Telecommun Syst DOI 10.1007/s11235-011-9569-2
Wait, focus and spray: efficient data delivery in wireless sensor networks 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 sensor devices, we can envision a world of ubiquitous sensor networks. Usually, sensor data needs to be disseminated from the source to data collectors, making the spatially distributed sensor data available for applications. The widespread and ubiquitous nature of mobile devices, e.g., PDAs and cell phones around the world, makes them attractive to be used as mobile data collectors (MDCs) to collect and deliver the sensor data. The goal of this work is to design a dissemination protocol that leads to efficient data delivery from the source sensors to ubiquitous MDCs. We propose the WaitFocus-Spray (WFS) data delivery scheme for wireless sensor networks with ubiquitous MDCs. The main objective of WFS is to balance the data delivery latency and transmission overhead when considering the existence of ubiquitous MDCs. In WFS, we also propose a corresponding L. Cheng () · W. Jiao · J. Ma State Key Lab of Networking & Switching Tech., Beijing Univ. of Posts and Telecomm., Beijing, China e-mail:
[email protected] W. Jiao e-mail:
[email protected] J. Ma e-mail:
[email protected] M. Chen School of Computer Sci. and Tech., Huazhong Univ. of Sci. and Tech., Wuhan, China e-mail:
[email protected] C. Chen Nokia Research Center, Beijing, China e-mail:
[email protected] J. Ma Wuxi 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. We then present an analytical model based on the Markov chain model to analyze the trade-off between delivery latency and transmission cost in WFS. Through extensive simulations, we demonstrate that our proposed scheme reduces the transmission cost per message while provides comparable delivery delay compared with the alternative approach. Keywords Wireless sensor networks · Ubiquitous mobile data collectors · Data delivery
1 Introduction A wireless sensor network (WSN) is usually composed of a large collections of small autonomous sensor devices that can sense environmental conditions about the ambient environment. Recent technological advance enables the widespread deployment of WSNs for many different applications, including smart battlefield, healthcare, environment and habitat monitoring, home automation, and traffic control, etc. [1]. With the decrease in cost and size of sensor devices, we envision a world of ubiquitous sensor networks, where connected sensor nodes are scattered everywhere. These sensors will generate a large amount of sensor data. It is expected that the increasing number of available sensor data could be easily accessed by applications. Usually, sensor data needs to be disseminated from the source sensors to data collectors (e.g., sink nodes). Then the collected data will be further processed (e.g., capturing the contextual information) at a central base station or remote data center, making those spatially distributed sensor data available for applications to
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access. Therefore, data collection is one of the substantial problems in WSNs. Since bandwidth and energy are scarce resources in WSNs, it is critical to design scalable and energy efficient data delivery schemes. The sensor data is usually disseminated to a static control point, e.g., a static data sink, via multi-hop communication. For a large scale network, this process consumes significant amounts of energy especially in the area near the sink where nodes need to relay data from nodes that are farther away. Consequently, the increased energy consumption and failure of these nodes (hopspot problem) may lead to a disconnected and dysfunctional network [2]. In order to increase the uniformity of energy consumption and bridge coverage and connectivity gaps within the network, researchers propose to exploit the mobile elements for energy efficient data collection in WSNs. The widespread and ubiquitous nature of mobile devices, e.g., PDAs and cell phones around the world, makes them attractive to be used as mobile data collectors (MDC)1 to collect and deliver the sensor data. By exploiting the existing mobile devices to collect sensor data from large scale WSNs has fourfold advantages [3]. First, the data can be delivered to the destination with fewer hops. For example in [4], where the MDCs are referred to as Data MULEs, sensor data is collected from nearby source sensors via onehop communication. This significantly improves the energy efficiency by reducing the need for multi-hop communication. Second, a large scale WSN might become disconnected (or partitioned) into several islands for a variety of reasons [5], thus the central data collection could be impossible. In this case, exploiting MDCs can bridge the connectivity gap. Third, there is no expenditure of deploying dedicated mobile data collection robots. Fourth, mobile users can access ambient sensor networks to get context-aware value added services [6]. However, the real mobility of mobile users is always random and uncontrollable (possibly path-constrained). In most situations, the communication costs are reduced to a short distance single-hop communication at the expense of increased delivery delay. The Data MULE [4] approach is only suitable for delay tolerant applications because source sensor nodes have to wait to transmit data until a Data MULE is nearby. One intuitive approach to alleviate the undesirable long delivery latency is to allow the multi-hop multicopy communication, which can increase the spatial diversity between data copies and MDCs. For example, the epidemic routing [7–9] is always used in challenged wireless networks,2 where there is no fixed route from the source 1 In
this work, a mobile data collector (MDC) is referred to as a mobile device that collects the sensor data and then unloads it to a remote service center or central base station.
