Bionanosensor networks are spatially distributed populations of sensors that use the methods of network engineering in a new way. Bionanosensors can be expressly engineered for a task, or be natural machines such as bacteria. The important point is that they communicate through chemical signals: information is encoded on molecules that are used as carriers.

All the authors of the book have worked in distributed nanomachines. This small book presents and summarizes under a unified view the results of their various approaches. The authors try to answer the question of whether a network of such devices could be used for practical applications, mainly for medical applications. Target detection and tracking are considered as the most important tasks, and they require static or dynamic positioning of the sensors.

The key problem for target detection is to determine the number of bionanosensors to introduce in the environment. Chapter 2 approaches the problem in a two-dimensional space; considering that the target generates a signal that degrades with time, the sensor placement should maximize the probability of successful detection.

For target tracking, the bionanosensors should be able to move with the target; in this case, signaling molecules to act as repellents (to explore the environment) and attractors (to move together to follow the target when found) are released by the bionanosensor. In chapter 3, the dynamics of their concentration are studied using differential equations.

Chapter 4 is about controlling the network by interfacing the bionanosensors to external devices to control their mobility. Bacterial chemotaxis is the proposed model.

Chapter 5 concludes the book and presents open research issues, in particular robust molecular communication, protocols, and “wet” experiments that will finally indicate how the models act in reality.

The book has a concise and clear presentation of the main results in using the paradigm of chemical communication. However, I think that some of the open issues indicated at the beginning, and in particular how to set such a large number of parameters, are not fully addressed.

The difficult physico-chemical process to create and deploy such networks is not considered. We do not know how many years we are away from applying in reality bionanosensor networks; however, the authors show that the problems of target detection and tracking can be solved using the traditional methods of networking, just adding the appropriate constraints. This result is encouraging.