Telecommun SystDOI 10.1007/s11235-011-9569-2

Wait, focus and spray: efficient data delivery in wireless sensornetworks with ubiquitous mobile data collectors

Long Cheng · Weiwei Jiao · Min Chen · Canfeng Chen ·Jian Ma

© Springer Science+Business Media, LLC 2011

Abstract With decrease in the cost and the size of sensordevices, we can envision a world of ubiquitous sensor net-works. Usually, sensor data needs to be disseminated fromthe source to data collectors, making the spatially distributedsensor data available for applications. The widespread andubiquitous nature of mobile devices, e.g., PDAs and cellphones around the world, makes them attractive to be usedas mobile data collectors (MDCs) to collect and deliver thesensor data. The goal of this work is to design a dissemi-nation protocol that leads to efficient data delivery from thesource sensors to ubiquitous MDCs. We propose the Wait-Focus-Spray (WFS) data delivery scheme for wireless sen-sor networks with ubiquitous MDCs. The main objectiveof WFS is to balance the data delivery latency and trans-mission overhead when considering the existence of ubiq-uitous MDCs. In WFS, we also propose a corresponding

L. Cheng (�) · W. Jiao · J. MaState Key Lab of Networking & Switching Tech., Beijing Univ.of Posts and Telecomm., Beijing, Chinae-mail: chenglong@bupt.edu.cn

W. Jiaoe-mail: jiaoww@bupt.edu.cn

J. Mae-mail: ma-jian@wsn.cn

M. ChenSchool of Computer Sci. and Tech., Huazhong Univ. of Sci.and Tech., Wuhan, Chinae-mail: minchen@ieee.org

C. ChenNokia Research Center, Beijing, Chinae-mail: canfeng-david.chen@nokia.com

J. MaWuxi Sensingnet Industrialization Research Institute, Wuxi,China

mechanism-probabilistic scattered binary spraying (PSBS),to reduce the spatial redundancy when spraying data copies,which can increase the probability of meeting a MDC. Wethen present an analytical model based on the Markov chainmodel to analyze the trade-off between delivery latency andtransmission cost in WFS. Through extensive simulations,we demonstrate that our proposed scheme reduces the trans-mission cost per message while provides comparable deliv-ery delay compared with the alternative approach.

Keywords Wireless sensor networks · Ubiquitous mobiledata collectors · Data delivery

1 Introduction

A wireless sensor network (WSN) is usually composedof a large collections of small autonomous sensor devicesthat can sense environmental conditions about the ambi-ent environment. Recent technological advance enables thewidespread deployment of WSNs for many different appli-cations, including smart battlefield, healthcare, environmentand habitat monitoring, home automation, and traffic con-trol, etc. [1].

With the decrease in cost and size of sensor devices, weenvision a world of ubiquitous sensor networks, where con-nected sensor nodes are scattered everywhere. These sensorswill generate a large amount of sensor data. It is expectedthat the increasing number of available sensor data could beeasily accessed by applications. Usually, sensor data needsto be disseminated from the source sensors to data collec-tors (e.g., sink nodes). Then the collected data will be fur-ther processed (e.g., capturing the contextual information)at a central base station or remote data center, making thosespatially distributed sensor data available for applications to

L. Cheng et al.

access. Therefore, data collection is one of the substantialproblems in WSNs. Since bandwidth and energy are scarceresources in WSNs, it is critical to design scalable and en-ergy efficient data delivery schemes.

The sensor data is usually disseminated to a static controlpoint, e.g., a static data sink, via multi-hop communication.For a large scale network, this process consumes significantamounts of energy especially in the area near the sink wherenodes need to relay data from nodes that are farther away.Consequently, the increased energy consumption and fail-ure of these nodes (hopspot problem) may lead to a discon-nected and dysfunctional network [2]. In order to increasethe uniformity of energy consumption and bridge coverageand connectivity gaps within the network, researchers pro-pose to exploit the mobile elements for energy efficient datacollection in WSNs.

The widespread and ubiquitous nature of mobile devices,e.g., PDAs and cell phones around the world, makes themattractive to be used as mobile data collectors (MDC)1 tocollect and deliver the sensor data. By exploiting the exist-ing mobile devices to collect sensor data from large scaleWSNs has fourfold advantages [3]. First, the data can bedelivered to the destination with fewer hops. For examplein [4], where the MDCs are referred to as Data MULEs,sensor data is collected from nearby source sensors via one-hop communication. This significantly improves the energyefficiency by reducing the need for multi-hop communi-cation. Second, a large scale WSN might become discon-nected (or partitioned) into several islands for a variety ofreasons [5], thus the central data collection could be impos-sible. In this case, exploiting MDCs can bridge the connec-tivity gap. Third, there is no expenditure of deploying dedi-cated mobile data collection robots. Fourth, mobile users canaccess ambient sensor networks to get context-aware valueadded services [6].

However, the real mobility of mobile users is always ran-dom and uncontrollable (possibly path-constrained). In mostsituations, the communication costs are reduced to a shortdistance single-hop communication at the expense of in-creased delivery delay. The Data MULE [4] approach is onlysuitable for delay tolerant applications because source sen-sor nodes have to wait to transmit data until a Data MULEis nearby. One intuitive approach to alleviate the undesir-able long delivery latency is to allow the multi-hop multi-copy communication, which can increase the spatial diver-sity between data copies and MDCs. For example, the epi-demic routing [7–9] is always used in challenged wirelessnetworks,2 where there is no fixed route from the source

1In this work, a mobile data collector (MDC) is referred to as a mobiledevice that collects the sensor data and then unloads it to a remoteservice center or central base station.2Also referred to as delay/disruption-tolerant networks, intermittentlyconnected, opportunistic networks.

to a MDC. In epidemic routing (flooding is the most sim-ple case), where nodes continuously replicate and transmitmessages to newly discovered contacts, the goal is to max-imize message delivery ratio and minimize the delivery la-tency. However, it may incur very high resource utilizationper message. So there exists a tradeoff between the deliverylatency and transmission overhead for data delivery in WSNwith MDCs.

In this work, we present the Wait-Focus-Spray (WFS), alow-latency energy efficient data delivery scheme for WSNswith ubiquitous MDCs. Different from existing work, wenot only introduce the data delivery scheme in the routinglayer, we also design a customized contention-based for-warding protocol to support the WFS scheme in the MAClayer. Moreover, we propose a corresponding mechanism-probabilistic scattered binary spraying (PSBS), to reducethe spatial redundancy when spraying data copies, whichcan increase the probability of meeting a MDC. WFS bal-ances the data delivery latency and energy efficiency and isbased on the locally obtainable information, thus scalablewith respect to the network size. To characterize the perfor-mance of the proposed WFS, we model the WFS routingprocess as a Markov chain, and analyze the trade-off be-tween delivery latency and transmission cost. With extensivesimulations, we show that WFS reduces the transmissioncost per message while provides comparable performancein terms of the delivery delay.