2 Also
referred to as delay/disruption-tolerant networks, intermittently connected, opportunistic networks.
to a MDC. In epidemic routing (flooding is the most simple case), where nodes continuously replicate and transmit messages to newly discovered contacts, the goal is to maximize message delivery ratio and minimize the delivery latency. However, it may incur very high resource utilization per message. So there exists a tradeoff between the delivery latency and transmission overhead for data delivery in WSN with MDCs. In this work, we present the Wait-Focus-Spray (WFS), a low-latency energy efficient data delivery scheme for WSNs with ubiquitous MDCs. Different from existing work, we not only introduce the data delivery scheme in the routing layer, we also design a customized contention-based forwarding protocol to support the WFS scheme in the MAC layer. Moreover, we propose a corresponding mechanismprobabilistic scattered binary spraying (PSBS), to reduce the spatial redundancy when spraying data copies, which can increase the probability of meeting a MDC. WFS balances the data delivery latency and energy efficiency and is based on the locally obtainable information, thus scalable with respect to the network size. To characterize the performance of the proposed WFS, we model the WFS routing process as a Markov chain, and analyze the trade-off between delivery latency and transmission cost. With extensive simulations, we show that WFS reduces the transmission cost per message while provides comparable performance in terms of the delivery delay. The rest of this paper is organized as follows. Section 2 surveys related work. Section 3 presents the network model and motivations of our design. Section 4 discusses the design of the Wait-Focus-Spray protocol in detail. Section 5 analyzes the trade-off between delivery latency and transmission cost, and compares our solution against other two alternative protocols. Simulation results are presented in Section 6. Finally, Section 7 concludes this paper.
2 Related work In this section, we briefly review the related work on exploiting mobile elements for energy efficient data collection in WSNs. Convergence of mobile devices and WSNs The convergence of mobile devices and WSNs can have a significant practical potential. The use of mobile devices to facilitate data collection in WSNs is not a new idea, which has been discussed by a number of researchers [3, 10–14]. The following characteristics make the existing mobile devices suitable as MDCs: (1) the widespread and ubiquitous nature in the pervasive computing environment; (2) mobility and opportunistic connectivity; (3) spare computing and communicating ability; and (4) predictable and rechargeable power supply.
Wait, focus and spray: efficient data delivery in wireless sensor networks with ubiquitous mobile data
Mobility-assisted data collection in dense WSNs For most of remote sensing applications, sensor nodes are battery powered, left unattended and expected to last over several months or years without recharging after the initial deployment. Therefore, optimizing energy consumption to extend the network lifetime is one of the important design goals in WSNs. In dense WSNs, where dense collections of networked sensors are deployed in the sensing field, the primary objective of mobility-assisted data collection is to move the MDC in the network to reduce the energy consumption in relaying 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 utilizing mobility to extend the network lifetime. Our work differs from existing related work in that we exploit the existing mobile devices as MDCs and take the ubiquitous nature of mobile devices into consideration. Thus, we investigate how to identify appropriate forwarding opportunities that could deliver the sensor data faster and more energy efficient to a MDC. While most existing work are based on the assumption that the number of sinks/MDCs is only one or far less than the number of sensor nodes. Mobility-assisted routing in challenged wireless networks Recent years have witnessed the emergence of a new kind of multi-hop mobile wireless network characterized by severely challenged connectivity. These challenged networks are different from traditional Mobile Ad Hoc Networks (MANET) in that no complete end-to-end paths exist most of the time, but only end-to-end path overtime might exist. Thus, the traditional protocols fail under such intermittent connectivity. Many research on challenged networks has been done [17–24]. Routing algorithms for delay tolerant networks are generally classified as either replication based or coding based [5]. In replication based algorithms, a number of message copies are generated and distributed to other relays in the network. Then, any of these nodes, independently of others, tries to deliver the message copy to the destination. Due to the node mobility, link connectivity between pairs of nodes comes up and down when they move into the radio range of each other. Finally, the first copy that arrives at the destination yields the optimum delivery latency. Epidemic routing [7–9], a replication based approach, has been proposed to reduce the data transmission delay in challenged wireless networks. In [25–27], the author 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. Unlike the previous work, in our network model, sensor nodes are static and any MDC could be the destination of the sensor data. In challenged wireless networks, it always requires a high amount of mobility by network nodes to achieve good
performance. We consider the large scale WSNs with ubiquitous MDCs, and study how to deliver sensor data to a MDC before the given deadline such that only a small number of copies are distributed in the network.
