The Network Lifetime Enhancement Using Elephant Swarm

Published Date: 02 Nov 2017

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 1.9: 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 influensive 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 influensive 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.

Chapter-2

LITERATURE SURVEY

J. Kennedy and R. C. Eberhart [60] introduced a consideration for the optimization of nonlinear function exploitation particle swarm methodology. The evolution of many paradigms is delineating, and an accomplishment of single paradigms has been mentioned. Benchmark has been experienced of the paradigm is represented, and applications, together with nonlinear operate optimization and neural network instruction, area unit planned. The relationships among particle swarm optimization and equal artificial life and genetic algorithms area unit represented.

W. R Heinzelman et.al [61] looked at communication protocols, which might have important impact on the general power dissipation of those networks. The researchers found that the standard protocols of transmission mechanism, minimum transmission power, multi hop routing, and static cluster might not be optimum for device networks, the researchers planned low-energy adaptive clustering hierarchy (LEACH), a clustering stand protocol that develop irregular rotation of inhabitant cluster based mostly station (cluster heads) to equally distribute the power load among the sensors within the network. Low- energy adaptive clustering hierarchy used localized coordination to alter quantifiability and lustiness for dynamic networks, and incorporates knowledge fusion into the routing protocol to scale back the quantity of knowledge that has to be transmitted to the bottom station. Simulations demonstrate the low-energy adaptive clustering hierarchy is able to do the maximum amount as an element of eight reductions in energy dissipation compared with typical outing protocols. Additionally, low-energy adaptive clustering hierarchy is ready to distribute energy dissipation equally throughout the sensors, doubling the helpful system life for the networks the researchers simulated.

W. B Heinzelman et.al [62] developed and evaluated low-energy adaptive clustering hierarchy (LEACH), a protocol design for small device networks that mixes the concepts of energy-efficient cluster-based routing and media access in conjunction with application-specific information aggregation to realize sensible presentation in terms of system lifespan, latency, and application-perceived quality. Low-energy adaptive clustering hierarchy contain a brand new, dispersed cluster formation method that permits self-organization of huge quantity of nodes, algorithms for adapting clusters and revolving cluster head situation to equally distribute the energy load amongst every nodes, and method to modify distributed indication process to save lots of communication resources. Our consequences demonstrate that low-energy adaptive clustering hierarchy will improve system lifespan by associate degree order of magnitude compared with general multihop approach.

S. Lindsey, C et.al [63] presented device webs consisting of nodes with restricted battery energy and wireless communications area unit deployed to gather helpful info from the sector. Gathering detected info in associate in energy economical manner is essential to operative the device network for an extended amount of your time. Data Information assortment downside is outlined wherever, in a very spherical of communication, every device node incorporates a packet to be send to the isolated stand station. There is a few mounted quantity of energy value within the physical science once transmittal or receiving a packet and a variable value once transmittal a packet that depends on the gap of transmission. If every node transmits its detected information on to the bottom station, then it will use up its power rapidly. The low-energy adaptive clustering hierarchy protocol conferred a chic resolution wherever clusters area unit fashioned to fuse information before transmittal to the bottom station. By randomly the cluster-heads selected to transmit to the bottom station, low-energy adaptive clustering hierarchy achieves an element of eight improvements compared to direct transmissions, as measured in terms of once nodes die. Associate in improved description of low-energy adaptive clustering hierarchy, referred to as LEACH-C, and is conferred wherever the central base station performs the bunch to enhance energy efficiency. During this research, the researchers conferred Associate and improved theme, referred to as PEGASIS (Power-Efficient Gathering in device data Systems), that could be a near optimal chain based protocol that minimizes power. In PEGASIS, every node communicates solely with an in depth neighbor and takes turns sending to the bottom station, so reducing the quantity of energy spent for each spherical. Simulation outcome demonstrate that PEGASIS present higher than low-energy adaptive clustering hierarchy by regarding one hundred to two hundred percentage when 1%, 25%, 50%, and 100% of nodes die for various network sizes and topologies. For several applications, additionally to minimizing energy, it’s conjointly necessary to contemplate the delay incurred in gathering perceived data. The researchers captured this with the energy  holdup metric and current schemes that commit to balance the energy and delay value for knowledge gathering from device networks. Since the majority of the delay issue is within the TRM time, the researchers calculated delay in terms of range of transmissions to achieve a spherical of data gathering. Consequently, delay will be reduced by permitting coinciding transmissions once potential within the network. With CDMA capable device nodes coincides data transmissions are unit potential with very little intervention. In this paper, the researchers gave 2 new method to reduce energy × delay mistreatment CDMA and non-CDMA device nodes. If the ambition is minimize only the delay price, then a binary combining theme is accustomed accomplish this assignment in concerning log N units of delay with parallel communications and acquisition a small increase in energy price. By CDMA capable machine nodes, a chain-based binary theme present greatest in terms of energy × delay. If the device node is not CDMA capable, then similar communications square is measure attainable merely between spatially alienated nodes and a chain-based three-level hierarchy theme performs glowing. The researchers compared the presentation of straight, low-energy adaptive clustering hierarchy, and the method with observe to energy × delay mistreatment intensive simulations for various network sizes. Results demonstrated that the subject perform eighty or a lot of times higher than the direct scheme and conjointly surmount the low-energy adaptive clustering hierarchy protocol.