The rest of this paper is organized as follows. Section 2surveys related work. Section 3 presents the network modeland motivations of our design. Section 4 discusses the de-sign of the Wait-Focus-Spray protocol in detail. Section 5analyzes the trade-off between delivery latency and trans-mission cost, and compares our solution against other twoalternative protocols. Simulation results are presented inSection 6. Finally, Section 7 concludes this paper.

2 Related work

In this section, we briefly review the related work on ex-ploiting mobile elements for energy efficient data collectionin WSNs.

Convergence of mobile devices and WSNs The conver-gence of mobile devices and WSNs can have a significantpractical potential. The use of mobile devices to facilitatedata collection in WSNs is not a new idea, which has beendiscussed by a number of researchers [3, 10–14]. The fol-lowing characteristics make the existing mobile devices suit-able as MDCs: (1) the widespread and ubiquitous nature inthe pervasive computing environment; (2) mobility and op-portunistic connectivity; (3) spare computing and commu-nicating ability; and (4) predictable and rechargeable powersupply.

Wait, focus and spray: efficient data delivery in wireless sensor networks with ubiquitous mobile data

Mobility-assisted data collection in dense WSNs For mostof remote sensing applications, sensor nodes are batterypowered, left unattended and expected to last over severalmonths or years without recharging after the initial deploy-ment. Therefore, optimizing energy consumption to extendthe network lifetime is one of the important design goals inWSNs.

In dense WSNs, where dense collections of networkedsensors are deployed in the sensing field, the primary ob-jective of mobility-assisted data collection is to move theMDC in the network to reduce the energy consumption inrelaying traffic and distribute energy consumption evenly,thus greatly extending the lifetime of the network. In [15]and [16], the authors survey the existing research on utiliz-ing mobility to extend the network lifetime.

Our work differs from existing related work in that we ex-ploit the existing mobile devices as MDCs and take the ubiq-uitous nature of mobile devices into consideration. Thus, weinvestigate how to identify appropriate forwarding opportu-nities that could deliver the sensor data faster and more en-ergy efficient to a MDC. While most existing work are basedon the assumption that the number of sinks/MDCs is onlyone or far less than the number of sensor nodes.

Mobility-assisted routing in challenged wireless networksRecent years have witnessed the emergence of a new kindof multi-hop mobile wireless network characterized byseverely challenged connectivity. These challenged net-works are different from traditional Mobile Ad Hoc Net-works (MANET) in that no complete end-to-end paths existmost of the time, but only end-to-end path overtime mightexist. Thus, the traditional protocols fail under such inter-mittent connectivity. Many research on challenged networkshas been done [17–24]. Routing algorithms for delay tol-erant networks are generally classified as either replicationbased or coding based [5]. In replication based algorithms,a number of message copies are generated and distributedto other relays in the network. Then, any of these nodes,independently of others, tries to deliver the message copyto the destination. Due to the node mobility, link connectiv-ity between pairs of nodes comes up and down when theymove into the radio range of each other. Finally, the firstcopy that arrives at the destination yields the optimum de-livery latency. Epidemic routing [7–9], a replication basedapproach, has been proposed to reduce the data transmis-sion delay in challenged wireless networks. In [25–27], theauthor use Markov model or ordinary differential equation(ODE) to study the characteristics of the epidemic routing.

Our work is closer to the replication-based approach. Un-like the previous work, in our network model, sensor nodesare static and any MDC could be the destination of the sen-sor data. In challenged wireless networks, it always requiresa high amount of mobility by network nodes to achieve good

performance. We consider the large scale WSNs with ubiq-uitous MDCs, and study how to deliver sensor data to aMDC before the given deadline such that only a small num-ber of copies are distributed in the network.

3 Network model and motivations

In this section, we list the assumptions of our network modeland then we describe the motivations of our design.

3.1 Assumptions

• Sensor nodes are identical and can wirelessly communi-cate with neighbors in a short range.

• The locations of sensor nodes are static or change slowly.High node mobility is not considered in this work.

• Ubiquitous MDCs are roaming in the widespread WSNs,and send the HELLO messages periodically to announcethe presence.

• For a message, the number of copies distributed to thenetwork is limited to n (we call n spraying tokens).

• Each copy is delivered to the destination independentlyof others and has a TTL (time-to-live) limit. If the TTLexpires, the data packet will be discarded.

3.2 Motivations

We have the following intuitive observations that motivatethe design of our scheme. When a sensor node has datato send, it has three different strategies to deliver the data.(1) Waiting until it encounters a MDC; (2) Forwarding thedata to a different neighbor (to be consistent with previouswork [22], we call it as the Focus strategy); (3) Sprayingsome additional copies of the data to neighbors. For the re-source constrained WSNs, it is desirable that the sensor datacould be delivered to a MDC timely with a small number ofdata copies distributed in the network.

For the first strategy, it may experience a very long de-livery delay, the data packet will be discarded when thedeadline expires. Therefore, the naively waiting strategy isnot suitable. Although spraying mechanism can increase thedelivery probability, it also incurs extra use of bandwidth,buffer space and energy. There is a tradeoff between the de-livery delay and the energy efficiency. Compared with thenaive waiting, forwarding to an appropriate neighbor mayincrease the probability of meeting a MDC. If there is noappropriate neighbor which can provide larger probabilityof meeting a MDC, after waiting for a certain time, sprayingscheme may help to increase the delivery probability beforethe given deadline by creating the diversity between copy-bearing relays and MDCs.

In our design, we adopt a combined strategy adaptively.Data copies are only sprayed to other nodes on demand

L. Cheng et al.

based on the locally obtainable knowledge. The details ofthe algorithm design are presented in the following section.

4 Wait-Focus-Spray description

4.1 Wait-Focus-Spray scheme overview

Definition 1 (Expected meeting time) Let N(i) denote thetimes that node i witnesses any MDC’s passing by withina given interval of time T . We have the estimated expectedmeeting time (EMT).

EMT(i) = T

N(i) + 1(1)

Note that EMT is a dynamic and local metric, where eachnode maintains its own EMT value. Whenever a node wit-nesses any MDC’s passing by, its EMT value will be up-dated.