3 Network model and motivations In this section, we list the assumptions of our network model and then we describe the motivations of our design. 3.1 Assumptions • Sensor nodes are identical and can wirelessly communicate 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 announce the presence. • For a message, the number of copies distributed to the network is limited to n (we call n spraying tokens). • Each copy is delivered to the destination independently of others and has a TTL (time-to-live) limit. If the TTL expires, the data packet will be discarded. 3.2 Motivations We have the following intuitive observations that motivate the design of our scheme. When a sensor node has data to send, it has three different strategies to deliver the data. (1) Waiting until it encounters a MDC; (2) Forwarding the data to a different neighbor (to be consistent with previous work [22], we call it as the Focus strategy); (3) Spraying some additional copies of the data to neighbors. For the resource constrained WSNs, it is desirable that the sensor data could be delivered to a MDC timely with a small number of data copies distributed in the network. For the first strategy, it may experience a very long delivery delay, the data packet will be discarded when the deadline expires. Therefore, the naively waiting strategy is not suitable. Although spraying mechanism can increase the delivery probability, it also incurs extra use of bandwidth, buffer space and energy. There is a tradeoff between the delivery delay and the energy efficiency. Compared with the naive waiting, forwarding to an appropriate neighbor may increase the probability of meeting a MDC. If there is no appropriate neighbor which can provide larger probability of meeting a MDC, after waiting for a certain time, spraying scheme may help to increase the delivery probability before the given deadline by creating the diversity between copybearing relays and MDCs. In our design, we adopt a combined strategy adaptively. Data copies are only sprayed to other nodes on demand
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based on the locally obtainable knowledge. The details of the 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 the times that node i witnesses any MDC’s passing by within a given interval of time T . We have the estimated expected meeting time (EMT). EMT (i) =
T N (i)
+1
(1)
Note that EMT is a dynamic and local metric, where each node maintains its own EMT value. Whenever a node witnesses any MDC’s passing by, its EMT value will be updated. When a new data gets generated at a source sensor (or a cluster head if in the cluster-based WSNs), and needs to be delivered to a MDC, the source node first waits for a Twait time. During this time, it will finish the delivery task if meeting a MDC. When Twait expires, if the copy-bearing node has not delivered the data to a MDC, it will check whether it has any neighbour with a smaller EMT than itself. If there is, this neighbor will take the delivering task instead of the source sensor. If there is not, the source node will enter into the Spray phase. This process repeats until either the message is delivered to a MDC or the delivery deadline expires. The Wait-Focus-Spray state transition graph is shown in Fig. 1. In this work, we introduce an improved version of the Binary Spraying [19] for our spraying mechanism. Each copybearing node is associated with n “spraying tokens”. When performing the spraying mechanism, if n > 1, it may spawn and forward a copy of the data to a neighbor node, hand over n2 spraying tokens and keep the rest n2 for itself. Or it may hand over n2 spraying tokens to two neighbors, respectively, which depends on the locally neighborhood knowledge (when n is an odd, dividing n into n2 and n2 ). For the latter case, it is noteworthy that the current copy-bearing node will release itself from the delivery task after spraying. When n = 1, it can only perform the Focus operation. When a neighbor takes over its delivery task, it will discard the data copy. Compared with Spray-Wait [19] and Spray-Focus [22], our scheme first tries to forward the sensor data to a preferred neighbor to increase the delivery probability. The spraying operation is only performed on demand when a copy-bearing relay has not encountered a MDC for Twait time. While [19] and [22] spread all its copies quickly to the source 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 borrows the contention-based forwarding idea [28, 29] and is customized for WFS. Usually, each node maintains a neighbor table and makes the forwarding decision by looking up the neighbor table. Nodes employ the beaconing mechanism to update the local neighborhood information, e.g., the exchanging of EMT. However, due to the dynamics of MDCs in pervasive computing environment, nodes have to increase the beaconing frequency to keep their neighbors updated, but this significantly increases the network load as well as the energy consumption. Thus, to avoid these periodic transmissions, we adopt the beacon-less contention-based forwarding scheme. WFS-MAC uses a three way handshake mechanism to make dynamic forwarding decisions, similar in [29]. When a copy-bearing node (the sender) has data to focus or spray (possibly when the Twait expires), it first listens to check whether the channel is clear. If it is, the sender broadcasts the DATA message, which contains its EMT value. Then it waits for response for a predefined maximum time of BD (Backoff Duration). The backoff duration is divided into two parts: the Focus Contention Period (FCP) and Spray Contention Period (SCP), as shown in Fig. 2. If the channel is busy, the sender backs off and reschedules another attempt at a later time. The sender’s neighbor who successfully receives the DATA compares its own EMT (denote as EMT r ) with the sender’s (denote as EMT s ). If EMT r < EMT s , the neighbor will contend to take over the delivery task in FCP, i.e., take over all n tokens. If EMT r ≥ EMT s 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 calculated in a distributed fashion. ⎧ EMT r ⎪ ⎪ EMT s · TFCP + random(0, τ ), ⎪ ⎪ ⎨ if EMT < EMT r s Tbackoff = (2) ⎪(1 − EMT s ) · TSCP + TFCP ⎪ EMT r ⎪ ⎪ ⎩ + random(0, τ ), otherwise where TFCP and TSCP are the time durations of the FCP and SCP, respectively. τ is the time duration of one time slot, the random function aims to mitigate the radio interference. When the backoff timer expires, the neighbor will send out an ACK message, which contains its own EMT. At the same time, other contenders will cancel their timers if overhearing this ACK. If the sender receives an ACK during the Focus Contention Period, it will broadcast a SELECT message to the selected receiver after TFCP . Notice that it is possible some