Adaptive clustering and transmission range adjustment for topology control in wireless sensor networks

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on August 10, 2007)Vita.Thesis (Ph. D.) University of Missouri-Columbia 2006.A wireless sensor network (WSN) is characterized by a limited energy supply and a large number of nodes. Topology control (TC) as one of the main ways to control energy consumption in WSNs has been the focus of a considerable body of research. Topology control algorithms can be divided into duty-cycle-based algorithms and transmission-power-based algorithms according to their energy saving approaches. By dynamically integrating the two approaches, I have developed a two-level topology control strategy to achieve further energy saving. Connected dominating set (CDS) as a very promising energy saving technique can be used with either a transmission-power-based algorithm or a dutycycle- based algorithm. I have designed a distributed algorithm, DSP-CDS, for constructing CDS quickly in a single phase. I have developed an energy consumption model for clustered WSNs and use it to solve the optimal transmission range problem. This model provides us an insight into the energy consumption behavior in clustered wireless sensor networks and the relationship among major factors. Observing that traffic load often has unpredictable changes after deployment and has great impact on the optimal transmission range, I have designed a traffic adaptive clustering algorithm, RDSP-CDS. RDSP-CDS is suitable for dynamic network topologies due to transmission range changes, node mobility, and/or node failure. As a summary, the contributions of the dissertation include a two-level topology control strategy, a distributed connected dominating set construction algorithm (DSP-CDS), an energy consumption analysis model to solve the optimal transmission range problem in clustered WSNs, and a distributed traffic-adaptive clustering algorithm (RDSP-CDS) for non-uniform traffic networks.Includes bibliographical reference

Energy efficiency in ad-hoc wireless networks

In ad-hoc wireless networks, nodes are typically battery-powered, therefore energy limitations are among the critical constraints in ad-hoc wireless networks' development. The approaches investigated in this thesis to achieve energy efficient performance in wireless networks can be grouped into three main categories. 1. Each wireless network node has four energy consumption states: transmitting, receiving, listening and sleeping states. The power consumed in the listening state is less than the power consumed in the transmitting and receiving states, but significantly greater than that in the sleeping state. Energy efficiency is achieved if as many nodes as possible are put into the sleeping states. 2) Since energy is consumed for transmission nonlinearly in terms of the transmission range, transmission range adjustment is another energy saving approach. In this work, the optimal transmission range is derived and applied to achieve energy efficient performance in a number of scenerios. 3) Since energy can be saved properly arranging the communication algorithms, network topology management or network routing is the third approach which can be utilised in combination with the above two approaches. In this work, Geographical Adaptive Fidelity (GAF) algorithms, clustering algorithms and Geographic Routing (GR) algorithms are all utilised to reduce the energy consumption of wireless networks, such as Sensor Networks and Vehicular Networks. These three approaches are used in this work to reduce the energy consumption of wireless networks. With the GAF algorithm. We derived the optimal transmission range and optimal grid size in both linear and rectangular networks and as a result we show how the network energy consumptions can be reduced and how the network lifetime can be prolonged. With Geographic Routing algorithms the author proposed the Optimal Range Forward (ORF) algorithm and Optimal Forward with Energy Balance (OFEB) algorithm to reduce the energy consumption and to prolong the network lifetime. The results show that compared to the traditional GR algorithms (Most Forward within Radius, Nearest Forward Progress), the network lifetime is prolonged. Other approaches have also been considered to improve the networks's energy efficient operation utilising Genetic Algorithms to find the optimal size of the grid or cluster. Furthermore realistic physical layer models, Rayleigh fading and LogNormal fading, are considered in evaluating energy efficiency in a realistic network environment

Non-Metaheuristic Clustering Algorithms for Energy-Efficient Cooperative Communication in Wireless Sensor Networks: A Comparative Study

 Wireless Sensor Networks (WSNs) are now considered a vital technology that enables the gathering and distribution of data in various applications, such as environmental monitoring and industrial automation. Nevertheless, the finite energy resources of sensor nodes pose significant obstacles to the long-term viability and effectiveness of these networks. Researchers have developed and studied various non-meta algorithms to improve energy efficiency, data transfer, and network lifespan. These efforts contribute to enhancing cooperative communication modules. This analysis conducts a detailed examination and comparative evaluation of different well-known clustering methods in the field of Wireless Sensor Networks (WSNs), providing significant insights for improving cooperative communication. Our purpose is to provide a comprehensive perspective on the contributions of these algorithms to improving energy efficiency in WSNs. This will be achieved by examining their practical implementations, underlying mathematical principles, strengths, shortcomings, real-world applications, and potential for further improvement