J. Tillet, R. Rao, and F. Sahin [64] proposed a new application of the optimization technique known as particle swarm optimization (PSO) to the problem of clustering nodes. The PSO approach is an evolutionary programming technique where a 'swarm' of test solutions, analogous to a natural swarm of bees, ants or termites, is allowed to interact and cooperate to find the best solution to the given problem. In a typical optimization, some function or fitness is used as a criterion for the optimization. Here the researchers use application specific criteria, where the researchers are equalizing the number of nodes, and candidate cluster-heads in each cluster, with the objective of minimizing the energy expended by the nodes while maximizing the total data gathered. The objective criteria fit with the implementation of a wireless, ad hoc, sensor network with cluster-head routing and data aggregation.

S. D. Muruganathan et.al [65] presented wireless sensor networks accommodates little battery powered device with restricted energy possessions. Once organized, the tiny detector nodes square measure typically inaccessible to the user, and therefore replacement of the energy supply isn't possible. Therefore, power potency may be a key style problem that must be increased so as to boost the life of the network. Many network layer protocols are planned to boost the effective time period of a network with a restricted energy provide. In this article the researchers planned a centralized routing protocol known as base station controlled dynamic clustering protocol (BCDCP), which distributes the power dissipation equally between every sensing node to enhance network life and average power savings. The presentation of BCDCP is after that compare to cluster based method like low energy adaptative clustering hierarchy (LEACH), LEACH centralized (LEACH-C), and power-efficient gathering in sensor information systems (PEGASIS). Simulation outcome demonstrate that BCDCP reduce on the whole power utilization and improves network life above its comparative.

S. Guru, et.al [66] described the results of a performance evaluation of four extensions of Particle Swarm Optimization (PSO) to reduce energy consumption in wireless sensor networks. Communication distances are an important factor to be reduced in sensor networks. By using clustering in a sensor network the researchers can reduced the total communication distance, thus increasing the life of a network. The researchers adopted a distance based clustering criterion for sensor network optimization. From PSO perspective, the researchers studied the suitability of four different PSO algorithms for our application and propose modifications. An important modification proposed is to use a boundary checking routine for re-initialization of a particle which moves outside the set boundary.

N.M.A. Latiff et.al [67] presented an energy conscious agglomeration for wireless detector networks exploitation particle swarm improvement (PSO) formula that is enforced at the bottom station. The researchers outlined a replacement value perform, with the target of at the same time minimizing the intra-cluster distance and optimizing the power consumption for the network. The presentation of protocol is compared with the accepted cluster-based protocol developed for wireless sensor networks, low energy adaptative clustering hierarchy (LEACH) and LEACH-C, the later organism an enhanced version of low energy adaptative clustering hierarchy. Simulation outcome shown that the designed protocol are able to do higher network period of time and data delivery on the bottom station above its comparatives.

