Monitoring Large-Scale Wireless Sensor Networks

Transcription

1 Monitoring Large-Scale Wireless Sensor Networks using Epidemic Protocols Daniela Gavidia, Spyros Voulgaris, Maarten van Steen Vrije Universiteit Amsterdam Faculty of Science Department of Computer Science De Boelelaan 181a, 181 HV Amsterdam, The Netherlands Tel: +31 (2) Fax: +31 (2) [daniela spyros Keywords: epidemic protocols, gossiping, wireless sensor networks, information dissemination Abstract This paper focuses on monitoring the state of the nodes in a wireless sensor network where the nodes are unaware of the location of the monitoring node. We introduce an epidemic protocol designed to propagate the state of the nodes in a simple, yet effective manner. The nodes in the network communicate their state through local interaction with their neighbors. The monitoring of the network is done by a special node that uses the same protocol for communication and keeps track of the state of the nodes as it is propagated. The protocol developed can effectively propagate the state of a node to all other nodes in the network, reaching the monitoring node as well. 1

2 1 Introduction Enhancing sensor nodes with wireless communication capabilities has opened the door to deploying massive numbers of networked nodes. By employing these wireless sensor networks, close and accurate monitoring of the environment could be possible. Although each node in the network operates individually, the aggregated information of the collection of nodes can provide a complete view of the state of the environment. To collect this information, the nodes - which have a limited range - would have to route their observations to one (or more) monitoring node(s). Efficient routing of a message in wireless sensor networks - and in ad-hoc networks in general - requires knowledge of the network topology to determine the optimal path to the destination. Since ad-hoc networks are subject to frequent changes due to mobility of the nodes as well as nodes joining or leaving the network unexpectedly, precise knowledge of the network topology poses a problem. Several routing algorithms [13] have been developed to reach a compromise between the need to know the topology and the overhead introduced to keep this information updated. However, the deployment of large-scale wireless sensor networks could be undermined by the amount of resources required to achieve efficient point-to-point communication when the number of nodes involved is increased. We believe that the nature of epidemic protocols makes them very suitable for dynamic topologies and that an epidemic approach to disseminating the state of the nodes can prove to be a scalable solution to monitoring large-scale sensor networks. Epidemic (or gossip-based) techniques have proved to be a robust, efficient and scalable [4, 5, 1] solution for disseminating information in peer-to-peer networks. In the case of ad-hoc networks, by using a gossip-based approach delivery of a packet to all other nodes in the network can be achieved with fewer messages and improved performance [6]. Additionally, the simplicity of epidemic protocols compared to other sophisticated network protocols makes them suitable to be run on sensor nodes with limited computing power. The problem we address in this paper is effectively monitoring large-scale wireless sensor networks containing tens or hundreds of thousands of nodes. We propose that each node in the network propagate its state in an epidemic fashion to its neighbors whenever the conditions it observes need to be reported. In such a scheme, some nodes inject new information into the network while others (that do not have anything new to report) only propagate this information. Assuming that each node has the capacity to store a limited amount of the messages that it has forwarded, we can say that the network as a whole has a collective memory. The data items injected into the network are replicated in the memory of a number of nodes inversely proportional to the number of nodes injecting information and passed around so that they eventually reach the totality of nodes in the network. Monitoring the state of the nodes is achieved by placing a monitoring node in the network which communicates with its neighbors using the same epidemic protocol, but keeps track of the content of the items that pass through. This is done in a simple and non-disruptive manner since the monitoring node just has to listen into the message exchange of other nodes and its location is not an issue. As a result, one or more nodes can be in charge of monitoring making it possible to deploy large-scale sensor networks. Our major contribution is that we demonstrate how an extremely simple epidemic protocol can be effectively deployed to monitor very large wireless sensor networks. Although there are ample opportunities for optimizing the basic protocol, at this stage we aim to explore simplicity rather than efficiency. This approach contributes to obtaining a better understanding of the fundamentals underlying gossip-based information dissemination in wireless sensor networks. The paper is organized as follows. We provide some background by means of a motivating example in Section 2. The protocol used for communication between nodes and its properties, illustrated 1

