A scalable code dissemination protocol in heterogeneous wireless sensor networks

Transcription

1 . RESEARCH PAPER. SCIENCE CHINA Information Sciences June 2012 Vol. 55 No. 6: doi: /s A scalable code dissemination protocol in heterogeneous wireless sensor networks PENG ShaoLiang, LI ShanShan, LIAO XiangKe, PENG YuXing & XIAO Nong Department of Computer Science, National University of Defense Technology, Changsha , China Received April 26, 2010; accepted April 15, 2011; published online December 21, 2011 Abstract Code dissemination is currently a major research issue in wireless sensor networks (WSNs). Many studies focus on code dissemination in homogeneous WSNs, mainly using a broadcast approach to solve this problem; few studies on code dissemination in heterogeneous WSNs. Furthermore, broadcasting cannot readily be used to solve the heterogenous WSN code dissemination problem directly, which is where we have focused our attention. We transformed this problem into a minimum non-leaf nodes (MNN) Steiner tree problem. We designed a scalable multicast protocol, named Heterogeneous Sensor Networks Scalable Reprogramming Protocol (HSR) to solve the MNN problem. HSR can build different multicast trees according to different nodes or code modules to disseminate different codes to them. HSR is able to approximate the MNN tree problem to a ratio of ln R (R is the set of all destinations) best known lowest bound. Therefore, the communication cost is significantly decreased and the total energy required by WSNs is reduced. We further designed two scalable schemes, special routing log and hops-restricted local broadcast, which compress the multicast tree information and deliver the multicast messages without loss. We also designed a 3-stage pipeline to speed up the transmission of packets, which alleviated interference and hidden terminal issues. We evaluated our design through comprehensive simulations and prototype implementations on Mica2 motes. Experimental results demonstrate that HSR outperforms previous protocols including the most recent studies on Sprinkler and ucast. Keywords wireless sensor networks, reprogramming, code dissemination, multicast, scalable Citation Peng S L, Li S S, Liao X K, et al. A scalable code dissemination protocol in heterogeneous wireless sensor networks. Sci China Inf Sci, 2012, 55: , doi: /s Introduction Wireless sensor networks (WSN) are composed of many source constrained sensor nodes. These nodes are self-organized into a wireless communication network, which senses the physical environment and transmits data to the sink. In most WSN applications, it is not easy to update the codes of nodes because of adverse environments and the large scale of the networks. Often, many nodes are randomly distributed into unfrequented areas, such as forests and Mars (the planet). Thus, it is virtually impossible to gather all the nodes to reprogram them. Therefore, reprogramming sensor nodes, i.e., changing the software running on sensor nodes after deployment, is necessary in sensor networks. Reprogramming can also be helpful for reutilizing the resources of WSNs [1]. Corresponding author ( pengshaoliang@nudt.edu.cn) c Science China Press and Springer-Verlag Berlin Heidelberg 2011 info.scichina.com

