International audienceThe autonomous data collector is a role recentlyintroduced to improve the performance of Wireless SensorNetworks. When a prompt response for data processing andoffloading is necessary, i.e. in the case of event-driven networks,a mobile flying sink could be a good option for that role.In this paper, we introduce FreeFall, a distributed algorithmfor the autonomous navigation of a mobile collector through aWSN for on-demand data offloading that does not rely on anabsolute coordinate system. We show that, under fairly commoncircumstances, it is possible to set the trajectory of the mobilesink and fulfill the offloading requests without the needs ofadditional equipment installed on nodes.We show how our systemis preferable over more classical routing solutions especially in thepresence of localized generation of large amounts of information

Mitton, Nathalie

Ruggeri, Giuseppe

Zema, Nicola Roberto

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A GPS-less on-demand mobile sink-assisted datacollection in wireless sensor networks

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HAL Id: hal-01074486https://hal.inria.fr/hal-01074486Submitted on 28 Sep 2015HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.A GPS-less on-demand mobile sink-assisted datacollection in wireless sensor networksNicola Roberto Zema, Nathalie Mitton, Giuseppe RuggeriTo cite this version:Nicola Roberto Zema, Nathalie Mitton, Giuseppe Ruggeri. A GPS-less on-demand mobile sink-assisteddata collection in wireless sensor networks. Short Paper at Wireless Days (WD), IFIP / IEEE, Nov2014, Rio de Janeiro, Brazil. ￿hal-01074486￿A GPS-less on-demand mobile sink-assisted datacollection in wireless sensor networksNicola Roberto ZemaUniversità Mediterranea di Reggio CalabriaEmail: nicola.zema@unirc.itNathalie MittonInria, FranceEmail: nathalie.mitton@inria.frGiuseppe RuggeriUniversità Mediterranea di Reggio CalabriaEmail: giuseppe.ruggeri@unirc.itAbstract—The autonomous data collector is a role recentlyintroduced to improve the performance of Wireless SensorNetworks. When a prompt response for data processing andoffloading is necessary, i.e. in the case of event-driven networks,a mobile flying sink could be a good option for that role.In this paper, we introduce FreeFall, a distributed algorithmfor the autonomous navigation of a mobile collector through aWSN for on-demand data offloading that does not rely on anabsolute coordinate system. We show that, under fairly commoncircumstances, it is possible to set the trajectory of the mobilesink and fulfill the offloading requests without the needs ofadditional equipment installed on nodes. We show how our systemis preferable over more classical routing solutions especially in thepresence of localized generation of large amounts of information.Keywords—WSN, Controlled Mobility, GPS-less.I. INTRODUCTIONThe use of controlled mobility as a degree of freedom hasbeen found as one of the most prominent methods to easethe energy consumption, increase the lifetime and enhance theperformances in a wireless sensor network [1]. For instance,sensors nodes around fixed data sinks deplete sooner theirenergy because of an increased traffic flow through them, andit is created an energy hole. This problem could be solvedusing a mobile collector [2], [3]. In a WSN where datageneration is linked to “events”, there would be nodes wherethe generation rate increases whenever their sensors detectenvironmental changes [4], and suddenly an huge amount ofdata has to be rapidly offloaded to a sink. Again the use ofa mobile collector has proven to be effective to improve theoffloading process. In fact, by using the mobile collector, mostof the transmission are carried on sigle hop connection. As animmediate consequence, the data offloading rate is increasedand the energy consumption is reduced.In [5] it has been shown that a good possibility for theimplementation of a controlled mobility framework is whenthe network itself drives the sink trajectory in a completelydistributed manner exploiting geographic and geometricalproperties. In this paper, following these previous studies, weintroduce FreeFall, a novel distributed GPS-less method for amobile collector which trajectory is driven by the ’off-loading’requests issued by the network sensor nodes without the aidof a global positioning system installed in them. More indetail, according to the FreeFall approach the nodes interestedto transmit their data explicitly advertise their position to amobile sink. The mobile sink will be capable to set its ownThis work has been partially supported by European Union (EU), European Social Fund (ESF), Calabria LocalGovernment and CPER CIA. This paper reflects the views only of the authors, and the EU, the ESF, Calabria LocalGovernment and Inria cannot be held responsible