X. Co, et.al [68] continued advance of wireless communication technologies have not able to the readying of huge scale wireless device networks. The sensors limited energy makes energy expenditure a vital issue. In particular hop wireless sensor networks, cluster heads election methodology supported residual energy will acquire higher energy potency than the strategy within which cluster heads area unit electoral in turns or by possibilities. Is it a parallel in multi-hop wireless sensor networks? During this article the researchers planned and estimated a routing improvement theme supported graph theory and particle swarm improvement formula for multi-hop wireless sensor network. Our prescriptions synthesize the philosophical theory blessings of graph theory and optimum investigates capability of PSO. The end in multi-hop networks is totally different from that in single-hop wireless sensor networks. The results demonstrate that there's little or no distinction from these ways. The explanation is mentioned very well.

A. Ratnaweera et.al [69] introduced a completely unique parameter automation strategy for the particle swarm rule and 2 any extensions to boost its performance when a predefined variety of generations. At the start, to with competence management the native search and convergence to the worldwide most advantageous response, time-varying acceleration coefficients (TVAC) is proposed additionally to the time-varying inactivity weight consider particle swarm optimization (PSO). From the idea of TVAC, 2 new methods area unit mentioned to increase the presentation of the PSO. At the first, the thought of "mutation" is identified to the particle swarm optimization in conjunction with TVAC (MPSO-TVAC), through adding up a tiny low perturbation to an every which way elite modulus of the speed vector of a random particle through predefine possibility. Next, the researchers introduced a unique particle swarm idea "self-organizing hierarchical particle swarm optimizer with TVAC (HPSO-TVAC)". Below this technique, merely the "social" half and therefore the "cognitive" an ingredient of the particle swarm strategy are thought of to estimate the novel speed of every particle and particles are reinitialized any instance they're stagnated within the explore space. Additionally, to beat the complexity of choosing associate degree applicable mutation step size for various issues, a time-varying mutation stride size has been introduced. Additional, for many of the standard, alteration likelihood is found to be insensitive to the performance of MPSO-TVAC technique. On the opposite hand, the result of re-initialization speed on the performance of HPSO-TVAC technique is additionally discovered. Time-varying and re-initialization stair dimension is found to be associate degree economical parameter optimization approach for HPSO-TVAC technique. The HPSO-TVAC approach out presented every strategy thought of during this exploration for many of the functions. What is more, it's additionally been discovered that each the MPSO and HPSO ways perform badly once the acceleration coefficients are fastened at 2.

V. Rodoplu and T. H. Meng [70] described a dispersed situation based network protocol optimized for least amount energy utilization in mobile wireless networks that hold up peer-to-peer communication. curtained several variety of indiscriminately deployed nodes over a vicinity, the researchers demonstrated that an easy native improvement theme dead at every node guarantees sturdy property of the complete network and attains the world minimum power resolution for motionless networks. Appropriate to its restricted nature, these procedures prove to be self-reconfiguring and continue near the minimum energy resolution once used to mobile networks.

R. Madan and S. Lall [71] proposed dispersed algorithms to calculate associate optimum routing theme that maximizes the time at that the primary node within the network drains away of power. The difficulty is developed as an applied math trouble and sub gradient algorithms square measure won’t to solve it in a very distributed manner. The ensuing algorithms have low procedure complexness associated square measure certain to converge to a most favorable routing theme that maximizes the network life. The algorithm square measure demonstrated through associate example within which associate optimum flow is calculated for a network of at random distributed nodes. The researchers additionally had exposed however the approach may be wont to acquire dispersed algorithms for several totally different extensions to the matter. At last, the researchers broaden disadvantage formulation to additional general definitions of network life to model realistic eventualities in sensor networks.

