Energy Optimal Transmission Scheduling in Wireless Sensor Networks

One of the main issues in the design of sensor networks is energy efficient communication of time-critical data. Energy wastage can be caused by failed packet transmission attempts at each node due to channel dynamics and interference. Therefore transmission control techniques that are unaware of the channel dynamics can lead to suboptimal channel use patterns. In this paper we propose a transmission controller that utilizes different "grades" of channel side information to schedule packet transmissions in an optimal way, while meeting a deadline constraint for all packets waiting in the transmission queue. The wireless channel is modeled as a finite-state Markov channel. We are specifically interested in the case where the transmitter has low-grade channel side information that can be obtained based solely on the ACK/NAK sequence for the previous transmissions. Our scheduler is readily implementable and it is based on the dynamic programming solution to the finite-horizon transmission control problem. We also calculate the information theoretic capacity of the finite state Markov channel with feedback containing different grades of channel side information including that, obtained through the ACK/NAK sequence. We illustrate that our scheduler achieves a given throughput at a power level that is fairly close to the fundamental limit achievable over the channel.Comment: Accepted for publication in the IEEE Transactions on Wireless Communication

Saving mobile device energy with multipath TCP

Multipath TCP is a backwards-compatible TCP extension that en-ables using multiple network paths between two end systems for a single TCP connection, increasing performance and reliability. It can also be used to “shift ” active connections from one network path to another without breakage. This feature is especially attrac-tive on mobile devices with multiple radio interfaces, because it can be used to continuously shift active connections to the most energy-efficient network path. This paper describes a novel method for deriving such a multipath scheduler using MPTCP that maximises energy savings. Based on energy models for the different radio in-terfaces as well as a continuously accumulated communication his-tory of the device user, we compute schedulers for different appli-cations by solving a Markov decision process offline. We evaluate these schedulers for a large number of random application mod-els and selected realistic applications derived from measurements. Evaluations based on energy models for a mobile device with Wifi and 3G radio interfaces show that it performs comparably in terms of energy efficiency to a theoretically optimal omniscient oracle scheduler

Intelligent and Secure Underwater Acoustic Communication Networks

Underwater acoustic (UWA) communication networks are promising techniques for medium- to long-range wireless information transfer in aquatic applications. The harsh and dynamic water environment poses grand challenges to the design of UWA networks. This dissertation leverages the advances in machine learning and signal processing to develop intelligent and secure UWA communication networks. Three research topics are studied: 1) reinforcement learning (RL)-based adaptive transmission in UWA channels; 2) reinforcement learning-based adaptive trajectory planning for autonomous underwater vehicles (AUVs) in under-ice environments; 3) signal alignment to secure underwater coordinated multipoint (CoMP) transmissions. First, a RL-based algorithm is developed for adaptive transmission in long-term operating UWA point-to-point communication systems. The UWA channel dynamics are learned and exploited to trade off energy consumption with information delivery latency. The adaptive transmission problem is formulated as a partially observable Markov decision process (POMDP) which is solved by a Monte Carlo sampling-based approach, and an expectation-maximization-type of algorithm is developed to recursively estimate the channel model parameters. The experimental data processing reveals that the proposed algorithm achieves a good balance between energy efficiency and information delivery latency. Secondly, an online learning-based algorithm is developed for adaptive trajectory planning of multiple AUVs in under-ice environments to reconstruct a water parameter field of interest. The field knowledge is learned online to guide the trajectories of AUVs for collection of informative water parameter samples in the near future. The trajectory planning problem is formulated as a Markov decision process (MDP) which is solved by an actor-critic algorithm, where the field knowledge is estimated online using the Gaussian process regression. The simulation results show that the proposed algorithm achieves the performance close to a benchmark method that assumes perfect field knowledge. Thirdly, the dissertation presents a signal alignment method to secure underwater CoMP transmissions of geographically distributed antenna elements (DAEs) against eavesdropping. Exploiting the low sound speed in water and the spatial diversity of DAEs, the signal alignment method is developed such that useful signals will collide at the eavesdropper while stay collision-free at the legitimate user. The signal alignment mechanism is formulated as a mixed integer and nonlinear optimization problem which is solved through a combination of the simulated annealing method and the linear programming. Taking the orthogonal frequency-division multiplexing (OFDM) as the modulation technique, simulation and emulated experimental results demonstrate that the proposed method significantly degrades the eavesdropper\u27s interception capability

