A Geography-free Routing Protocol for Wireless Sensor Networks Yunhuai Liu 1, Lionel M. Ni 1 and Minglu Li 2
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1 A Geography-free Routing Protocol for Wireless Sensor Networks Yunhuai Liu, Lionel M. Ni and Minglu Li Department of Computer Science Hong Kong University of Science & Technology {yunhuai, Abstract --- In wireless sensor networks, it is critical to provide the data delivery services between sensors and the data collection unit (called sink). Some of the existing approaches require location or ID information which is quite expensive. For those without location or ID information, the usage of the collected information is rather limited. In this paper, we propose a geography-free coordinate system, called GREENWIS, to assist routing. In GREENWIS, sensors are identified by a -tuple with which messages are universally transmitted. Our analysis and experiments show that GREENWIS is reliable and power efficient compared with existing approaches. I. INTRODUCTION Sensor network has become one of the most promising technologies for its extensible capabilities []. The ad-hoc mode and flexible means of deployment make it appropriate for a broad spectrum of applications ranging from environmental and habitat monitoring to military and security surveillance (e.g., []). Some unique features make the wireless sensor networks inherently different from the tradition mobile ad hoc networks [3]. Typically, sensors are characterized as resource critical devices with very limited power, thus the energy consumption arises to be the major concern. In order to lower down the cost and communication overhead, the global ID, such as the widely utilized IP address and the MAC address, is not always available. In the meantime, the large scale and the high density of the deployment also make the routing problem more challenging than in other systems. To provide efficient data delivery services, a first requirement and challenge is to define an effective node ID for nodes. In a purely content-driven computing environment without any ID, messages have to be flooded in the network, which is not power efficient obviously. Most applications also need a scheme to uniquely label the generated data. Otherwise, the collected information has the quite limited usage. Note This research was supported in part by Hong Kong RGC Grants HKUST66/3E and AoE/E-/99. In this paper, we use the term hop count to measure the distance. Unless otherwise specified, the terms hop count and logical distance are used interchangeably. Deptment of Computer Science & Engineering Shanghai Jiao Tong University mlli@sjtu.edu.cn that the location information is a specific kind of ID. It may make the problem more complicate to apply the location information as the id by imposing the localization techniques. On the other hand, too long a node ID does not lead to an efficient solution either, for the introduced communication and storage overhead rely on the number of bits. Thus, a suitable ID mechanism must strike the balance between no ID and a globally, location-dependent ID. Although some work has been done in this area [], few researchers have considered the impact of the ID scheme to the routing tasks. With the assumption of available ID schemes, existing routing protocols can be classified into three categories according to their basic methods. Geography based approaches assume that sensors are aware of their physical locations. Some of them [] setup a small region to flood instead of the whole field. Others seek to deliver messages along a single path by greedy forwarding. (e.g., [6-8]). However, it is too strong an assumption for simple sensors to acquire the location information. Gradient based approaches [9-] use the logical distance rather than the geometric distance to tackle the routing tasks. They weaken on the limited application domain. Random approaches [] introduce the random factors when establishing the routing path. However, such paths are not guaranteed to be optimal in any level. Moreover, the delivery rate of messages is not satisfactory either. In this paper, we propose a geography-free coordinate system GREENWIS, to assist routing. In GREENWIS, nodes are described by a -tuple coordinate (latitude, region-id, longitude, spread-mark) which can be utilized as the node ID. As illustrated in Fig., latitude is the logical distance of a node away from the sink. The sink will then establish a set of meridians as reference lines, where each meridian is identified by a region-id. Each node accordingly measures its distance to the closest meridian, called longitude. Spread-mark is another form of latitude with respect to the selected meridian. Once the GREENWIS coordinate system is established, nodes are roughly positioned and data can be transmitted universally.