T. ElBatt and A. Ephremides [72] introduced a cross-layer style structure to the numerous access drawback in contention-based wireless circumstantial networks. The inspiration for this revision is double, limiting multiuser interfering to extend single-hop output and reducing power expenditure to prolong battery life. The researchers centered on after that neighbor transmission where nodes are needed to send data package to their individual receivers subject to a constraint on the signal-to-interference-and-noise quantitative relation. The multiple access issues area unit solved by using 2 alternating phases, specifically scheduling and power control. The planning formula is important to coordinate the transmissions of freelance users so as to eliminate robust levels of interference (e.g., self-intervention) that can't be overcome through power control. On the opposite hand, power authority management is departed in a very distributed fashion to see the admit table power vector, if single one exist, which will be utilized by the regular users to satisfy their single-hop transmission needs. This is often in deep trouble 2 varieties of networks, specifically time-division multiple-access (TDMA) and TDMA-code-division multiple-access wireless circumstantial networks.

R. Bhatia and M. Kodialam [73] increasing attention in energy unnatural multi-hop wireless networks a basic downside is one in every of determinative energy economical communication ways over these multi-hop networks. The best disadvantage is one wherever a given supply node desires to speak with a given destination, with a given rate over a multi-hop wireless network, utilization minimum power. Now the power facility refers to the entire quantity of power consumed over the complete network so as to realize this rate between the supply and therefore the destination. There area unit 3 choices that have to be compelled to be created (jointly) so as to attenuate the facility demand. 1. (Routing) the path(s) that the info has got to take between the supply and also the destination. 2. (Control) the ability with every link transmission is completed. 3. (Power) looking on the interference or the MAC characteristics, the time slots within which specific link transmissions got to occur. 4. (Scheduling) to the most effective of our information, ours is that the initial commit to derive a performance warranted polynomial time approximation rule for together determination these 3 issues. The researchers developed the drawback as associate improvement drawback with non-linear objective operates and non-linear constraints. The researchers then derive a polynomial time 3-approximation rule to resolve this drawback. The researchers conjointly bestowed a straightforward version of the rule, with a similar performance certain, that involve determination solely shortest path issues and that is kind of economical in observe. Our approach without delay extends to the case wherever there area unit multiple source-destination pairs that got to communicate at the same time over the multi-hop network.

V. Balakrishnan et.al [74] considered linear systems among with any old parameters that lie between given higher and lounge bounds. Apart from many special cases, the computation of the several amount of interest for such systems will be present merely during Associate in complete search in parameter space. The researchers specified a general branch and convinced algorithmic rule that implements this explore in an exceedingly methodical manner and concern it to computing the minimum constancy degree.

WANG Xing-wei et.al [75] introducing the knowledge of the fuzzy mathematics, probability theory and gaming theory, a QoS(Quality of Service)multicast routing scheme with ABC(Always Best Connected)supported is proposed. It uses the interval to describe the user QoS requirement and the edge (link) parameter with the edge parameter probability and the user satisfaction degree introduced. With the help of the edge evaluation and the gaming analysis, it tries to find a QoS multicast tree with the Pareto optimum under the Nash equilibrium on both the network provider utility and the user utility achieved or approached based on the particle swarm optimization algorithm. Simulation results have shown that it is both feasible and effective.

Deng Qiang and Xie Dong-liang [76] proposed a holistic QoS model to evaluate the performance of WSN. QoS requirements within a WSN reference architecture is presented, and then a two-way mapping between application layer parameters and network layer parameters is carefully analyzed using fuzzy logic. Finally, the researchers proposed a fuzzy logic based comprehensive evaluation model for systematic QoS in WSN, and use a case study to illustrate its effectiveness.

