The use of wireless sensor networks (WSNs) for emergency navigation systems
suffer disadvantages such as limited computing capacity, restricted battery
power and high likelihood of malfunction due to the harsh physical environment.
By making use of the powerful sensing ability of smart phones, this paper
presents a cloud-enabled emergency navigation framework to guide evacuees in a
coordinated manner and improve the reliability and resilience in both
communication and localization. By using social potential fields (SPF),
evacuees form clusters during an evacuation process and are directed to
egresses with the aid of a Cognitive Packet Networks (CPN) based algorithm.
Rather than just rely on the conventional telecommunications infrastructures,
we suggest an Ad hoc Cognitive Packet Network (AHCPN) based protocol to prolong
the life time of smart phones, that adaptively searches optimal communication
routes between portable devices and the egress node that provides access to a
cloud server with respect to the remaining battery power of smart phones and
the time latency.Comment: This document contains 8 pages and 3 figures and has been accepted by
  ISCIS 2014 (29th International Symposium on Computer and Information
  Sciences

B Horling

E Gelenbe

H Bi

JH Reif

L Pelusi

L Yang

N Dimakis

X Zheng

Y Cao

English

arXiv

Huibo Bi

Erol Gelenbe

Crossref

A Cooperative Emergency Navigation Framework Using Mobile Cloud Computing

The use of wireless sensor networks (WSNs) for emergency navigation systems suffer disadvantages such as limited computing capacity, restricted battery power and high likelihood of malfunction due to the harsh physical environment. By making use of the powerful sensing ability of smart phones, this paper presents a cloud-enabled emergency navigation framework to guide evacuees in a coordinated manner and improve the reliability and resilience in both communication and localization. By using social potential fields (SPF), evacuees form clusters during an evacuation process and are directed to egresses with the aid of a Cognitive Packet Networks (CPN)-based algorithm. Rather than just rely on the conventional telecommunications infrastructures, we suggest an Ad hoc Cognitive Packet Network (AHCPN)-based protocol to prolong the life time of smart phones, that adaptively searches optimal communication routes between portable devices and the egress node that provides access to a cloud server with respect to the remaining battery power of smart phones and the time latency