2 The rest of this paper is organized as follows. Section presents the design of the GREENWIS and its applications. Section 3 gives the complexity analysis and shows some performance results. In the final section, we conclude this work with our future plan. II. THE GREENWIS COORDINATE SYSTEM In GREENWIS, the application field is divided into several regions by meridians. Each node belongs to one region and maintains a -tuples coordinate to represent its relative position. The greedy forwarding based routing algorithm is based on this coordinate. We assume that there is a local-id scheme available so that every node can distinguish its neighbors without confusion. This local-id can be randomly generated. We also assume that the links are symmetric. This can be easily acquired by remove all the asymmetric links. A. Concept and terminologies Definition : The latitude (lat) of a node is the shortest hop number from the node to the sink. Definition : A meridian is a multi-hop communication path connecting the sink to a node which has the max value of the latitude. Here the max value of the latitude is determined by the sink with the reference of the globally maxima of the latitude. The node on the other end of a meridian is called a pole node. Definition 3: The region-id is the ID of the meridian from which a node measures its coordinates. Definition : Given a meridian, the longitude (long) of a node is its shortest distance from the node to the meridian.. Definition : Given a node M with its associated meridian, the spread-mark (sm) of M is the latitude of the node in the meridian that M reaches with the shortest path (M s longitude). Whenever two paths from a meridian to a node M have the same length, M chooses the one who has the closer latitude with M s owns. For example shown in Fig., both A and B have paths to M that length=3. M.lat =, A.lat = and B.lat =. Then M.sm = A.lat = Definition 6: A cell is the set of nodes having the identical -tuple coordinates. B. Establishment of a single meridian GREENWIS The procedure to establish a single meridian GREENWIS mainly consists of: () setup the latitude; () find a pole node having the max latitude; (3) let the pole node sends a message, an initiator, back to the sink; the initiator triggers all the passing by nodes as the first meridian, called prime meridian; and ()the initiator is flooded to the network to facilitate the setup of other s longitude and sm. ) Determination of latitude Initially only the sink has the coordinates (,,, ). The first step is to assign the latitude by a sink initialized flooding. Many works have been done on how to do this. In particular, the authors of [3] proposed a back-off based flooding scheme which ensure that each node advertises its latitude only once with the eventually correct value. This mechanism avoids unnecessary traffic and improves the performance of flooding. In the process of flooding, another operation for each node is to determine if it has a maximum latitude among all its neighbors by overhearing others flooding messages. A node with the maximum latitude value among its neighbors is called latitude locally max (lmax) nodes. ) Looking for the prime pole To find a prime pole, the sink must determine the latitude globally max (gmax) first. Those lmax nodes should report their coordinates to the sink. Note that the latitude fields are available in present. The technique to deliver a message from a sensor to the sink only by latitude will be described later. The sink then can determine max=gmax. However, the number of nodes having lat=gmax could be very low. Accordingly, max=gmax may incur too few number of candidate pole nodes having lat=max, and thus may introduce too much overhead on finding a pole node. In order to avoid this, the sink can determine the max=gmax- or max=gmax-. Note that the direction of the prime meridian is less relevant to the performance of the GREENWIS coordinates. Although a longer prime meridian can make the transmission path better to some extent, choosing a non-perfect max will not deteriorate GEENWIS too much but can save a lot of efforts in some cases. With the determined max value, the sink disseminates a probe to the network. A probe is a special, long-lived message initiated by the sink and travels the network like a worm. The main objective of a probe is to find a node having lat=max. Any intermediate node randomly picks up a neighbor as the next hop until the probe arrives to a lat=max node. In order to avoid excessive wandering of the probe, for each forwarding node, the neighbors with higher latitude are selected with a higher priority so that the probe is gently encouraged to climb to the high latitude region.
