached the techniques for enhancing the node or network lifetime but majorities of them are having serious drawbacks. Few of them approaches are software oriented and few have been emphasized over hardware enhancements and modification. But the real time scenario states that software protocol enhancement can play a great role in enhancing the system’s effectiveness and quality. Few approaches have performed good results but still are having huge gap for further enhancement and optimization. The requirement and high level integration of WSNs has alarmed that there is a serious requirement of an effective solution of nodes sustaining for long time with higher throughput and efficiency. In spite of conventional protocols, a number of protocols or optimization techniques are being developed for enhancing the system throughput. Now days the most optimized techniques being implemented are based on the natural characteristics and genetic behaviour. Evolutionary computing is the technology being implemented for superior system optimization. Few of the dominating techniques that come under evolutionary computing are like genetic algorithm, particle swarm optimization, ant colony optimization, simulated annealing based optimization. These all techniques do enhance the system performance. Among these mentioned techniques particle swarm optimization has exhibited better result. Meanwhile, considering some popular protocols for system enhancement, LEACH protocol has performed better result, but these all techniques cannot be assured to be the final result or the best architecture. The requirement of efficient and highly optimized network architecture and the optimistic view of evolutionary computing motivates the author to think about a further optimum solution. And the author considers an evolutionary technique that is based on the unique and robust swarm behaviour of elephant swarm in nature. Elephant species has a number of unique behaviour and characteristics that can be implemented with the real time systems and of course the optimum solution can be achieved for a defined problem. Few of the characteristics like memory capacity, leadership, way of communication, group isolation, and handing over etc are the dominant character that strikes the author to develop a system architecture based on those facts for optimizing the life time and hence the throughput of the considered network. On the other hand the cross layered system architectures to minimize the computational complexity and increase the efficiency, also motivates the author to step ahead for formulating and developing a elephant swarm based system architecture for lifetime maximization and enhancement.

1.3 Objective of the Research work:

In wireless sensing network, lifetime of the communicating node is considered as the dominating characteristics that ensure the QoS of the considered network. Therefore, enhancing and hence optimizing the network lifetime is considered as the dominant issue in communication researchers. In the effort of optimizing the system lifetime and hence longer sustainability a number of systems have been developed but majority of them are found to be either ineffective or confined to a particular extent. On the other hand the concept of evolutionary computing has ignited the research arena to come with some highly optimized solutions. Considering the research domain for network lifetime maximization here in this research work the author has considered elephant behaviour to implement with a robust architecture at TDMA-MAC as a cross layered architecture development. The dominant goal of this research work is to implement the behaviour of elephant with cross layer architecture, so that the lifetime of the nodes can be increased.

The overall research goal can be summarized as follows:

3.1 Specific Goal:

Design a wireless sensing network that can effectively accommodate cross layer design and the network QoS optimization issues.

Define a robust cross layered network architecture that may provide a rich set of parameters for an effective simulation.

The predominant goal or objective of this research work is to develop system architecture by implementing the behaviors of elephant swarm in constructing a cross layered CDMA-MAC architecture for enhancing the lifetime of nodes in WSN.

Develop a system architecture that could facilitate a robust as well as optimized routing technique, adaptive radio link optimization and balanced scheduling so that a cumulative enhanced network performance can be achieved.

Develop parallel system architecture for popular approaches like LEACH protocol and PSO based cross layered architecture so that the proposed system can be compared for its robustness against them.

Develop system model for PSO, LEACH and proposed elephant swarm optimization based cross layer architecture for the network topology of various size and with various operating conditions so that the robustness of the proposed system can be analyzed for real time performance perspective.

1.4 Problem definition:

This is the matter of fact that the real time implementation of elephant swarm model is a complex as well as mammoth work therefore in order to realize the characteristics or behaviors in considered wireless sensor networks the author has proposed a system architecture that adopts a robust cross layer approach for incorporating the elephant swarm model. QoS oriented network optimization is the dominant factor for adopting at routing layer, MAC layer and its Radio layer (PHY Layer) of the wireless sensor node. The presented research paper introduces an enhanced and robust cross layered approach that incorporates elephant swarm optimization technique that will be further compared with existing techniques like Particle swarm optimization (PSO) and LEACH protocol.