3 through simulation results, are described in Sections 3 and 4, respectively. Section 5 gives a concrete example on how the protocol can be used to monitor a sensor network where nodes have a binary state. Related work is discussed in Section 6 followed by conclusions and final remarks in the last section. 2 Motivating Example The research described in this paper was partly motivated by a practical, real-world problem. Consider a large geographical area such as a city or district, in which street lights are used in abundance. In an area where the number of street lamps runs in the thousands, the task of discovering faulty units could be, to say the least, daunting. For example, the City of San Diego already has more than 4 street lights to maintain; a similar number is used in the Cornwall district (UK), whereas Amsterdam airport appears to have more than 8 lights. A normal procedure for maintenance is that people inform a special public service that lights are broken after which they can be scheduled for repair. In addition, the service responsible for lighting proactively checks the conditions of street lights. Cheap wireless sensor networks can improve maintenance quality. The basic idea is to use a cheap, solar-powered sensor that simply records when a lamp is broken. This state can be detected by two light sensors: one detecting whether it is day or night; the other whether the lamp is on or off. If the lamp is off during the night, the sensor reports the lamp to be broken. The problem to be solved is propagating such reports to a monitoring node in an unknown location, using only the sensors that are attached to the street lights. Using wireless communication allows the sensor network to be easily deployed. By keeping the sensors as simple as possible, total investments can be kept to a minimum. Failures could be monitored by having the sensor node insert alarm entries into the network whenever the behavior of the lamp does not adhere to the standard. The entries produced by each sensor node should be uniquely identified by an ID that can be resolved to the physical location of the lamp. A monitoring node should be able to provide the mapping from ID to location to ensure that the lamps are fixed in a timely fashion. Additional information regarding the state of the lamp - such as kind of problem detected, type of lamp - could also be included in the entry. In the next section, we present a protocol that can be used for the propagation of alarm entries from the defective lamp to the monitoring node. 3 Communication Protocol To explain our solution, we adopt the following system model. We consider a large collection of nodes that are spread across a large geographical area. Nodes communicate only through a wireless protocol, effectively meaning that each node can reach only a limited number of nearby neighbors. To keep matters simple, we assume nodes do not fail (although our protocol can be shown to be highly robust). Also, a node is not preconfigured with any global knowledge such as its position, the position of the monitoring node, or who its neighbors are. Each node, however, does have a unique ID. In order to engage in an exchange of information, each node in the system maintains a list of c data entries, which we will refer to as its cache. In its most basic form, a cache entry contains the ID of the node that created it and possibly other fields of information relevant to the application it has been designed for. The spread of information across the network is accomplished by having the nodes execute periodically a simple, yet effective, epidemic algorithm. Following a peer-to-peer communication approach, 2

4 each node selects randomly a neighbor within its range to exchange messages with. The data exchange as initiated by a node P proceeds as follows: 1. Randomly select 1 l c entries from P s cache. 2. Send the set of l entries to a randomly chosen neighbor Q. 3. Receive a set of, at most, l entries from Q. 4. Inspect the received entries and, if an entry with the same ID (which we call a repeated entry) is present in P s cache, discard one of the two entries. The criterion to select which entry to discard might be application-specific. 5. Update P s cache to include all remaining entries, by firstly using empty cache slots (if any), and secondly by replacing the entries originally sent to Q with the received ones. When contacted by P in step 2, node Q executes step 4 of the protocol. If there are repeated entries and Q has information to report, it takes this opportunity to insert an entry of its own in the group of entries to be sent back to P and picks the rest randomly from its cache. Otherwise, all entries to be sent back to P are randomly selected. Subsequently, Q executes step 5 to update its own cache. This protocol, which we will refer to as shuffling, is partly based on a peer-to-peer protocol used for handling flash crowds [12], which we recently enhanced in order to maintain unstructured overlays that share important properties with random graphs [14]. The most important observation to make is that P and Q essentially swap a number of entries. In doing so, they not only preserve the data that are collectively stored in the network, but also move these data around in a seemingly random fashion. The underlying idea is that by randomly shuffling data entries between nodes, the monitoring node will eventually get to see all data entries. A complete formulation of the protocol for the specific task of monitoring the network is presented in Section Properties In this section we present the basic properties of the communication protocol described in Section 3. In order to observe the behavior of the protocol in large-scale settings, a series of simulations were conducted. The results presented here correspond to a network of 1 nodes where all nodes have a cache size of 5. Unless explicitly stated, the nodes were set up in a rectangular grid topology, with 1 nodes on each side. 4.1 Convergence When a node P wishes to insert data into the network, it does so by creating an entry containing its ID and including it when engaging in a data exchange with a neighbor Q. After the exchange has concluded, Q will have P s entry in its cache. The next time Q takes part in a data exchange with a neighbor - let s call it R -, it might send a copy of P s entry to R and, if there are empty slots in its cache after receiving R s entries, Q will keep a copy of P s entry for itself. Whenever possible, a node will keep the entries it already has in its cache and will remove them only when they have been selected to be sent to a neighbor who has promised to store them in its cache. This characteristic of the protocol suggests that after repeated execution, the entries belonging to a node P will be replicated in the caches of a number of nodes in the network. 3

