WSN ROUTING TECHNIQUES

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INTRODUCTION

WSNs are application-specific sensor network technologies which are widely deployed in applications such as in; environmental monitoring, atmospheric monitoring, process monitoring, material sensing, security or surveillance systems, medical systems, etc. These networks operate on collective computing capabilities of individual sensors based on their physical sensing properties and processing capabilities. Sensors nodes, cooperatively communicate and relay aggregated data to the main network control system for further processing and acting. In this regard, these sensors, must have some kind of ability to conform to the collective networking functionalities as governed by their respective network policies. In WSNs, sensor nodes can be randomly deployed, in essence allowing opportunities for applications even in inaccessible areas [1]. This feature about sensor networks, allows the possibility of deploying a large number of sensors over intuited areas for as long as communications can be established and sustained among these sensor nodes. A WSN consists of but not limited to; a wireless sensor network server (attached to a power source), routers, switches, sensor nodes, etc. depending on the design setup as required for its purpose. Depending on the purpose of the WSN, sensor nodes communicate amongst themselves as a means to forward sensed data to the network sink, which will then be passed by the network sink to the WSN server for further processing. Even though there are some network stability reflections to be noted about node failures in WSNs such as network partitioning [2], [3] (which lead to poor Quality of Service (QoS)) and more energy depletion due to neighbouring node rerouting as an effort to amend faults [4], [5] a failure in one of the sensor nodes does not collapse the network [6], [7]; rather network traffic is routed [8] through adjacent sensor nodes thereby sustaining the traffic flow [9], [10]. In cases when accessing a WSN remotely, a network sink may also be used as gateway to connect to the internet [11]. This phenomenon about WSNs can be regarded as an opportunity for network computing innovation. An overview of a wireless sensor network for a monitored field is shown in FIGURE 1.1. WSNs are envisioned to be deployed on a larger scale, as millions of wireless sensor nodes will be working cooperatively to transmit critical data and at the same time being connected to the internet as an effort towardsthe realization of IoT [12]. Commonly reported challenges include; sensor node energy limitations, low memory and processing capacity, low channel bandwidth and being application specific [13], [14]. A lot of work has been done particularly on energy limitations such as in [15], [16], as an effort to improve the node energy utilization on WSNs. In addition to that, other methods of energy harvesting have been proposed as a means to leverage this limitation on sensor nodes [17], [18]. However, only to a certain extent, this particular energy issue has been moderated, as it is still one of the serious challenges in WSNs [19], since it affects the lifetime of a WSN directly. Other reported These application technologies benefit from the use of sensing devices. However due to limitations in WSN systems, there are very limited opportunities to bring innovation into the network. Computing aspects such as memory and processing power are the mostly critical resources of WSN systems. Therefore, to improve these technologies, it is necessary to develop efficient resource-aware strategies that will rather enhance their operations. The cooperative nature of sensors in terms of data aggregation and routing exhibits some level of system intelligence that can be used to improve their applications. Sensor technologies allows smart applications such as in acquisition, control and monitoring [22], [23]. These technologies are commonly deployed for sophisticated operations that requires lightweight but efficient sensing capabilities. Hence, the use of sensor applications for modern control technologies is rapidly growing. To optimize sensor communication or traffic routing in a simulation environment, an understanding of how data packets are transmitted is critical. Figure 1.3 below, illustrates this process given different sensors numbers acting as both data sources and receivers. The figure describes collaborative data transmission to the base station from different sources. This data transmission mechanism provides the foundation for developing efficient data transmission techniques, which will result in commendable performances. It is also important to consider transmission or channel distortions as data (in the form of electrical signals) is propagated within the system. Different network devices share all the network communication or radio channels to transmit traffic to various destinations On the basis that it is not yet possible to realize WSNs that have a prolonged network lifetime (as sensor nodes are battery-power reliant), important considerations must be made before the deployment phase of the network. These considerations includes (but not limited to); understanding phenomena/event requirements and the deployment environment (as this will assist in deciding on relevant equipment and their capability, and also whether there are no radio frequency disturbances within the area (in case of industrial or city/infrastructure deployments )), sensor network connectivity (i.e. critical measure must be taken to maintain connectivity), the ability of the network to self-configure in adverse or intrusion situation [24], [25], choice of wireless protocol depending on the sensor type to be used (as protocols/standards differ in power utilization, throughput and communication range) [26], [27]. We emphasis on this so that if there are counter measure (either software-based or enhanced equipment/devices) on these consideration, be it that they are accounted for on the network planning phase. Network programmability, such as that introduced by SDN, is seen as a potential direction to fully evolve computing networks especially those that used for human survival. SDN proposes a system whose controlling functionality is central but well-resourced to facilitate the underlying network infrastructure from its point. In terms of WSNs, this means, the centralized SDN controller will be responsible for executing network intelligence, so that the underlying network devices, perform data transmission. This framework is presented as software defined wireless sensor network (SDWSN). However, it is yet to be realized how SDN can improve computing capabilities of WSNs application technologies. It is also noteworthy to mention that, because of its early state of activity, current SDN systems are still under a lot of developmental work and testing. Therefore, it must be appreciated that, a lot of work spanning different proposals and approaches is still to be done. However, this does not disregard the already achieved work under the area. This work aims to develop intelligent software-oriented strategies that will be implemented in resource limited WSNs, with application focus on monitoring systems. Machine learning approaches will be used to develop network intelligent algorithms which will form the basis for improved network knowledge presentation and efficient data aggregation.