When a new data gets generated at a source sensor (or acluster head if in the cluster-based WSNs), and needs to bedelivered to a MDC, the source node first waits for a Twait

time. During this time, it will finish the delivery task if meet-ing a MDC. When Twait expires, if the copy-bearing nodehas not delivered the data to a MDC, it will check whetherit has any neighbour with a smaller EMT than itself. If thereis, this neighbor will take the delivering task instead of thesource sensor. If there is not, the source node will enterinto the Spray phase. This process repeats until either themessage is delivered to a MDC or the delivery deadline ex-pires. The Wait-Focus-Spray state transition graph is shownin Fig. 1.

In this work, we introduce an improved version of the Bi-nary Spraying [19] for our spraying mechanism. Each copy-bearing node is associated with n “spraying tokens”. Whenperforming the spraying mechanism, if n > 1, it may spawnand forward a copy of the data to a neighbor node, handover n

2 spraying tokens and keep the rest n2 for itself. Or it

may hand over n2 spraying tokens to two neighbors, respec-

tively, which depends on the locally neighborhood knowl-edge (when n is an odd, dividing n into �n

2 � and �n2 �). For

the latter case, it is noteworthy that the current copy-bearingnode will release itself from the delivery task after spray-ing. When n = 1, it can only perform the Focus operation.When a neighbor takes over its delivery task, it will discardthe data copy.

Compared with Spray-Wait [19] and Spray-Focus [22],our scheme first tries to forward the sensor data to a pre-ferred neighbor to increase the delivery probability. Thespraying operation is only performed on demand when acopy-bearing relay has not encountered a MDC for Twait

time. While [19] and [22] spread all its copies quickly to thesource node’s immediate neighborhood at the initial phase.

Fig. 1 Wait-Focus-Spray state transitions

4.2 WFS-MAC protocol

To support the Wait-Focus-Spray scheme in the MAC layer,we propose the WFS-MAC protocol. WFS-MAC borrowsthe contention-based forwarding idea [28, 29] and is cus-tomized for WFS. Usually, each node maintains a neigh-bor table and makes the forwarding decision by looking upthe neighbor table. Nodes employ the beaconing mechanismto update the local neighborhood information, e.g., the ex-changing of EMT. However, due to the dynamics of MDCsin pervasive computing environment, nodes have to increasethe beaconing frequency to keep their neighbors updated,but this significantly increases the network load as well asthe energy consumption. Thus, to avoid these periodic trans-missions, we adopt the beacon-less contention-based for-warding scheme.

WFS-MAC uses a three way handshake mechanism tomake dynamic forwarding decisions, similar in [29]. Whena copy-bearing node (the sender) has data to focus or spray(possibly when the Twait expires), it first listens to checkwhether the channel is clear. If it is, the sender broadcaststhe DATA message, which contains its EMT value. Thenit waits for response for a predefined maximum time of BD(Backoff Duration). The backoff duration is divided into twoparts: the Focus Contention Period (FCP) and Spray Con-tention Period (SCP), as shown in Fig. 2. If the channel isbusy, the sender backs off and reschedules another attemptat a later time. The sender’s neighbor who successfully re-ceives the DATA compares its own EMT (denote as EMTr )with the sender’s (denote as EMTs ). If EMTr < EMTs , theneighbor will contend to take over the delivery task in FCP,i.e., take over all n tokens. If EMTr ≥ EMTs and n > 1, the

Wait, focus and spray: efficient data delivery in wireless sensor networks with ubiquitous mobile data

Fig. 2 The Backoff Duration structure

neighbor competes to get n2 spraying tokens. Otherwise, it

enters into the Wait state.The contention process is achieved by starting a backoff

timer whose value Tbackoff is defined as (2), which is calcu-lated in a distributed fashion.

Tbackoff =

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

EMTr

EMTs· TFCP + random(0, τ ),

if EMTr < EMTs

(1 − EMTs

EMTr) · TSCP + TFCP

+ random(0, τ ), otherwise

(2)

where TFCP and TSCP are the time durations of the FCP andSCP, respectively. τ is the time duration of one time slot, therandom function aims to mitigate the radio interference.

When the backoff timer expires, the neighbor will sendout an ACK message, which contains its own EMT . At thesame time, other contenders will cancel their timers if over-hearing this ACK. If the sender receives an ACK duringthe Focus Contention Period, it will broadcast a SELECTmessage to the selected receiver after TFCP. Notice that it ispossible some neighbors cannot overhear the replied ACKbecause of their positions, thus the sender may receive mul-tiple ACK messages. The neighbor that first replies an ACKwill win the competition as a subsequent copy-bearing node.The SELECT message has twofold functions: (1) confirm-ing the selection with a final control packet; (2) suppressingall other contenders. After sending out the SELECT, theselected new copy-bearing node takes over the n sprayingtokens, and the previous sender will discard it’s own datacopy.

If the sender receives an ACK during the Spray Con-tention Period, which means there is no neighbor havingshorter EMT than the sender, it will enter into the Sprayphase. In WFS-MAC, we propose a new binary sprayingmechanism, named probabilistic scattered binary spraying(PSBS), to reduce the spatial redundancy. Consider an ex-ample as shown in Fig. 3, where the current copy-bearingnode A sprays the data copies to its neighbors. It may sprayto different neighbors. For example in Fig. 3(a), nodes Band E get the spraying tokens. Figure 3(b) shows that PSBStends to reduce the spatial redundancy, which can improvethe probability of meeting a MDC. The corresponding back-off time in view of the MAC layer is shown in Fig. 4.

PSBS takes advantage of the broadcast nature of wire-less communication to achieve the reduction of the spatialredundancy. If a neighbor has larger EMT than the sender, itwill contend to get n

2 spraying tokens when its backoff timer

Fig. 3 An example illustrating the probabilistic scattered binaryspraying (PSBS) mechanism. (a) Randomly selected token receivers;(b) Selecting scattered token receivers to reduce the spatial redundancy

Fig. 4 The corresponding backoff time in Fig. 3(b)

expires. Take Fig. 3 for example, suppose node B first sendsan ACK to the sender to announce itself as a token receiver.Nodes D and E will cancel their backoff timers and withdrawfrom the contention if overhearing this ACK. But nodes Cand F cannot overhear the replied ACK, and will contendto be another token receiver. Finally, node C’s timer firesearlier than F’s. It is also possible that the sender receivesmultiple ACK messages after the Spray Contention Period,the first two neighbors who reply the ACK messages will beconsidered as the potential token receivers.