5 % of nodes holding a replica Percentage of nodes holding an entry from source node X source node 1 source node 2 source node 3 source node 4 source node 5 source node 6 source node 7 source node 8 source node 9 source node Figure 1: Convergent behavior illustrated by having 1 nodes insert data entries into a network of 1 nodes. The value to which the number of entries inserted by a particular node converges is dictated by the number of nodes inserting information into the network, which we will refer to as source nodes. Given a network of size N where all nodes have a cache size of c, the network has a total capacity of N c. These N c available slots have to be filled with the entries of the d source nodes. Because of the randomness introduced when choosing which entries to exchange, the total capacity should eventually be evenly divided between the number of source nodes resulting in an average of N c/d entries for each of the d nodes. Considering that the protocol does not allow more than one entry with the same ID in the same cache, this means that c/d of the nodes have an entry from one of the d nodes in its cache. Figure 1 shows the convergent behavior of the protocol. 1 nodes were randomly selected to insert entries into the network. Time is measured in rounds, where a round consists of letting each node execute the exchange protocol once. After an initial stabilization period, the number of entries in the system for each of the 1 nodes converges to the same value. According to our previous reasoning, this value should be 1 5/1 = 5, meaning that 5% of the nodes in the network have a replica of an entry inserted by one of the 1 nodes. The system shows, therefore, emergent self-stabilizing behavior. 4.2 Linearity To illustrate the effectiveness of shuffling for disseminating information, Figure 2 shows a comparison between a random gossiping protocol and shuffling. Random gossiping is defined for this experiment by the following steps: 1. Node P randomly picks one of its neighbors, Q, and sends it its cache. 2. Node Q makes a list of its local cache entries and the received cache entries. 3. If Q is a source node, it adds its own entry to the list. 4. Repeated entries (having same ID) are removed, leaving only one entry of each kind. 5. Fill the new cache with c randomly selected entries from the list. 4

6 Time required for all nodes to have seen an entry time (random) average (random) Time required for all nodes to have seen an entry time (shuffling) average (random) average (shuffling) Number of source nodes (a) Number of source nodes (b) Figure 2: Average number of rounds required for all the nodes in the network to see an entry using (a) random gossiping (b) shuffling. Figure 2 shows the average number of rounds required for all nodes to see an entry. Several experiments with different numbers of source nodes were conducted. The time (measured in number of rounds) taken for the entries of each source node to pass through the cache of every node in the network was recorded. The figures show the average, along with the individual values. Figure 2(a) shows the results for random gossiping while (b) shows the results for shuffling including the curve from (a) for comparison. (Note that difference between (a) and (b) in the number of rounds we consider.) It is apparent that although random gossiping works well for small numbers of source nodes, its performance is disappointing with many sources. On the other hand, the shuffling algorithm shows linear behavior when increasing the number of source nodes. A closer look also reveals that the individual measurements fall closer to the average with shuffling than with random gossiping. In fact, the standard deviation for random gossiping with 1 source nodes is visibly higher than for shuffling with 5 nodes. The slope of the curve is directly related to the number of entries being shuffled. As Figure 3 shows, there is an inversely proportional relationship between the number of entries being exchanged and the slope of the curve. The three curves shown correspond to experiments with a cache size of 5, 1 and 2 entries. In all cases, all of the entries in the cache were exchanged. By doubling the number of entries shuffled, the average time for the entries generated by a particular source node to pass through every node in the network is virtually divided in half. Such a characteristic, as well as the predictable behavior with an increasing number of source nodes, are important factors to consider when choosing an appropriate value for the cache size c. 4.3 Behavior on Different Topologies The results presented so far correspond to experiments conducted on a rectangular grid. In this section, we analyze the effect of using various network topologies on the dissemination of data entries. Three kinds of configurations were used for the experiments: nodes in a rectangular grid of 1x1, nodes positioned in blocks of various sizes and nodes distributed randomly in an area of 1x1 units. In all cases, the total number of nodes in the network was 1. In the case of the grid topology, the range of each node was 1 unit, making communication possible 5

