Key Words: Encounter-based Localization, Cooperative Localization, Rendezvous-based Localization
- How to achieve encounter detection: Audio, Wi-Fi
- How to obtain encounter location: Audio beacon, Wi-Fi
- How to infer between encounters: Dead reckoning on IMU
- Mathematical methods: Graph (MDS)
- Another related topic is “Range-free” localization where only “connectivity” information is used for localization.
- One of the major differences of P2Loc compared with cooperative localization is that P2Loc relies purely on the communication between nodes, but not relie on anchors.
- Locating nodes with EASE, [INFOCOM 2003]
- Taxonomy: encounter detection method not mentioned,
- Opportunity: “Distance Effect”: If the node is far away from the destination, an imprecise estimate is sufficient, and vice versa.
- (1) Intellectual Novelty: only used the location and time of last encounter of the nodes to infer the locations of all nodes;
- (2) Pros. compare with SoA: No communication overhead is needed between nodes;
- (1) This is a theoretical paper based on simulation, but the idea, observation, and conclusion are valuable.
- (2) Technical details like how to detect encounter, how to obtain node’s location are not discussed.
- Cooperative Localization in Wireless Networks, [IEEE 2009]
- Taxonomy: Comprehensive/Survey paper on cooperative localization
- Highly cited paper (>1000), one of the pioneer works on cooperative localization.
- Cooperative localization: localize a node with the help of other nodes instead of purly relying in anchors.
- Cooperative localization is one of the related topics of encounter-based localization.
- Good taxonomy and summary.
- Did You See Bob? [MobiCom 2010, Escort]
- Taxonomy: Audio for encounter detection, Dead reckoning on IMU
- (1) One of the ealiest work on “encounter-based localization”;
- (2) No need of calibration, war-driving and floor plan;
- (1) Need deploy audio beacons in the building;
- Collaborative PDR Localisation with Mobile Phones, [ISWC 2011]
- Taxonomy: no fixed encounter detection methods, using problistic methods for localization;
- (1) Intellectual Novelty: use encounter information to improve dead reckoning accuracy.
- Push the Limit of WiFi based Localization for Smartphones, [MobiCom 2012]
- Taxonomy: Acoustic for encounter detection, Wi-Fi for localization;
- (1) Observation: Discover the root cause of large errors in Wi-Fi fingerprinting works: existence of faraway locations sharing similar radio signatures;
- (2) Intellectual Novelty: Wi-Fi+Acoustic for localization;
- Encounter Based Sensor Tracking, [MobileHoc 2012, EBT]
- Taxonomy: Dead reckoning on IMU to infer between encounters, Graph (MDS)
- Core Idea: Treats encounters as constraints in a graph realization problem, minimizing positional estimates in an offline manner.
- (1) Intellectual Novelty: model the encounter-based localiazation as a graph realization problem.
- (1) encounter detection is aussumed to achieve by radio;
- (2) Only simulation, no in-field experiment conducted.
- Social-Loc: Improving Indoor Localization with Social Sensing, [SenSys 2013, Social-Loc]
- Taxonomy: Dead reckoning, Wi-Fi
- (1) Intellectual Novelty: use encounter and non-encounter information to calibrate the underlying localization erros.
- (2) Technical Novelty: a probabilistic approach to cooperatively calibrate the locations.
- (3) Experimental Novelty: implement the system on Android.
- (1) Limitations: is a middleware based on existing localization methods.
- (1) Not purely theoretical paper, but also not a pure system.
- (2) Specifically, its’ a middleware, and the design is implemented using with dead reckoning and Wi-Fi based methods.
- Social Spring, [BuildSys 2014]
- Taxonomy: Graph (MDS)
- (1) Intellectual Novelty: model the encounter as springs in a graph.
- (1) Only 1 citation untial 2021/03.
- (2) Need distance information between users.
- CoSMiC: Mobile Crowd-sourced Collaborative Ap plication to Find a Missing Child, [MobileHCI 2014]
- Taxonomy: Wi-Fi for encounter detection and localization
- (1) Problem Novelty: a crowd-sourced collaborative application to help parents find a missing child quickly.
- (2) Technical Novelty: New Recover the trace of the missing child;
- (1) “Virtual encounter” instead of “Physical encounter” is considered in the paper. “Virtual encounter” is obtained through Wi-Fi-based localization.
- (2) It’s more like a “CHI” paper instead of a “Mobile Computing” paper.
- Spring Model Based Collaborative Indoor Position Estimation with Neighbor Mobile Devices, [IEEE 2015]
- Taxonomy: Wi-Fi for localization, BLE for encounter detection
- M2M Encountering, [Sensor Journal 2016]
- Taxonomy: ZigBee for localization, Wi-Fi for encounter detection;
- BLE-based Collaborative Indoor Localization, [WCNC 2016]
- Taxonomy: BLE for localization (iBeacon devices), BLE for encounter detection
- Peer-to-Peer Indoor Navigation Using Smartphones, [IEEE 2017]
- Taxonomy: Wi-Fi (fingerprints) for localization
- Navigation work, based on previous user’s trace.
- NOT an encounter-based work.