2 1324 Peng S L, et al. Sci China Inf Sci June 2012 Vol. 55 No. 6 WSNs usually operate for extended periods of time unattended, where evolving analysis and environments can change application requirements, creating the need to alter the network s behavior by introducing new code. Unlike the traditional method of programming a node over a dedicated link, the embedded nature of these systems requires the propagation of new code over the network. As WSN research matures, growing test bed environments consisting of tens of thousands of nodes are now becoming a reality, making code propagation over the network a necessity in the debugging and testing cycle. These factors suggest that network programming (the programming of nodes by disseminating code over the network) is required for successful WSNs. In particular, we consider the propagation of complete binary images. Most of these studies [2 9] assume the WSN is homogeneous and a broadcast approach is a straightforward and efficient method. Much research has focused on transmission costs real time dissemination and reliability in homogeneous WSNs. To the best of our knowledge, research on network reprogramming in heterogeneous WSNs is less evident. Obviously, the broadcast approach cannot be used directly for reprogramming in heterogeneous WSNs. The key issue is that different codes need be burned into different nodes in heterogeneous WSNs. Network reprogramming in heterogeneous WSNs poses several new challenges: 1. Heterogeneity: Different nodes need different code images; thus broadcasting cannot solve this problem. In this paper, we consider using a multicast approach for reprogramming. 2. Transmission cost: Transmission is the main source of energy consumption, especially for sending packets as shown in Table Reliability: Network reprogramming requires 100% delivery, with two requirements: every node in the network must receive the program code, and the code image must be received in its entirety (Figure 1 illustrates the structure of codes data). This is very different from traditional sensor network applications in which occasional loss of data is tolerable. 4. Real time dissemination: The transmission cost of code dissemination is large because a code module is composed of over 40 packets [1]. All codes need to be disseminated to the right nodes as soon as possible. 5. Scalability: The scale of WSNs increases to cover more objects and areas. Consequently the scalability problem of code dissemination needs to be solved. Many other problems exist in code dissemination in heterogeneous WSNs, such as hidden terminals and storage problems. These problems and challenges must also be dealt with when we design our code dissemination protocol. We propose a Heterogeneous sensor networks scalable reprogramming protocol (HSR) in this paper. The main concept behind HSR is to construct different multicast trees for different types of nodes, and then compress and transmit the tree information to help the dissemination of the codes. First, global topology information needs to be collected in the base station. Then we must construct and approximate many minimum multicast trees for different type nodes to reduce transmission costs. It is well known that this problem is an NP-complete problem [10, 11]. Second, the information on the multicast trees needs to be compressed without loss. Finally, we must consider how to maintain these multicast trees and how to design a scalable, reliable, and real-time code dissemination protocol. A minimizing energy for code dissemination (MECD) problem is formed according to the assumptions above. More importantly, the MECD problem can be transformed into a minimum non-leaf nodes (MNN) Steiner tree problem. We designed the scalable multicast protocol HSR to solve the MNN problem. HSR can build different multicast trees according to different nodes or code modules to disseminate different codes to them. The key points of our contribution are as follows: 1. We formulated the problem of code dissemination in heterogeneous WSNs as an MNN problem, and designed a code dissemination protocol named HSR. The protocol included: a. The design of a scalable and energy-efficient multicast protocol named HSR, which is a centralized algorithm. By introducing an MNN problem, HSR builds an MNN multicast tree, and in theory achieves a best known lowest bound, ln R (R is the set of all destinations). Consequently, the multicast traffic can be decreased significantly. b. The design of two scalable schemes, the special routing log (SRL) and the hops-restricted local

3 Peng S L, et al. Sci China Inf Sci June 2012 Vol. 55 No Figure 1 Codes data in heterogeneous WSNs. Table 1 Mica2 node energy consumption Operation Energy consumption (nah) Sending a packet Receiving a packet Idle broadcast (HLB), which compress the multicast tree information and deliver the multicast messages without loss. They work well particularly when the scale of the WSN is large or with a huge number of destination nodes. 2. The HSR protocol was further extended including: a. Proposing a 3-stage pipeline TDMA scheme to speed up the transmission of packets and alleviate interference and the hidden terminal problem. The code dissemination time was decreased from O(m n) to O(3(m 1) + n)(wheren is the number of destinations, and m is the number of sending packets). b. Using HSR to build MNN multicast trees for heterogeneous sensor nodes and different code modules. Therefore, HSR can successfully solve the code dissemination problem in heterogeneous WSNs. 3. We conducted comprehensive simulations and prototype implementations on Glomosim [12] and Berkeley Mica2 motes to evaluate our HSR design. Moreover, we compared HSR with existing protocols such as unicast, Sprinkler [7] and ucast [10] The rest of the paper is organized as follows. We outline the related studies in Section 2. Problem statements and modeling are described in Section 3. Section 4 presents the details of the HSR protocol including proof of the bound of our algorithm in approximating the MNN problem, a real-time scheduling scheme, and scalability of HSR. In Section 5, we report on simulations based on prototype implementations. Our conclusions are presented in Section 6. 2 Related studies Several reprogramming systems have been designed and studied in the past few years, as summarized in Table 2. With the exception of Sprinkler, all are designed for the Berkeley TinyOS/Mote platform. Most reprogramming systems disseminate the compiled program image across the network. The overhead is usually large in cases when only minor changes occur between the new and old versions. Incremental update approaches compare the differences between the old and new programs, and only transmit the delta patch, script or module. Incremental network programming uses a differing algorithm optimized for sensors and assumes no prior knowledge of the program code structure (hardware independent). XNP [2] provides a single-hop, in-network programming facility for TinyOS. A special Boot Loader must be resident in a reserved section of program memory, and the XNP protocol module must be wired into an application to allow for subsequent XNP updates. A host PC application XNP loads the image via a base station mote running TOSBase (this acts as a serial-to-wireless bridge) to one (mote-id specific) or many (group-id specific) nodes within direct radio range of the base. The image is sent in capsules, one per packet, and there is a fixed time delay between packet transmissions. In unicast mode, XNP checks delivery for each capsule and in broadcast mode, missing packets are handled, after the full image download has been completed, using a follow-up query request (nodes respond with a list of missing capsules). The program is loaded into external (nonprogram) memory. Applications are halted during