To simplify the problem, following sub-problems are identified.

These are as presented below:

To define WSN protocol architecture that can explicitly accommodate cross layer design and optimization issues. The lack of standard architecture prohibits software reusability resulting in waste of time, effort, and money. Also the existing architectures do not support the cross layer design explicitly and therefore, the benefits that one can achieve from cross layer information exchange cannot be achieved. If one wants to use existing architectures, there is always a tradeoff between plug-and-play supportive design and getting benefits from the cross layer design. So the task is to define a WSN architecture which supports cross layer approach and provides plug-and-play features at the same time.

To define a cross layer management plane as part of the above envisioned architecture. It will provide a rich set of network parameters explicitly to different layers of protocol architectures which can benefit from these parameters. This would equip different modules of the protocol stack with plug-and-play features and at the same time these modules will be able to avail cross layer benefits. What are these parameters and how different layers would adapt themselves to these parameters is a challenging issue.

To develop routing protocols for the reference application which use cross layer information and evaluate them in context of the proposed architecture. Any routing protocol which uses cross layer information is also regarded as cross layer design.

1.5 Methodology

Here in this research work the author has proposed cross layer architecture based on elephant swarm optimization for enhancing QoS oriented network parameters like lifetime, least delay and communication overheads. The cross layer model has been modeled at CDMA-MAC and for comparing the system robustness, in this research work the researcher proposes to build the parallel architecture of LEACH and PSO based cross layer model and simulate with homogenous network conditions. The behaviour of elephant swarm has been adopted as the system characteristics and model has been prepared on SENSORIA, a WSN simulation platform with C# programming language.

The presented research work has been developed while considering the homogeneity of the WSNs and nodal diversity in the area of deployment where nodes are deployed randomly. The characteristics of elephant swarm have been incorporated for developing cross layered system architecture for optimization, TDMA MAC scheduling and advanced radio layer control techniques. In this research methodology the elephant swam optimization is applied taking into account unconstrained scheduling on the network links. The elephant swarm optimization enables simultaneous scheduling of the sensing data on the interfering wireless communication links in the current considered scheduling time slot. The elephant swarm optimization iterates to obtain an optimal routing, power consumption and schedule to enhance the considered network lifetime. In order to facilitate a comparative study and research justification a number of QoS oriented parameters have been simulated and plotted.

The overall research and hence thesis methodology can be presented as below:

At first, a reference application, generally referred as wireless container management system, has been outlined for two purposes, first is to outline a significant WSN application and then utilize it as a standard benchmark for implementation and evaluation of the proposed systems. Second purpose is to construct protocol architecture for WSN that can explicitly support cross layer design and optimization. The third and last step is to develop the parallel reference systems that can be considered for comparison purpose and a standard justification can be presented against the existing systems.

The overall research work and thesis has been developed in the sequence as presented below:

Figure 1-5: Overall Methodology of thesis

1.6 Thesis Contribution:

In this research work the researcher has proposed robust cross layered system architecture based on elephant swarm behaviour. As the evolutionary technologies have magnified the system enhancement and optimization many folds, like wise considering that optimistic scope here in this research work the author has developed an elephant swarm based cross layered architecture for lifetime maximization.

The presented thesis has a number of contributions. Few of these contributions are as mentioned below:

The presented research work implements the evolutionary computing technique based on elephant swarm behaviour, which provides a robust system architecture as well as optimized throughput for WSNs.

In spite of the elephant swarm based cross layered optimization, the presented research work and the thesis, the research work and hence the thesis presented here provides a parallel system development of popular LEACH protocol as well as particle Swarm optimization (PSO) based protocol, that may provide the readers a better platform to understand the algorithms of different generations and their comparative study.

The presented thesis has been prepared while considering the every theoretical as well as technical prospects of WSN, PSO, LEACH and proposed elephant swarm based technique. It makes the presentation easier and feasible to understand.

