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

Performance Review of Selected Topology-Aware Routing Strategies for Clustering Sensor Networks

In this paper, cluster-based routing (CBR) protocols for addressing issues pertinent to energy consumption, network lifespan, resource allocation and network coverage are reviewed. The paper presents an indepth  performance analysis and critical review of selected CBR algorithms. The study is domain-specific and simulation-based with emphasis on the tripartite trade-off between coverage, connectivity and lifespan. The rigorous statistical analysis of selected CBR schemes was also presented. Network simulation was conducted with Java-based Atarraya discrete-event simulation toolkit while statistical analysis was carried out using MATLAB. It was observed that the Periodic, Event-Driven and Query-Based Routing (PEQ) schemes performs better than Low-Energy Adaptive Clustering Hierarchy (LEACH), Threshold-Sensitive Energy-Efficient Sensor Network (TEEN) and Geographic Adaptive Fidelity (GAF) in terms of network lifespan, energy consumption and network throughput.Keywords: Wireless sensor network, Hierarchical topologies, Cluster-based routing, Statistical analysis, Network simulatio

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Geographic Gossip: Efficient Averaging for Sensor Networks

Gossip algorithms for distributed computation are attractive due to their simplicity, distributed nature, and robustness in noisy and uncertain environments. However, using standard gossip algorithms can lead to a significant waste in energy by repeatedly recirculating redundant information. For realistic sensor network model topologies like grids and random geometric graphs, the inefficiency of gossip schemes is related to the slow mixing times of random walks on the communication graph. We propose and analyze an alternative gossiping scheme that exploits geographic information. By utilizing geographic routing combined with a simple resampling method, we demonstrate substantial gains over previously proposed gossip protocols. For regular graphs such as the ring or grid, our algorithm improves standard gossip by factors of $n$ and $\sqrt{n}$ respectively. For the more challenging case of random geometric graphs, our algorithm computes the true average to accuracy $\epsilon$ using $O(\frac{n^{1.5}}{\sqrt{\log n}} \log \epsilon^{-1})$ radio transmissions, which yields a $\sqrt{\frac{n}{\log n}}$ factor improvement over standard gossip algorithms. We illustrate these theoretical results with experimental comparisons between our algorithm and standard methods as applied to various classes of random fields.Comment: To appear, IEEE Transactions on Signal Processin