4 1326 Peng S L, et al. Sci China Inf Sci June 2012 Vol. 55 No. 6 Figure 2 Code dissemination in heterogeneous WSNs. Table 2 Related studies [1] Protocol name Reprogrammin pattern MAC Hop Pipeline XNP [2] Complete program CSMA Single-hop No Trickle [3] Script CSMA Multihop No MOAP [4] Complete program CSMA Multihop No Deluge [5] Complete program CSMA Multihop Yes MNP [6] Complete program CSMA Multihop Yes Sprinkler [7] Complete program TDMA Multihop No Firecracker [8] Complete program CSMA Multihop No TinyCubus [9] Modular update CSMA Multihop No the program download. When a reboot command is issued (via the XNP host program), the boot loader is called which copies the program from the external to program memory, and then jumps to the start of the new program. MOAP [4] is a multi-hop, over-the-air code distribution mechanism specifically targeted at Mica2 motes running TinyOS. It uses store-and-forward, providing a ripple pattern of updates. Lost segments are identified by the receiver using a sliding window, and are re-requested using a unicast message to prevent duplication. A keepalive timer is used to recover from unanswered unicast retransmission requests which sends a broadcast request when it expires. The base station broadcasts published messages advertising the version number of the new code. There is no support for rate control or suppression of multiple senders apart from link statistics. Deluge [5] is a data dissemination protocol and algorithm for propagating large amounts of data throughout a WSN using incremental upgrades for enhanced performance. It is aimed in particular at disseminating software image updates identified by incremental version numbers for network reprogramming. There is no support for heterogeneity because the same image is disseminated to all nodes in the network. The program image is split into fixed size pages that can be reasonably buffered in RAM, and each page is split into fixed size packets so that a packet can be sent without fragmentation by the TinyOS network stack. MNP [6] is targeted at MICA2 motes running TinyOS and uses the XNP boot loader along with a dedicated network protocol to provide multi-hop, in-network programming. The MNP protocol operates in four phases. MNP supports pipelining to accelerate reprogramming in multihop networks, while MOAP uses a sliding window concept. MNP uses negotiation to suppress hidden terminals. The source node sends out the ADV twice. In Table 2, the reprogramming pattern type, MAC, hop type, and whether a pipeline is used are shown. Most of them are homogeneous WSNs, and broadcast is the main method to disseminate codes. These methods cannot work well in heterogeneous WSNs. As is shown in Figure 2, heterogeneous sensor nodes need different code modules. For example, cluster header and nodes intra cluster need to be burned with different programs in hierarchical WSNs. We propose a code dissemination protocol for heterogeneous WSNs, named HSR, and analyze the bound of the approximate algorithm. We also presents the details of HSR protocol, and evaluate our design through comprehensive simulations and prototype implementations on Mica2 motes.

5 Peng S L, et al. Sci China Inf Sci June 2012 Vol. 55 No Figure 3 Two kinds of multicast tree. 3 Problem statements 3.1 Assumptions To simplify the problem model and analysis we make some assumptions as shown below: V = n nodes are randomly distributed in a two-dimensional area and the number of destinations is R(R <n). The communication radius of node is R = 10 log n/n [13] to connect the WSN. There are m-type different nodes in the WSN, so m-type different codes need to be disseminated to them as shown in Figure 2. Considering the local broadcast in WSNs, we assume all one-hop neighbors of the node can overhear the packet if a node sends one (Figure 3 illustrates the local broadcast). There are two typical traffic patterns in sensor networks: base station and peer-to-peer [14]. The peer-to-peer pattern is usually used for data aggregation and consensus in a small area where a set of nearby motes interact with each other. Furthermore, each node is resource-constrained, and only knows the local topology. The base station pattern usually issues multiple queries, thresholds or commands from the base station (sink) to a large number of destinations and pursuers. The base station pattern is the most representative traffic pattern inside sensor networks [14]. Therefore, we assume the traffic pattern is a base station pattern. In a period of t, most nodes in a heterogeneous WSN are static and the topology of the WSN is less changeable. All nodes can sleep and awake periodically, but the total energy consumption is proportional to the transmission cost in the WSN. 3.2 Problem statements The m-type different nodes need m-type different codes in heterogeneous WSNs. We model the MECD problem to minimize the transmission cost. We build one MECD multicast tree for every type of node. The problem of finding the multiple optimal multicast trees in the networks can be treated as searching for multiple edge weighted Steiner trees [14]. Specifically, the problem can be formally defined as follows: Given: A graph G =(V,R,E,d), where V is a set of vertices, containing a subset R i V of m-type destinations (R = m i=1 R i, also called terminals), E = V V is the set of edges, and d is a weight function d: E R. Find: A set of edges T i E that connect together the elements of R i (and possibly some of V R i ) into a tree, such that the cost of T, m i=1 e T i d(e) is minimized. Note: The elements of V R i will be referred to as optional vertices, which are also called Steiner nodes [10]. Thus the MECD problem is turned into a problem of minimizing the total cost of multiple multicast trees. It is well known that the edge weighted Steiner tree problem in an undirected graph is NPcomplete [10]. Many efficient heuristics [10] have been proposed in the literature to approximate a minimum Steiner tree in graph theory. Therefore, the problem of code dissemination in heterogeneous