The chapters incorporated in this thesis work describes every required facts with better presentation so that any reader can get it and understand it with less effort and may understand the protocols of three generations on a single platform.

The network conditions or the parametric values like homogeneity or heterogeneity being considered in this research works are so robust that the real time system development can be considered for the proposed system.

The resulting output depicts that the developed system architecture can be considered for a real time system development while considering few enhancements as future work, like decay rate minimization.

The developed system employs diversified topological size, thus making it very effective for real time implementation.

The developed system can be further enhanced with certain modification in cross layered architecture and making it robust for future ahead.

The results and analysis of the proposed work states that the developed technique effectively dominates the most popular techniques like LEACH and PSO based systems, so the proposed technique can be preceded for further research and development.

1.7 Thesis Organization:

The presented thesis work has been emphasized over achieving an optimum solution for lifetime maximization or optimization. The quality of presentation plays a vital role in presenting the work with influencive and attractive arrangement. In order to present the work in better way it is must to present the thesis in a proper sequence so that the reader can understand the work in easy way with the better understanding. In order to provide an influencive way to present the research work, here we have arranged the contents in the proper sequence.

The arrangement or the flow of the thesis work has been implemented in the following sequence:

Chapter-1: Introduction

This chapter mainly discusses about the introductory and background of the domain or the research work. In this chapter the background of research domain with key technologies have been mentioned. The dominant contents of this chapter are the brief of research domain, motivation for research, aim and objective of research, proposed system and its problem formulation, research methodology, scope of thesis and its contribution. At last in this chapter the organization of entire thesis has been presented. Thus the main goal of this chapter to let reader introduced with each aspects of the research work.

Chapter-2: Literature Survey

This chapter discusses about the literature survey made for the research work. It discusses about the previous research works carried for optimizing the lifetime or QoS optimization of WSNs. The dominant purpose of this chapter is to brief the researches made earlier and to provide a short description of the existing systems. The literature survey chapter has been prepared for techniques developed based on cross layer approach, particle swarm optimization based approach and LEACH based system developments and their scopes. It also facilitates the technical understanding of the overall scenario in which the development and hence the enhancement can be made.

Chapter-3: Theoretical background

This chapter mainly discusses the theoretical background of the research work and techniques being implemented. The main purpose of this chapter is to provide a fundamental and deep routed understanding of various techniques, domains, and methods that are being introduced or implemented in this research work. This chapter consists of the detailed knowledge transfer about the techniques like LEACH protocol, particle swarm optimization based cross layer architecture, and clustering in various techniques etc. this chapter also describes a number of approaches and methodologies that are implemented or even considered for optimizing lifetime in wireless sensing networks. In summary, this chapter encompasses every theoretical description of the topics or technologies being implemented in this research work or thesis prepared.

Chapter-4: Network Lifetime enhancement using Elephant Swarm Optimization in WSNs

This chapter mainly discusses the research work done by the researcher and his mathematical derivations, methodologies, conceptual description and its theoretical justification. This chapter has been dedicated to the work done by researcher and his approach of development. Initially this chapter describes the characteristics of elephant swarm and its unique behaviors that have been incorporates for constructing a cross layered architecture for lifetime maximization. The presented section of this paper elaborates the elephant swarm optimization algorithm for routing, scheduling and advanced radio layer control techniques. The dominant goal of this chapter is to present the research development and its mathematical approach to come up with an optimal solution for QoS oriented lifetime maximization in WSNs.

Chapter-5: Result and Analysis

This chapter mainly discusses about the technical aspects of research implementation and its resulting outputs. This chapter encompasses the presentation of the research work and its implementation with other techniques like LEACH and PSO based cross layered architecture. Here in this chapter the result analysis for the developed model or the scheme has been discussed. This chapter also discusses the comparative study for the other techniques made for achieving the goal of the research work. In this section the results, graphs and their individual explanation with goal justification has been presented.

Chapter-8: Conclusion and Future work

This chapter concludes the research done by the author. This chapter will be discussing the strength as well as the limitation of the developed model. Here the enhancement or the optimization made by the author will be discussed. The future scope for the research work would also be discussed here.