Shahram Mohrehkesh

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

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.

Toward a wireless charging for battery electric vehicles at traffic intersections

In near future, Battery Electric Vehicles (BEVs) will become widely accepted and used. One of the main challenges of BEVs is their limited energy capacity. Current battery technologies require BEVs to make frequent trips, in comparison to traditional refuel, to charging stations for recharging. In this paper, we envision a new scheme for charging of BEV based on wireless charging. Our scheme exploits the frequent BEVs stops at traffic intersections to charge their batteries via wireless charging device. We study how to integrate control strategy at traffic intersections for maximizing charging while minimizing waiting delays. Simulation results are provided to show the effectiveness of proposed charging schemes.

Optimizing Energy Consumption in Terahertz Band Nanonetworks

In this paper, we develop a technique for achieving the maximum utilization of harvested energy in perpetual wireless nanonetworks, where nanonodes communicate in the THz frequency band. Because of their nano-scale sizes, nanonodes cannot store large amounts of energy. Compounding the problem, the arrival of energy is not constant, but follows a stochastic process. Therefore, an optimum design for the consumption of this limited amount of energy is required. We model the problem as a Markov decision process, where we include the energy for both reception and transmission of packets. We analyze the performance of the energy harvesting and consumption processes for very low energy harvesting rates and small energy storage capacity. We compare the performance of the optimal policy with intuitive energy consumption policies. Next, since solving an optimized problem of this sort is too compute-intensive for nanonodes with limited resources, we propose a light-weight heuristic method that can perform close to optimal. Simulation results show that our heuristic model and the optimal model can serve as a framework for the design of nanonodes that operate in low rate stochastic energy harvesting conditions with limited energy storage.

Optimizing communication energy consumption in perpetual wireless nanosensor networks

This paper investigates the effect of various parameters of energy consumption for communication in pulse-based wireless nanosensor networks that exploit energy harvesting to supply energy. Finding the optimum combination of parameters to minimize energy consumption while satisfying the QoS requirements (e.g. delay and reliability) of communication is a challenging task. We model this problem as a multiobjective function problem. We evaluate the effect of packet size, repetition and code weight on this optimization problem. Through simulation, the effect of network parameters, i.e. topology and energy for pulse transmission/reception, on the optimization problem is studied as well. The model enables optimum energy consumption design in wireless nanosensor networks.

DRIH-MAC: A Distributed Receiver-Initiated Harvesting-Aware MAC for Nanonetworks

In this paper, we introduce DRIH-MAC, a distributed receiver-initiated medium access control protocol for communication among nanonodes in a wireless electromagnetic nanonetwork. DRIH-MAC is developed based on the following principles: 1) communication starts via the receiver with the goal of maximizing the energy utilization; 2) the distributed scheme for accessing the medium is designed based on graph coloring; and 3) communication scheduling works in coordination with the energy harvesting process. DRIH-MAC is based on a probabilistic scheme to create a scalable and light-weight solution, which minimizes collisions and maximizes the utilization of harvested energy, and can be used in a wide variety of applications. Through simulation experiments, we demonstrate the efficiency of DRIH- MAC in a sample medical monitoring application. In particular, DRIH-MAC can improve energy utilization by 50% as compared to a random MAC protocol. Furthermore, it can satisfy application requirements such as delay, even with low energy harvesting rates.

Toward aggregating time-discounted information

This paper provides a way to think formally about the aggregation processes that take place in networks where individual actors (whether sensors, robots, or people) possess data whose value is discounted over time. The various actors use data to make decisions: the larger the value, the better (i.e. more informed) the decision. At every moment, individual actors have the choice of making a decision or else to defer decision to a later time. However, the longer they wait, the lower the value of the data they hold. To counter-balance the effect of time discounting, we define an algebraic operation that we call aggregation, whereby two or more actors integrate their data in the hope of increasing its value. Our main contribution is a formal look at the value of time-discounted information and at the algebra of its aggregation. We allow aggregation of time-discounted information to proceed in an arbitrary, not necessarily pairwise, manner. Our model relates aggregation decisions to the ensuing value of information and suggests natural thresholding strategies for the aggregation of the information collected by sets of network actors. A sensor network with the mission of intrusion detection is used throughout as an illustrative example. The accuracy of our theoretical predictions was confirmed by simulating a number of realistic scenarios.

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 micro meters in size. A nanonode consisting of nan-memory, a nano-processor, nano-harvesters, 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.

Optimized inquiry mechanisms for Bluetooth devices

In this paper, we propose a simple and practical scheme for optimization the inquiry process in Bluetooth standards in order to achieve fast, reliable and controlled responses. We show how the inquiry process is related to clock setting in Bluetooth devices and how clock optimization could be used to shorten the inquiry process time. Theoretical analysis and simulation results shows the efficiency of the proposed optimized scheme. We also develop a traffic transportation application, i.e. speed measurement, as an example of many applications where this scheme could benefit. Results show how the proposed optimized scheme outperforms in comparison with others.

On aggregating information in actor networks

This paper provides a way to think formally about the aggregation processes that take place in networks where individual actors (whether sensors, robots, or people) possess data whose value may decay over time. The various actors use data to make decisions: the larger the value, the better (i.e. more informed) the decision. At every moment, individual actors have the choice of making a decision or else to defer the decision to a later time. However, the longer they wait, the lower the value of the data they hold. To counter-balance the effect of time discounting, we define an algebraic operation that we call aggregation, whereby two or more actors integrate their data in the hope of increasing its value. Our main contribution is a formal look at the value of time-discounted information and at the algebra of its aggregation. We allow aggregation of time-discounted information to proceed in an arbitrary, not necessarily pairwise, manner. Our model relates aggregation decisions to the ensuing value of information and suggests natural thresholding strategies for the aggregation of the information collected by sets of network actors. Extensive simulations have confirmed the accuracy of our theoretical predictions.

Context-aware content adaptation in access point

Instead of traditional content adaptation at servers or clients, we propose context-aware content adaptation at the Access Point (AP). We show that because of the special characteristics of the AP such as: being the last node at the edge of the network, no power constraint, and powerful computing capabilities, it can be the best candidate for context-aware content adaptation in high dynamic wireless mobile networks. In fact, it can adapt content more accurately and faster in a high diverse and dynamic context. Content adaptation at the application and MAC layers of the AP is introduced in this paper.