I.F. Akyildiz et.al [77] described the elemental thought of sensor networks that has been created viable by the convergence of small electro-mechanical systems technology, wireless communications as well as electronics. Initial, the sensing works and therefore the potential device networks applications are discovered, and an evaluated of things influencing the look of sensor networks has been provided. After that, the communication design intended for sensor networks is made outline, and therefore the algorithms and protocols developed for every layer within the literature are discovered. Open analysis problems for the belief of sensor networks also are mentioned.

Supiya Ujjin and Peter J. Bentley [78] conversed afresh recommender system, that employs a particle swarm optimization (PSO) algorithmic program to find out individual preferences of users and supply tailored proposal. Recommender systems square measure new styles of i r spherical. Reproduction results demonstrate that PEG internet-based package tools that square measure designed to assist users notice their manner through today’s complicated on-line outlets and diversion websites. The presentation and consequences outcome of the planned system were measure up to to those acquired from the genetic algorithmic program (GA) recommender system and a regular, non-adaptive system supported the Pearson algorithmic program. The proposed was based around a collaborative filtering approach, building up profiles of users and then using an algorithm to find profiles similar to the current user. To build recommendations selected data from those profiles were then used. The task of finding appropriate similarities was obviously difficult because profiles contain many attributes, many of which have sparse or incomplete data. To overcome these problems, current systems (such as Movie Lens) used stochastic and heuristic-based models to speed up and improve the quality of profile matching. This work takes such ideas one step further, by applying a particle swarm optimization algorithm to the problem of profile matching. The PSO was found performing very well compared to the other two systems for all experiments with both Zero and At-Most-One tolerance levels. However, as the number of users goes up, the probability of finding a better matched profile should be higher and hence the accuracy of the predictions should increase, still applies to the GA system here, but is not the case for the PSO recommender. This fall in the average accuracy level as the number of user’s increases is because of originally the users that were selected to be part of the neighborhood to give recommendations are highly similar to the active user. As the number of user’s increases, more users are being considered and this could sometimes result in many less similar users being added to the neighborhoods and hence, lower overall prediction accuracy. In this way particle swarm optimization can be employed to fine-tune a profile-matching algorithm within a recommender system, tailoring it to the preferences of individual users. As compared to the GA approach, the PSO algorithm achieved the final solution significantly faster, making it a more efficient way of improving performance where computation speed is an important factor in recommender systems.

Sheikh I. Ahamed et.al [79] recently, middleware has started playing an important role in embedded devices and applications. The recent advances in wireless communication and micro system technology have evolved the construction of sensor networks for sensing environmental parameters, processors, wireless communication capabilities, and autonomous power supplies in tiny devices in large quantities at low cost. Large and dense networks of these devices can then be deployed discreetly in the physical environment in order to observe a wide variety of real world phenomena. With the continuous growth of sensor networks, monitoring has become an important problem. In this paper, authors present a middleware service for monitoring sensor networks and applications with an illustrative example.

Raquel A. F. Mini et.al [80] discussed the problem of constructing the energy map of a wireless sensor network using prediction-based approaches. Along with this they also presented an energy dissipation model that is used to simulate the behavior of a sensor node in terms of energy consumption. The performance of the prediction-based approaches with a naive one in which no prediction is used was compared. The results show that the prediction-based approaches outperform the naive in a variety of parameters. In the designing of wireless sensor networks the key challenge is maximizing their lifetime. The information about the amount of available energy in each part of the network which is useful to increase the lifetime of the network is called the energy map. They proposed the mechanisms to predict the energy consumption of a sensor node in order to construct the energy map of a wireless sensor network. If a sensor can predict efficiently the amount of energy it will dissipate in the future, it will not be necessary to transmit its available energy often. One message with its available energy and the parameters of the model that describing its energy dissipation can be sent by this node. With this information, the monitoring node will update often its local information about the available energy of this node. The effectiveness of this paradigm was dependent on the accuracy with which prediction models can be generated. Using the energy dissipation model proposed in this work, simulations were conducted in order to compare the performance of two prediction-based approaches with a naive one, in which only the available energy is sent to the monitoring node. This work can be extended in two main directions. The first one is to decrease the amount of energy necessary to construct the energy map and other objective is to determine the length of time for which a sensor node will be able to sense, rather than merely being concerned with the amount of energy a sensor node has. The most concern is on the expected lifetime of each geographical area of the network.

