Efficient Communication Protocols for Wireless Nanoscale Sensor Networks

Advances in nanotechnology are paving the way for wireless nanoscale sensor networks (WNSNs), promising radically new applications in medical, biological, and chemical fields. However, the small scale poses formidable challenges for communication. First, small nanomaterial-based antennas communicate in the terahertz band, which coincides with the natural resonance frequencies of many types of molecules causing severe molecular absorption and noise. The problem is particularly complicated if the molecular composition of the channel changes over time, causing time-varying absorption and noise. Second, as it is not practical to fit large batteries or replace batteries in a small device, these devices are expected to power themselves by harvesting ambient energy from the environment. However, the amount of energy that can be harvested is directly proportional to the size of the harvester. A nanodevice therefore can generate only a tiny fraction of its total power consumption, which requires us to rethink the design of communication protocols for self-powering WNSNs. In order to address aforementioned challenges, this thesis makes three fundamental contributions. First, it proposes dynamic frequency and power selection as a means to overcome the first problem, i.e, changing molecular composition problem in a time-varying terahertz channel. The dynamic frequency/power selection problem is modelled as a Markov Decision Process to derive the optimal solutions, while several practical heuristics are proposed that achieve close to optimal solutions. Second, to address the severe power shortage problem in a self-powering nanodevice, this thesis proposes a mechanism to exploit the information contained in the energy harvesting data to detect the energy-dissipating events occurring in the environment. This form of event monitoring makes dual use of the energy-harvesting unit in the nanodevice, i.e., it is used to generate power as well as monitor the environment, thus saving significant energy, which otherwise would have been used to power the onboard sensors. Finally, novel WNSN applications are designed and analysed to monitor and control chemical reactors at the molecular level with the ultimate goal of increasing the selectivity of the reactor. It is shown that using the proposed communication protocols for a time-varying terahertz channel, the selectivity of the reactor can be significantly increased, beyond what can be achieved with conventional solutions

Power Optimization in Nano Sensor Networks for Chemical Reactors

ABSTRACT Chemical reactors are designed to efficiently produce highvalue chemical products but at the same time they also produce low-value by-products. The selectivity of a chemical process refers to the proportion of high-value product produced. A nano sensor network (NSN) monitoring the chemical process at the molecule level could help improving the selectivity by preventing the reactions that lead to low value by-products. Therefore, a central requirement to achieve high selectivity by NSN is reliable communication. A challenge to realising reliable communication within a chemical reactor is its time-varying chemical composition, which in turn creates a time-varying radio channel and noise. The sensor nodes therefore need to adjust their transmission power according to the chemical composition while maintaining a low overall power budget. We show that this problem can be modelled as a Markov Decision Process (MDP). However, the MDP solution requires the sensors to know the composition of the reactor at each time instance, which is prohibitive. We therefore derive off-line time-based policies that these sensors can use. We illustrate our work by using an important chemical process for fuel production and demonstrate the performance of our proposed off-line policies against the optimal MDP policy

Innovative Approach to Improving Gas-to-Liquid Fuel Catalysis via Nanosensor Network Modulation

Fischer–Tropsch synthesis, a major process for converting natural gas to liquid hydrocarbons (GTL), suffers from selectivity limitations that refer to the ratio of highly useful hydrocarbons to the total product output. Existing strategies for selectivity improvement, such as manipulation of reactor operating conditions (temperature, pressure, etc.) and catalyst design variables may be classified as top-down approaches. In this work, a bottom-up approach is proposed in which surface processes can be controlled via a nanosensor network (NSN) involving the turning on or off of elementary steps creating undesired species and redirection of surface efforts to step(s) leading to the desired products. The overall effect of these nanolevel communications offers superior selectivity to that hitherto possible by reducing the rate of hydrogenation of surface unsaturated species to paraffin (HTP) reactions. Our numerical and simulation results confirm substantial improvement of overall selectivity in a catalyst that is equipped with a highly reliable NSN