Energy Efficient Node Deployment in Wireless Ad-hoc Sensor Networks

We study a wireless ad-hoc sensor network (WASN) where $N$ sensors gather data from the surrounding environment and transmit their sensed information to $M$ fusion centers (FCs) via multi-hop wireless communications. This node deployment problem is formulated as an optimization problem to make a trade-off between the sensing uncertainty and energy consumption of the network. Our primary goal is to find an optimal deployment of sensors and FCs to minimize a Lagrange combination of the sensing uncertainty and energy consumption. To support arbitrary routing protocols in WASNs, the routing-dependent necessary conditions for the optimal deployment are explored. Based on these necessary conditions, we propose a routing-aware Lloyd algorithm to optimize node deployment. Simulation results show that, on average, the proposed algorithm outperforms the existing deployment algorithms.Comment: 7 pages, 6 figure

Node Deployment in Heterogeneous Rayleigh Fading Sensor Networks

We study a heterogeneous Rayleigh fading wireless sensor network (WSN) in which densely deployed sensor nodes monitor an environment and transmit their sensed information to base stations (BSs) using access points (APs) as relays to facilitate the data transfer. We consider both large-scale and small-scale propagation effects in our system model and formulate the node deployment problem as an optimization problem aimed at minimizing the wireless communication network's power consumption. By imposing a desired outage probability constraint on all communication channels, we derive the necessary conditions for the optimal deployment that not only minimize the power consumption, but also guarantee all wireless links to have an outage probability below the given threshold. In addition, we study the necessary conditions for an optimal deployment given ergodic capacity constraints. We compare our node deployment algorithms with similar algorithms in the literature and demonstrate their efficacy and superiority

Energy-Efficient Node Deployment in Static and Mobile Heterogeneous Multi-Hop Wireless Sensor Networks

We study a heterogeneous wireless sensor network (WSN) where N heterogeneous access points (APs) gather data from densely deployed sensors and transmit their sensed information to M heterogeneous fusion centers (FCs) via multi-hop wireless communication. This heterogeneous node deployment problem is modeled as an optimization problem with total wireless communication power consumption of the network as its objective function. We consider both static WSNs, where nodes retain their deployed position, and mobile WSNs where nodes can move from their initial deployment to their optimal locations. Based on the derived necessary conditions for the optimal node deployment in static WSNs, we propose an iterative algorithm to deploy nodes. In addition, we study the necessary conditions of the optimal movement-efficient node deployment in mobile WSNs with constrained movement energy, and present iterative algorithms to find such deployments, accordingly. Simulation results show that our proposed node deployment algorithms outperform the existing methods in the literature, and achieves a lower total wireless communication power in both static and mobile WSNs, on average

Using Quantization to Deploy Heterogeneous Nodes in Two-Tier Wireless Sensor Networks

We study a heterogeneous two-tier wireless sensor network in which N heterogeneous access points (APs) collect sensing data from densely distributed sensors and then forward the data to M heterogeneous fusion centers (FCs). This heterogeneous node deployment problem is modeled as a quantization problem with distortion defined as the total power consumption of the network. The necessary conditions of the optimal AP and FC node deployment are explored in this paper. We provide a variation of Voronoi Diagram as the optimal cell partition for this network, and show that each AP should be placed between its connected FC and the geometric center of its cell partition. In addition, we propose a heterogeneous two-tier Lloyd algorithm to optimize the node deployment. Simulation results show that our proposed algorithm outperforms the existing clustering methods like Minimum Energy Routing, Agglomerative Clustering, and Divisive Clustering, on average

Structural Equation Modeling with Latent Variables

Discovering causal relationships between variables is a difficult unsupervised learning task, which becomes more challenging if there are unobserved common causes between pairs of variables. Often it is not feasible to uniquely recover causal relations when only observational data is available. When experimental data is obtainable through interventions, we present a method for guaranteed identification under mild assumptions. We consider a linear structural equation model where there are independent unobserved common causes between pairs of observed variables. The generative process of latent effects is given by the mixing method of blind source separation problem. Our objective is to disentangle the observed causal effects from latent confounders and learn the model parameters that are consistent with observational and experimental data. By exploiting the invariance of latent factors across various interventions, we present matching methods as a way to combine the information across various interventions. Finally, we propose an identification algorithm that uses efficient tensor decomposition for a unique recovery of model parameters and disentangling the latent confounders from observed causal effects

Structural Equation Modeling with Latent Variables

Discovering causal relationships between variables is a difficult unsupervised learning task, which becomes more challenging if there are unobserved common causes between pairs of variables. Often it is not feasible to uniquely recover causal relations when only observational data is available. When experimental data is obtainable through interventions, we present a method for guaranteed identification under mild assumptions. We consider a linear structural equation model where there are independent unobserved common causes between pairs of observed variables. The generative process of latent effects is given by the mixing method of blind source separation problem. Our objective is to disentangle the observed causal effects from latent confounders and learn the model parameters that are consistent with observational and experimental data. By exploiting the invariance of latent factors across various interventions, we present matching methods as a way to combine the information across various interventions. Finally, we propose an identification algorithm that uses efficient tensor decomposition for a unique recovery of model parameters and disentangling the latent confounders from observed causal effects

Energy-Efficient Node Deployment in Wireless Sensor Networks

With recent advances in communication, sensing, computing, and battery capacity, wireless sensor networks (WSNs) have emerged as a viable technology for monitoring and surveillance purposes in numerous applications such as precision agriculture, healthcare monitoring, and industrial monitoring. However, battery power depletion has remained the most pivotal factor in network failure since sensors are driven by battery that are infeasible to replenish, especially in hostile environments. This calls for innovative approaches for improving the energy-efficiency of WSNs and extending their lifetime.Empirical measurements have demonstrated that wireless communication dominates the network’s energy consumption. Node deployment plays a crucial role in energy-efficiency of the WSN since electromagnetic wave propagation dampens as a power law function of the distance between the transmitter and receiver. In this dissertation, by making a resemblance between network nodes/cells and quantization points/regions, I aim to find the optimal deployment, cell partitioning, and data routing that minimizes the wireless communication power consumption of these networks. In particular, I considered the effect of both large-scale path-loss signal attenuation and small-scale signal fading and modeled the node deployment problem as an optimization problem with the total power consumption of the network as its cost function. To tackle the resulting NP-hard optimization problem, I derived the necessary conditions for optimal deployment, cell partitioning, and data routing under various network setups and environmental conditions. My theoretical results are then embedded in iterative algorithms to yield energy-efficient deployment and optimal intercommunication protocol for network nodes.One of key contributions in this dissertation is addressing challenges that arise under various hardware settings, such as homogeneous versus heterogeneous and static versus mobile nodes, in addition to various network architectures, such as two-tier versus multi-hop. Simulation results show that, regardless of the distribution of events to be sensed by sensor nodes, the proposed deployment algorithms outperform previous state-of-the-art methods in the literature by a significant margin. In particular, the proposed algorithms improved these networks' energy efficiency and lifetime by up to a factor of two compared to existing work in the literature. This, in turn, reduces the cost of such networks and demonstrates their potential as a sustainable, rigorous, and cost-effective monitoring system