Since all the neighbors in the Spray phase have largerEMT than the sender, in our design, the sender makes aprobabilistic spraying decision based on the EMT informa-tion. Let Pj denote the probability that the sender spraysn2 tokens to the neighbor j who replies the second ACK.Pj is calculated as Pj = EMTs

EMTj. After the Spray Contention

Period, the sender generates a random number Prandom (0 ≤Prandom ≤ 1). If Prandom ≤ Pj , the sender will spray all n to-kens to the first two neighbors who reply the ACK messageswithout reservation. Otherwise, it reserves n

2 tokens for it-self and sprays n

2 tokens to the neighbor who first replies theACK. After the sender makes the spraying decision, it willsend out the SELECT message, in which the selection re-sult and the number of tokens are piggybacked. The selectedneighbors will then take the delivery task, i.e., waiting theMDCs for a maximum Twait time.

L. Cheng et al.

If the sender receives no ACK message during the Back-off Duration, that means there is no neighbor who can takeover the data delivery task. In this case, the sender will iter-atively wait for another Twait time until either the message isdelivered to a MDC or the delivery deadline expires.

4.3 Suppressing spraying

Once the tokens have been sprayed out, all copy-bearingnodes will deliver the data to a MDC parallelly. Hence, dif-ferent MDCs may receive multiple copies of a data. In [5],the authors assumed that the destination acknowledges re-ceived messages using a broadcast to all nodes, thereby sup-pressing any spraying after the message delivery. This as-sumption might be quite strong and impractical for largescale WSNs. In this work, although this issue is not our fo-cus, we would like to point out there is a possible solutionto avoid too much energy consumption after the data is de-livered to a MDC in the pervasive computing environment.Since we consider the ubiquitous mobile devices as MDCs,the location information of a MDC is always available, e.g.,most latest mobile phones are equipped with GPS radios.So, if a MDC unloads the data to a remote service center,the service center can inform all other MDCs in the neigh-borhood based on the real-time location of MDCs. Then,the identifier of that received data can be piggybacked onthe HELLO message sent from those MDCs periodically. Ifcurrent copy-bearing nodes encounters a MDC, it will be in-formed and discard the copy that has already been deliveredby another node.

5 Theoretic analysis

In this section, we introduce a Markov chain model to de-scribe the discrete stochastic process of the WFS and studyits behavior based on the discrete Markov chain. We are in-terested in characterizing two important performance met-rics of WFS: the upper bound of the expected message delayand the number of copies (the copy count) of the message atthe time the message is first delivered to a MDC.

5.1 WFS scheme

For simplicity and without loss of generalization, we assumethat each node has a possibility p to meet a MDC duringTwait period. Twait is defined as a time slot. Every Twait, ifthe current copy-bearing node does not encounter a MDC, itwill firstly try to spray the copy to a preferred neighbor to in-crease the delivery probability.3 If no such a neighbor, it will

3Since we assume each node has a probability p to meet a MDC, infact, each spraying operation will more or less increase the deliveryprobability, we derive the upper bound of the expected message delayand the copy count.

adopt the probabilistic scattered binary spraying scheme toincrease the copy count, thus to increase the delivery prob-ability. We assume each node has a probability q to have apreferred neighbor who has less EMT than itself. Note thatp and q are random variables in a practical system. Trans-missions between two nodes may take place if they locatewithin the radio range and are assumed to be instantaneous.Therefore, at each time slot, each copy-bearing node has aprobability (1−p)q to Focus, and probability (1−p)(1−q)

to Spray. Each copy-bearing node works independently ofothers. The delivery deadline is K · Twait. We assume thenumber of spraying tokens is larger than 2(K−1).

5.1.1 Markov chain model

Let D(t) denote the delivery delay, N(t) denote the numberof duplicate copies of the message in the network at timeslot t , respectively. The message duplication process in WFScan be modeled as a discrete-time Markov chain as shown inFig. 5, where {D(t),N(t)} is a two-dimensional stochasticprocess. The state transition starts from {1,1}. The sourcenode encounters a MDC at the first time slot with probabilityof p. If not, a transition will be triggered from {1,1} to {2,1}or {2,2}, with probability of (1 − p)q and (1 − p)(1 − q),respectively.

Denote Pm,n to be the state probability of {m,n}, which isstarted from state {1,1}. Defining P d

m,n to be the probabilitythat at the mth time slot, the message is first delivered to aMDC; P t

m,n to be the probability that the one-step transitionfrom {D(m),N(m)} to {D(m + 1),N(m + 1)} occurs. Wehave

P1,1 = 1

P d1,1 = p

P t1,1 = 1 − p

(3)

P dm,n = (1 − (1 − p)n) · Pm,n

P tm,n = (1 − p)n · Pm,n

(4)

Let P {m + 1, j | m,n} (j ∈ [n,2n]) denote the one-step transition probability for the Markov chain model, i.e.,P {m + 1, j | m,n} = P {D(m + 1) = m + 1,N(m + 1) =j |D(m) = m,N(m) = n}. We have the recursive equationof state probability,

Pm+1,j =j∑

k=� j2 �

P tm,k · P {m + 1, j | m,k} (5)

Next, we derive the one-step transition probability. Asshown in Fig. 6, for the state {m,n}, it has n possible tran-sitions, from {m + 1, n} to {m + 1,2n}. The one-step tran-sition P {m + 1, n| m,n} means all n copy-bearing nodes

Wait, focus and spray: efficient data delivery in wireless sensor networks with ubiquitous mobile data

Fig. 5 WFS scheme: transitiondiagram of the Markov chain forthe message delay and copycount

Fig. 6 The one-step transition diagram

forward the data copies to their preferred neighbors. WhileP {m+1,2n| m,n} indicates all n copy-bearing nodes spraythe tokens, thus the copy count is doubled. We have the one-step transition probability

P {m + 1, j |m,n} = C(j−n)n · (1 − q)(j−n) · q(2n−j) (6)

Defining Pfail to be the probability that the delivery dead-line expires, we have

Pfail =2(K−1)∑

j=1

P tK,j (7)

5.1.2 Expected first delivery delay and copy count

Let E(D) denote the expected delivery delay at the time themessage is first delivered to a MDC. From the Markov chainmodel in Fig. 5, we can calculate E(D) by substituting (3)(4) (5) into (8).