- WAIPO: A Fusion-Based Collaborative Indoor Localization System on Smartphones, [IEEE TON 2017]
- Comment: Too many data sources used: Wi-Fi, Photo, Magnetic.
- Crowdx, [IMWUT 2018]
- Taxonomy: IMU, Dead Reckoning
- Contributions: Use encounter event to calibrate dead reckoning to build indoor floor plan.
- Comment: Need IMU data for dead reckoning.
- Smartphone-based user positioning in a multiple-user context with Wi-Fi and Bluetooth, [IEEE IPIN 2018]
- Taxonomy: Wi-Fi for localization, BLE for encounter detection
- Contribution: Merge Wi-Fi and BLE data for localization
[INFOCOM 2003] Grossglauser, Matthias, and Martin Vetterli. “Locating nodes with EASE: Last encounter routing in ad hoc networks through mobility diffusion.” In IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No. 03CH37428), vol. 3, pp. 1954-1964. IEEE, 2003.
[IEEE 2009] Wymeersch, Henk, Jaime Lien, and Moe Z. Win. “Cooperative localization in wireless networks.” Proceedings of the IEEE 97, no. 2 (2009): 427-450
[MobiCom 2010, Escort] Constandache, Ionut, Xuan Bao, Martin Azizyan, and Romit Roy Choudhury. “Did you see Bob? Human localization using mobile phones.” In Proceedings of the sixteenth annual international conference on Mobile computing and networking, pp. 149-160. 2010.
[ISWC 2011] Kloch, Kamil, Paul Lukowicz, and Carl Fischer. “Collaborative PDR localisation with mobile phones.” In 2011 15th Annual International Symposium on Wearable Computers, pp. 37-40. IEEE, 2011.
[MobiCom 2012] Liu, Hongbo, Yu Gan, Jie Yang, Simon Sidhom, Yan Wang, Yingying Chen, and Fan Ye. “Push the limit of WiFi based localization for smartphones.” In Proceedings of the 18th annual international conference on Mobile computing and networking, pp. 305-316. 2012.
[MobileHoc 2012, EBT] Symington, Andrew, and Niki Trigoni. “Encounter based sensor tracking.” In Proceedings of the thirteenth ACM international symposium on Mobile Ad Hoc Networking and Computing, pp. 15-24. 2012.
[SenSys 2013, Social-Loc] Jun, Junghyun, Yu Gu, Long Cheng, Banghui Lu, Jun Sun, Ting Zhu, and Jianwei Niu. “Social-Loc: Improving indoor localization with social sensing.” In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, pp. 1-14. 2013.
[BuildSys 2014] Martin, Paul, Yasser Shoukry, Prashanth Swaminathan, Robin Wentao Ouyang, and Mani Srivastava. “Social spring: encounter-based path refinement for indoor tracking systems.” In Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings, pp. 156-159. 2014.
[MobileHCI 2014] Shin, Hyojeong, Taiwoo Park, Seungwoo Kang, Bupjae Lee, Junehwa Song, Yohan Chon, and Hojung Cha. “CoSMiC: Designing a mobile crowd-sourced collaborative application to find a missing child in situ.” In Proceedings of the 16th international conference on Human-computer interaction with mobile devices & services, pp. 389-398. 2014.
[IEEE 2015] Taniuchi, Daisuke, Xiaopeng Liu, Daisuke Nakai, and Takuya Maekawa. “Spring model based collaborative indoor position estimation with neighbor mobile devices.” IEEE Journal of Selected Topics in Signal Processing 9, no. 2 (2015): 268-277.
[Sensor Journal 2016] Qiu, Jun-Wei, and Yu-Chee Tseng. “M2M encountering: Collaborative localization via instant inter-particle filter data fusion.” IEEE Sensors Journal 16, no. 14 (2016): 5715-5724.
[WCNC 2016] Qiu, Jun-Wei, Chien-Pu Lin, and Yu-Chee Tseng. “BLE-based collaborative indoor localization with adaptive multi-lateration and mobile encountering.” In 2016 IEEE Wireless Communications and Networking Conference, pp. 1-7. IEEE, 2016.
[IEEE 2017] Yin, Zuwei, Chenshu Wu, Zheng Yang, and Yunhao Liu. “Peer-to-peer indoor navigation using smartphones.” IEEE Journal on Selected Areas in Communications 35, no. 5 (2017): 1141-1153.
[IEEE TON 2017] Gu, Fei, Jianwei Niu, and Lingjie Duan. “WAIPO: A fusion-based collaborative indoor localization system on smartphones.” IEEE/ACM Transactions on Networking 25, no. 4 (2017): 2267-2280.
[IMWUT 2018] Chen, Huijie, Fan Li, Xiaojun Hei, and Yu Wang. “Crowdx: Enhancing automatic construction of indoor floorplan with opportunistic encounters.” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, no. 4 (2018): 1-21.
[IEEE IPIN 2018] Ta, Viet-Cuong, Trung-Kien Dao, Dominique Vaufreydaz, and Eric Castelli. “Smartphone-based user positioning in a multiple-user context with Wi-Fi and Bluetooth.” In 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 206-212. IEEE, 2018.