Michael I. Brownfield et.al [81] proposed a new radio power management (RPM) algorithm for optimizing radio sleep capabilities by transitioning nodes to intermediate power level states. They also discussed the radio power levels, state transition times, and state transition energy costs of an IEEE 802.15.4 compliant sensor platform for improved accuracy in simulating WSN energy consumption. the same as the development of computer networks is growing and change of integrity distant locations, to gather and method data from remote locations wireless sensor networks (WSNs) come to the fore because the latest frontier in increasing opportunities. To create detector field deployments each reasonable and durable while not maintenance support WSNs consider hardware effortlessness. Keeping in mind application-specific throughput and latency requirements, WSN designers attempt to extend network lifetimes. Based upon the sleep period duration and the current state of the radio, effective power management places sensor nodes into one of the available energy-saving modes. The newest generation of sensor platform radios with a 250 kbps data rate does not provide adequate time to completely power off the radio during overheard 128-byte constrained IEEE 802.15.4transmissions.This research makes two major contributions to the up to date wireless sensor networks. First, the experimental measurements characterize the sleep mode transitions for the newest generation of WSN mote devices which establish sleep transition thresholds for the proposed RPM algorithm and provide an accurate energy simulation model for future research. The second contribution is the introduction of the wireless sensor network radio power management (RPM) algorithm designed to develop further power-saving opportunities required for the newest generation of faster sensor platform transceivers. Based upon the power and response characteristics of the sensor platform’s transceiver, the RPM algorithm optimizes sleep transition decisions. The ability to attain a 56% increase in the SMAC network lifetime utilizing the current technology’s realistic data patterns and a 40% increase in the TMAC lifetime was demonstrated on implementation of the RPM algorithm into a WSNMAC protocol. It is the IEEE 802.15.4 WSN platform characterizations and the RPM algorithm which provides the tools for researchers to continue their progress with the next generation of wireless sensor network platforms. The results show that the developed framework accurately models the distribution of the energy consumption and captures the randomness of multi-hop WSNs.

Ian F. Akyildiz et.al [82] severed energy constraint of powered device nodes necessitate energy-efficient communication protocols so as to satisfy application objectives of wireless device networks (WSN). Though, the overwhelming preponderance of the present solutions area unit supported classical bedded protocols approach. It’s method more resource-efficient to own a combined theme that melts common protocol layer functionalities into a cross-layer component for resource controlled device nodes. To the simplest of our information, to date, there's no unified cross-layer communication protocol for economical and reliable event communication that regard as transport, routing, medium access functionalities among with physical layer (wireless channel) consequence for wireless sensor networks. During this research a combined cross-layer protocol has been developed, that replaces the whole ancient bedded protocol design that has been used to date in WSNs. The main designing principle is complete unified cross-layering such that both the information and the functionalities of traditional communication layers are melted in a single protocol. The objective of the proposed cross-layer protocol is highly reliable communication with minimal energy consumption, adaptive communication decisions and local congestion avoidance. To this end, the protocol operation is governed by the new concept of initiative determination. Based on this concept, the cross-layer protocol performs received based contention, local congestion control, and distributed duty cycle operation in order to realize efficient and reliable communication in WSN. Performance evaluation results show that the proposed cross-layer protocol significantly improves the communication efficiency and outperforms the traditional layered protocol architectures.

Lei Zhang and Zhi Wang [83] presented the vision of pervasive computing is based on the idea that future computers will merge with their environment. Radio Frequency Identification (RFID) and Wireless Sensor Network (WSN) are two important components of pervasive computing, since both technologies can be used for coupling the physical and the virtual world. However, RFID and WSN almost are under development in parallel method, few integration schemes and related opportunities are investigated in detail. Through deep analysis of RFID and WSN, three forms of new system architecture that combines the two technologies are proposed and its feasibility, technical challenges are discussed thoroughly.