neighbors cannot overhear the replied ACK because of their positions, thus the sender may receive multiple ACK messages. The neighbor that first replies an ACK will win the competition as a subsequent copy-bearing node. The SELECT message has twofold functions: (1) confirming the selection with a final control packet; (2) suppressing all other contenders. After sending out the SELECT, the selected new copy-bearing node takes over the n spraying tokens, and the previous sender will discard it’s own data copy. If the sender receives an ACK during the Spray Contention Period, which means there is no neighbor having shorter EMT than the sender, it will enter into the Spray phase. In WFS-MAC, we propose a new binary spraying mechanism, named probabilistic scattered binary spraying (PSBS), to reduce the spatial redundancy. Consider an example as shown in Fig. 3, where the current copy-bearing node A sprays the data copies to its neighbors. It may spray to different neighbors. For example in Fig. 3(a), nodes B and E get the spraying tokens. Figure 3(b) shows that PSBS tends to reduce the spatial redundancy, which can improve the probability of meeting a MDC. The corresponding backoff time in view of the MAC layer is shown in Fig. 4. PSBS takes advantage of the broadcast nature of wireless communication to achieve the reduction of the spatial redundancy. If a neighbor has larger EMT than the sender, it will contend to get n2 spraying tokens when its backoff timer
Fig. 3 An example illustrating the probabilistic scattered binary spraying (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 sends an ACK to the sender to announce itself as a token receiver. Nodes D and E will cancel their backoff timers and withdraw from the contention if overhearing this ACK. But nodes C and F cannot overhear the replied ACK, and will contend to be another token receiver. Finally, node C’s timer fires earlier than F’s. It is also possible that the sender receives multiple ACK messages after the Spray Contention Period, the first two neighbors who reply the ACK messages will be considered as the potential token receivers. Since all the neighbors in the Spray phase have larger EMT than the sender, in our design, the sender makes a probabilistic spraying decision based on the EMT information. Let Pj denote the probability that the sender sprays n 2 tokens to the neighbor j who replies the second ACK. EMT s . After the Spray Contention Pj is calculated as Pj = EMT j Period, the sender generates a random number Prandom (0 ≤ Prandom ≤ 1). If Prandom ≤ Pj , the sender will spray all n tokens to the first two neighbors who reply the ACK messages without reservation. Otherwise, it reserves n2 tokens for itself and sprays n2 tokens to the neighbor who first replies the ACK. After the sender makes the spraying decision, it will send out the SELECT message, in which the selection result and the number of tokens are piggybacked. The selected neighbors will then take the delivery task, i.e., waiting the MDCs for a maximum Twait time.
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If the sender receives no ACK message during the Backoff Duration, that means there is no neighbor who can take over the data delivery task. In this case, the sender will iteratively wait for another Twait time until either the message is delivered to a MDC or the delivery deadline expires. 4.3 Suppressing spraying Once the tokens have been sprayed out, all copy-bearing nodes will deliver the data to a MDC parallelly. Hence, different MDCs may receive multiple copies of a data. In [5], the authors assumed that the destination acknowledges received messages using a broadcast to all nodes, thereby suppressing any spraying after the message delivery. This assumption might be quite strong and impractical for large scale WSNs. In this work, although this issue is not our focus, we would like to point out there is a possible solution to avoid too much energy consumption after the data is delivered 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 neighborhood based on the real-time location of MDCs. Then, the identifier of that received data can be piggybacked on the HELLO message sent from those MDCs periodically. If current copy-bearing nodes encounters a MDC, it will be informed and discard the copy that has already been delivered by another node.
adopt the probabilistic scattered binary spraying scheme to increase the copy count, thus to increase the delivery probability. We assume each node has a probability q to have a preferred neighbor who has less EMT than itself. Note that p and q are random variables in a practical system. Transmissions between two nodes may take place if they locate within the radio range and are assumed to be instantaneous. Therefore, at each time slot, each copy-bearing node has a probability (1−p)q to Focus, and probability (1−p)(1−q) to Spray. Each copy-bearing node works independently of others. The delivery deadline is K · Twait . We assume the number 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 number of duplicate copies of the message in the network at time slot t, respectively. The message duplication process in WFS can be modeled as a discrete-time Markov chain as shown in Fig. 5, where {D(t), N (t)} is a two-dimensional stochastic process. The state transition starts from {1, 1}. The source node encounters a MDC at the first time slot with probability of 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 is d to be the probability started from state {1, 1}. Defining Pm,n that at the mth time slot, the message is first delivered to a t MDC; Pm,n to be the probability that the one-step transition from {D(m), N (m)} to {D(m + 1), N (m + 1)} occurs. We have
5 Theoretic analysis
P1,1 = 1
In this section, we introduce a Markov chain model to describe the discrete stochastic process of the WFS and study its behavior based on the discrete Markov chain. We are interested in characterizing two important performance metrics of WFS: the upper bound of the expected message delay and the number of copies (the copy count) of the message at the time the message is first delivered to a MDC.
d =p P1,1
5.1 WFS scheme For simplicity and without loss of generalization, we assume that each node has a possibility p to meet a MDC during Twait period. Twait is defined as a time slot. Every Twait , if the current copy-bearing node does not encounter a MDC, it will firstly try to spray the copy to a preferred neighbor to increase the delivery probability.3 If no such a neighbor, it will 3 Since
we assume each node has a probability p to meet a MDC, in fact, each spraying operation will more or less increase the delivery probability, we derive the upper bound of the expected message delay and the copy count.