Bi, H

Gelenbe, E

Spiral - Imperial College Digital Repository

arXiv:1406.0775v1  [cs.NI]  3 Jun 2014A Cooperative Emergency NavigationFramework using Mobile Cloud ComputingHuibo Bi and Erol GelenbeAbstract The use of wireless sensor networks (WSNs) for emergency navigationsystems suffer disadvantages such as limited computing capacity, restricted bat-tery power and high likelihood of malfunction due to the harsphysical environ-ment. By making use of the powerful sensing ability of smart phones, this paperpresents a cloud-enabled emergency navigation framework to guide evacuees in acoordinated manner and improve the reliability and resilience in both communica-tion and localization. By using social potential fields (SPF), evacuees form clustersduring an evacuation process and are directed to egresses with the aid of a Cogni-tive Packet Networks (CPN) based algorithm. Rather than just rely on the conven-tional telecommunications infrastructures, we suggest anAd hoc Cognitive PacketNetwork (AHCPN) based protocol to prolong the life time of smart phones, thatadaptively searches optimal communication routes betweenportable devices andthe egress node that provides access to a cloud server with respect to the remainingbattery power of smart phones and the time latency.Key words: Coordinated emergency navigation; Infrastructure-less;Cloud com-puting; Ad hoc Cognitive Packet Networks1 IntroductionWith the rapid development of sensor technology, emergencyvacuation in builtenvironments requires time-critical response with respect to multi-domain sens-ing, data interpreting and information transmitting. WSN based emergency man-agement systems cannot provide optimal solutions due to their limited computingcapability, battery power and storage capacity. Although the evolution from homo-geneous architecture with functionality-identical sensors t heterogeneous architec-Imperial College London Department of Electrical and Electronic Engineering Intelligent Systemsand Networks Group, e-mail:{huibo.bi12,e.gelenbe}@imperial.ac.uk12 Huibo Bi and Erol Gelenbetures with separated sensing and decision subsystems makesWSN based systemsmore energy-efficient and fault-tolerant, most decision supporting subsystems thatconsist of lightweight decision nodes still suffer from resource restriction problems.Owing to the high processing power and low risk level, cloud-computing hasbecome a dominating technology and tends to revolutionise the mergency manage-ment landscape. Accompanying this tendency, a new interesthas been aroused toconsider smart phones as simple clients for the back-end Cloud due to their sensingability and popularity [1]. For example, Ref. [2] presents an emergency navigationsystem based on smart phones and active temperature RFID sensor tags to calcu-late evacuation routes and intensive computations are offloaded to a cloud server.Due to the limitation of indoor positioning and unavailability of GPS, in [3] a smartphone assisted system to locate evacuees with pedometry-based localization andimage-based positioning is suggested. The cloud based server can obtain the posi-tion of individuals by matching the image snapshots uploaded by evacuees and thenprovide uncrowded routes for users. However, current cloudenabled emergency re-sponse systems with the aid of portable devices do not consider the impact of thesignificant energy consumption in the client side during thecommunication process.On the other hand smart phones using the UMTS protocol can easily come underattack [4, 5] including various forms of denial of service attacks [6].Much research has demonstrated cooperative strategies hava significant posi-tive influence on multi-agent systems and related studies have emerged for decadesas organizational paradigms in agent based structures [7] and autonomous searchby large scale robotic systems [8, 9, 10]. However, most research on emergencyevacuation focuses on developing different models [11] to simulate the crowd or co-ordinated behaviours such as kin behaviour [12]. Although previous work [13] hasproposed a resilient emergency support system (ESS) with the aid of opportunisticcommunications [14] to study the impact of “passive” cooperation among evacueeswhich exchange emergent messages with other civilians within the communicationrange, no active mechanism is used to improve the information dissemination effi-ciency.Thus, in this paper we propose a cloud based emergency navigation system whichuses a cooperative strategy to egress evacuees in loose clusters, and we also presenta power-aware quality of service (QoS) metric to prolong thelife time of smartphones used in an evacuation to connect to the access point(s) f he cloud systemthat carries out intensive computations.The remainder of the paper is organized as follows: In Section 2 we recall theconcept of Cognitive Packet Networks, and then describe