E(D) = 1 · P d1,1 + 2 · (P d

2,1 + P d2,2) + 3 · (P d

2,1 + P d2,2)

+ · · · + K · (P dK,1 + · · · + P d

K,2(K−1) )

=K∑

i=1

i ·2i−1∑

j=1

P di,j (8)

Denote E(C) to be the expected copy count at the timethe message is first delivered to a MDC. We have

E(C) = 1 · (P d1,1 + · · · + P d

K,1) + 2 · (P d2,2 + · · · + P d

K,2)

+ · · · + 2(K−1) · P dK,2(K−1)

=2(K−1)∑

j=1

j · (P d

(�logj2�+1),j

+ · · · + P dK,j )

=2(K−1)∑

j=1

j ·K∑

i=(�logj2�+1)

P di,j (9)

5.2 BSWF scheme

We then analyze an alternative approach, which we refer toas the BSWF (Binary Spraying-Wait-Focus) [22]. In BSWF,when a new message gets generated at a source, and needsto be routed to a MDC, it first enters the Binary Spray phasefor this message. Then, it will wait for Twait time. If meet-ing a MDC, it will deliver the data to the MDC and set thespraying tokens zero. When a relay for a given message hasonly one spraying token left for that message, it switchesto the Focus phase. The transition diagram of the Markovchain corresponding to the BSWF scheme is given in Fig. 7,we have

P di,2(i−1) = (1 − (1 − p)2(i−1)

) · Pi,2(i−1)

P ti,2(i−1) = (1 − p)2(i−1) · Pi,2(i−1)

Pi,2(i−1) = P t(i−1),2(i−2) (i ≥ 2)

(10)

L. Cheng et al.

Fig. 7 BSWF scheme: transition diagram of the Markov chain for themessage delay and copy count

Substituting the initial value (3) into the recursive equation(10), we have

Pi,2(i−1) =i−1∏

m=1

(1 − p)2(m−1) = (1 − p)(2(i−1)−1)

P di,2(i−1) = (1 − (1 − p)2(i−1)

) · (1 − p)(2(i−1)−1)

= (1 − p)(2(i−1)−1) − (1 − p)(2

i−1)

(11)

Therefore, we derive the expected delivery delay andcopy count at the time the message is first delivered to aMDC.

E(D) = 1 · P d1,1 + 2 · P d

2,2 + 3 · P d3,4 + · · · + K · P d

K,2(K−1)

=K∑

i=1

i · {(1 − p)(2(i−1)−1) − (1 − p)(2

i−1)} (12)

E(C) = 1 · P d1,1 + 2 · P d

2,2 + 4 · P d3,4 + · · ·

+ 2(K−1) · P dK,2(K−1)

=K∑

i=1

2(i−1) · {(1 − p)(2(i−1)−1) − (1 − p)(2

i−1)} (13)

The probability that the delivery deadline expires,

Pfail = (1 − p)(2K−1) (14)

5.3 Naive Wait scheme

Serving as a baseline of comparison, we analyze the NaiveWait scheme, where the source sensor transmits the data to aMDC via only the one-hop communication, i.e., no duplica-tion is allowed and a message from the source naively waitsthe chance to be directly transmitted to a MDC. The Markovchain represented the Naive Wait scheme is shown in Fig. 8,we have the initial value

P di,1 = p · Pi,1

P ti,1 = (1 − p) · Pi,1

Pi,1 = P t(i−1),1 (i ≥ 2)

(15)

Fig. 8 Naive Wait protocol: transition diagram of the Markov chainfor the message delay and copy count

Substituting the initial value (3) into the recursive equation(15), we have

Pi,1 = (1 − p) · P(i−1),1 = (1 − p)(i−1)

P di,1 = p · (1 − p)(i−1)

(16)

Since no duplication is allowed, the copy count is alwaysone. We have the expected delivery delay

E(D) =1 · P d1,1 + 2 · P d

2,1 + 3 · P d3,1 + · · · + K · P d

K,1

=K∑

i=1

i · p · (1 − p)(i−1) (17)

The probability that the delivery deadline expires,

Pfail = (1 − p)K (18)

5.4 Analytical results

In the test, we vary the meeting MDC probability p from0.05 to 0.5. K is set 9, that is, the delivery deadline is9 · Twait. We compare WFS with BSWF and the Naive Waitscheme. In WFS, the Focus probability q of each hop isset 0.2, 0.5 and 0.8, respectively. The analytical results areshown in Figs. 9, 10, 11.

Figure 9 presents the analytical result for the expected de-livery delay when the message is first delivered to a MDC.It is observed that when the Focus probability q (0 ≤ q ≤ 1)is a very small value, WFS approaches to BSWF. As q in-creases, WFS degrades to the Naive Wait scheme. Note thatonly the successful delivery are counted, that is why the de-livery delay of the Naive Wait scheme seems shorter thanother schemes when p < 0.15. However, the fact is that, forthe Naive Wait scheme, its expected delivery ratio is muchlower than other schemes when p is a small value, as shownin Fig. 11.

Figure 10 shows the analytical result for expected copycount when the message is first delivered to a MDC. FromFigs. 9 and 10, it is clear to see that WFS achieves a bettertradeoff between the data delivery latency and transmissioncost. When q = 0.8 and p < 0.3, the expected copy count of

Wait, focus and spray: efficient data delivery in wireless sensor networks with ubiquitous mobile data

Fig. 9 Expected first delivery delay vs. Meeting MDC probability

Fig. 10 Expected first delivery copy count vs. Meeting MDC proba-bility

WFS approaches to constant one. However, BSWF incursmuch higher energy cost. Actually, when each copy-bearingrelay sprays the copy to a preferred neighbor, the meetingMDC probability p will more or less increase. Although ourmodel derives the upper bound analytical results of WFS,compared with the Naive Wait scheme, the delivery ratio hasbeen significantly improved. And it can also provide compa-rable delivery delay compared with the BSWF.

6 Simulation

In this section, we present simulation results to justifythe WFS scheme using ns-2 simulator [30]. We compareour protocol with the alternative approach Spray & Fo-cus [22]. In order to highlight the difference between ourwork and [22], we refer to Spray & Focus as the BSWF(Binary Spraying-Wait-Focus). Acting as the baseline ofcomparison, we also report the simulation results of the

Fig. 11 Expected delivery ratio vs. Meeting MDC probability

Naive Wait scheme. All the results have been averaged over100 runs.

6.1 Simulation settings

In the implementation of our simulation, 400 sensor nodesare uniformly placed in a 400 m × 400 m field. The nodetransmission range is set 30 m. Each MDC sends HELLOmessages every 5 seconds. The simulation starts at 0 s andstops at 300 s. The source node which has data to send israndomly selected. It generates the sensor data at the time of200 s. Twait is set 8.0 s. For calculating the expected meetingtime (EMT), T is set 600 s. The TTL limit is set 10 hops, andthe initial spraying token number is 10. We implement ourWFS-MAC protocol based on the modification of the IEEE802.11 MAC.