7 25 2 Time required for all nodes to have seen an entry average (shuffle, cache size: 5, shuffling 5) average (shufle, cache size: 1, shuffling 1) average (shuffle, cache size: 2, shuffling 2) Number of source nodes Figure 3: Using different cache sizes. All entries in the cache are exchanged Time required for all nodes to have seen an entry average (shuffle, cache size: 5, shuffling 5, grid) average (shuffle, cache size: 5, shuffling 5, blocks) average (shuffle, cache size: 5, shuffling 5, random) Number of source nodes (a) Time required for all nodes to have seen an entry average (shuffle, cache size: 5, shuffling 5, 2x2) average (shuffle, cache size: 5, shuffling 5, 5x5) average (shuffle, cache size: 5, shuffling 5, 6x6) average (shuffle, cache size: 5, shuffling 5, 11x11) Number of source nodes (b) Figure 4: Different topologies: (a) grid, blocks and random topologies (b) different block sizes. with the node s immediate neighbors to the North, South, East and West. On average, each node had 3.96 neighbors (due to the effect of boundary nodes with less than 4 neighbors). To create the block topology, nodes were positioned forming square blocks with 5 nodes on each side separated by 1 unit. By arranging the nodes in this manner and keeping the range of the nodes at 1 unit, the average number of neighbors decreased to For the random topology, the range of the nodes had to be increased to 2 units in order to ensure that each node had at least one neighbor. The average number of neighbors in this case was The curves in Figure 4(a) show the average time in rounds needed for entries with the same ID to travel throughout the network. The effect of using different topologies is manifested in the different slopes of the curves. The graph suggests a correlation between the average number of neighbors of a node and the speed of dissemination of the data entries. In fact, this argument is supported by Figure 4(b) where different sizes of blocks were used. The number of nodes on the side of the blocks increased while the distance between nodes remained at 1 unit. As a result, larger blocks had a lower number of neighbors per node and are represented by curves with higher slopes in the graph. 6

8 5 Monitoring The development of the algorithm introduced in Section 3 was motivated by the desire to provide a simple mechanism to inject data items into a network of wireless nodes with limited range and let it find its way to where it is needed. Going back to our street lights example, we explained how entries inserted into the network by nodes aware of a defective lamp can propagate their message to the monitoring node thanks to the shuffling protocol. In this section, we will describe the complete solution to the monitoring problem by detailing the procedure after the monitoring node has been notified of a defective lamp including sending feedback to the source nodes and optimizing the performance of the system. 5.1 Ceasing the reporting of alarms Once an alarm entry from a defective lamp reaches the monitoring node, it is no longer desirable for that node to keep injecting entries into the network. In fact, as noted in Section 4.2, the higher the number of source nodes that have injected information, the longer it will take for a particular alarm signal to be propagated through the network. In other words, keeping the entries of a node whose alarm signal has already been received at the monitoring node will only cripple the performance of the system and slow down the discovery of other failing nodes. Once an alarm signal has been received, two important steps have to be executed. First, the flow of alarm entries has to be stopped by notifying the source node and, second, the entries that are already in the network have to be eliminated since they have already accomplished their task. Having logged the message from the source node, the monitoring node can take advantage of the probabilistic guarantees of the protocol to deliver an entry to every node in the network. In the same way that a source node injects an entry with the hope that it will reach the monitoring node, the monitoring node can mark an entry and put it back into the network. By applying a policy of favouring marked entries over unmarked ones in Step 4 of the protocol, over time marked entries will replace the unmarked ones and eventually reach the source node. At that point, the node becomes aware of the fact that its alarm signal has been received and can stop publicizing its state. Figure 5 shows the number of entries belonging to a particular source node in a network of 1 nodes with a cache size of 5 where 2 nodes inject entries. As expected, the number of replicas of the entry converges to 25, which means that 25% of the nodes have a copy. It can be seen that after an initial period of unmarked entries spreading through the network the entries reach the monitoring node where they are marked. From that moment on, the number of marked entries climbs steadily until all unmarked entries have been replaced. Later on, the marked entries are eliminated as explained in the next section. 5.2 Removal of entries When a source node stops injecting entries, the ones already present in the network do not disappear, but linger indefinitely unless a removal mechanism is implemented. To actually remove entries from the network, we added a counter to each entry. This counter is incremented whenever the node that holds the entry participates in a shuffle operation. Each entry also includes a maximum value for the counter. Once this value is reached, the entry is removed from the cache. The maximum value of the counter, to be referred as maximum shuffle count, should be high enough to let the entries traverse the network and at some point reach the monitoring node. When received at the monitoring node, all entries are marked and their counters (and the maximum shuffle count) are reset so that they can 7