related algorithms in Sec-tion 3. The simulation model and assumptions are introducedin Section 4 and resultsare presented in Section 5. Finally, we draw conclusions in Section 6.A Cooperative Emergency Navigation Framework using MobileCloud Computing 32 Cognitive Packet NetworkThe Cognitive Packet Network (CPN) introduced in [15, 16, 17] was originallyproposed for large-scale and fast-changing packet networks. By employing cog-nitive or smart packets (SPs), CPN can discover optimal routes rapidly and heuris-tically and realise continuous self-improvement. Contrary to conventional routingprotocols, in the CPN, intelligence is realised by using Random Neural Networks[18, 19, 20, 21, 22, 23] and is constructed into smart packetsother than protocols.Hence, cognitive packets can discover optimised routes with their predefined goalsand improve QoS by learning from their own investigations and experience fromother packets.The AHCPN [24] is a variant of the original CPN. It can replacemost of broad-cast transmissions with unicast transmissions and adapt tohighly dynamic Ad hocnetwork environments rapidly.3 The Cloud-enabled Emergency Navigation frameworkThe framework contains a user layer which is composed of evacuees with portabledevices and a Cloud layer which performs intensive computations. In the followingsubsections, we concentrate on the coordinated emergency navigation algorithm andthe energy efficient protocol due to the space constraints.3.1 Coordinated Emergency NavigationEvacuating in a cooperative manner can increase the probability for evacuees to ob-tain assistance when at risk and reduce energy utilization of smart phones by relay-ing data. In this section, we leverage the SPF to cluster evacu es into groups duringan evacuation. The initial SPF algorithm [25] consists of glbal control forces andlocal control forces. The global control forces coordinatethe individuals and deter-mine the distribution of the individuals while the local control forces dominate thepersonal behaviours. In our treatment, we replace the localcontrol forces with theCPN based algorithm to control the individual behaviour of evacuees and use theglobal forces to regulate intra-group behaviours. Detailsof the CPN based emer-gency evacuation algorithm can be seen in [26] and [27]. The force between twoevacuees can be calculated from the following equation:f (r) =−c1rσ1+c2rσ2(1)wherec1, c2, σ1 andσ2 are constants andr is the distance between two evacuees.Term− c1rσ1 presents the repulsive force whilec2rσ2 depicts the attractive force. Weassume that each civilian can only be affected by other civilians within 20 meters.4 Huibo Bi and Erol GelenbeIf the distance between two civilians is smaller than 7 meters, a repulsive force willbe generated, otherwise, a attractive force will be produce. To achieve this, we setc1 to 20,c2 to 15,σ1 to 0.9478 andσ2 to 0.8, respectively. This ensures the civiliansto evacuate in loose groups without increasing the level of congestion.To combine the SPF and the CPN based algorithm, we use a simplescheme torandomly choose either the decision of the SPF or the CPN as the next decision ata time. When a SPF related decision is chosen, we adopt the associ ted neighbournode which has the most matched direction with the resultantforce as the next hop.3.2 Power-aware ProtocolAs the front-end component of the cloud enabled framework, the operational timeof smart phones becomes a bottleneck of the system due to the larg amount ofenergy consumption during information exchanges. Becausethe remaining batterypower of the portable devices yields normal distribution when an emergency eventoccurs and the power consumption of different communication m des varies, it isnot a optimal strategy for each smart phone to exchange information with the Cloudthrough 3G directly. To realise energy efficiency and maximise the average batterylifetime of smart phones, we employ an AHCPN based algorithmto relay sensorydata before ultimately uploading to the Cloud. The AHCPN based algorithm is de-ployed on the smart phones to search power-saving paths to convey sensory datato the Cloud. The QoS criterion we used is inspired by the energy aware metric in[24]. Here we also employ the “path availability” notion andconstruct the metric asfollows.Ged = αn−1∏i=0Pa(π(i),π(i +1)){n−1∑i=0D(π(i),π(i +1))}(2)whereπ represents a particular path,n is the number of nodes (smart phones) onthe pathπ , andπ(i) is the i-th node on the pathπ . TermPa(π(i),π(i + 1)) is theavailability of the edge betweenπ(i) andπ(i+1). D(π(i),π(i+1)) is the delay costfor a packet to transmit fromπ(i) to π(i +1). Termα is a constant that coordinatesthe relation between the path availability and delay.Path availability is affected by the remaining battery power of a smart phone andthe estimated power consumption of transmitting a piece of inf rmation:Pa(π(i),π(i +1)) =BCiBCi −BUi(3)whereBCi represents the current remaining battery power of a node (smart phone)π(i) andBUi depicts the power utilisation at nodeπ(i) to convey a certain piece ofinformation. If the potential power utilisation is larger than the remaining battery,this node will be excluded.A Cooperative Emergency Navigation Framework using MobileCloud Computing 54 Simulation Model and AssumptionsSince mathematical models [28] cannot handle the full complexity that is encoun-tered during an emergency, the performance of the proposed algorithms is evaluatedin fire-related scenarios based on the Distributed BuildingEvacuation Simulator(DBES)[29, 30]. The building model is a three story canary wharf shopping mallas shown in Fig. 1. Initially, evacuees are randomly scattered in the building andare equipped with smart phones with random remaining battery power. When thebattery power of a smart phone is depleted, the evacuee will wander or follow otherevacuees in the line of sight to exits.Fig. 1 Graph representation of the building model: vertices are positions with landmarks that evac-uees can easily identify their locations by uploading snapshots and matching them with pre-knownlandmarks which are stored in cloud severs, edges are physical links that evacuees can move insidethe building. The two black stars on the ground floor mark the position of the building’s exits.We assume that no related sensors are pre-installed in the built environment.Hence, hazard information and location of civilians will becollected by mobilephones with built-in cameras and analysed in the Cloud. We also hypothesis that in-door communication infrastructures such as Wi-Fi access points are unavailable buta few cloud access points can be rapidly deployed between theemergency locationand the Cloud when a hazard occurs. According to literature [31, 32, 33, 34], theenergy consumption model of an individual smart phone is shown in Table 1.Communication modelDownload Upload Signal Rate3G 0.001224x 0.0003375x 2 Mb/sBluetooth 0.0001377x 0.00012012x 1 Mb/sTable 1 The energy consumption of a smart phone in Joule with regard to the transferred datax inbyte and the related signal rate.6 Huibo Bi and Erol Gelenbe5 Results and DiscussionTo evaluate the navigation algorithm which combines CPN andSPF (CPN&SPF)as well as the AHCPN based energy efficient protocol, we design an experimentwhich involves 4 scenarios with 30, 60, 90, 120 evacuees, respectively. The Dijk-stra’s shortest path algorithm and CPN based algorithm withtime metric (CPNST)[35] are performed for comparison purpose.Fig. 2 indicates that both CPNST and CPN&SPF achieve more survivors than Di-jkstra’s algorithm due to the congestion-ease mechanisms in both CPNST and SPF.Compared with CPNST, CPN&SPF performs slightly better in desely-populatedscenarios because CPN&SPF organises evacuees as loose clusters and is easier tobalance the power utilisation among smart phones. As a result, the probability ofbeing trapped due to the depletion of smart phones is decreased. Furthermore, thecoordinated behaviour increases the probability for an evacuee with depleted smartphone to follow other evacuees other than wander in the building.30 Evacuees 60 Evacuees 90 Evacuees 120 Evacuees0102030405060708090100Percentage of survivors  Dijkstra’s Algorithm + 3GDijkstra’s Algorithm + 3G and BluetoothCPN with Time Metric + 3GCPN with Time Metric + 3G and BluetoothCPN with Time Metric and SPF + 3GCPN with Time Metric and SPF + 3G and BluetoothFig. 2 Percentage of survivors for each scenario. The results are the average of five randomizedsimulation runs, and error bars shows the min/max result in any of the five simulation runs.As can be seen from Fig. 3, Dijkstra’s algorithm achieves leat number of drainedsmart phones in 3G mode. This is because both the CPNST and CPN&S F do notfollow the shortest path. Hence, evacuees will traverse more landmarks and uploadmore photos to the Cloud. However, by employing the AHCPN protoc l, the num-ber of depleted smart phones decreases significantly for allthree algorithms. More-over, there is no drained smart phone when combining the CPN&SPF algorithmwith the AHCPN protocol. This confirms that routing evacueesin loose groups cancontribute to the power-balancing of smart phones. The results also indicates thatalthough AHCPN may consume extra energy because of sending smart packets pe-riodically, it can effectively reduce the number of drainedsmart phones by balancingthe remaining battery power of smart handsets.A Cooperative Emergency Navigation Framework using MobileCloud Computing 730 Evacuees 60 Evacuees 90 Evacuees 120 Evacuees024681012Number of Depleted Smart Phones  Dijkstra’s Algorithm + 3GDijkstra’s Algorithm + 3G and BluetoothCPN with Time Metric + 3GCPN with Time Metric + 3G and BluetoothCPN with Time Metric and SPF + 3GCPN with Time Metric and SPF + 3G and BluetoothFig. 