for any use which may be made of the information contained therein.waypoints in order to reach the interested areas using onlyan inertial navigation system. To the best of our knowledge,the only strictly similar work in the literature is our previousDirected Movement approach, introduced in [2], which relieson the same principles than FreeFall but which assumes thatnodes are aware of their absolute geographical coordinates.The location discovery system used in FreeFall does not usecomplex and expensive absolute positioning system (GPS) nordirection finding devices. FreeFall has been evaluated througha simulation study where the performances of our system havebeen compared with significant related works. Our approachshows a decreased energy expenditure over more commonsolutions for data delivery and an improvement in the end-to-end delay when the number of sources is limited.II. MAIN ASSUMPTIONSThe main assumptions for our framework to be effectiveare:(i) the network is composed by static sensor nodes and amobile sink (either a ground robot or a flying drone); (ii) itis dense enough to uniformly cover the whole sensing areagiven the capabilities of a general sensor networking device;(iii) each node is able to infer the relative position of itsneighbors using an r-map [6]. The r-map is the implementationof a polar coordinate system specifying the relative locationof the neighbors of a node from its point of view. Thanksto the implementation of a r-map, every node i sets its ownreference originate axis, later referred as→xi. Through therelative positions of nodes inferred by the r-map, each node iis able to estimate in this reference system for every of itsneighbor j both the angle αij =→̂xi,→ij and the Euclideandistance diij between nodes i and j in the reference systemof i. A detailed algorithm for the construction of an r-map isdescribed in [6].III. OUR CONTRIBUTIONWhen a source s has to offload data, it needs to advertisethe sink S. As the sink is mobile and s has no informationabout its mobility pattern, s is not able to reach it directly. Itwill thus send a request in the network using a source adver-tisement packet. Specifically we suppose that each advertise-ment packet is forwarded along two perpendicular directions asin [7]. Also the sink advertises its position by using a sink ad-vertisement packet. Once again sink advertisement packets areforwarded along two perpendicular directions. Eventually, bothsource advertisement packet and sink advertisement packetreach a common node that will forward the request to thesink with a backtrace advertisement packet, as illustrated onFigure 1-b. More details about the aforementioned process willbe given in the following sections, however, for the sake ofclarity the following description is restricted to a single sendingdirection which forms an angle β with the reference axis→xiof the node i.Neighbor of interest: Given a node i, a node j is saidto be a Neighbour of Interest of node i in respect to thepropagation angle β if |β−αiij | < 90◦ and diij×cos(β−αiij) <∆width,i; with ∆width,i set as the communication range of thenodes. As an example, in Figure 1-a, the source node has threeneighbors: N1, N2 and N3 but only N1 and N2 are selectedas Neighbour of Interest since laying in the ∆width-wide stripalong the direction chosen by the source.A. Source and sink advertisement packetsLet’s assume a source s needs to offload data. First of allnode s determines a random direction along which to send itsrequest, identified by the angle β, then determines all its Neigh-bor of Interest in respect the same angle β and, finally a sourceadvertisement packet is sent every ∆T sadv to the Neighbors ofInterest. By properly selecting the Neighbor of Interest it isassured a source advertisement packet is forwarded followingthe direction which has been previously determined. Thesource advertisement packet contains the source node ID, thepropagation angle β, the list of the forwarders, dtrans(si) anddlong(si). The meaning of source node ID, the propagationangle β is straightforward. The list of the forwarders is meantto contain the list of the nodes which have already forwardedthe packet and it is used for the backtrace process. Of coursewhen a packet is freshly generated this list is empty and itwill be populated by the forwarders themselves. dtrans(sj)and dlong(sj) are the longitudinal and transversal distancebetween the last node j which processed the packet and theSource s which had generated the packet. More in details,the transverse distance is the distance between node j andits projection on the β axis. The longitudinal distance is thedistance between the source and the projection of the nodeon the β axis. For instance, on Figure 1-a, the transversedistance of N1 is D1 and its longitudinal distance is D2. Againwhen a packet is freshly generated