6 1328 Peng S L, et al. Sci China Inf Sci June 2012 Vol. 55 No. 6 WSNs is NP-complete. The code dissemination for m-type nodes is independent, so the m multicast trees are constructed independently. Therefore, our goal can be transformed by constructing an optimal Steiner multicast tree. Even though we can well approximate the Steiner tree problem [11], these approximation solutions may not apply to a WSN because of the unique characteristics of WSN communications. Given the example in Figure 3, the lengths of the two trees in both the figures are 6, while the WSN context traffic for the left-hand tree is 6 and that for the right-hand tree is only 4. The traditional solution to the Steiner tree only pursues minimizing the total length of tree, which is not enough to achieve our goal of minimizing the multicast traffic in WSNs. We formulated this new problem into an MNN problem. Given a WSN graph and a multicast request consisting of a sink and a destination set, the MNN problem is to construct a multicast tree rooted at the sink spanning all destinations such that the sum of the number of transmissions at non-leaf nodes is minimized. The problem involves the choice of non-leaf and leaf nodes. Note that the leaf nodes do not contribute to any transmission power consumption because they do not transmit any messages. The problem is at least as hard as the set-cover problem [10], for which the best known lower bound on the approximation ratio is ln R. In the next section, we will present a heuristic algorithm with the lowest theoretical approximation ratio of ln R. Moreover, we focus not only on minimizing transmission cost but on real-time and reliable transmission. We will design a code dissemination protocol, named HSR, to solve these problems as shown below. 4 HSR HSR includes five major components: Initialization; Tree Computing; Message Broadcasting; and Maintenance. The sink gathers network topology to first compute the multicast tree, and then send out the routing and multicast message. Next, HSR needs to maintain the multicast procedure. Finally, a pipeline based TDMA scheme is proposed. Figure 4 describes the major components of HSR. 4.1 Initialization The sink collects the neighborhood information on each sensor. The WSN is different from ad-hoc because most of the nodes are static. The sink can also implement an intelligent restricted flooding [11] toward the destinations to reduce the flooding cost. The topology information may include node location, ID, or the relationship of neighboring connections. The global topology and real wireless link graph G can be formed at the sink. Flooding will only be used again to recollect the information when the whole topology changes significantly. 4.2 Computing the multicast tree In this section we present a global approximation algorithm with an approximation ratio of ln R i (R i :the ith-type destination nodes) when we compute the multicast tree, T i.wehave R i terminals in a graph G, which we want to connect using the least number of non-leaf nodes. We assume that the non-leaf nodes have weight 1, and the leaf nodes have weight 0. Some adjacent destination nodes can be formed into a component as shown in Figure 5. Where each non-leaf node has the same cost, and we count the number of non-leaf nodes in our final solution, we can show that an approximation guarantee of ln( R i + θ(1)) is possible. We can also show that if the value of the optimal solution is at least a fixed constant then we can achieve an approximation factor of ln R i, the lowest theoretical bound [10]. We first note that connected components induced by terminals can always be shrunk to a single terminal. Our algorithm runs in two phases. In the first phase, the algorithm greedily picks high degree stars(a star is a vertex that has at least two required vertices as neighbors) and merges them, until very few components are left. In the second phase, the algorithm runs a Steiner tree (edge) approximation algorithm with each edge having unit weight.