The MDCs move according to the Manhattan Mobil-ity [31] model or the Random Waypoint model [32]. Forthe Manhattan Mobility model, the speed of a MDC is ran-domly selected from the range [2,20] m/s. The number ofhorizontal and vertical streets is set 3, and the number oflanes is 12. For the Random Waypoint model, the speed ofa MDC is randomly selected from the range [1,15] m/s.The Manhattan Mobility model can be useful in modelingmovement in an urban area where the pervasive computingservice between portable mobile devices is provided [31].In Random Waypoint model, the mobile nodes move ran-domly and freely without restrictions. This mobility modelis always used to describe the movement pattern of mobileusers over time in a random manner. The simulation param-eters are summarized in Table 1.

We select six evaluation metrics:

• First delivery delay: We define this metric as the timetaken for the first copy of a given data transmitted fromthe source to a MDC.

L. Cheng et al.

Table 1 Simulation parameters

Parameter Value

Simulation area 400 m × 400 m

Number of nodes 400

Twait 8.0 s

T (for calculating EMT) 600 s

TTL limit 10 hops

HELLO period 5 s

TSCP 15 time unit

TFCP 15 time unit

Manhattan Mobility model speed range [2,20] m/s

Random Waypoint model speed range [1,15] m/s

• First delivery copy count: The total amount of copies of agiven data message scattered in the network so far whenthe first copy is delivered to a MDC.

• First delivery transmission cost: The total number ofDATA transmissions so far when the first copy is deliv-ered to a MDC.

• Copy count: The total number of copies of a given datascattered in the network when the simulation stops.

• Transmission cost: The total number of DATA transmis-sions when the simulation stops.

• Data delivery ratio: We define this metric as the ratio ofthe amount of data packets delivered to MDCs before thesimulation stops to the total amount of packets sent by thesource.

Note that the copy count metric and transmission cost metricmeasure not only the communication overhead, but also theenergy efficiency.

6.2 Simulation results

We examine the performance difference between our WFSand BSWF by varying the number of MDCs under the Man-hattan Mobility model and Random Waypoint model, re-spectively. Figures 12 13, 14, 15, 16 report the evaluationresults under the Manhattan Mobility model. Figures 17,18, 19, 20, 21 show the evaluation results under the Ran-dom Waypoint model. Both WFS and BSWF achieve nearly100% data delivery ratio with the ubiquitous MDCs. Fig-ures 22 and 23 illustrate the average delivery delay and de-livery ratio for the Naive Wait scheme under the ManhattanMobility model and Random Waypoint model, respectively.

6.2.1 WFS vs. BSWF under the Manhattan Mobility model

Figure 12 illustrates the changes of the average first deliv-ery delay at increasing the number of MDCs. The figureshows that the first delivery delay decreases from 16 s to 5 son average with increasing the number of MDCs for both

Fig. 12 Average first delivery delay vs. number of MDCs under theManhattan Mobility model

Fig. 13 Average first delivery copy count vs. number of MDCs underthe Manhattan Mobility model

schemes. BSWF only behaves a little better than WFS. Thisis because BSWF first enters the Spray phase, i.e., scatteringthe spraying tokens to neighbors. From Fig. 12, we see WFSachieves the comparable first delivery delay in the pervasivecomputing environment.

Figure 13 shows the average first delivery copy count asthe number of MDCs changes. It is seen that WFS decreasesthe copy count scattered in the network on average. Note thatfor the BSWF implementation in our simulation, the senderwon’t spray all tokens in one time, it will spray half of thetokens to a neighbor every Twait time. Thus, once meeting aMDC, it will zero clear its spraying tokens. Averagely, WFScan save one less copy count compared with BSWF whenthe first copy is delivered to a MDC.

Figure 14 reports the average first delivery transmissioncost under different number of MDCs. Note that this met-ric is not linearly proportional with the first delivery copycount. This is partly because our WFS-MAC protocol usesDATA/ACK/SELECT three way handshake mechanism.

Wait, focus and spray: efficient data delivery in wireless sensor networks with ubiquitous mobile data

Fig. 14 Average first delivery transmission cost vs. number of MDCsunder the Manhattan Mobility model

Fig. 15 Average copy count vs. number of MDCs under the Manhat-tan Mobility model

Therefore, when a copy-bearing node has only one sprayingtoken left, it will still send DATA every Twait time beforethe TTL expires. Even this, WFS still behaves a little betterthan BSWF.

Figure 15 plots the number of copies of a given messagescattered in the network when the simulation stops. As thenumber of MDCs increases, the copy count for both schemesdecreases as well. The figure clearly shows that WFS cansave up to 50% improvements over BSWF.

Figure 16 is the result of the transmission cost at increas-ing the number of MDCs. Revisiting Fig. 14, we know thatafter the first copy is delivered to a MDC, the transmissioncost does not increase much for WFS. While for BSWF,more copy-bearing nodes may incur the higher transmissioncost.

Fig. 16 Average transmission cost vs. number of MDCs under theManhattan Mobility model

Fig. 17 Average first delivery delay vs. number of MDCs under theRandom Waypoint model

6.2.2 WFS vs. BSWF under the Random Waypoint model

As under the Manhattan Mobility model, WFS yields com-parable delivery delay compared with the BSWF under theRandom Waypoint model, as shown in Fig. 17. However,both schemes experience longer delivery delay under theRandom Waypoint model. This is due to the random mobil-ity of MDCs. For the same reason, the result is not monoton-ically decreasing as the number of MDCs increases. Similarresult in Fig. 18, it takes a little more copy count for bothschemes under the Random Waypoint model than that in theManhattan Mobility model.

Figure 19 illustrates the average first delivery transmis-sion cost as the number of MDCs increases. The simulationresult corresponds well with the analytical result. WFS cansignificantly reduces the transmission cost per data message.

Figures 20 and 21 show the similar results as in Figs. 15and 16 under the Manhattan Mobility model. WFS shows

L. Cheng et al.

Fig. 18 Average first delivery copy count vs. number of MDCs underthe Random Waypoint model

Fig. 19 Average first delivery transmission cost vs. number of MDCsunder the Random Waypoint model

obvious advantage over BSWF in terms of the energy costfor both mobility models.

6.2.3 Naive Wait scheme

Figure 22 depicts the average delivery delay and data deliv-ery ratio of the Naive Wait scheme under the Manhattan Mo-bility model. It is seen that Naive Wait scheme suffers a verylong delivery delay and low data delivery ratio. Comparedwith the Naive Wait scheme, both WFS and BSWF signifi-cantly decrease the delivery delay and improve the data de-livery ratio. We know the price is the increased transmis-sion cost. However, WFS balances the data delivery latencyand transmission overhead, improves the energy efficiencyremarkably. It is demonstrated that WFS is a preferred dis-semination protocol when considering the existence of ubiq-uitous MDCs.