those distances are zero.Every time a packet is forwarded by a node j dtrans(sj) anddlong(sj) are updated accordingly. It should be noted that invery dense networks, where the list of Neighbors of Interest islong it could be important to properly prune that list in orderto avoid jeopardizing the scalability of FreeFall. In this workthe number of neighbors has randomly selected at each ∆T sadvand a smarter pruning strategy is left as a future work. Fromits side, the sink applies the same algorithm and issues a sinkadvertisement packet.B. Advertisements processingUpon receiving an advertisement packet a node j first itchecks the forwarder list to ensure that the packet it is not aduplicate. If the packet is a duplicate it is discarded and noother action is taken. If it is not the case then node j checks fora matching advertisement, i.e. if the received packet is a sourceadvertisement a corresponding sink advertisement is searched,if the received packet is a sink advertisement a correspondingsource advertisement is searched for. If a matching is found abacktrace packet is generated and no other action is taken. Ifnothing is found in the buffers then node j forward the packetsagain. In order to do so node j has (i) to determine how thedirection β relates to its own reference axis→xj . This can be(a) node-level detail. (b) network-level detail.Fig. 1. Working Scenariodone by leveraging the values dij and αij contained in ther-map; (ii) update the values of dtrans(sj) and longitudinaldlong(sj) or the equivalent in case of a sink; (iii) add itselfto the forwarder list. To show ho the values of dtrans(sj) andlongitudinal dlong(sj) are computed let assume that a node jreceives from node i a packet which has been produced by theSource s then: dtrans(sj) = dtrans(si) + djij × cos(β − αjij)and dlong(sj) = dlong(si) + djij × sin(β − αjij). The sign ofdjij can be negative in case of axis crossing.C. Backtrace packet generation and forwardingWhen it have both the source and sink advertisementpackets, node j has already the estimated transverse and lon-gitudinal distances to the source(s) (djtrans(sj) and djlong(sj))and the sink(s) (djtrans(Sj) and djlong(Sj)). From them, it canestimate αj(sS) as following:αj(sS) = βj(S)−βj(s)−arctan(djtrans(sj)djlong(sj))−arctan(djtrans(Sj)djlong(Sj))Node j generates a backtrace packet with the sink Id, αj(sS),dj(jS), dj(js) . The backtrace packet is sent back followingthe reverse path of the sink advertisement.D. Backtrace Packet reception at sinkThe sink takes a decision about its movements every∆movement seconds. If one or more backtrace packets havebeen received during this interval each one of the will beprcessed as follows. Using the laws of sines and cosines thesink extrapolates the remaining angles and sides of the trianglewhose vertexes are the original source, the mobile sink andthe node which generated the backtrace packet . Hence thesink will be able to calculate the direction and the distanceto travel for reaching the source as in Figure 1-b. In caseof multiple sources, the node stores information about themand creates a distance-ranked list of way points using simplevectorial operations, closest first Just after the expiration of∆tmovement control the sink set its course to the first node inthe list and, upon arrival, starts the data transfer procedure.While in the movement phase the sink will not receive anyother packets but will resume the network operation uponarrival and store eventual new information. At the end of thedata transfer it will move again to a new destination if present.IV. RESULTSWe run a set of simulation under the WSNet environment[8]. We set a topology of 600 nodes distributed uniformly ona 500m × 500m square area . Nodes use a single-channel,ricean faded, 2400MHz bpsk 802.15.4 compliant MAC andradio interfaces. The values of ∆tmovement control is set asa 802.15.4 beacon-enabled time-slot and ∆Tadv as the formermultiplied by the network diameter. We compare FreeFall with(i) our previous GPS-driven approach [2], where the nodesuse the absolute position system to send the advertisementpackets; (ii) the plain Greedy Geographical Routing (iii) arandom mobility framework later referred as RandMob wherethe mobile sink will move randomly around the networkand, if it gets into contact with a source, it will start thedata offloading procedure. The signaling times are kept thesame of the other mobility frameworks. Using FreeFall andGPS-driven, the sinks and the sources signal each other’spresence and then the former will reach the selected positionsto transfer the data. We are fist interested in the capabilityof our FreeFall to handle flows of multiple sizes. To thispurpose, a source and a sink are set at the two oppositecorners of the topology and a bursty, acked application isactivated between them. Using the maximum