Cooperative target tracking by a mobile bionanosensor network

On the Target Detection Performance of a Molecular Communication Network With Multiple Mobile Nanomachines

A network of nanomachines (NMs) can be used to build a target detection system for a variety of promising applications. They have the potential to detect toxic chemicals, infectious bacteria, and biomarkers of dangerous diseases such as cancer within the human body. Many diseases and health disorders can be detected early and efficiently treated in the future by utilizing these systems. To fully grasp the potential of these systems, mathematical analysis is required. This paper describes an analytical framework for modeling and analyzing the performance of target detection systems composed of multiple mobile nanomachines of varying sizes with passive/absorbing boundaries. We consider both direct contact detection, in which NMs must physically contact the target to detect it, and indirect sensing, in which NMs must detect the marker molecules emitted by the target. The detection performance of such systems is calculated for degradable and non-degradable targets, as well as mobile and stationary targets. The derived expressions provide various insights, such as the effect of NM density and target degradation on detection probability.

Target Convergence Analysis of Cancer-Inspired Swarms for Early Disease Diagnosis and Targeted Collective Therapy

Sensing and perception is generally a challenging aspect of decision-making. In the nanoscale, however, these processes face further complications due to the physical limitations of devising the nanomachines with more limited perception, more noise, and fewer sensors. There is, hence, higher dependence on swarm sensing and perception of many nanomachines. Here, taking hardware and software bioinspiration, we propose Chemo-Mechanical Cancer-Inspired Swarm Perception (CMCISP) based on online nano fuzzy haptic feedback for early disease diagnosis and targeted therapy. Particularly, we use epithelial cancer cell's scaffold as a carrier, its properties as a distributed perception mechanism, and its motility patterns as the swarm movements such as anti-durotaxis, blebbing, and chemotaxis. We implement the in-silico model of CMCISP using a hybrid computational framework of the cellular Potts model, swarm intelligence, and fuzzy decision-making. Furthermore, the target convergence of CMCISP is analytically proved using swarm control theory. Finally, several numerical experiments and validations for cancer target therapy, cellular stiffness measurement, anti-durotaxis movement, and robustness analysis are also conducted and compared with a mathematical chemotherapy model and authors' previous works on targeted therapy. Results show improvements of up to 57.49% in early cancer detection, 26.64% in target convergence, and 68.01% in increased normoxic cell density. The study also reveals the strategy's robustness to environmental/sensory noise by applying six SNR levels of 0, 2, 5, 10, 30, and 50 dB, with an average diagnosis error of only 0.98% and at most 2.51%.

Cooperative Target Tracking Algorithm based on Massive Beacon Coordinates System in Directional Molecular Communication

As nanotechnology advances, it is possible to build nanomachines to conduct tasks on the nano-scale. Since the EM wave, the traditional communication medium does not apply in nano-scale, Molecular Communication (MC) has been recently given increasing attention for its excellent biocompatibility and low energy consumption within the human body. Diffusion channel-based MC(DbMC) is mainly studied in the aquatic environment. However, due to the randomness of diffusion, DbMC suffers high losses, low propagation speed, and limited communication range. Directional Molecular Communication(DMC) is proposed by introducing chemotaxis. DMC can significantly improve the efficiency of molecular communication because it keeps molecules moving along a predetermined path. It is particularly suitable in the scenario of target tracking related to some applications such as drug delivery. In this paper, we have proposed a novel massive beacon coordinates system model to aid target tracking. Beacons in this system navigate nanomachines, and the beacon system can uniquely determine their position coordinates. Each nanomachine carries a lot of bacteria carrier (E.coli) to share information. Information is encoded in DNA molecules and transferred to other nanomachines by bacteria carriers. With the help of bacteria carriers, nanomachines can share their current position information with others to realize cooperated fast target tracking. We have evaluated its performance in target tracking through simulation by comparison with the diffusion-based model. Some key factors that may influence target tracking are also taken into consideration.

Swarm-Fuzzy Rule-Based Targeted Nano Delivery Using Bioinspired Nanomachines

Cooperative navigation and swarm decision making take center stage in a broad range of distributed applications with high-environmental and measurement uncertainties. Here, we propose a fuzzy carotid body-inspired nanonetwork (FCBN) of autonomous nanomachines as a swarm computational framework for cooperative navigation and decision making at the nanoscale. The carotid body is a complex sensory system in animals that trigger the global breathing decision based on locally sensed data. Similarly, each nanomachine senses relevant local data and uses a local fuzzy inference system (LFIS) to determine a local environmental trigger that, along with triggers of other nanomachines, collectively estimate the overall environmental state and the appropriate course of action by the nanonetwork. To illustrate, here, we consider the cooperative therapy of a cancer site by targeted drug delivery. The general paradigm, however, is equally applicable to large-scale networks and swarms of cooperative machines. The simulation results show that the FCBN outperforms the benchmark mathematical therapy model by decreasing cancer markers including hypoxic and endothelial cell densities by as much as 99.75% and 33.34%, respectively, while also increasing normoxic cell density by 76.18%. The FCBN also outperforms our earlier reported non-fuzzy BN approach by decreasing hypoxic cell and endothelial cell densities by as much as 29.37% and 30%, respectively, while also increasing normoxic cell density by 45.21%.

Molecular Communication: A Personal Perspective

The authors of this paper have been involved in molecular communication since its conception. They describe their decade-and-a-half long personal journey of the molecular communication research and share their observations and thoughts on how the molecular communication research started and expanded to flourish. The authors also share their thoughts on research challenges that they hope the molecular communication research community addresses in the coming decade.

Towards Concurrent Data Transmission: Exploiting Plasmid Diversity by Bacterial Conjugation

The progress of molecular communication (MC) is tightly connected to the progress of nanomachine design. State-of-the-art states that nanomachines can be built either from novel nanomaterials by the help of nanotechnology or they can be built from living cells which are modified to function as intended by synthetic biology. With the growing need of the biomedical applications of MC, we focus on developing bio-compatible communication systems by engineering the cells to become MC nanomachines. Since this approach relies on modifying cellular functions, the improvements in the performance can only be achieved by integrating new biological properties. A previously proposed model for molecular communication is using bacteria as information carriers between transmitters and receivers, also known as bacterial nanonetworks. This approach has suggested encoding information into the plasmids inserted into the bacteria which leads to extra overhead for the receivers to decode and analyze the plasmids to obtain the encoded information. Another scheme, which is proposed in this paper, is to determine the digital information transmitted based on the quantity of bacteria emitted. While this scheme has its simplicity, the major drawback is the low-data rate resulting from the long propagation of the bacteria. To improve the performance, this paper proposes a distributed modulation scheme utilizing three bacterial properties, namely, engineering of plasmids, conjugation, and bacterial motility. In particular, genetic engineering allows us to engineer the different combinations of genes representing the different series of bits. When compared with binary density modulation and the M-ary density modulation, it is shown that the distributed modulation scheme outperforms the other two approaches in terms of bit error probability as well as the achievable rate for varying quantity of bacteria transmitted, distances, as well as time slot length.