3 The number of drained smart phones of five iterations. The error bars represent the min/maxvalues found in the five simulations.6 ConclusionsIn this paper we propose an infrastructure-less indoor emergency response systemto evacuate civilians with the aid of smart handsets and cloud servers. A coordinatedemergency navigation algorithm is proposed to guide evacuees in loose groups. Theexperimental results prove that the algorithm can increasethe survival rate by re-ducing the number of drained smart phones in an evacuation process and raising thelikelihood for an evacuee with a depleted mobile device to encou ter other evac-uees in the line of sight and follow them to egresses. Due to the considerable energyconsumption between the Cloud and the smart phones during communication pro-cesses, an AHCPN based energy efficient protocol is also presented to prolong thelife time of smart handsets. Simulations indicate that the protocol can significantlydecrease the number of drained smart phones in an evacuationprocess and balancethe remaining battery power among portable devices.References1. E. Gelenbe, F.J. Wu, Future Internet5(3), 336 (2013)2. L. Chu, S.J. Wu, inNano, Information Technology and Reliability (NASNIT), 2011 15th North-East Asia Symposium on(IEEE, 2011), pp. 45–483. J. Ahn, R. Han, inServices Computing Conference (APSCC), 2011 IEEE Asia-Pacific (IEEE,2011), pp. 70–774. E. Gelenbe, G. Görbil, D. Tzovaras, S. Liebergeld, D. Garcia, M. Baltatu, G.L. Lyberopou-los, in ISCIS, Lecture Notes in Electrical Engineering, vol. 264, ed. by E. Gelenbe, R. Lent(Springer, 2013),Lecture Notes in Electrical Engineering, vol. 264, pp. 369–3785. E. Gelenbe, G. Gorbil, D. Tzovaras, S. Liebergeld, D. Garcia, M. Baltatu, G. Lyberopoulos,in Proceedings of the 2013 IEEE Global High Tech Congress on Electronics (GHTCE’13)(2013)8 Huibo Bi and Erol Gelenbe6. O.H. Abdelrahman, E. Gelenbe, inProceedings of IEEE International Conference on Com-munications, ICC 2014, Sydney(2014)7. B. Horling, V. Lesser, The Knowledge Engineering Review19(4), 281 (2004)8. E. Gelenbe, N. Schmajuk, J. Staddon, J. Reif, Robotics andAutonomous Systems22(1), 23(1997)9. Y. Cao, E. Gelenbe, inProc. SPIE 3079, Detection and Remediation Technologies for Minesand Minelike Targets II(1997), p. 691. DOI 10.1117/12.28089810. Y. Cao, E. Gelenbe, European Journal of Operational Research108(2), 319 (1998)11. X. Zheng, T. Zhong, M. Liu, Building and Environment44(3), 437 (2009)12. L. Yang, D. Zhao, J. Li, T. Fang, Building and Environment40(3), 411 (2005)13. G. Gorbil, E. Gelenbe, inComputer and Information Sciences III(Springer, 2013), pp. 249–25714. L. Pelusi, A. Passarella, M. Conti, Communications Magazine, IEEE44(11), 134 (2006)15. E. Gelenbe, R. Lent, Z. Xu, inPerformance and QoS of next generation networking(SpringerLondon, 2001), pp. 3–1716. E. Gelenbe, R. Lent, A. Nunez, Proceedings of the IEEE92(9), 1478 (2004)17. E. Gelenbe, Communications of the ACM52(7), 66 (2009)18. E. Gelenbe, Neural computation1(4), 502 (1989)19. E. Gelenbe, Neural computation2(2), 239 (1990)20. E. Gelenbe, Neural Computation5(1), 154 (1993)21. E. Gelenbe, S. Timotheou, Neural Computation20(9), 2308 (2008)22. E. Gelenbe, E. Seref, Z. Xu, Proceedings of the IEEE89(2), 148 (2001)23. E. Gelenbe, Computer J.55(7), 848 (2012)24. E. Gelenbe, R. Lent, Ad Hoc Networks2(3), 205 (2004)25. J.H. Reif, H. Wang, Robotics and Autonomous Systems27(3), 171 (1999)26. H. Bi, A. Desmet, E. Gelenbe, inProceedings of the 28th International Symposium on Com-puter and Information Sciences (ISCIS’13)(Springer, London, 2013)27. H. Bi, Future Internet6(2), 203 (2014)28. E. Gelenbe, Acta Inf.12, 285 (1979)29. A. Filippoupolitis, E. Gelenbe, inHuman System Interactions, 2009. HSI’09. 2nd Conferenceon (2009), pp. 323–33030. N. Dimakis, A. Filippoupolitis, E. Gelenbe, Comput. J.53(9), 1384 (2010)31. A. Carroll, G. Heiser, inProceedings of the 2010 USENIX conference on USENIX annualtechnical conference(2010), pp. 21–2132. C. Haas, J. Wilke, V. Stöhr, inWireless Sensor Networks(Springer, 2012), pp. 82–9733. N. Balasubramanian, A. Balasubramanian, A. Venkataramni, inProceedings of the 9th ACMSIGCOMM conference on Internet measurement conference(ACM, 2009), pp. 280–29334. G. Kalic, I. Bojic, M. Kusek, inMIPRO, 2012 Proceedings of the 35th International Conven-tion (IEEE, 2012), pp. 754–75935. H. Bi, E. Gelenbe, inIn Proceedings of the 4th International Workshop on Pervasive Networksfor Emergency Management (PerNEM’14)(IEEE, 2014), pp. 1–6

A cooperative emergency navigation framework using mobile cloud computing

A Cooperative Emergency Navigation Framework using Mobile Cloud
  Computing

A Cooperative Emergency Navigation Framework using Mobile Cloud Computing

Abstract

Similar works

Full text

Available Versions

Crossref

Spiral - Imperial College Digital Repository