An autonomous Vehicle to Vehicle (V2V) communication mode, which allows V2V communication in out of eNB (Evolved Node B) coverage areas, has been recently introduced into the Long Term Evolution (LTE) standard. Recent research has studied the performance of this LTE V2V autonomous mode for an urban intersection use case, where non-line-of-sight (NLOS) communication links are present and it is observed that the overall message transmission performance in LTE V2V is found to degrade drastically in NLOS link conditions. To improve the performance, a vehicle-assisted relaying strategy was proposed. In this scheme, at each time instance, distance of each vehicle from intersection point is computed. Then the vehicle close to the intersection point is nominated as relaying vehicle and will rebroadcast the BSMs (Basic Safety Message) in a selective manner. However, security aspects while relaying the BSM is not being addressed in the suggested model. We cannot reject the possibility that a vehicle can be malicious. A malicious vehicle can manipulate its distance from the intersection point, thereby nominating itself as the relaying vehicle with malicious motives such as to use extra infrastructural faciltiy, curiosity in the messages sent by other vehicles, etc. This work proposes a secure algorithm for finding the most appropriate relaying vehicle to address this security vulnerability so that a vehicle can no longer falsify its location information.

Variation in the acoustic structure of vocal signals is important to communicate social information. However, relatively little is known about the features that receivers extract to decipher relevant social information. Here, we took an expansive, bottom-up approach to delineate the feature space that could be important for processing social information in zebra finch song. Using operant techniques, we discovered that female zebra finches can consistently discriminate brief song phrases ("motifs") from different social contexts. We then applied machine learning algorithms to classify motifs based on thousands of time-series features and to uncover acoustic features for motif discrimination. In addition to highlighting classic acoustic features, the resulting algorithm revealed novel features for song discrimination, for example, measures of time irreversibility (i.e., the degree to which the statistical properties of the actual and time-reversed signal differ). Moreover, the algorithm accurately predicted female performance on individual motif exemplars. These data underscore and expand the promise of broad time-series phenotyping to acoustic analyses and social decision-making.