Effective Node Clustering and Data Dissemination In Large-Scale Wireless Sensor Networks

The denseness and random distribution of large-scale WSNs makes it quite difficult to replace or recharge nodes. Energy efficiency and management is a major design goal in these networks. In addition, reliability and scalability are two other major goals that have been identified by researchers as necessary in order to further expand the deployment of such networks for their use in various applications. This thesis aims to provide an energy efficient and effective node clustering and data dissemination algorithm in large-scale wireless sensor networks. In the area of clustering, the proposed research prolongs the lifetime of the network by saving energy through the use of node ranking to elect cluster heads, contrary to other existing cluster-based work that selects a random node or the node with the highest energy at a particular time instance as the new cluster head. Moreover, a global knowledge strategy is used to maintain a level of universal awareness of existing nodes in the subject area and to avoid the problem of disconnected or forgotten nodes. In the area of data dissemination, the aim of this research is to effectively manage the data collection by developing an efficient data collection scheme using a ferry node and applying a selective duty cycle strategy to the sensor nodes. Depending on the application, mobile ferries can be used for collecting data in a WSN, especially those that are large in scale, with delay tolerant applications. Unlike data collection via multi-hop forwarding among the sensing nodes, ferries travel across the sensing field to collect data. A ferry-based approach thus eliminates, or minimizes, the need for the multi-hop forwarding of data, and as a result, energy consumption at the nodes will be significantly reduced. This is especially true for nodes that are near the base station as they are used by other nodes to forward data to the base station. MATLAB is used to design, simulate and evaluate the proposed work against the work that has already been done by others by using various performance criteria

Design Methodology for Self-organized Mobile Networks Based

The methodology proposed in this article enables a systematic design of routing algorithms based on schemes of biclustering, which allows you to respond with timely techniques, clustering heuristics proposed by a researcher, and a focused approach to routing in the choice of clusterhead nodes. This process uses heuristics aimed at improving the different costs in communication surface groups called biclusters. This methodology globally enables a variety of techniques and heuristics of clustering that have been addressed in routing algorithms, but we have not explored all possible alternatives and their different assessments. Therefore, the methodology oriented design research of routing algorithms based on biclustering schemes will allow new concepts of evolutionary routing along with the ability to adapt the topological changes that occur in self-organized data networks

Efficient Aerial Data Collection with UAV in Large-Scale Wireless Sensor Networks

Data collection from deployed sensor networks can be with static sink, ground-based mobile sink, or Unmanned Aerial Vehicle (UAV) based mobile aerial data collector. Considering the large-scale sensor networks and peculiarity of the deployed environments, aerial data collection based on controllable UAV has more advantages. In this paper, we have designed a basic framework for aerial data collection, which includes the following five components: deployment of networks, nodes positioning, anchor points searching, fast path planning for UAV, and data collection from network. We have identified the key challenges in each of them and have proposed efficient solutions. This includes proposal of a Fast Path Planning with Rules (FPPWR) algorithm based on grid division, to increase the efficiency of path planning, while guaranteeing the length of the path to be relatively short. We have designed and implemented a simulation platform for aerial data collection from sensor networks and have validated performance efficiency of the proposed framework based on the following parameters: time consumption of the aerial data collection, flight path distance, and volume of collected data

PFARS: Enhancing Throughput and Lifetime of Heterogeneous WSNs through Power-aware Fusion, Aggregation and Routing Scheme

Heterogeneous wireless sensor networks (WSNs) consist of resource-starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy-efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature , these issues are addressed individually and most of the proposed solutions are either application-specific or too complex that make their implementation unrealis-tic, specifically, in a resource-constrained environment. In this paper, we propose a novel node level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in-network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, i.e., the residual energy of neighboring nodes and their importance from a network's con-nectivity perspective. All our proposed algorithms were tested on a real-time dataset obtained through our deployed heterogeneous WSN in an orange orchard, and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in term of various performance metrics such as throughput, lifetime, data accuracy, computational time and delay