Figure 23 reports the average delivery delay and data de-livery ratio of the Naive Wait scheme under the Random

Fig. 20 Average copy count vs. number of MDCs under the RandomWaypoint model

Fig. 21 Average transmission cost vs. number of MDCs under theRandom Waypoint model

Fig. 22 Average delivery delay and data delivery ratio of the NaiveWait scheme under the Manhattan Mobility model

Wait, focus and spray: efficient data delivery in wireless sensor networks with ubiquitous mobile data

Fig. 23 Average delivery delay and data delivery ratio of the NaiveWait scheme under the Random Waypoint model

Table 2 Performance summary of WFS compared with BSWF

Under ManhattanMobility model

Under RandomWaypoint model

First delivery delay −19% −11%

First delivery copycount

+38% +29%

First deliverytransmission cost

+16% +40%

Copy count +55% +33%

Transmission cost +68% +58%

Waypoint model. Compared with the results under the Man-hattan Mobility model, the delivery delay is increased. Thereason is that MDCs move in the sensing field randomly un-der the Random Waypoint model, thus, increasing the timerequired to meet a MDC. However, the fully random mo-bility can also increase the data delivery ratio. Because inthe Manhattan Mobility model, the movement of MDCs onstreets is limited along the predefined roads, i.e., defined bymaps.

6.2.4 Summary

Simulation results above are shown that WFS minimizesthe delivery cost per message with acceptable data deliv-ery delay when compared with BSWF. Table 2 summarizesthe performance improvement of WFS. In the table, positivevalues indicate the improvement of WFS, while the nega-tive values reveal that WFS is inferior to BSWF. For ex-ample, −19% indicates that WFS incurs 19% longer firstdelivery delay than BSWF under the Manhattan Mobilitymodel, +58% means that WFS provides 58% improvementover BSWF in terms of the transmission cost under the Ran-dom Waypoint model.

7 Conclusion

In this work we have proposed the WFS, an efficient datadissemination protocol when considering the existence ofubiquitous MDCs. Different from existing work, we notonly introduced the data delivery scheme in the routinglayer, we also designed a customized forwarding proto-col to support the WFS scheme in the MAC layer, namedWFS-MAC. In WFS, we further introduced the probabilisticscattered binary spraying (PSBS) mechanism to reduce thespatial redundancy when spraying data copies, which canincrease the probability of meeting MDCs. Moreover, wepresented an analytical model based on the Markov chainmodel to analyze the trade-off between delivery latency andtransmission cost in WFS. We conducted extensive simu-lations to study the performance of WFS compared withthe alternative approach BSWF. Simulation results demon-strated the suitability of the WFS scheme when ubiquitousMDCs are available. It significantly reduces the transmis-sion cost per data message while still provides comparableperformance in terms of the delivery delay.

Acknowledgements The support provided by China ScholarshipCouncil (CSC) during a visit of Long Cheng to University of Texasat Arlington is acknowledged. This work was in part supported byNational Natural Science Foundation of China (NSFC) under grantNo. 60873241; National 863 High Technology Program of China undergrant No. 2008AA01Z217 and No. 2009AA01Z210.

References

1. Romer, K., & Mattern, F. (2004). The design space of wirelesssensor networks. IEEE Wireless Communications, 11(6), 54–61.

2. Kinalis, A., & Nikoletseas, S. (2007). Scalable data collection pro-tocols for wireless sensor networks with multiple mobile sinks. InProc. ANSS ’07 (pp. 60–72).

3. Dyo, V. (2005). Middleware design for integration of sensor net-work and mobile devices. In Proc. DSM ’05 (pp. 1–5).

4. Jain, S., Shah, R. C., Brunette, W., Borriello, G., & Roy, S. (2006).Exploiting mobility for energy efficient data collection in wirelesssensor networks. Mobile Networks and Applications, 11(3), 327–339.

5. Almasaeid, H., & Kamal, A. (2008). Modeling mobility-assisteddata collection in wireless sensor networks. In Proc. IEEEGLOBECOM ’08 (pp. 1–5), Nov.

6. Kabadayı, S., & Julien, C. (2007). Scenes: Abstracting interactionin immersive sensor networks. Pervasive and Mobile Computing,3(6), 635–658.

7. Vahdat, A., & Becker, D. (2000). Epidemic routing for partially-connected ad hoc networks. Duke University Technical ReportCS-200006, April.

8. Haas, Z., Halpern, J., & Li, L. (2006). Gossip-based ad hoc rout-ing. IEEE/ACM Transactions on Networking, 14(3), 479–491.

9. Lindgren, A., Doria, A., & Schelén, O. (2003). Probabilistic rout-ing in intermittently connected networks. Mobile Computing andCommunications Review , 7(3), 19–20.

L. Cheng et al.

10. Ren, B., Ma, J., & Chen, C. (2006). The hybrid mobile wire-less sensor networks for data gathering. In Proc. IWCMC ’06 (pp.1085–1090).

11. Trossen, D., & Pavel, D. (2005). Building a ubiquitous platformfor remote sensing using smartphones. In Proc. MOBIQUITOUS’05 (pp. 485–489).

12. Jayaraman, P. P., Zaslavsky, A., & Delsing, J. (2007). Sensor datacollection using heterogeneous mobile devices. In Proc. IEEE in-ternational conference on pervasive services (pp. 161–164).

13. Frank, C., Bolliger, P., Mattern, F., & Kellerer, W. (2008). Thesensor internet at work: Locating everyday items using mobilephones. Pervasive and Mobile Computing, 4(3), 421–447.

14. Cheng, L., Chen, C., Ma, J., & Chen, Y. (2009). A group-levelincentive scheme for data collection in wireless sensor networks.In Proc. IEEE CCNC’09 (pp. 346–350).

15. Ekici, E., Gu, Y., & Bozdag, D. (2006). Mobility-based communi-cation in wireless sensor networks. IEEE Communications Maga-zine, 44(7), 56–62.

16. Yang, Y., Fonoage, M. I., & Cardei, M. (2010). Improving networklifetime with mobile wireless sensor networks. Computer Commu-nications, 33(4), 409–419.

17. Jain, S., Fall, K., & Patra, R. (2004). Routing in a delay tolerantnetwork. In Proc. ACM SIGCOMM ’04 (pp. 145–158).

18. Zhang, P., Sadler, C. M., Lyon, S. A., & Martonosi, M. (2004).Hardware design experiences in zebranet. In Proc. ACM SenSys’04 (pp. 227–238).

