A stateless opportunistic routing protocol for underwater sensor networks

Routing packets in Underwater Sensor Networks (UWSNs) face different challenges, the most notable of which is perhaps how to deal with void communication areas. While this issue is not addressed in some underwater routing protocols, there exist some partially state-full protocols which can guarantee the delivery of packets using excessive communication overhead. However, there is no fully stateless underwater routing protocol, to the best of our knowledge, which can detect and bypass trapped nodes. A trapped node is a node which only leads packets to arrive finally at a void node. In this paper, we propose a Stateless Opportunistic Routing Protocol (SORP), in which the void and trapped nodes are locally detected in the different area of network topology to be excluded during the routing phase using a passive participation approach. SORP also uses a novel scheme to employ an adaptive forwarding area which can be resized and replaced according to the local density and placement of the candidate forwarding nodes to enhance the energy efficiency and reliability. We also make a theoretical analysis on the routing performance in case of considering the shadow zone and variable propagation delays. The results of our extensive simulation study indicate that SORP outperforms other protocols regarding the routing performance metrics

Categories, Quantum Computing, and Swarm Robotics: A Case Study

The swarms of robots are examples of artificial collective intelligence, with simple individual autonomous behavior and emerging swarm effect to accomplish even complex tasks. Modeling approaches for robotic swarm development is one of the main challenges in this field of research. Here, we present a robot-instantiated theoretical framework and a quantitative worked-out example. Aiming to build up a general model, we first sketch a diagrammatic classification of swarms relating ideal swarms to existing implementations, inspired by category theory. Then, we propose a matrix representation to relate local and global behaviors in a swarm, with diagonal sub-matrices describing individual features and off-diagonal sub-matrices as pairwise interaction terms. Thus, we attempt to shape the structure of such an interaction term, using language and tools of quantum computing for a quantitative simulation of a toy model. We choose quantum computing because of its computational efficiency. This case study can shed light on potentialities of quantum computing in the realm of swarm robotics, leaving room for progressive enrichment and refinement