(3)
t P1,1 =1−p d Pm,n = (1 − (1 − p)n ) · Pm,n t Pm,n = (1 − p)n · Pm,n
(4)
Let P {m + 1, j | m, n} (j ∈ [n, 2n]) denote the onestep 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 equation of state probability, Pm+1,j =
j
t Pm,k · P {m + 1, j | m, k}
(5)
k= j2
Next, we derive the one-step transition probability. As shown in Fig. 6, for the state {m, n}, it has n possible transitions, from {m + 1, n} to {m + 1, 2n}. The one-step transition 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: transition diagram of the Markov chain for the message delay and copy count
Denote E(C) to be the expected copy count at the time the message is first delivered to a MDC. We have d d d d + · · · + PK,1 ) + 2 · (P2,2 + · · · + PK,2 ) E(C) = 1 · (P1,1 d + · · · + 2(K−1) · PK,2 (K−1)
=
Fig. 6 The one-step transition diagram
(K−1) 2
j · (P d
j =1
forward the data copies to their preferred neighbors. While P {m + 1, 2n| m, n} indicates all n copy-bearing nodes spray the tokens, thus the copy count is doubled. We have the onestep transition probability (j −n)
P {m + 1, j |m, n} = Cn
· (1 − q)(j −n) · q (2n−j )
(6)
Defining Pfail to be the probability that the delivery deadline expires, we have Pfail =
(K−1) 2
t PK,j
(7)
j =1
5.1.2 Expected first delivery delay and copy count Let E(D) denote the expected delivery delay at the time the message is first delivered to a MDC. From the Markov chain model in Fig. 5, we can calculate E(D) by substituting (3) (4) (5) into (8). d d d d d E(D) = 1 · P1,1 + 2 · (P2,1 + P2,2 ) + 3 · (P2,1 + P2,2 )
=
i=1
i·
i−1 2
j =1
j =1
K
j·
d Pi,j
We then analyze an alternative approach, which we refer to as the BSWF (Binary Spraying-Wait-Focus) [22]. In BSWF, when a new message gets generated at a source, and needs to be routed to a MDC, it first enters the Binary Spray phase for this message. Then, it will wait for Twait time. If meeting a MDC, it will deliver the data to the MDC and set the spraying tokens zero. When a relay for a given message has only one spraying token left for that message, it switches to the Focus phase. The transition diagram of the Markov chain corresponding to the BSWF scheme is given in Fig. 7, we have (i−1)
(8)
(9)
5.2 BSWF scheme
(i−1)
d Pi,j
d + · · · + PK,j )
j i=(log2 +1)
d 2 Pi,2 (i−1) = (1 − (1 − p)
d d + · · · + PK,2 + · · · + K · (PK,1 (K−1) ) K
=
(K−1) 2
j
(log2 +1),j
t 2 Pi,2 (i−1) = (1 − p)
t Pi,2(i−1) = P(i−1),2 (i−2)
) · Pi,2(i−1)
· Pi,2(i−1) (i ≥ 2)
(10)
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Fig. 7 BSWF scheme: transition diagram of the Markov chain for the message delay and copy count
Substituting the initial value (3) into the recursive equation (10), we have Pi,2(i−1) =
i−1
= (1 − p)(2
m=1 (i−1)
d 2 Pi,2 (i−1) = (1 − (1 − p)
(i−1) −1)
= (1 − p)(2
(11)
(i−1) −1)
) · (1 − p)(2
E(D) = =
K
d + 2 · P2,2
i
d + 3 · P3,4
+ ··· + K
d · PK,2 (K−1)
i · {(1 − p)
(2i −1)
− (1 − p)
}
(12)
d d d d + 2 · P2,1 + 3 · P3,1 + · · · + K · PK,1 E(D) =1 · P1,1
=
K
i · p · (1 − p)(i−1)
(17)
i=1
d + 2(K−1) · PK,2 (K−1) (i−1) −1)
2(i−1) · {(1 − p)(2
− (1 − p)(2 −1) } (13) i
i=1
The probability that the delivery deadline expires, K −1)
Pfail = (1 − p)(2
(14)
5.3 Naive Wait scheme Serving as a baseline of comparison, we analyze the Naive Wait scheme, where the source sensor transmits the data to a MDC via only the one-hop communication, i.e., no duplication is allowed and a message from the source naively waits the chance to be directly transmitted to a MDC. The Markov chain represented the Naive Wait scheme is shown in Fig. 8, we have the initial value d = p · Pi,1 Pi,1 t Pi,1 = (1 − p) · Pi,1 t Pi,1 = P(i−1),1
(i ≥ 2)
Pfail = (1 − p)K
(18)
5.4 Analytical results
d d d E(C) = 1 · P1,1 + 2 · P2,2 + 4 · P3,4 + ···
K
(16)
The probability that the delivery deadline expires, (2(i−1) −1)
i=1
=
d Pi,1 = p · (1 − p)(i−1)
Since no duplication is allowed, the copy count is always one. We have the expected delivery delay
− (1 − p)(2 −1)
Therefore, we derive the expected delivery delay and copy count at the time the message is first delivered to a MDC. d 1 · P1,1
Substituting the initial value (3) into the recursive equation (15), we have Pi,1 = (1 − p) · P(i−1),1 = (1 − p)(i−1)
(i−1) −1)
(m−1)
(1 − p)2
Fig. 8 Naive Wait protocol: transition diagram of the Markov chain for the message delay and copy count
(15)