Configuring heterogeneous wireless sensor networks under quality-of-service constraints

Wireless sensor networks (WSNs) are useful for a diversity of applications, such as structural monitoring of buildings, farming, assistance in rescue operations, in-home entertainment systems or to monitor people's health. A WSN is a large collection of small sensor devices that provide a detailed view on all sides of the area or object one is interested in. A large variety of WSN hardware platforms is readily available these days. Many operating systems and protocols exist to support essential functionality such as communication, power management, data fusion, localisation, and much more. A typical sensor node has a number of settings that affect its behaviour and the function of the network itself, such as the transmission power of its radio and the number of measurements taken by its sensor per minute. As the number of nodes in a WSN may be very large, the collection of independent parameters in these networks – the configuration space – tends to be enormous. The user of the WSN would have certain expectations on the Quality of Service (QoS) of the network. A WSN is deployed for a specific purpose, and has a number of measurable properties that indicate how well the network's task is being performed. Examples of such quality metrics are the time needed for measured information to reach the user, the degree of coverage of the area, or the lifetime of the network. Each point in the configuration space of the network gives rise to a certain value in each of the quality metrics. The user may place constraints on the quality metrics, and wishes to optimise the configuration to meet their goals. Work on sensor networks often focuses on optimising only one metric at the time, ignoring the fact that improving one aspect of the system may deteriorate other important performance characteristics. The study of trade-offs between multiple quality metrics, and a method to optimally configure a WSN for several objectives simultaneously – until now a rather unexplored field – is the main contribution of this thesis. There are many steps involved in the realisation of a WSN that is fulfilling a task as desired. First of all, the task needs to be defined and specified, and appropriate hardware (sensor nodes) needs to be selected. After that, the network needs to be deployed and properly configured. This thesis deals with the configuration problem, starting with a possibly heterogeneous collection of nodes distributed in an area of interest, suitable models of the nodes and their interaction, and a set of task-level requirements in terms of quality metrics. We target the class of WSNs with a single data sink that use a routing tree for communication. We introduce two models of tasks running on a sensor network – target tracking and spatial mapping – which are used in the experiments in this thesis. The configuration process is split in a number of phases. After an initialisation phase to collect information about the network, the routing tree is formed in the second configuration phase. We explore the trade-off between two attributes of a tree: the average path length and the maximum node degree. These properties do not only affect the quality metrics, but also the complexity of the remaining optimisation trajectory. We introduce new algorithms to efficiently construct a shortest-path spanning tree in which all nodes have a degree not higher than a given target value. The next phase represents the core of the configuration method: it features a QoS optimiser that determines the Pareto-optimal configurations of the network given the routing tree. A configuration contains settings for the parameters of all nodes in the network, plus the metric values they give rise to. The Pareto-optimal configurations, also known as Pareto points, represent the best possible trade-offs between the quality metrics. Given the vastness of the configuration space, which is exponential in the size of the network, it is impossible to use a brute-force approach and try all possibilities. Still our method efficiently finds all Pareto points, by incrementally searching the configuration space, and discarding potential solutions immediately when they appear to be not Pareto optimal. An important condition for this to work is the ability to compute quality metrics for a group of nodes from the quality metrics of smaller groups of nodes. The precise requirements are derived and shown to hold for the example tasks. Experimental results show that the practical complexity of this algorithm is approximately linear in the number of nodes in the network, and thus scalable to very large networks. After computing the set of Pareto points, a configuration that satisfies the QoS constraints is selected, and the nodes are configured accordingly (the selection and loading phases). The configuration process can be executed in either a centralised or a distributed way. Centralised means that all computations are carried out on a central node, while the distributed algorithms do all the work on the sensor nodes themselves. Simulations show run times in the order of seconds for the centralised configuration of WSNs of hundreds of TelosB sensor nodes. The distributed algorithms take in the order of minutes for the same networks, but have a lower communication overhead. Hence, both approaches have their own pros and cons, and even a combination is possible in which the heavy work is performed by dedicated compute nodes spread across the network. Besides the trade-offs between quality metrics, there is a meta trade-off between the quality and the cost of the configuration process itself. A speed-up of the configuration process can be achieved in exchange for a reduction in the quality of the solutions. We provide complexity-control functionality to fine-tune this quality/cost trade-off. The methods described thus far configure a WSN given a fixed state (node locations, environmental conditions). WSNs, however, are notoriously dynamic during operation: nodes may move or run out of battery, channel conditions may fluctuate, or the demands from the user may change. The final part of this thesis describes methods to adapt the configuration to such dynamism at run time. Especially the case of a mobile sink is treated in detail. As frequently doing global reconfigurations would likely be too slow and too expensive, we use localised algorithms to maintain the routing tree and reconfigure the node parameters. Again, we are able to control the quality/cost trade-off, this time by adjusting the size of the locality in which the reconfiguration takes place. To conclude the thesis, a case study is presented, which highlights the use of the configuration method on a more complex example containing a lot of heterogeneity