PROBLEM STATEMENT

This section describes the challenges and networking demands that informs the efforts of developing network-computing systems that are highly efficient. Trough experimentation, the performance potential of proposed approach in terms of providing realistic strategies that can be implemented to improve WSN application systems is also justified, especially those deployed for life-concerning missions. Context of the problem The modern era of network computing is confronted with a high demand of powerful systems that not only are reliable and fast but also are, adaptable to requirement changes. This is predominantly due to the radical advancement in wide computing platforms operating at highly descriptive and abstracted mediums such as; reconfigurable computing systems, smart automation systems, parallel programming and cognitive systems which communicate using highly complex resources or methods. Hence, such systems must integrate the best forms of technologies to accommodate the rapidly growing and heterogeneously connected platforms which uses different means of interconnecting diverse machines or devices. In addition, these systems must be able to reach unusual environmental spaces, be cost effective, easy to deploy and manage. Due to their deployment simplicity and cost advantages, WSNs have emerged as favourable platforms that can be used to advance application technologies for mission critical systems. However, these technologies have not yet been deployed as fully trusted systems due to their resource limitations, spanning from; memory and computing limitations, limited power sustainability, communication and delay constraints. Therefore, there is a need to enhance these network computing technologies for them to be realized as fully capable systems for mission critical systems. To put into perspective, these technologies must be optimized to fully support today’s highly sophisticated life-oriented systems such as medical, surveillance, aeronautical and agricultural systems. However, to effectively manage such multifaceted high-end computing resources, requires well-organized and carefully implemented systems. These systems must be able to cater for any change that counts to the benefit of the users and customers at large. They must be designed with a focus to be efficient in terms of performance and be scalable throughout. Research gap Due to the technical constraints as well as limited network performance of WSNs, a lot of research approaches has been applied and tested in this field as a means to improve their application capabilities. Even though some work has resulted into great achievements, WSN application systems are still experiencing issues especially in their network capability and resource management. Some approaches work to a certain extent or for specific conditions only, which still reflects considerable limitations in their applications. This work applies SDN strategies to these application technologies as an effort to enhance their network performance as well as their application capacity using a high-level programming language. Software driven policies and communication protocols are implemented to the actual network infrastructure to bring innovation, allow efficient resource management, introduce network flexible as well as to improve their overall network performance. RESEARCH GOAL The main goal of this research is to develop and implement efficient programming methods to improve WSNs monitoring applications using SDN strategies and Machine Learning techniques. These strategies are focused on improving network flexibility, enable efficient resource management, and improve sensor data aggregation as well as ensuring efficient communication between the SDN controller and the underlying network infrastructure. 1.5 RESEARCH CONTRIBUTION This study directly contributes in the area of WSNs by exhibiting innovative strategies to the management and operational functionality of the said application technologies with emphasis to develop scalable and flexible systems. Furthermore, SDN programmable strategies for network computing will be developed in essence to mitigate sensor node limitations such as; memory and computing capacity, bandwidth utilization and load balancing. Lastly, in this work, Machine Learning techniques were developed and successfully coupled with SDWSN strategies to improve networking intelligence for monitoring application systems. It is further highlighted that, though the systematic view of an SDN architecture seems to be complex, its capability to managing and customizing WSNs will make a huge impact towards the evolution of computing networks. This work also contribute immensely towards the manufacturing industry for future customizable wired and wireless networking devices. Moreover, this research work open up research opportunities for programmable WSNs applications, especially for large scale developments. This work further open up opportunities for programmable network prototyping and also the integration of numerous network operations or applications development using different high-level programming languages in WSN networks. From this work, developmental analysis and directions towards effective applications of WSNs will be given as for future work. It is emphasized that this…