In the test, we vary the meeting MDC probability p from 0.05 to 0.5. K is set 9, that is, the delivery deadline is 9 · Twait . We compare WFS with BSWF and the Naive Wait scheme. In WFS, the Focus probability q of each hop is set 0.2, 0.5 and 0.8, respectively. The analytical results are shown in Figs. 9, 10, 11. Figure 9 presents the analytical result for the expected delivery 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 increases, WFS degrades to the Naive Wait scheme. Note that only the successful delivery are counted, that is why the delivery delay of the Naive Wait scheme seems shorter than other schemes when p < 0.15. However, the fact is that, for the Naive Wait scheme, its expected delivery ratio is much lower than other schemes when p is a small value, as shown in Fig. 11. Figure 10 shows the analytical result for expected copy count when the message is first delivered to a MDC. From Figs. 9 and 10, it is clear to see that WFS achieves a better tradeoff between the data delivery latency and transmission cost. 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. 11 Expected delivery ratio vs. Meeting MDC probability
Naive Wait scheme. All the results have been averaged over 100 runs. 6.1 Simulation settings
Fig. 10 Expected first delivery copy count vs. Meeting MDC probability
WFS approaches to constant one. However, BSWF incurs much higher energy cost. Actually, when each copy-bearing relay sprays the copy to a preferred neighbor, the meeting MDC probability p will more or less increase. Although our model derives the upper bound analytical results of WFS, compared with the Naive Wait scheme, the delivery ratio has been significantly improved. And it can also provide comparable delivery delay compared with the BSWF.
6 Simulation In this section, we present simulation results to justify the WFS scheme using ns-2 simulator [30]. We compare our protocol with the alternative approach Spray & Focus [22]. In order to highlight the difference between our work and [22], we refer to Spray & Focus as the BSWF (Binary Spraying-Wait-Focus). Acting as the baseline of comparison, we also report the simulation results of the
In the implementation of our simulation, 400 sensor nodes are uniformly placed in a 400 m × 400 m field. The node transmission range is set 30 m. Each MDC sends HELLO messages every 5 seconds. The simulation starts at 0 s and stops at 300 s. The source node which has data to send is randomly selected. It generates the sensor data at the time of 200 s. Twait is set 8.0 s. For calculating the expected meeting time (EMT), T is set 600 s. The TTL limit is set 10 hops, and the initial spraying token number is 10. We implement our WFS-MAC protocol based on the modification of the IEEE 802.11 MAC. The MDCs move according to the Manhattan Mobility [31] model or the Random Waypoint model [32]. For the Manhattan Mobility model, the speed of a MDC is randomly selected from the range [2, 20] m/s. The number of horizontal and vertical streets is set 3, and the number of lanes is 12. For the Random Waypoint model, the speed of a MDC is randomly selected from the range [1, 15] m/s. The Manhattan Mobility model can be useful in modeling movement in an urban area where the pervasive computing service between portable mobile devices is provided [31]. In Random Waypoint model, the mobile nodes move randomly and freely without restrictions. This mobility model is always used to describe the movement pattern of mobile users over time in a random manner. The simulation parameters are summarized in Table 1. We select six evaluation metrics: • First delivery delay: We define this metric as the time taken for the first copy of a given data transmitted from the 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
TSCP
5s 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 a given data message scattered in the network so far when the first copy is delivered to a MDC. • First delivery transmission cost: The total number of DATA transmissions so far when the first copy is delivered to a MDC. • Copy count: The total number of copies of a given data scattered in the network when the simulation stops. • Transmission cost: The total number of DATA transmissions when the simulation stops. • Data delivery ratio: We define this metric as the ratio of the amount of data packets delivered to MDCs before the simulation stops to the total amount of packets sent by the source. Note that the copy count metric and transmission cost metric measure not only the communication overhead, but also the energy efficiency.