Liang Song, and Dimitrios Hatzinakos [84] proposed the Low Energy Self-Organizing Protocol (LESOP) for target tracking in dense wireless sensor networks. A cross-layer design perspective is adopted in LESOP for high protocol efficiency, where direct interactions between the Application layer and the Medium Access Control (MAC) layer are exploited. Unlike the classical Open Systems Interconnect (OSI) paradigm of communication networks, the Transport and Network layers are excluded in LESOP to simplify the protocol stack. A lightweight yet efficient target localization algorithm is proposed and implemented, and a Quality of Service (QoS) knob is found to control the tradeoff between the tracking error and the network energy consumption. Furthermore, LESOP serves as the first example in demonstrating the migration from the OSI paradigm to the Embedded Wireless Interconnect (EWI) architecture platform, a two-layer efficient architecture proposed here for wireless sensor networks.

Min Shao et.al [85] proposed for sensor networks deployed to monitor and report real events, event source anonymity is an attractive and critical security property, which unfortunately is also very difficult and expensive to achieve. This is not only because adversaries may attack against sensor source privacy through traffic analysis, but also because sensor networks are very limited in resources. As such, a practical tradeoff between security and performance is desirable. In this paper, for the first time authors propose the notion of statistically strong source anonymity, under a challenging attack model where a global attacker is able to monitor the traffic in the entire network. Authors propose a scheme called FitProbRate, which realizes statistically strong source anonymity for sensor networks. They also demonstrate the robustness of our scheme under various statistical tests that might be employed by the attacker to detect real events. In this paper analysis and simulation results show that proposed scheme, besides providing source anonymity, can significantly reduce real event reporting latency compared to two baseline schemes.

Raju Kumar et.al [86] presented data generated in wireless sensor networks may not all is alike: some data may be more important than others and hence may have different delivery requirements. In this paper, authors address differentiated data delivery in the presence of congestion in wireless sensor networks. Authors propose a class of algorithms that enforce differentiated routing based on the congested areas of a network and data priority. The basic protocol, called Congestion-Aware Routing (CAR), discovers the congested zone of the network that exists between high-priority data sources and the data sink and, using simple forwarding rules, dedicates this portion of the network to forwarding primarily high-priority traffic. Since CAR requires some overhead for establishing the high-priority routing zone, it is unsuitable for highly mobile data sources. To accommodate these, they define MAC-Enhanced CAR (MCAR), which includes MAC-layer enhancements and a protocol for forming high-priority paths on the fly for each burst of data. MCAR effectively handles the mobility of high-priority data sources, at the expense of degrading the performance of low-priority traffic. Authors present extensive simulation results for CAR and MCAR, and an implementation of MCAR on a 48-node test bed.

Raghavendra V. Kulkarni et.al [87] discussed to the non-beacon nodes estimation their locality exploitation using distance measurements from 3 or a lot of non-collinear beacon they will be receive indication from. The range based localization work was developed as a dimensional optimization difficulty, and addressed exploitation bio-inspired algorithms, utilizing their fast convergence to superiority explanation. Location data of the haphazardly deployed nodes is that the demand in several applications of wireless sensor networks (WSNs). To organize little special beacon nodes have locality consciousness, which facilitate the standard nodes to localize may be a general answer to the localization trouble. Here, the nodes that acquire localize in iteration act as orientation for remaining nodes to localize. Utilizing particle swarm optimization (PSO) as well as bacterial foraging algorithm (BFA) the disadvantage has been self-addressed. Comparison of the performances of PSO and BFA in terms of the quantity of nodes localized, localization accuracy and calculation time has been proposed. BFA may be a new organic process optimization formula introduced that mimics the search behavior of E. coli (commonly referred to as E. coli) bacterium that board human internal organ. There are eminent applications of BFA and its hybrids in optimization issues like PID controller standardization and economic load dispatch. The first preparation of nodes and beacons for particle swarm optimization and bacterial foraging algorithm based localization is that the similar in a very experimentation. The outcome of particle swarm optimization and bacterial foraging algorithm based localization demonstrate that each random algorithms used here contain presented fairly well in wireless sensor network localization. The localization drawback has been treat as a multidimensional optimization drawback and addressed during the aforesaid population stand optimization algorithms. The improvement of reduced range of transmissions to the bottom station is taken by dispersed localization planned that assist the nodes preserve their power that may be a serious apprehension in most wireless sensor networks applications. The results illustrated that the intended algorithms have a trade off issue. Whereas the particle swarm optimization concludes the node coordinates additional quickly, the bacterial foraging algorithm will thus additional accurately.