7 Peng S L, et al. Sci China Inf Sci June 2012 Vol. 55 No Figure 4 HSR protocol. Figure 5 Using component to compute the MNN multicast tree. We pick λ =2C S +1 where C S is the best approximation ratio for the unweighted Steiner tree problem [10]. Algorithm A Step 1. In each iteration we choose a vertex that merges the largest number of required vertices until iteration count we reach the stage where the number of components left to merge is less than the ln R λ + e λ or no merging is possible. Step 2. We apply an (edge weighted) Steiner tree approximation algorithm, with each edge having unit weight. Theorem 1. The above Algorithm A finds a solution to the MNN multicast tree problem with an approximation factor of ln R i (which is the best possible), when the optimal solution is greater than C s e λ. Proof. Assume that the set of components remaining after the first phase is A. We claim that there is a Steiner tree with at most A + OPT edges. Thus when we apply an (edge weighted) Steiner tree approximation, we get a tree with at most C s ( A + OPT ) edges. If the number of iterations in the first phase is r, the final solution has a cost r + C s ( A + OPT ). We now proceed to give a bound on r. Let a i components be left after the ith iteration. Since OPT nodes are capable of merging these components, for each i, intheith iteration, there must be a node that merges ai 1 OPT components. This gives a bound on a i, ( ) ai 1 1 a i a i 1 +1 a i OPT OPT 1 We can easily verify that a i a 0 (1 OPT )i + i 1 j=0 (1 1 OPT )j. The second term is a geometric series that sums to at most OPT. Thus when i=(ln R λ) OPT the first term is a geometric series that sums to at most e λ, and the number of components a i OPT +e λ i ln R λ + eλ. This guarantees that the number of iterations, r (ln R λ) OPT. If we stop because merging by stars is not possible, then the components have disjoint neighborhoods, and OPT has to pick at least one vertex from each neighborhood. Thus A OPT. If we stop because the number of components is small, then A OPT + e λ. In any case, A OPT + e λ and this yields a solution of cost at most ln R OPT + C s e λ +(2C s λ) OPT. Putting λ =2C s +1 gives at most ln R OPT vertices in our solution when OPT C s e 2Cs+1. We can modify the above algorithm, to run until no further merging is possible. Algorithm B Step 1. In each iteration choose a vertex that merges the largest number of required vertices (at least two). Step 2. Apply an (edge weighted) Steiner tree approximation algorithm, with each edge having unit weight.

8 1330 Peng S L, et al. Sci China Inf Sci June 2012 Vol. 55 No. 6 Theorem 2. The above Algorithm B finds a solution to the MNN multicast tree problem with an approximation factor of lnδ + 2Cs +1, where Δ is the maximum degree. Proof. As before, let a i denote the number of be vertices left after the ith iteration and a 0 = R. Then after OPT ln a0 OPT, there are at most 2 OPT components to connect. Hence we will continue to merge by stars for OPT more iterations until the number of components is definitely less than OPT. Because each Steiner vertex can be adjacent to at most Δ required vertices, OPT a 0 /Δ. If at this stage we use a f more iterations before invoking the edge weighted Steiner tree algorithm, there is a tree with OPT a f + OPT edges. Thus we can find a solution of cost at most C s (OPT a f + OPT ). The final solution has at most OPT ln a0 OPT + OPT + a f + c s ( OPT a f + OPT ) nodes. Because OPT a 0 /Δ, we get a performance guarantee of lnδ + 2C s + 1 for the algorithm. Algorithms A and B are two approximation algorithms for the MNN multicast tree problem. Theorems 1 and 2 show that the HSR can achieve a best known performance ratio of about ln R [5]. Consequently, the multicast traffic can be decreased significantly. We will further test our algorithms through simulations and experiments in the next section. 4.3 Sending routing and multicast messages When the sink computes a minimal cost multicast tree T i for the ith nodes, it compresses, encodes the tree and sends the routing messages out. HSR adopts two techniques, the SRL and the HLB to save cost when the multicast tree is deep and wide. They can also make HSR more scalable, especially in large scale WSNs and where there are destination nodes SRL When the ith destinations nodes are far from the sink, the routing path is long, and the routing information cannot be included in the packet header. Generally in HSR, a remote node is connected to the sink along the shortest path, obtained through a local routing protocol like GPSR. Therefore, it is not necessary to encode all routing nodes into packet headers. If some routing nodes calculated by the sink are not in accordance with those based on GPSR, the nodes are indicated as special nodes. All special next-hop nodes form a SRL. Obviously, the length of SRL is much shorter HLB As mentioned before, HSR uses the ARG algorithm to partition the adjacent nodes into a k-component (cluster) to maximize the local broadcast. In this case only the cluster header and the hops of local broadcast need to be encoded into the packet header to compress the multicast tree. As shown in Figure 6, all destinations and forking nodes are divided into four different components, A, B, C and D. We need only to encode the information of A, B, C and D into the packet header, so all destination nodes can receive the code. The HLB scheme works well when the multicast tree is wide. SRL and HLB are both helpful for the scalable problem of code dissemination in heterogeneous WSNs. 4.4 Maintaining the multicast tree We focus on two types of dissemination protocol problems in heterogeneous WSNs: node failure and packet loss Node failure For node failure, we mainly deal with the nodes on the multicast tree whose failure will influence the routing and local broadcast. A new reinforced path should be established in a timely manner in case of node failure and the sink should be notified to reconstruct the optimal multicast tree. In HSR, different measures are taken to deal with the failure of different kinds of nodes. When a cluster header fails, a new cluster header needs to be selected to carry on the local broadcast. A secondary cluster header can be selected from other nodes in the cluster when we partition nodes into