3 3 Prime Meridian Sink Prime Meridian A 3 B 3 3 Node M Sink the prime meridian Figure : The sink builds the prime meridian and sensor nodes measure their coordinates The first lat=max node receiving a probe becomes the pole node of this prime meridian, called the prime pole. 3) Establishment of the prime meridian The prime pole sends an initiator message back to the sink, and all the passing by nodes form the prime meridian (e.g., Fig.3). As all the downstream nodes of the prime pole are involved, the shape of the meridian is not a single path but an ellipse. ) The assignment of the region-id, longitude and sm The initiator message is not sent back only but flooded into the network to facilitate others to establish their region-id, longitude, and sm fields, respectively. C. Routing based on GREENWIS With the available GREENWIS coordinates, messages are greedily forwarded toward the destination. The region-id helps to restrict the transmission within the region. For a sensor-to-sink operation, the value of latitude is sufficient (e.g., [, 3]). Each sender broadcasts the message to the direct neighbors with the sender s latitude. Each receiver then forwards the message if its latitude is less than the sender s which implies that the receiver is on the downstream of the sender. Otherwise, the receiver discards the overhearing message. For the transmissions of a sink-to-sensor message, the longitude and the spread-mark(sm) fields are utilized. Consider the case that sender M at (sm, longitude, region-id) = (,,) wants to send a message to node N at (, 8,). The line MN has an angle more than π/. M then selects nodes at (6,, ) as the next hop. This kind of forwarding never stops until the receiver (, 8, ) is in the same cell of the destination node. This receiver then floods the message within the destination cell. It is possible that an intermediate sender does not have the desirable next hop node. For the example mentioned above, there is no node in cell (6,, ). It implies that there are voids between M and N and the known right-hand rule is followed to traverse the void: the message is sent to nodes at (, 3, ). Figure: M.latitude=; M.region-id=(prime); M.longitude=3; M.sm=. Figure 3: The establishment of the prime meridian (roughly bounded by the ellipse) D. Establishment of other three meridians Instead of employing the single meridian GREENWIS, we are more interested in GREENWIS with multiple meridians. Similar to the procedure of determining the local maxima of latitude, nodes having a local maxima longitude also report to the sink their coordinates. Among all reported nodes, the sink randomly selects one of the nodes having both the local maxima of longitude and the max value of latitude as the second pole node. The sink then acknowledges the second pole by its coordinate. Similarly, the second meridian can be established. Then another turn of flooding the coordinate begins. The only difference is that this time the region-id is the second meridian but not the prime one. The third meridian and the fourth meridian can be repeatedly established. III. ANALYSIS AND PERFORMANCE EVALUATION In this section, we give some analysis and evaluate the performance of GREENWIS. A. Complexity analysis ) Number of flooding to establish GREENWIS Suppose there are K meridians. Consider simple cases of K= m. The setup of the latitude and longitude for the prime meridian require one flooding each. Suppose the field is equally divided into K= m regions. Then each region roughly has the area A/ m where A is the area of the whole field. When K = m+ meridians are imposed in, half of each region is flooded to assign the new coordinates. So the number of flooding log K F = + (). Equation () can be easily extended to cases that are more general when K is any integer.
4 Transmission overhead (number of packets) x Number of meridians 6 RADIUS (hops) Energy(Kilo Jouels) Total Consumed Energy Vs Size of Field GREENWIS GRAB Rumor Routing 7 Radius(hops) Energy (Jouels) 3 3 Max Consumed Energy Vs Size of Field GREENWIS GRAB Rumor Routing 7 Radius(hops) Figure : K vs. R vs. communication overhead Figure : With density=6, the total consumed energy Figure 6: With density=6, the maximum consumed energy for individual nodes Reliability of GREENWIS on Different Node Density Reliability of GRAB on Different Node Density Reliability of Rumor on Different Node Density d= d=7 d= d= d= % % % % % % 3% 6 d= d=7 d= d= d= % % % % % % 3% 6 d= d=7 d= d= d= % % % % % % 3% 3% Figure 7: The reliability of GREENWIS vs. node failure ) Number of meridians With greedy forwarding algorithm on GREENWIS, it is guaranteed that a node at (long,sm) can be reached by at most long+sm hops. More meridians make the longitude smaller and thus a shorter transmission path. On the other hand, more meridians requires more control overhead to establish GREENWIS and storage spaces for sensors to memorize the region-id by the factor log (K). Thus, the number of meridians is a tradeoff between the transmission overhead and the maintenance overhead. Here we present the relationship between the path quality and the number of meridians. Due to page limitation, we ignore the procedure of derivation. Suppose N nodes are deployed in a field with radius R = N / Dπ hops where D is the average node density. Here the density is defined as the average number of neighbors per sensor node []. The network is partitioned by K= m meridians. Suppose the sink has one interaction with each of the sensor. The sum of the transmission overhead (number of messages) ε is: π ε ( ) R 3 Total = + D 3 K The overhead of the control message (flooding) is: log K ε control = ( + ) π R D Combining these two, Fig. shows the relationship among transmission overhead (number of messages), the number of meridians, and radius (hops). Comparing with the single meridian, two meridians can Figure 8: The reliability of GRAB vs. node failure Figure 9: The reliability of Rumor routing vs. node failure significantly reduce the transmission overhead by %. Increasing from to, the reduction is also near %. From to 8 it is only about % improvement. From 8 to 6, the reduction of the overhead is less than %. In this paper, we