Modelling users in networks with path choice: four studies in telecommunications and transit

Networks of interacting users arise in many important modelling applications. Commuters interact with each other and form traffic jams during peak-time. Network protocols are users in a communication network that control sending rate and server choice. When protocols send with too high rates, network links get overloaded resulting in lost data and high delays. Although these two example users seem very different, they are similar on a conceptual modelling level. Accurate user models are essential to study complex interactions in networks. The behaviour of a user with access to different paths in a network can be modelled as an optimisation problem. Users who choose paths with the highest utility are common in many different application areas, for example road traffic, Internet protocol modelling, and general societal networks, i.e. networks of humans in everyday life. Optimisation-based user models are also attractive from the perspective of a modeller since they often allow the derivation of insights about the behaviour of the entire system by only describing a user model. The aim of this thesis is to show, in four practical studies from telecommunications and transit networks, where optimisation-based models have limitations when modelling users with path choice. We study users who have access to a limited number of paths in large scale data centers and investigate how many paths per user are realistically needed in order to get high throughput in the network. In multimedia streaming, we study a protocol that streams data on multiple paths and path properties matter. We also investigate complex energy models for data interfaces on mobile phones and evaluate how to switch interfaces to save energy. Finally, we analyse a long-term data set from 20,000 transit commuters and give insights on how they change their travel behaviour in response to incentives and targeted offers. We use tools from optimisation, simulation, and statistics to evaluate the four studies and point out problems we faced when modelling and implementing the system. The findings of this thesis indicate where user models need to be extended in order to be of practical use. The results can serve as a guide towards better user models for future modelling applications

From Sleeping to Stockpiling: Energy Conservation via Stochastic Scheduling in Wireless Networks.

Motivated by the need to conserve energy in wireless networks, we study three stochastic dynamic scheduling problems. In the first problem, we consider a wireless sensor node that can turn its radio off for fixed durations of time in order to conserve energy. We formulate finite horizon expected cost and infinite horizon average expected cost problems to model the fundamental tradeoff between packet delay and energy consumption. Through analysis of the dynamic programming equations, we derive structural results on the optimal policies for both formulations. For the infinite horizon problem, we identify a threshold decision rule to determine the optimal control action when the queue is empty. In the second problem, we consider a sensor node with an inaccurate timer in the ultra-low power sleep mode. The loss in timing accuracy in the sleep mode can result in unnecessary energy consumption from two unsynchronized devices trying to communicate. We develop a novel method for the node to calibrate its timer: occasionally waking up to measure the ambient temperature, upon which the timer speed depends. The objective is to dynamically schedule a limited number of temperature measurements in a manner most useful to improving the accuracy of the timer. We formulate optimization problems with both continuous and discrete underlying time scales, and implement a numerical solution to an equivalent reduction of the second formulation. In the third problem, we consider a single source transmitting data to one or more receivers over a shared wireless channel. Each receiver has a buffer to store received packets before they are drained. The transmitter's goal is to minimize total power consumption by exploiting the temporal and spatial variation of the channel, while preventing the receivers' buffers from emptying. In the case of a single receiver, we show that modified base-stock and finite generalized base-stock policies are optimal when the power-rate curves are linear and piecewise-linear convex, respectively. We also present the sequences of critical numbers that complete the characterizations of the optimal policies when additional technical conditions are satisfied. We then analyze the structure of the optimal policy for the case of two receivers.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/77839/1/dishuman_1.pd