TABLE OF CONTENTS :

• CHAPTER 1 INTRODUCTION
  • 1.1 PROBLEM STATEMENT
    • 1.1.1 Context of the problem
    • 1.1.2 Research gap
  • 1.2 RESEARCH QUESTIONS AND OBJECTIVE
    • 1.2.1 Research Questions
    • 1.2.2 Research Objectives
  • 1.3 APPROACH
  • 1.4 RESEARCH GOAL
  • 1.5 RESEARCH CONTRIBUTION
  • 1.6 RESEARCH OUTPUTS
  • 1.7 OVERVIEW OF STUDY
• CHAPTER 2 LITERATURE STUDY
  • 2.1 CHAPTER OBJECTIVES
  • 2.2 WSN ROUTING TECHNIQUES
    • 2.2.1 Design Constraints for WSN Routing Protocols
    • 2.2.2 WSNs Communication and Routing Protocols
  • 2.3 WSN: APPLICATIONS AND SYSTEM CHALLENGES
    • 2.3.1 WSN APPLICATION SYSTEMS
    • 2.3.2 WSN SYSTEM CHALLENGES
  • 2.4 SDN PARADIGM
    • 2.4.1 SDN Controllers
  • 2.5 SDN FOR FLEXIBILE RESOURCE MANAGEMENT IN IOT NETWORKS
    • 2.5.1 Application Challenges in IoT Networks
    • 2.5.2 SDN Benefits for IoT Networks
    • 2.5.3 In-Network Cache Computing for IoT Objects
    • 2.5.4 IoT Objects Communication and Data Integrity
    • 2.5.5 SDN Enabling Secure WSN Based IoT Systems
  • 2.6 SDWSN: APPLICATION IMPROVEMENTS FOR WSN SYSTEMS
    • 2.6.1 Network Resource Management
    • 2.6.2 Network Flexibility
    • 2.6.3 SDN for Scalable WSN Networks
    • 2.6.4 Machine Learning Approaches in WSNs and SDWSNs
  • 2.7 CHAPTER SUMARY
• CHAPTER 3 SDWSN: A FLEXIBLE WSN NETWORK
  • 3.1 CHAPTER OBJECTIVES
  • 3.2 SIMULATION ENVIRONMENT
    • 3.2.1 NS-3 Network Modules and Model Library
    • 3.2.2 ODL: Enabling the ODL Controller
    • 3.2.3 OpenFlow Communication Standard
    • 3.2.4 BGP AND NETCONF
  • 3.3 EXPERIMENTAL SETUP
  • 3.4 EXPERIMENTAL RESULTS
  • 3.5 CHAPTER CONCLUSION
• CHAPTER 4 APPLYING MACHINE LEARNING TECHNIQUES IN SDWSN
  • 4.1 CHAPTER OBJECTIVES
  • 4.2 THE KNN ALGORITHM
    • 4.2.1 KNN CLIQ
  • 4.3 EXPERIMENTAL SETUP
  • 4.4 EXPERIMENTAL RESULTS
  • 4.5 CHAPTER CONCLUSSION
  • CHAPTER 5 DISCUSSION
  • CHAPTER 6 CONCLUSION

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