Energy-Efficient Adaptive Geosource Multicast Routing for Wireless Sensor Networks

We propose an energy-efficient adaptive geosource multicast routing (EAGER) for WSNs. It addresses the energy and scalability issues of previous location based stateless multicast protocols in WSNs. EAGER is a novel stateless multicast protocol that optimizes location-based and source-based multicast approaches in various ways. First, it uses the receiver's geographic location information to save the cost of building a multicast tree. The information can be obtained during the receiver's membership establishment stage without flooding. Second, it reduces packet overhead, and in turn, energy usage by encoding with a small sized node ID instead of potentially large bytes of location information and by dynamically using branch geographic information for common source routing path segments. Third, it decreases computation overhead at each forwarding node by determining the multicast routing paths at a multicast node (or rendezvous point (RP)). Our extensive simulation results validate that EAGER outperforms existing stateless multicast protocols in computation time, packet overhead, and energy consumption while maintaining the advantages of stateless protocols

Performance optimization of wireless sensor networks for remote monitoring

Wireless sensor networks (WSNs) have gained worldwide attention in recent years because of their great potential for a variety of applications such as hazardous environment exploration, military surveillance, habitat monitoring, seismic sensing, and so on. In this thesis we study the use of WSNs for remote monitoring, where a wireless sensor network is deployed in a remote region for sensing phenomena of interest while its data monitoring center is located in a metropolitan area that is geographically distant from the monitored region. This application scenario poses great challenges since such kind of monitoring is typically large scale and expected to be operational for a prolonged period without human involvement. Also, the long distance between the monitored region and the data monitoring center requires that the sensed data must be transferred by the employment of a third-party communication service, which incurs service costs. Existing methodologies for performance optimization of WSNs base on that both the sensor network and its data monitoring center are co-located, and therefore are no longer applicable to the remote monitoring scenario. Thus, developing new techniques and approaches for severely resource-constrained WSNs is desperately needed to maintain sustainable, unattended remote monitoring with low cost. Specifically, this thesis addresses the key issues and tackles problems in the deployment of WSNs for remote monitoring from the following aspects. To maximize the lifetime of large-scale monitoring, we deal with the energy consumption imbalance issue by exploring multiple sinks. We develop scalable algorithms which determine the optimal number of sinks needed and their locations, thereby dynamically identifying the energy bottlenecks and balancing the data relay workload throughout the network. We conduct experiments and the experimental results demonstrate that the proposed algorithms significantly prolong the network lifetime. To eliminate imbalance of energy consumption among sensor nodes, a complementary strategy is to introduce a mobile sink for data gathering. However, the limited communication time between the mobile sink and nodes results in that only part of sensed data will be collected and the rest will be lost, for which we propose the concept of monitoring quality with the exploration of sensed data correlation among nodes. We devise a heuristic for monitoring quality maximization, which schedules the sink to collect data from selected nodes, and uses the collected data to recover the missing ones. We study the performance of the proposed heuristic and validate its effectiveness in improving the monitoring quality. To strive for the fine trade-off between two performance metrics: throughput and cost, we investigate novel problems of minimizing cost with guaranteed throughput, and maximizing throughput with minimal cost. We develop approximation algorithms which find reliable data routing in the WSN and strategically balance workload on the sinks. We prove that the delivered solutions are fractional of the optimum. We finally conclude our work and discuss potential research topics which derive from the studies of this thesis