Dynamic Source Routing (DSR) algorithm which computes a new route when the loss of packet occurs has been presented by N. Chilamkurti et.al [88]. To find that the packet loss is caused by whether node failure or it is due to the result of congestion which causes DSR to find a new route there is not any in-built mechanism in DSR algorithm. Thus when DSR used in the wireless sensor networks it leads to inefficient energy utilization. Authors developed cross layer optimization mechanism that widens DSR to enhance its routing energy more proficiently by lowering the frequency of recalculated route in the proposed work. This approach makes DSR to start a route discovery only when the link breakdown takes place. Extensive simulation has been done to calculate the performance of the cross layer DSR routing algorithm by the authors. The results which is obtained by simulation of our extended DSR routing algorithm shows that the frequency is 50 % decreased with the new routes which are recomputed when compared with the original DSR protocol. The researchers exposed that with the proposed cross layer DRS that distinguish between congestion and the link failure and the new routes are recomputed only for the link failure.

A multimedia sensor node that can extensively improve the capability of wireless sensor network has been used by Lei Shu et.al [89] for event description. The WSN’s cannot be use effectively for extremely long times in a number of scenario we can take an erupting volcano as an example for it. Instead of this the WSNs are aimed to transfer constant and reliable data as possible in the large number within an expected lifetime. A proficient collection of multimedia data within an expected lifetime in WSNs has been expected by the authors in this paper. By taking into consideration the communication between physical, network and the transport layer, an adaptive method to adjust the transfer radius and the data generating rate adjustment (RRA) dynamically is proposed which is based on the cross layer design. By using the minimum data generation rate the designer first minimize the end to end data transfer delay in WSNs. A finest transmission radius thus can be derived or obtained in this phase. Then by using this transmission radius, designers adaptively fix the rate of generating data to increase the amount of collected data. By dynamically fixing the transmission radius of the sensor nodes and increasing the data generation rate of the source node, the proposed RRA scheme can enhance the data collection performance effectively in wireless multimedia sensor networks (WMSNs) as shown by the simulation results.

Jianping Yu et.al [90] presented the multi-constrained any cast routing is an important issue in the ad hoc networks because of the mobility of nodes. To improve the any-cast success rate and shorten the any-cast delay when considering the special constraints of the bandwidth and delay during the network deployment period, an any-cast algorithm inspired by swarm intelligence of ants is proposed, in which the improved pheromone diffusion mechanism is adopted to promote the performance of any-casting emerging in self-organized way from the positive interaction of ants. The simulation results demonstrated that not only the performance of any-cast success rate can be improved but also significant smaller delay can be achieved by the proposed algorithm when compared with other any-cast routing algorithms.

Xin Fei et.al [91] proposed in the densely deployed wireless sensor networks, sensors are scheduled over time in order to maintain the coverage while saving energy of networks. In this article, Authors investigate the coverage-aware scheduling problem using genetic algorithms. Sensors are optimally scheduled in different time slots to maximize the overall coverage under the given k-cover requirement and lifetime of networks. A set of simulation is carried out. The simulation result shows that, using the optimal schedule generated by genetic algorithm, this algorithm can optimize the coverage performance of wireless sensor network in terms of overall coverage rate and number of active sensors.