Energy Harvesting-Aware Design for Wireless Nanonetworks

Nanotechnology advancement promises to enable a new era of computing and communication devices by shifting micro scale chip design to nano scale chip design. Nanonetworks are envisioned as artifacts of nanotechnology in the domain of networking and communication. These networks will consist of nodes of nanometer to micrometer in size, with a communication range up to 1 meter. These nodes could be used in various biomedical, industrial, and environmental monitoring applications, where a nanoscale level of sensing, monitoring, control and communication is required. The special characteristics of nanonetworks require the revisiting of network design. More specifically, nanoscale limitations, new paradigms of THz communication, and power supply via energy harvesting are the main issues that are not included in traditional network design methods. In this regard, this dissertation investigates and develops some solutions in the realization of nanonetworks. Particularly, the following major solutions are investigated. (I) The energy harvesting and energy consumption processes are modeled and evaluated simultaneously. This model includes the stochastic nature of energy arrival as well as the pulse-based communication model for energy consumption. The model identifies the effect of various parameters in this joint process. (II) Next, an optimization problem is developed to find the best combination of these parameters. Specifically, optimum values for packet size, code weight, and repetition are found in order to minimize the energy consumption while satisfying some application requirements (i.e., delay and reliability). (III) An optimum policy for energy consumption to achieve the maximum utilization of harvested energy is developed. The goal of this scheme is to take advantage of available harvested energy as much as possible while satisfying defined performance metrics. (IV) A communication scheme that tries to maximize the data throughput via a distributed and scalable coordination while avoiding the collision among neighbors is the last problem to be investigated. The goal is to design an energy harvesting-aware and distributed mechanism that could coordinate data transmission among neighbors. (V) Finally, all these solutions are combined together to create a data link layer model for nanonodes. We believe resolving these issues could be the first step towards an energy harvesting-aware network design for wireless nanosensor networks

Demographic Prediction of Mobile User from Phone Usage

In this paper, we describe how we use the mobile phone usage of users to predict their demographic attributes. Using call log, visited GSM cells information, visited Bluetooth devices, visited Wireless LAN devices, accelerometer data, and so on, we predict the gender, age, marital status, job and number of people in household of users. The accuracy of developed classifiers for these classification problems ranges from 45-87% depending upon the particular classification problem

RIH-MAC: Receiver-Initiated Harvesting-aware MAC for

In this paper, we introduce RIH-MAC, a receiver-initiated MAC protocol, for communication among nanonodes in a wireless electromagnetic nanonetwork. The protocol can be used for a wide family of applications and operates in both distributed and centralized communication models. Furthermore, RIH-MAC is designed to operate adaptively with energy harvesting nanonodes. RIH-MAC is developed based on distributed and probabilistic schemes to create a scalable solution, which minimizes collisions and maximizes the utilization of harvested energy. Through simulation, we show the efficiency of RIH-MAC. 1

Toward Aggregating Time-Discounted Information ∗

This paper provides a way to think formally about the aggregation processes in networks where individual actors (whether sensors, robots, or people) possess information whose value decays over time. Our contribution is a formal look at the value of time-discounted information and at the algebra of its aggregation. Our model relates aggregation decisions to the ensuing value of information. A sensor network with the mission of intrusion detection is used throughout as an illustrative example. Our theoretical predictions were confirmed by extensive simulation

A Framework for Assessing the Quality of Event Detection in Sensor Networks ∗

In spite of the multitude of protocols that have been designed for event monitoring in sensor networks, very little work has been devoted to assessing the quality of these protocols in terms of their ability to provide timely and accurate information about events. The problem is challenging because of the combined effects of the stochastic nature of event arrival and duration and the stochastic nature of sensor sleep-awake schedule. These various stochastic processes need to be modelled jointly in order to be able to reason, with any degree of confidence, about the quality of the event detection. The main contribution of this paper is a framework for assessing our confidence about the occurrence of events in sensor networks. Our framework models the interaction between the stochastic nature of an event arrival, the event duration, and the sensor sleep/awake schedule. By employing our framework, it is possible to configure individual sensor duty cycles to meet the requirements of missionoriented applications in terms of timely and accurate information about events of interest. field

Energy Harvesting in Nanonetworks

The goal of this chapter is to review the process, issues, and challenges of energy harvesting in nanonetworks, composed of nanonodes that are nano to micrometers in size. A nanonode consisting of nan-memory, a nano-processor, nanoharvesters, ultra nano-capacitor, and a nano-transceiver harvests the energy required for its operations, such as processing and communication. The energy harvesting process in nanonetworks differs from traditional networks (e.g. wireless sensor networks, RFID) due to their unique characteristics such as nanoscale, communication model, and molecular operating environment. After reviewing the energy harvesting process and sources, we introduce the communication model, which is the main source of energy consumption for nanonodes. This is followed by a discussion on the models for joint energy harvesting and consumption processes. Finally, we describe approaches for optimizing the energy consumption process, which includes optimum data packet design, optimal energy utilization, energy consumption scheduling, and energy-harvesting-aware protocols

Energy Harvesting in Electromagnetic Nanonetworks

The ability of nanonetworks to exploit harvested energy from ambient sources efficiently and economically will determine the extent of their future application. Research has already identified processes, issues, and challenges for these networks, most of which center on the energy-harvesting process, optimal energy consumption, and communication protocols