9 Peng S L, et al. Sci China Inf Sci June 2012 Vol. 55 No Figure 6 HLB scheme. k-components (clusters). The secondary cluster header always has more neighbors than other nodes in the cluster, but fewer than the first cluster header When a node in the routing path fails, its upstream node will choose another next-hop neighbor like GPSR and continue to transmit the packet because all routing information is encoded into the packet header using the SRL scheme. This path may not be the shortestormostenergy-efficient. Therefore, this failure information needs to be notified to the sink to recalculate the optimal multicast tree. When a node finds its downstream neighbor in the tree cannot work, it adds the neighbor node ID into its data packet. The failure information can then be piggybacked on the back-channel to deliver this failure information to the sink. The creation of the back-channel is free of charge in that no additional transmissions or control data traffic are needed to create it. We only need a few additional bytes in the data packet to hold the ID of the failure nodes Packet loss In sensor networks, data that flows from sources to the sink generally tolerate loss. However, data that flow from the sink to multiple sources similar to multicast are usually for the purpose of control or management (e.g. retasking sensors [1]) and are sensitive to message losses. In this case, a negative acknowledgment (NACK) can be used to support reliable data transmission in HSR because this kind of application always needs to transmit continuous queries, value thresholds and instructions. When a node detects a missing fragment after a TTL period, a repair request is sent downstream on the reverse path toward the sink. If the requested fragment is in the local cache, a response is sent; otherwise the NACK is forwarded to the next hop toward the sink. We can also use the method of three times handshake to improve the reliability of packet transmission, as done in Stream [15]; however, the control overhead and delay in the negotiation method are significantly large 4.5 Real-time scheduling Our HSR protocol takes advantage of pipelining [6] to allow parallel transfers of data in networks. Pipelining is done through segmentation: a program is divided into several segments, each of which contains a fixed number of packets. Instead of completely receiving a whole program before forwarding it, a node becomes a source node after it receives only one complete segment. For transmission of a large program, pipelining can significantly increase overall throughput. Other than pipelining, there is another major benefit of segmentation: without segmentation, a large program has thousands of packets. As a consequence, each node needs a large number of states to record the packet information (received or not). We propose a 3-stage pipeline real-time scheduling method based on TDMA. In Figure 7, corresponding to a simple channel model, a dashed line represents the interference range, and a solid arrow represents the reliable communication range. Due to the hidden terminal problem, the simultaneous data transfer from A to B will collide with the transfer from C to D. The parallel transfers should take place with at least three-hop spacing. As is shown in Figures 7 and 8, we can build a 3-stage TDMA pipeline if the hidden terminal problem can be avoided within three hops in the WSN. In stage 3, nodes A and D can

10 1332 Peng S L, et al. Sci China Inf Sci June 2012 Vol. 55 No. 6 Figure 7 A 3-stage pipeline. Figure 8 Real-time scheduling of a 3-stage pipeline. (a) Routing trace; (b) 3-stage pipeline. Figure 9 MNN multicast trees for different types of destination nodes. Figure 10 MNN multicast trees for different types of code modules. send packets simultaneously. Node A can send packet 2 after packet 1. Figure 8 combined with Figure 7 gives an example in which node D sends packet 1 to node E, and node A can send packet 2 to node B at the same time. We draw a conclusion from Figures 7 and 8 that in a network of n nodes, without pipelining, transferring m packets needs a total time of O(m n). With pipelining, the total time is reduced to O(3(m 1)+ n). HSR adopts an adaptive sleeping scheme. It reduces idle listening time by putting a node into the sleep state when its neighbors are transmitting its unneeded data. The sleep duration is carefully calculated according to the transmission time. HSR supports pipelining to accelerate reprogramming in multi-hop networks. As shown in Figure 1, m-type different codes need to be disseminated to the m-type different destination nodes. Thus m-type different MNN multicast trees need to be built for m-type different destination nodes. In Figure 9, we can build the MNN multicast tree T i for the ith destination node and i MNN multicast trees for all the destination nodes (i: the total number of destination node types). In Figure 10, we can apply the same method for code modules and build the MNN multicast tree T i for the ith destination nodes and i MNN multicast trees for all the destination nodes (i: the total number of code module types). Therefore, the HSR protocol can build different multicast trees according to different nodes or code modules to disseminate different codes to them. The problem of code dissemination in homogeneous WSNs can be solved successfully using the HSR protocol. We will evaluate our design through comprehensive simulations and prototype implementations on Mica2 motes in the next section. 5 Performance evaluation of HSR In this section, we present the performance evaluation results of HSR using GloMoSim [12] and Matlab 2007 with respect to three main aspects: energy consumption, multicast traffic delay, and reliability. To demonstrate the superiority of HSR, we compare it with the other four representative protocols, Sprinkler [1], MNP [1], the most recent release of the ucast multicast protocol [10], and the plain unicast protocol [11].