capabilities, interms of packets size and transmission rate possible for the802.15.4 MAC, the source have to deliver to the network astream of packets which sum is respectively of 0.25, 0.5, 1and 1.5 MB, representing the application data. Each valueis the result of a 30 simulative runs which outputs wereprocessed with 95% confidence intervals and shown whereapplicable. According to [9] we set the maximum speed ofthe sink to 60km/h. Figure 2 displays the time difference,at the source, between the first data packet enqueuing (andthe activation of the delivery systems) and the reception ofthe ack for the last packet. FreeFall shows a great reductioneither versus the routed approach and the random mobility. Iteven outperforms the GPS-driven approach as ,geometrically,the latter configures a specific case of the former when thetwo corridors intersect at a right angle, maximizing the hopdistance. Figure 3 shows the same evaluation in the case ofmultiple traffic flows with up to 4 source nodes that need tooffload 1 MiB of application data. In terms of sheer trafficconsumption, the Random Mobility is the best option but, asshown in the figures that the time necessary for complete thetransfer up to four sources, is much higher and it represent thepossibility of completely missing the source or the data. 0 100 200 300 400 500 600 700 800 9000.25 0.5 1 1.5Time in secondsBurst Size [MB]Time to end transferGreedy GeographicGPS-DrivenFreeFallRandMobFig. 2. Time to end data transfer for different burst sizes.V. CONCLUSIONS AND FUTURE WORKSIn this paper, we have introduced FreeFall, a completelydistributed, practical signaling and movement protocols for amobile sink to set a trajectory across the network and perform 0 500 1000 1500 20001 2 3 4Time in secondsSources of TrafficTime to end transferGreedy GeographicGPS-DrivenFreeFallRandMobFig. 3. Time to end data transfer for different number of sources.data collection in a dense Wireless Sensor Network in whichnodes have to offload non-negligible amount of data. Oursystem is remarkable because it does not use any absolute po-sitioning system and can be employed without additional coston the nodes and in all those environments where, for instance,GPS is not available. There are, however, some issues that weplan to address in our future research. A multi-sink variantshould be investigated together with a framework to make theminteroperate, efficiently schedule the source visitation orderand the information buffering space needed to avoid starvationand improve fairness. FreeFall should implement an obstacleavoidance system that would make the packets route aroundcoverage holes and also a careful tune of the parameters, anaccurate evaluation of either the mobility inherent cost and theevaluation of a noisy r-map is needed.REFERENCES[1] S. Gao, H. Zhang, and S. K. Das, “Efficient data collection in wirelesssensor networks with path-constrained mobile sinks,” Mobile Computing,IEEE Transactions on, vol. 10, no. 4, pp. 592–608, 2011.[2] N. R. Zema, N. Mitton, and G. Ruggeri, “Using location services toautonomously drive flying mobile sinks in wireless sensor networks,” inInt. Conf. on Ad Hoc Networks (AdHocNets), Rhodes, Greece, 2014.[3] W. Liang, J. Luo, and X. Xu, “Prolonging network lifetime via acontrolled mobile sink in wireless sensor networks,” in Global Telecom-munications Conference (GLOBECOM),, Austin, USA, 2010.[4] L. Filipponi, A. Vitaletti, G. Landi, V. Memeo, G. Laura, and P. Pucci,“Smart city: an event driven architecture for monitoring public spaceswith heterogeneous sensors,” in Int. Conf. on Sensor Technologies andApplications (SENSORCOMM), 2010.[5] C. Tunca, S. Isik, M. Donmez, and C. Ersoy, “Distributed mobile sinkrouting for wireless sensor networks: A survey,” Communications SurveysTutorials, IEEE, vol. 16, no. 2, pp. 877–897, Second 2014.[6] Y. Bejerano, “Simple and efficient k-coverage verification without loca-tion information,” in Int. Conf. on Computer Communications (INFO-COM), Phoenix, USA, 2008.[7] G. Shi, J. Zheng, J. Yang, and Z. Zhao, “Double-blind data discoveryusing double cross for large-scale wireless sensor networks with mobilesinks,” IEEE Trans. Vehicular Technology, vol. 61:5, 2012.[8] A. Fraboulet, G. Chelius, and E. Fleury, “Worldsens: Development andprototyping tools for application specific wireless sensors networks,” inProc. Int. Conf. on Information Processing in Sensor Networks, ser.IPSN, New York, NY, USA, 2007, pp. 176–185.[9] E. Natalizio, R. Surace, V. Loscri, F. Guerriero, and T. Melodia, “FilmingSport Events with Mobile Camera Drones: Mathematical Modeling andAlgorithms,” Research Report, Dec. 2012.

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A GPS-less on-demand mobile sink-assisted datacollection in wireless sensor networks

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