Abstract-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.

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Abstract-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.

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.