Fig. 12 Average first delivery delay vs. number of MDCs under the Manhattan Mobility model
Fig. 13 Average first delivery copy count vs. number of MDCs under the Manhattan Mobility model
6.2 Simulation results We examine the performance difference between our WFS and BSWF by varying the number of MDCs under the Manhattan Mobility model and Random Waypoint model, respectively. Figures 12 13, 14, 15, 16 report the evaluation results under the Manhattan Mobility model. Figures 17, 18, 19, 20, 21 show the evaluation results under the Random Waypoint model. Both WFS and BSWF achieve nearly 100% data delivery ratio with the ubiquitous MDCs. Figures 22 and 23 illustrate the average delivery delay and delivery ratio for the Naive Wait scheme under the Manhattan Mobility 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 delivery delay at increasing the number of MDCs. The figure shows that the first delivery delay decreases from 16 s to 5 s on average with increasing the number of MDCs for both
schemes. BSWF only behaves a little better than WFS. This is because BSWF first enters the Spray phase, i.e., scattering the spraying tokens to neighbors. From Fig. 12, we see WFS achieves the comparable first delivery delay in the pervasive computing environment. Figure 13 shows the average first delivery copy count as the number of MDCs changes. It is seen that WFS decreases the copy count scattered in the network on average. Note that for the BSWF implementation in our simulation, the sender won’t spray all tokens in one time, it will spray half of the tokens to a neighbor every Twait time. Thus, once meeting a MDC, it will zero clear its spraying tokens. Averagely, WFS can save one less copy count compared with BSWF when the first copy is delivered to a MDC. Figure 14 reports the average first delivery transmission cost under different number of MDCs. Note that this metric is not linearly proportional with the first delivery copy count. This is partly because our WFS-MAC protocol uses DATA/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 MDCs under the Manhattan Mobility model
Fig. 15 Average copy count vs. number of MDCs under the Manhattan Mobility model
Fig. 16 Average transmission cost vs. number of MDCs under the Manhattan Mobility model
Fig. 17 Average first delivery delay vs. number of MDCs under the Random Waypoint model
6.2.2 WFS vs. BSWF under the Random Waypoint model Therefore, when a copy-bearing node has only one spraying token left, it will still send DATA every Twait time before the TTL expires. Even this, WFS still behaves a little better than BSWF. Figure 15 plots the number of copies of a given message scattered in the network when the simulation stops. As the number of MDCs increases, the copy count for both schemes decreases as well. The figure clearly shows that WFS can save up to 50% improvements over BSWF. Figure 16 is the result of the transmission cost at increasing the number of MDCs. Revisiting Fig. 14, we know that after the first copy is delivered to a MDC, the transmission cost does not increase much for WFS. While for BSWF, more copy-bearing nodes may incur the higher transmission cost.
As under the Manhattan Mobility model, WFS yields comparable delivery delay compared with the BSWF under the Random Waypoint model, as shown in Fig. 17. However, both schemes experience longer delivery delay under the Random Waypoint model. This is due to the random mobility of MDCs. For the same reason, the result is not monotonically decreasing as the number of MDCs increases. Similar result in Fig. 18, it takes a little more copy count for both schemes under the Random Waypoint model than that in the Manhattan Mobility model. Figure 19 illustrates the average first delivery transmission cost as the number of MDCs increases. The simulation result corresponds well with the analytical result. WFS can significantly reduces the transmission cost per data message. Figures 20 and 21 show the similar results as in Figs. 15 and 16 under the Manhattan Mobility model. WFS shows
L. Cheng et al.
Fig. 18 Average first delivery copy count vs. number of MDCs under the Random Waypoint model
Fig. 20 Average copy count vs. number of MDCs under the Random Waypoint model
Fig. 19 Average first delivery transmission cost vs. number of MDCs under the Random Waypoint model Fig. 21 Average transmission cost vs. number of MDCs under the Random Waypoint model
obvious advantage over BSWF in terms of the energy cost for both mobility models. 6.2.3 Naive Wait scheme Figure 22 depicts the average delivery delay and data delivery ratio of the Naive Wait scheme under the Manhattan Mobility model. It is seen that Naive Wait scheme suffers a very long delivery delay and low data delivery ratio. Compared with the Naive Wait scheme, both WFS and BSWF significantly decrease the delivery delay and improve the data delivery ratio. We know the price is the increased transmission cost. However, WFS balances the data delivery latency and transmission overhead, improves the energy efficiency remarkably. It is demonstrated that WFS is a preferred dissemination protocol when considering the existence of ubiquitous MDCs. Figure 23 reports the average delivery delay and data delivery ratio of the Naive Wait scheme under the Random
Fig. 22 Average delivery delay and data delivery ratio of the Naive Wait scheme under the Manhattan Mobility model
Wait, focus and spray: efficient data delivery in wireless sensor networks with ubiquitous mobile data
7 Conclusion
Fig. 23 Average delivery delay and data delivery ratio of the Naive Wait scheme under the Random Waypoint model Table 2 Performance summary of WFS compared with BSWF Under Manhattan Mobility model
Under Random Waypoint model
First delivery delay
−19%
−11%
First delivery copy count
+38%
+29%
First delivery transmission cost
+16%
+40%
Copy count
+55%
+33%
Transmission cost
+68%
+58%
Waypoint model. Compared with the results under the Manhattan Mobility model, the delivery delay is increased. The reason is that MDCs move in the sensing field randomly under the Random Waypoint model, thus, increasing the time required to meet a MDC. However, the fully random mobility can also increase the data delivery ratio. Because in the Manhattan Mobility model, the movement of MDCs on streets is limited along the predefined roads, i.e., defined by maps. 6.2.4 Summary Simulation results above are shown that WFS minimizes the delivery cost per message with acceptable data delivery delay when compared with BSWF. Table 2 summarizes the performance improvement of WFS. In the table, positive values indicate the improvement of WFS, while the negative values reveal that WFS is inferior to BSWF. For example, −19% indicates that WFS incurs 19% longer first delivery delay than BSWF under the Manhattan Mobility model, +58% means that WFS provides 58% improvement over BSWF in terms of the transmission cost under the Random Waypoint model.