11 Peng S L, et al. Sci China Inf Sci June 2012 Vol. 55 No Table 3 Simulation parameter settings MAC layer TDMA Radio layer RADIO-ACCNOISE Model of transmission TWO-RAY Bandwidth 256 Kb/s Payload 32 bytes Area of deployment ( m) Number of nodes 636 Number of packets 100 Radius of transmission 50 m Simulation time 3600 s Nodes deployment Uniform random Number of destinations 3 15 We assume m-type different codes need to be disseminated to the m-type different destination nodes in Figure 10 and m-type different code modules need to be disseminated to the m-type different destination nodes in Figure 11. Therefore, our simulations will show that the HSR protocol can build different approximate optimal MNN multicast trees according to different nodes or code modules. Thus the total energy consumption and delay is shown to be approximately optimal. All the nodes in our simulation can be homogeneous or heterogeneous. We will show that our HSR can work well whether the WSN is homogeneous or heterogeneous. Unless otherwise stated, the default parameters are those in Table 3. The radius of the pie shape area is 250 m. We selected different time lengths for evaluation. The sink node and destination nodes are randomly selected. We assume that each node has the same transmission power level. The simulations were conducted in the GloMoSim [12] environment as MNP [6] and ucast [10]. The simulations were repeated five times every 5 minutes and the average values were saved. 5.1 Transmission cost Figure 11 shows the transmission cost of five protocols when the number of destinations differ. We assume the transmission cost of one packet sent from one node to its neighbor is 1 [10]. The total traffic is related to the number of packets and the hops from the sink to the destination. We note that the HSR protocol refines the idea of the MNN multicast tree theory, which can build approximately optimal multicast trees for different types of destination nodes and code modules as shown in Figures 9 and 10. We can clearly see that HSR performs best among the five protocols. As the theoretical analysis and result of Section 4 indicate, the transmission cost of HSR is the least. HSR achieves 40% performance improvement compared with that of Sprinkler [1] in traffic conditions. 5.2 Energy consumption To accurately estimate energy consumption, we use realistic parameters of MicaZ [2] nodes in energy consumption simulations. More specifically, energy consumption comes from both sending and receiving packets. According to the data sheet of the CC2420 radio on MicaZ, sending and receiving have current levels of 17.4 and 18.8 ma. The voltage supply is assumed to be 3 V, and the data rate is 250 Kbps. Packets are assumed to have a payload of 32 bytes, and each destination requires 4 bytes in the header. The important metric we use is the total energy consumption, in joules, for sending 100 packets to all destinations from the sink. In the following simulations, ucast and unicast are integrated with GPSR. Figures 11 and 12 show the transmission overhead of HSR compared with those of the other four protocols. We make several observations. HSR outperforms the other existing protocols in traffic and energy consumption. Wherever the destinations are placed, HSR is more energy-efficient, and is less affected by the positions of the destination nodes than any other protocols. As emphasized earlier, ucast is designed primarily for small group multicast, and is a local approximately optimal multicast protocol. As expected, HSR performs better than Sprinkler and MNP [1]. We note that the HSR protocol refines

