Offloading Floating Car Data through V2V Communications

Transcription

1 Offloading Floating Car Data through V2V Communications Razvan Stanica (INSA Lyon), Marco Fiore (IEIIT - CNR), Francesco Malandrino (Politecnico di Torino) 3èmes Journées Nationales des Communications dans les Transports (JNCT) Nevers - 30 May 2013

2 Floating Car Data Mobility Trace Optimal Gain Degree-based Mechanism Degree-based with Confirmation Reservation-based Mechanism 1 Offloading Floating Car Data

3 Floating Car Data Up to 100 Electronic Control Units in a car Large amount of data produced, but not stored Possible applications: Fleet management Remote diagnostic Customized car insurance Collaborative urban sensing Road traffic monitoring Navigation systems 2 Offloading Floating Car Data

4 Floating Car Data Up to 100 Electronic Control Units in a car Large amount of data produced, but not stored Possible applications: Fleet management Remote diagnostic Customized car insurance Collaborative urban sensing Road traffic monitoring Navigation systems To enable such applications, data needs to be centralized and mined 2 Offloading Floating Car Data

5 Current model Tom Tom HD Traffic, Meihui TrafficCast, Peugeot Connect Apps 3 Offloading Floating Car Data

6 Current model Tom Tom HD Traffic, Meihui TrafficCast, Peugeot Connect Apps Very expensive: price limits the frequency of collection The advantage of the low penetration ratio vs. Market share and application success 3 Offloading Floating Car Data

7 Current model Tom Tom HD Traffic, Meihui TrafficCast, Peugeot Connect Apps Very expensive: price limits the frequency of collection The advantage of the low penetration ratio vs. Market share and application success can represent a serious challenge for both cellular operators (network capacity) and service providers (collection price) 3 Offloading Floating Car Data

8 Offloading Use of Vehicle-to-Vehicle communication 4 Offloading Floating Car Data

9 Offloading Use of Vehicle-to-Vehicle communication Local gathering and, perhaps, fusion of : reduced network load, reduced cost, more data 4 Offloading Floating Car Data

10 Offloading Use of Vehicle-to-Vehicle communication Local gathering and, perhaps, fusion of : reduced network load, reduced cost, more data Addressed questions: What is the possible gain brought by offloading? Is there a simple, practical mechanism for offload? 4 Offloading Floating Car Data

11 Vehicular mobility trace Largest freely available vehicular mobility dataset 24h on a 400km 2 region in the Köln area: more than 700k trips Synthetic trace respecting real macroscopic measures OpenStreetMap, SUMO, TAPAS, Gawron s relaxation For more information, see: S. Uppoor, O. Trullols-Cruces, M. Fiore, J.M. Barcelo-Ordinas, Generation and Analysis of a Large-scale Urban Vehicular Mobility Dataset, IEEE TMC Offloading Floating Car Data

12 V2V connectivity Each second in the trace results in a connectivity graph Two vehicles separated by less than R meters are considered connected (this does not imply a unit disk graph radio propagation model) This allows the detection of important properties: assortative network 6 Offloading Floating Car Data

13 Optimal gain Gain measured as percentage of vehicles that do not use the cellular uplink The offloading problem maps to a classical Minimum Dominating Set problem MDS is NP-hard, but bounded approximation algorithms exist More than 60% of the can be offloaded through V2V communication in more than 70 % of the cases 7 Offloading Floating Car Data

14 Impact of daytime Up to 90% gain in the time intervals 6:30am-9am and 3:30pm-7pm This second time interval maps to the daily peak in cellular traffic 8 Offloading Floating Car Data

15 Impact of geographical area The gain depends on the vehicular density, so it is not evenly distributed Some areas present a limited gain throughout the entire day, while a 95% gain can be achieved at peak hours in the central region 9 Offloading Floating Car Data

16 Looking for practical solutions Centralized MDS already NP-hard Many distributed algorithms proposed for backbone construction in wireless sensor networks Trade-off between the quality of the MDS approximation and the amount of needed communication Best algorithms find a dominating set after multiple communication rounds (a different message can be exchanged with each neighbor during a round) Convergence time too long for a dynamic vehicular network 10 Offloading Floating Car Data

17 Looking for practical solutions Centralized MDS already NP-hard Many distributed algorithms proposed for backbone construction in wireless sensor networks Trade-off between the quality of the MDS approximation and the amount of needed communication Best algorithms find a dominating set after multiple communication rounds (a different message can be exchanged with each neighbor during a round) Convergence time too long for a dynamic vehicular network We relax the MDS condition, and focus on convergence time 10 Offloading Floating Car Data

18 Degree-based mechanism Every vehicle recovers the useful from its neighbors through regular V2V beaconing (already standardized, transmitted on the control channel) A vehicle uses the cellular uplink with probability parameter of the mechanism node degree DB does not recover from all the vehicles Ratio of uncovered nodes: probability that a node has degree d 11 Offloading Floating Car Data

19 Degree-based mechanism k=1 k=2 With k=1, we obtain the same gain as for the MDS, but only 70% of vehicles are covered With k=2, 90% of the vehicles are covered, but the offloading gain decreases 12 Offloading Floating Car Data

20 Degree-based with confirmation A simple confirmation mechanism for 100% coverage: a node that uses the cellular uplink announces this to its neighbors Nodes uncovered at the end of a collection period transmit on the cellular uplink Maps to a cost optimization problem: nodes with less than k neighbors always transmit nodes relaying for their neighbors nodes transmitting at the end of the collection period 13 Offloading Floating Car Data

21 Degree-based with confirmation The optimal k value varies, but the differences between the gain obtained with an optimal k and a fixed k=2 are minimal A 20% difference compared with the optimal gain can be noticed at peak hours 14 Offloading Floating Car Data

22 Reservation-based mechanism Try to improve the performance of DB-C, while keeping 100% coverage Reservation period at the beginning of every collection period Each vehicle randomly chooses a slot to transmit a reservation message All the vehicles receiving a reservation message cancel their back-off In the ideal scenario the RB mechanism reaches the same solution as a centralized MDS heuristic In reality: limited number of slots, message collision, radio propagation problems 15 Offloading Floating Car Data

23 Reservation-based mechanism The performance of the mechanism quickly increases with the number of slots The gain matches the one obtained with a centralized MDS algorithm even in realistic scenarios 16 Offloading Floating Car Data

24 Conclusion Floating Car Data is becoming an important challenge for operators, providers and users We show that using V2V communication to offload can bring important benefits Three simple mechanism are discussed, and their performance indicates that offloading is feasible in practice Still a lot of work to do: penetration ratio, actual gain on the cellular network, actual gain for service providers, different offloading mechanisms 17 Offloading Floating Car Data

25 Offloading Floating Car Data through V2V Communications Razvan Stanica (INSA Lyon), Marco Fiore (IEIIT - CNR), Francesco Malandrino (Politecnico di Torino) 3èmes Journées Nationales des Communications dans les Transports (JNCT) Nevers - 30 May 2013