Distributed Service Discovery for Heterogeneous Wireless Sensor Networks

Service discovery in heterogeneous Wireless Sensor Networks is a challenging research objective, due to the inherent limitations of sensor nodes and their extensive and dense deployment. The protocols proposed for ad hoc networks are too heavy for sensor environments. This paper presents a resourceaware solution for the service discovery problem, which exploits the heterogeneous nature of the sensor network and alleviates the high-density problem from the flood-based approaches. The idea is to organize nodes into clusters, based on the available resources and the dynamics of nodes. The clusterhead nodes act as a distributed directory of service registrations. Service discovery messages are exchanged among the nodes in the distributed directory. The simulation results show the performance of the service discovery protocol in heterogeneous dense environments

Enabling limited traffic scheduling in asynchronous ad hoc networks

We present work-in-progress developing a communication framework that addresses the communication challenges of the decentralized multihop wireless environment. The main contribution is the combination of a fully distributed, asynchronous power save mechanism with adaptation of the timing patterns defined by the power save mechanism to improve the energy and bandwidth efficiency of communication in multihop wireless networks. The possibility of leveraging this strategy to provide more complex forms of traffic management is explored

Cross-layer Balanced and Reliable Opportunistic Routing Algorithm for Mobile Ad Hoc Networks

For improving the efficiency and the reliability of the opportunistic routing algorithm, in this paper, we propose the cross-layer and reliable opportunistic routing algorithm (CBRT) for Mobile Ad Hoc Networks, which introduces the improved efficiency fuzzy logic and humoral regulation inspired topology control into the opportunistic routing algorithm. In CBRT, the inputs of the fuzzy logic system are the relative variance (rv) of the metrics rather than the values of the metrics, which reduces the number of fuzzy rules dramatically. Moreover, the number of fuzzy rules does not increase when the number of inputs increases. For reducing the control cost, in CBRT, the node degree in the candidate relays set is a range rather than a constant number. The nodes are divided into different categories based on their node degree in the candidate relays set. The nodes adjust their transmission range based on which categories that they belong to. Additionally, for investigating the effection of the node mobility on routing performance, we propose a link lifetime prediction algorithm which takes both the moving speed and moving direction into account. In CBRT, the source node determines the relaying priorities of the relaying nodes based on their utilities. The relaying node which the utility is large will have high priority to relay the data packet. By these innovations, the network performance in CBRT is much better than that in ExOR, however, the computation complexity is not increased in CBRT.Comment: 14 pages, 17 figures, 31 formulas, IEEE Sensors Journal, 201

Load-balancing rendezvous approach for mobility-enabled adaptive energy-efficient data collection in WSNs

Copyright © 2020 KSII The tradeoff between energy conservation and traffic balancing is a dilemma problem in Wireless Sensor Networks (WSNs). By analyzing the intrinsic relationship between cluster properties and long distance transmission energy consumption, we characterize three node sets of the cluster as a theoretical foundation to enhance high performance of WSNs, and propose optimal solutions by introducing rendezvous and Mobile Elements (MEs) to optimize energy consumption for prolonging the lifetime of WSNs. First, we exploit an approximate method based on the transmission distance from the different node to an ME to select suboptimal Rendezvous Point (RP) on the trajectory for ME to collect data. Then, we define data transmission routing sequence and model rendezvous planning for the cluster. In order to achieve optimization of energy consumption, we specifically apply the economic theory called Diminishing Marginal Utility Rule (DMUR) and create the utility function with regard to energy to develop an adaptive energy consumption optimization framework to achieve energy efficiency for data collection. At last, Rendezvous Transmission Algorithm (RTA) is proposed to better tradeoff between energy conservation and traffic balancing. Furthermore, via collaborations among multiple MEs, we design Two-Orbit Back-Propagation Algorithm (TOBPA) which concurrently handles load imbalance phenomenon to improve the efficiency of data collection. The simulation results show that our solutions can improve energy efficiency of the whole network and reduce the energy consumption of sensor nodes, which in turn prolong the lifetime of WSNs