Research routing and MAC based on LEACH and S-MAC for energy efficiency and QoS in wireless sensor network

The wireless sensor is a micro-embedded device with weak data processing capability and small storage space. These nodes need to complete complex jobs, including data monitoring, acquisition and conversion, and data processing. Energy efficiency should be considered as one of the important aspects of the Wireless Sensor Network (WSN) throughout architecture and protocol design. At the same time, supporting Quality of Service (QoS) in WSNs is a research field, because the time-sensitive and important information is expected for the transmitting to to the sink node immediately. The thesis is supported by the projects entitled “The information and control system for preventing forest fires”, and “The Erhai information management system”, funded by the Chinese Government. Energy consumption and QoS are two main objectives of the projects. The thesis discusses the two aspects in route and Media Access Control (MAC). For energy efficiency, the research is based on Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. LEACH is a benchmark clustering routing protocol which imposes upon cluster heads to complete a lot of aggregation and relay of messages to the base-station. However, there are limitations in LEACH. LEACH does not suit a wide area in clustering strategy and multi-hop routing. Moreover, routing protocols only focus on one factor, combining the clustering strategy and multi-hop routing mechanism were not considered in routing protocol for performance of network. QoS is supported by the MAC and routing protocol. Sensor MAC(S-MAC) makes the use of the periodically monitoring / sleeping mechanism, as well as collision and crosstalk avoidance mechanism. The mechanism reduces energy costs. Meanwhile, it supports good scalability and avoids the collision. However, the protocols do not take the differentiated services. For supporting QoS,A new route protocol needs to be designed and realized on embed platforms, which has WIFI mode and a Linux operation system to apply on the actual system. This research project was conducted as following the steps: A new protocol called RBLEACH is proposed to solve cluster on a widely scale based on LEACH. The area is divided into a few areas, where LEACH is improved to alter the selecting function in each area. RBLEACH creates routes selected by using a new algorithm to optimize the performance of the network. A new clustering method that has been developed to use several factors is PS-ACO-LEACH. The factors include the residual energy of the cluster head and Euclidean distances between cluster members and a cluster head. It can optimally solve fitness function and maintain a load balance in between the cluster head nodes, a cluster head and the base station. Based on the “Ant Colony” algorithm and transition of probability, a new routing protocol was created by “Pheromone” to find the optimal path of cluster heads to the base station. This protocol can reduce energy consumption of cluster heads and unbalanced energy consumption. Simulations prove that the improved protocol can enhance the performance of the network, including lifetime and energy conservation. Additionally, Multi Index Adaptive Routing Algorithm (MIA-QR) was designed based on network delay, packet loss rate and signal strength for QoS. The protocol is achieved by VC on an embedded Linux system. The MIA-QR is tested and verified by experiment and the protocol is to support QoS. Finally, an improved protocol (SMAC -SD) for wireless sensor networks is proposed, in order to solve the problem of S-MAC protocol that consider either service differentiation or ensure quality of service. According to service differentiation, SMAC-SD adopts an access mechanism based on different priorities including the adjustment of priority mechanisms of channel access probability, channel multi-request mechanisms and the configuring of waiting queues with different priorities and RTS backoff for different service, which makes the important service receive high channel access probability, ensuring the transmission quality of the important service. The simulation results show that the improved protocol is able to gain amount of important service and shortens the delay at the same time. Meanwhile, it improves the performance of the network effectivel

Configuring heterogeneous wireless sensor networks under quality-of-service constraints

Ph.DNUS-TU/E JOINT PH.D. PROGRAMM