In-network computation in sensor networks

Sensor networks are an important emerging class of networks that have many applications. A sink in these networks acts as a bridge between the sensor nodes and the end-user (which may be automated and/or part of the sink). Typically, convergecast is performed in which all the data collected by the sensors is relayed to the sink, which in turn presents the relevant information to the end-user. Interestingly, some applications require the sink to relay just a function of the data collected by the sensors. For instance, in a fire alarm system, the sinks needs to monitor the maximum of the temperature readings of all the sensors. For these applications, instead of performing convergecast, we can let the intermediate nodes process the data they receive, to significantly reduce the volume of traffic transmitted and increase the rate at which the data is collected and processed at the sink: this is known as in-network computation. Most of the current literature on this novel technique focuses on asymptotic results for large networks and for very elementary functions. In this dissertation, we study a new class of functions for which we want to compute explicit solutions for networks of practical size. We consider the applications where the sink is interested in the first M statistical moments of the data collected at a certain time. The k-th statistical moment is defined as the expectation of the k-th power of the data. The M=1 case represents the elementary functions like MAX, MIN, MEAN, etc. that are commonly considered in the literature. For this class of functions, we are interested in explicitly computing the maximum achievable throughput including routing, scheduling and queue management for any given network when in-network computation is allowed. Flow models have been routinely used to solve optimal joint routing and scheduling problems when there is no in-network computation and they are typically tractable for relatively large networks. However, deriving such models is not obvious when in-network computation is allowed. Considering a single rate wireless network and the physical model of interference, we develop a discrete-time model for the real-time network operation and perform two transformations to obtain a flow model that keeps the essence of in-network computation. This model gives an upper bound on the maximum achievable throughput. To show the tightness of that upper bound, we derive a numerical lower bound by computing a feasible solution to the discrete-time model. This lower bound turns out to be close to the upper bound proving that the flow model is an excellent approximation to the discrete-time model. We then adapt the flow model to a wired multi-rate network with asynchronous transmissions on links with different capacities. To compute the lower bound for wired networks, we propose a heuristic strategy involving the generation of multiple trees and effective queue management that achieves a throughput close to the one computed by the flow model. This cross validates the tightness of the upper bound and the goodness of our heuristic strategy. Finally, we provide several engineering insights on what in-network computation can achieve in both types of networks

Power Consumption and Throughput in Mobile Ad Hoc Networks Using Directional Antennas

We present medium access control (MAC) protocols for mobile ad hoc networks that utilize directional antennas. The use of directional antennas in place of traditional omnidirectional antennas reduces interference and thereby improves the throughput performance of the network. An additional advantage of using directional antennas is due to its higher gain from its directivity, which can be utilized to reduce the transmission power during a directional transmission. In order to maximally utilize the savings in the average power consumption in the network, we propose a power control scheme that maintains a minimum transmission power level for effective transmission of packets using directional antennas. We present simulation results showing the throughput advantage and the savings in the average consumed power in the network using the proposed protocol. We also present results showing the maximum possible savings in power consumption in the same network when an ideal power control scheme is applied

Extraction of Gd³⁺ and UO₂²⁺ Ions Using Polystyrene Grafted Dibenzo Crown Ether (DB18C6) with Octanol and Nitrobenzene: A Molecular Dynamics Study

Atomistic molecular dynamics (MD) simulations are performed in order to derive thermodynamic properties important to understand the extraction of gadolinium (Gd³⁺) and uranium dioxide (UO₂) with dibenzo crown ether (DBCE) in nitrobenzene (NB) and octanol (OCT) solvents. The effect of polystyrene graft length, on DBCE, on the binding behavior of Gd³⁺ and UO₂²⁺ is investigated for the first time. Our simulation results demonstrate that the binding of Gd³⁺ and UO₂²⁺ onto the oxygens of crown ethers is favorable for polystyrene grafted crown ether in the organic solvents OCT and NB. The metal ion binding free energy (<i>Δ<i>G</i></i>_Binding) in different solvent environments is calculated using the thermodynamic integration (TI) method. <i>Δ<i>G</i></i>_Binding becomes more favorable in both solvents, NB and OCT, with an increase in the polystyrene monomer length. The metal ion transferability from an aqueous phase to an organic phase is estimated by calculating transfer free-energy calculations (<i>Δ<i>G</i></i>_Transfer). Δ<i>G</i>_Transfer is significantly favorable for both Gd³⁺ and UO₂²⁺ for the transfer from the aqueous phase to the organic phase (i.e., NB and OCT) via ion-complexation to DBCE with an increase in polystyrene length. The partition coefficient (log <i>P</i>) values for Gd³⁺ and UO₂²⁺ show a 5-fold increase in separation capacity with polystyrene grafted DBCE. We corroborate the observed behavior by further analyzing the structural and dynamical properties of the ions in different phases