In time-slotted molecular communication systems, clock synchronization between transmitter and receiver is crucial. In this paper, training-based synchronization schemes in a molecular communication system with quantity-based modulation are proposed by utilizing the information of arrival times of molecules. For timing offset estimation, three estimators are proposed, including maximum likelihood estimation (MLE), unbiased linear estimation (ULE), and iterative ULE. For clock skew estimation, two algorithms, arithmetic mean estimation and weighted mean estimation, are proposed. Simulation results demonstrate that the proposed estimators are effective.

This paper proposes a lane-level beacon-less, infrastructure-less, and GPS-less cooperative collision avoidance (BIG-CCA) framework for preventing rear-end collisions. The BIG-CCA framework applies vehicular sensor networks to prevent chain vehicle collisions, which are common road accidents that occur when vehicles make sudden stops. BIG-CCA enables vehicles equipped with only onboard sensors to prevent such accidents. Based on extensive research, BIG-CCA is the first lane-level CCA solution that provides the following features: 1) BIG-CCA does not maintain a list of neighboring vehicles through beacons and, thus, the overall signaling overhead can be reduced to conserve bandwidth; 2) BIG-CCA does not use the GPS positions of vehicles to facilitate collision avoidance and, thus, the inaccuracy and unavailability of a GPS can be avoided; and 3) BIG-CCA does not rely on costly roadside infrastructures but employs only vehicle-to-vehicle communications to form warning groups for vehicles driving along in the same lane. BIG-CCA consists of a distributed grouping mechanism and a receiver-based forwarding scheme. Vehicles join or leave a warning group through only single-hop transmissions with the low overhead of group maintenance. When sudden braking occurs in a warning group, the proposed receiver-based forwarding scheme can specify a single forwarder without message contention. The performance of BIG-CCA is evaluated by conducting mathematical analysis and computer simulations, which outperform existing methods. An Android-based prototype is also implemented to verify the feasibility of BIG-CCA.

In this paper, we design and implement a lane-level cooperative collision avoidance (LCCA) system using vehicle-to-vehicle communications. The LCCA system applies vehicular sensor networks to preventing chain vehicle collisions, which allows vehicles with merely onboard sensors to prevent such collisions on the road because of sharp stops. To the best of our knowledge, this is the first CCA system that does not use inaccurate GPS locations and costly roadside infrastructures to avoid chain vehicle collisions. LCCA employs inter-vehicle communications and onboard sensing to form warning groups, where each warning group is a set of vehicles that drive along the same lane and every pair of adjacent cars is within a certain distance. Only single-hop transmissions are needed to join/leave a warning group, thus keeping the group maintenance overhead low. When a sudden braking is taken in a warning group, LCCA can quickly propagate warning messages among group members. This paper demonstrates our current prototype.