employ meridians. B. Performance evaluation For the experiment, we select the parameters of the sensor hardware similar to Berkeley motes []. The transmission range of a node is meters with the unit disk model. The power consumptions of full power transmitting, receiving and listening are.66w,.39w and.39w, respectively. The size of the application field varies form m to m. Varied numbers of nodes are deployed for desired node density to examine the scalability of protocols. We also simulate the failure of sensors to evaluate the reliability. To test if GREENWIS achieves its goal in efficient data delivery, we measure the total energy consumption. It is the total amount of energy to support all nodes having one interaction with the sink. An interaction includes one sink-to-sensor and one sensor-to-sink transmission. We also examine the maximum consumed energy for individual nodes. To examine if GREENWIS satisfies the power efficiency with the cost of reliability, we measure the success ratio which is the number of completed interactions to the total number of generated interactions. GREENWIS is compared with the known GRAB [] and Rumor routing []. All of these three protocols are implemented in our simulator. For GRAB,
5 we select the hop count as the cost field, and for Rumor routing, each query generates agents [] to detect an event. C. Transmission overhead We study the relationship between the energy and the size of application field R with fixed node density being 6. Figure shows that in GREENWIS the total energy consumption is linearly increased with a low slope on the radius R. Both Rumor routing and GRAB have exponential increase with that GRAB consumes the most power. Figure 6 shows the maximum consumed energy of individual nodes. GREENWIS also exhibits good performance over both GRAB and Rumor routing. The maximum consumed energy has direct relation to the lifetime of nodes. Reducing the maximum consumed energy can dramatically increase the connectivity and hence extend the lifetime of the network. D. Reliability of transmissions Figures 7-9 compare the transmission reliabilities of GREENWIS, GRAB and Rumor routing, respectively. We study their performances in terms of node failures under different node densities. For GREENWIS (shown in Fig. 7), when sensor nodes are sparsely deployed, a low number of node failure will degrade the performance dramatically. When d=, a % of node failure will decrease the reliability by 3%. On the other hand, increasing the node density can significantly improve the reliability. d=7 can deliver 9% of transmissions even with % of node failure, and d= can withstand 3% of node failure. Rumor routing performs the worst due to the inherent random fashion that Rumor routing employs. Figure 9 shows that the successful rate is no more than 8% even there is no node failure. IV. CONCLUSIONS AND FUTURE WORK In this paper, we presented the promising coordinate system, called GREENWIS. GREENWIS is a geography-free system in which nodes are identified by a -tuple with which messages can be easily delivered. Our preliminary results showed that GREENWIS is power efficient without sacrificing any communication reliability. In our future work, we will explore more applications based on GREENWIS such as maintenance and density control. We will also investigate the GREENWIS under the scenarios of multiple sinks, mobile sensors, and the dynamic addition/deletion of sensors. REFERENCE: [] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "A survey on Sensor Networks," IEEE Communications Magazine. Augest, [] D. Culler, D. Estrin, and M. Srivastava, "Sensor Network Applications," IEEE Computer, pp August, [3] E. M. Royer and C.-K. Toh, "A Review of Current Routing Protocols for Ad Hoc Mobile Wireless Networks," IEEE Personal Communications. April, 999 [] J. N. Al-Karaki and A. E. Kamal, "A Taxonomy of Routing Techniques in Wireless Sensor Networks," in Sensor Networks Handbook: CRC Publishers,. [] S. Basagni, I. Chlamtac, V. R. Syrotiuk, and B. A. Woodward, "A Distance Routing Effect Algorithm for Mobility (DREAM)," presented at ACM MobiCom, 998. [6] B. Karp and H. T. Kung, "GPSR: Greedy Perimeter Stateless Routing for Wireless Networks," presented at ACM MobiCom,. [7] Y. Xu, J. Heidemann, and D. Estrin, "Geography-informed Energy Conservation for Ad Hoc Routing," presented at ACM MobiCom,. [8] F. Ye, H. Luo, J. Cheng, S. Lu, and L. Zhang, "A TwoTier Data Dissemination Model for Largescale Wireless Sensor Networks," presented at ACM MobiCom,. [9] C. Intanagonwiwat, R. Govindan, and D. Estrin, "Directed Diffusion: A scalable and roubst communication paradigm for sensor networks," presented at ACM MobiCom,. [] D. Ganesan, R. Govindan, S. Shenker, and D. Estrin, "Highly-resilient, energy-efficient multipath routing in wireless sensor networks," presented at ACM MobiCom,. [] F. Ye, G. Zhong, S. Lu, and L. Zhang, "GRAB: A Robust Data Delivery Protocol for Large Scale Sensor Networks," presented at IEEE IPSN, 3. [] D. Braginsky and D. Estrin, "Rumor Routing Algorithm For Sensor Networks," presented at ACM MobiCom,. [3] F. Ye, A. Chen, S. Lu, and L. Zhang, "A Scalable Solution to Minimum Cost Forwarding in Large Sensor Network," presented at ICCCN,. [] J. Hill, R. Szewczyk, A. Woo, S. Hollar, D. Cullar, and K. Pister, "System Architecture Directions for Networked Sensors," presented at ASPLOS-IX,.
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