19. Spyropoulos, T., Psounis, K., & Raghavendra, C. S. (2005). Sprayand wait: an efficient routing scheme for intermittently connectedmobile networks. In Proc. ACM SIGCOMM workshop on delay-tolerant networking (pp. 252–259).

20. Hull, B., Bychkovsky, V., Zhang, Y., Chen, K., Goraczko, M.,Miu, A., Shih, E., Balakrishnan, H., & Madden, S. (2006). Car-Tel: a distributed mobile sensor computing system. In Proc. ACMSenSys ’06 (pp. 125–138).

21. Ramanathan, R., Basu, P., & Krishnan, R. (2007). Towards a for-malism for routing in challenged networks. In Proc. ACM work-shop on challenged networks (pp. 3–10).

22. Spyropoulos, T., Psounis, K., & Raghavendra, C. S. (2007). Sprayand focus: Efficient mobility-assisted routing for heterogeneousand correlated mobility. In Proc. PERCOMW ’07 (pp. 79–85).

23. Spyropoulos, T., Psounis, K., & Raghavendra, C. S. (2008).Efficient routing in intermittently connected mobile networks:the multiple-copy case. IEEE/ACM Transactions on Networking,16(1), 77–90.

24. Liu, C., & Wu, J. (2009). An optimal probabilistic forwarding pro-tocoling delay tolerant networks. In Proc. ACM MobiHoc ’09 (pp.105–114).

25. Groenevelt, R., Nain, P., & Koole, G. (2005). The message delay inmobile ad hoc networks. Performance Evaluation, 62(1–4), 210–228.

26. Zhang, X., Neglia, G., Kurose, J., & Towsley, D. (2007). Per-formance modeling of epidemic routing. Computer Networks,51(10), 2867–2891.

27. Zhou, S., Ying, L., & Tirthapura, S. (2010). Delay, cost and infras-tructure tradeoff of epidemic routing in mobile sensor networks. InProc. ACM IWCMC ’10 (pp. 1242–1246).

28. Füßler, H, Widmer, J, Käsemann, M, Mauve, M, & Hartenstein, H.(2003). Contention-based forwarding for mobile ad-hoc networks.Ad Hoc Networks, 1(4), 351–369.

29. Sanchez, J., Marin-Perez, R., & Ruiz, P. (2007). BOSS: beacon-less on demand strategy for geographic routing inwireless sensornetworks. In Proc. IEEE MASS ’07 (pp. 1–10).

30. Network simulator (2011). Available: http://www.isi.edu/nsnam/ns/.

31. Bai, F., Sadagopan, N., & Helmy, A. (2003). IMPORTANT:aframework to systematically analyze the impact of mobility on

performance of routing protocols for adhoc networks. In Proc.IEEE INFOCOM ’03 (pp. 825–835).

32. Bettstetter, C., Resta, G., & Santi, P. (2003). The node distributionof the random waypoint mobility model for wireless ad hoc net-works. IEEE Transactions on Mobile Computing, 2(3), 257–269.

Long Cheng received his B.S. de-gree in Computer Science fromXian Telecommunication Institute,China, in 2004, and M.S. degreein Telecommunication Engineeringfrom XiDian University, China, in2007, and is pursuing his Ph.D.in State Key Laboratory of Net-working and Switching Technol-ogy, Beijing University of Posts andTelecommunications, China. He iscurrently a visiting scholar in theCReWMan Laboratory, Departmentof Computer Science and Engineer-ing, University of Texas at Arling-

ton, USA. He is a student member of IEEE, and his main researchinterests cover wireless sensor networks, Internet of Things, mobilecomputing, and pervasive computing.

Weiwei Jiao received her B.S. de-gree in Computer Science from JilinUniversity, China, in 2001, and ispursuing her Ph.D. in the StateKey Laboratory of Networking andSwitching Technology, Beijing Uni-versity of Posts and Telecommuni-cations, China since 2005. Her mainresearch interests focus on wire-less sensor networks and Internet ofThings.

Min Chen is a professor atHuazhong University of Scienceand Technology, he was an assis-tant professor in School of Com-puter Science and Engineering atSeoul National University (SNU).He has worked as a Post-DoctoralFellow in Dept. of Electrical andComputer Engineering at UBC forthree years since Mar. 2006. Be-fore joining UBC, he was a Post-Doctoral Fellow at SNU for one andhalf years. He has published morethan 120 technical papers. He re-ceived the Best Paper Runner-up

Award from QShine 2008. He serves as editor or AE for Wiley I. J. ofWireless Communication and Mobile Computing, IET Communica-tions, Wiley I. J. of Security and Communication Networks, Journalof Internet Technology, KSII Transactions on Internet and InformationSystems, and International Journal of Sensor Networks. He is a man-aging editor for IJAACS. He is a TPC co-chair of BodyNets 2010. Heis a symposia co-chair and workshop chair of CHINACOM 2010. He isthe co-chair of MMASN-09, UBSN-10, GCCN-10 and NCAS-11. Hewas the TPC chair of ASIT-09, ASIT 2010, TPC co-chair of PCSI-09

Wait, focus and spray: efficient data delivery in wireless sensor networks with ubiquitous mobile data

and PCSI-10. He serves as the corresponding guest editors for severalinternational journals, such as ACM MONET, IJCS. He is an IEEEsenior member.

Canfeng Chen received his Bache-lor in Information Engineering andPh.D. in Signal and InformationProcessing from Beijing Universityof Posts and Telecommunications in2000 and 2005, respectively. SinceJuly 2005, he has been working forNokia Research Center as a PostdocResearcher, and joined Nokia Re-search Center as a Member of Re-search Staff in July 2007. He is amember of IEEE, and his main re-search interests cover wireless sen-sor networks, mobile networks andservices, and radio resource man-agement.

Jian Ma received his B.Sc. andM.Sc. in 1982 and 1987, respec-tively, from Beijing University ofPosts and Telecommunications, andhis Ph.D. degree in 1994 fromthe Department of Electronics En-gineering, Helsinki University ofTechnology, Finland. Since then hehad worked as principal scientist forNokia Research Center for last 16years. He is now a chief scientist atWuxi SensingNet IndustrializationResearch Institute, Wuxi, China,and also act as the general secre-tary of Alliance of Sensing China.

He has 47 granted patents, and is author or co-author of more than300 publications in journals and conferences, as well as a couple ofbooks. He is also adjunct professor of Beijing University of Posts andTelecommunications. His current research focuses are the mobility ofIoT technologies and services, as well as LBS services.