In this work we have proposed the WFS, an efficient data dissemination protocol when considering the existence of ubiquitous MDCs. Different from existing work, we not only introduced the data delivery scheme in the routing layer, we also designed a customized forwarding protocol to support the WFS scheme in the MAC layer, named WFS-MAC. In WFS, we further introduced the probabilistic scattered binary spraying (PSBS) mechanism to reduce the spatial redundancy when spraying data copies, which can increase the probability of meeting MDCs. Moreover, we presented an analytical model based on the Markov chain model to analyze the trade-off between delivery latency and transmission cost in WFS. We conducted extensive simulations to study the performance of WFS compared with the alternative approach BSWF. Simulation results demonstrated the suitability of the WFS scheme when ubiquitous MDCs are available. It significantly reduces the transmission cost per data message while still provides comparable performance in terms of the delivery delay. Acknowledgements The support provided by China Scholarship Council (CSC) during a visit of Long Cheng to University of Texas at Arlington is acknowledged. This work was in part supported by National Natural Science Foundation of China (NSFC) under grant No. 60873241; National 863 High Technology Program of China under grant No. 2008AA01Z217 and No. 2009AA01Z210.
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Long Cheng received his B.S. degree in Computer Science from Xian Telecommunication Institute, China, in 2004, and M.S. degree in Telecommunication Engineering from XiDian University, China, in 2007, and is pursuing his Ph.D. in State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China. He is currently a visiting scholar in the CReWMan Laboratory, Department of Computer Science and Engineering, University of Texas at Arlington, USA. He is a student member of IEEE, and his main research interests cover wireless sensor networks, Internet of Things, mobile computing, and pervasive computing. Weiwei Jiao received her B.S. degree in Computer Science from Jilin University, China, in 2001, and is pursuing her Ph.D. in the State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China since 2005. Her main research interests focus on wireless sensor networks and Internet of Things.
Min Chen is a professor at Huazhong University of Science and Technology, he was an assistant professor in School of Computer Science and Engineering at Seoul National University (SNU). He has worked as a Post-Doctoral Fellow in Dept. of Electrical and Computer Engineering at UBC for three years since Mar. 2006. Before joining UBC, he was a PostDoctoral Fellow at SNU for one and half years. He has published more than 120 technical papers. He received the Best Paper Runner-up Award from QShine 2008. He serves as editor or AE for Wiley I. J. of Wireless Communication and Mobile Computing, IET Communications, Wiley I. J. of Security and Communication Networks, Journal of Internet Technology, KSII Transactions on Internet and Information Systems, and International Journal of Sensor Networks. He is a managing editor for IJAACS. He is a TPC co-chair of BodyNets 2010. He is a symposia co-chair and workshop chair of CHINACOM 2010. He is the co-chair of MMASN-09, UBSN-10, GCCN-10 and NCAS-11. He was 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 several international journals, such as ACM MONET, IJCS. He is an IEEE senior member.
Canfeng Chen received his Bachelor in Information Engineering and Ph.D. in Signal and Information Processing from Beijing University of Posts and Telecommunications in 2000 and 2005, respectively. Since July 2005, he has been working for Nokia Research Center as a Postdoc Researcher, and joined Nokia Research Center as a Member of Research Staff in July 2007. He is a member of IEEE, and his main research interests cover wireless sensor networks, mobile networks and services, and radio resource management.
Jian Ma received his B.Sc. and M.Sc. in 1982 and 1987, respectively, from Beijing University of Posts and Telecommunications, and his Ph.D. degree in 1994 from the Department of Electronics Engineering, Helsinki University of Technology, Finland. Since then he had worked as principal scientist for Nokia Research Center for last 16 years. He is now a chief scientist at Wuxi SensingNet Industrialization Research Institute, Wuxi, China, and also act as the general secretary of Alliance of Sensing China. He has 47 granted patents, and is author or co-author of more than 300 publications in journals and conferences, as well as a couple of books. He is also adjunct professor of Beijing University of Posts and Telecommunications. His current research focuses are the mobility of IoT technologies and services, as well as LBS services.