12 1334 Peng S L, et al. Sci China Inf Sci June 2012 Vol. 55 No. 6 Figure 11 Traffic comparison. Figure 12 Energy consumption comparison. Figure 13 Delay comparison. Figure 14 Reliability comparison. the idea of the MNN multicast tree theory, which can further enhance wireless link sharing and maximize local broadcasts among the neighboring destinations. On the contrary, Sprinkler only tries to find the shortest path in each step, which may not consider the effect of link sharing and local broadcasting. The preliminary results also indicate that the HSR has many advantages over unicast which demonstrates that multicast in WSN deserves further study. The cost of sink flooding and gathering the global topology information may not be small if only a few data packets are sent to destinations. In this case, ucast can perform well because ucast is connectionless and local, and does not need to maintain the global topology information. This is the great merit of ucast. In practice, in many applications of WSNs, a large number of packets need to be sent to different destinations. HSR is mainly designed for long-term and large-scale multicast. The number of packets and destinations is huge in HSR. Thus in Figures 11 and 12, HSR is more energy-efficient than ucast when the 100 packets are sent to different destinations in 100 multicast sessions. The more packets sent to different destinations, the more energy-efficient SenCast becomes. Therefore, the cost of flooding and initialization in HSR can be ignored when a large number of data packets are sent to different destinations. 5.3 Delay Figure 13 shows the comparison results of the delay. Due to the effect of path aggregation, we observe that HSR, MNP, ucast, and Sprinkler deliver packets along longer routes compared with unicast. This is intuitive, because unicast typically finds near-optimal paths. The increase in the path length means that HSR, MNP, ucast, and Sprinkler may have a slightly higher end-to-end delay. However, the average path length of HSR is shorter than that of ucast and unicast because HSR considers the link share and path aggregation and uses the idea of the shortest path. HSR tries to find a good tradeoff point between path length and link sharing to minimize multicast traffic.

13 Peng S L, et al. Sci China Inf Sci June 2012 Vol. 55 No A 3-stage pipeline real-time scheduling method is designed in Subsection 4.5 (Figure 8). The method can successfully solve the problems of the hidden terminal and interference. Thus HSR performs the best of the five protocols for delay. With the 3-stage pipelining, the total delay of HSR is reduced from O(m n)too(3(m 1) + n). The results are shown in Figure Reliability Network reprogramming requires high percentage delivery, which includes two parts: every node in the network must receive the program code, and the code image must be received in its entirety. This is very different from traditional sensor network applications in which occasional loss of data is tolerable [16]. The energy conservation model we use is random sleep scheduling to prolong the lifetime of the whole WSN. For example, in Figure 14, 10% or 20% sleep scheduling with a 10 s toggle period means those 10% or 20% nodes in the whole WSN sleep for 10 s in every 10 s. Each node has the same toggle period. Figure 14 shows the performance evaluation results. These two experiments are carried out using a data rate of 6 packets per minute. The comparison results demonstrate the superiority of HSR in the presence of node state transitions. The HSR performs nearly as well as the stateless ucast owing to Piggyback and Hop-by-Hop NACK-based Repair schemes (see Subsection 4.4). We observe that HSR achieves a delivery ratio of around 90%, which is good enough for common multicast purposes. 6 Conclusions and future studies This study focuses on code dissemination in heterogeneous WSNs, proposing HSR based on multicast. HSR can build different optimal multicast trees according to different nodes or code modules and encode the tree using SRL and HLB schemes. A 3-stage pipeline method is added into the HSR protocol to speed up the transmission of packets, which alleviates the interference and hidden terminal problems. We also introduce novel schemes to reinforce and complement HSR, which are helpful to address reliability and scalability on code dissemination in heterogeneous WSNs. We prove theoretically that the performance ratio of our design is ln R, which is the best among the existing code dissemination schemes. Our simulations on Glomosim and Matlab 2007 demonstrate HSR s performance compared with those of MNP, Sprinkler, unicast and ucast. Our future studies will continue the integration of energy consideration into HSR, i.e., we will consider different energy management goals and adjust HSR to further reduce the energy costs. In addition, HSR is based on the concept of reactive routing; our future work will consider other cases and problems such as multiple sinks, proactive, interference, and opportunistic routing as well as geographical routing. Acknowledgements This work was supported by National Natural Science Foundation of China (Grant Nos , ), Hunan Natural Science Foundation (Grant No. 11jj4053), National Basic Research Program of China (Grant No. 2011CB302601), and China 973 WSNs Joint Lab at Shanghai. The authors would like to thank Professor Tian He and Dr. Qing Cao for providing the source code and valuable data on ucast. We are also grateful to Professor Weijia Jia, Guoliang Xing, Yunhao Liu and the anonymous reviewers for their comments and suggestions. References 1 Wang Q, Zhu Y, Cheng L. Reprogramming wireless sensor networks: challenges and approaches. IEEE Network, 2006, 20: Crossbow Technology Inc. Mote In-Network Programming User Reference. Version , Levis P, Patel S, Shenker S, et al. Trickle: A self-regulating algorithm for code propagation and maintenance in wireless sensor networks. Technical Report, UCB/CDS , Computer Science Dept. 2003

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