9 % of nodes holding a replica Percentage of nodes holding an entry from source node X total # of entries marked entries Figure 5: Total number of entries compared to number of marked entries for one source node. stay in the network long enough to provide feedback to the node where they originated. The selection criterion in Step 4 of the protocol keeps marked entries over unmarked ones to ensure that the source node receives an acknowledgment. Setting the maximum shuffle count is not a trivial task. Although a given value might work under certain conditions, it might not be appropriate at other times. Varying conditions in the network preclude the use of a fixed value as a general solution. The best approach to this problem would be to use an algorithm to make the maximum shuffle count an adaptive parameter. Developing such an algorithm is the focus of current research. Some preliminary results are shown in Figure 6. In the experiment, 5 randomly chosen nodes act as source nodes every 3 rounds. The experiment has four phases, each one spanning 3 rounds. In each phase, the entries reach the monitoring node, are marked and eventually reach their source node deactivating further injection of entries. In the first phase, nothing is known regarding the size of the network or the position of the monitoring node, so a very high value is chosen for the maximum shuffle count. As a result, the entries stay in the network longer than necessary. Although in all phases the defective lamps are detected and the nodes are notified of this, the time that entries remain in the network varies according to the maximum shuffle count. Subsequent phases demonstrate the effect of dynamically adjusting the value of the maximum shuffle count. When the monitoring node marks an entry, it calculates a value for the maximum shuffle count by taking into account the entry s counter and the counters of previously received entries. The values of these counters serve to estimate how distant the source nodes are and set an adequate value for the maximum shuffle count. At the same time, by actively scanning the entries that pass through their caches, the nodes in the network can make a more reasonable choice for the maximum shuffle count they use for their own entries. 5.3 Monitoring Protocol To conclude our example, we present an extended version of the protocol introduced in Section 3, with the appropriate steps for removal of entries. The data exchange, initiated by node P, proceeds as follows: 1. Randomly select 1 l c entries from P s cache. 2. Send the set of l entries to a randomly chosen neighbor Q. 3. Receive a set of, at most, l entries from Q. 8

10 % of nodes holding a replica Percentage of nodes holding an entry from source node X source node 1 source node 2 source node 3 source node 4 source node 5 % of nodes holding a replica Percentage of nodes holding an entry from source node X total average marked average (a) (b) Figure 6: Setting the maximum shuffle count dynamically: a) number of entries belonging to five random source nodes, b) average values for the total number of entries and number of marked entries associated with a source node. 4. Inspect the received entries and, if an entry with the same ID is present in P s cache, discard one of the two entries. The selection criterion is the following: If one of the entries is marked, keep the marked entry. In any other case (both or none are marked) keep the one with the highest counter value. 5. Increase the counter of the entries in P s cache and remove the ones that exceed their maximum shuffle count. 6. Update P s cache to include all remaining entries, by firstly using empty cache slots (if any), and secondly by replacing the entries originally sent to Q with the received ones. When contacted by P in step 2, node Q executes step 4 of the protocol. If there are repeated entries and Q has information to report, it takes this opportunity to insert an entry of its own in the group of entries to be sent back to P and picks the rest randomly from its cache. Otherwise, all entries to be sent back to P are randomly selected. Subsequently, Q executes steps 5 and 6 to update its own cache. 6 Related Work Different approaches have been explored to achieve information dissemination in ad-hoc environments. From the simplicity of classic flooding to the sophistication of negotiation-based schemes [7], the focus has been on delivering the observations of individual sensor nodes to all nodes in the network. In this regard, gossiping has proved to be an efficient way of spreading information with reduced redundancy of packets [6], sparking interest in its use for information dissemination [1], as well as to improve reliability in mobile ad-hoc networks [2]. In the applications area, considerable efforts have been focused on using a database approach to sensor networks, where the network can be queried for information [8, 15]. From this point of view, individual readings of sensors are not particularly useful by themselves, but the real value comes from aggregating [11, 3] these readings according to queries. The intent of these projects is to provide high-level interfaces for users and applications to extract information from sensor networks. 9

11 We approach sensor networks from a distributed systems perspective. Our research is concerned with providing a scalable and robust data exchange mechanism for sensor network applications. In this sense, our vision comes somewhat close to Directed Diffusion [9]. However, at this point we are not particularly concerned with energy efficiency. Our interest lies in exploring the benefits of using epidemics in ad-hoc environments. 7 Conclusions and Future Work In this paper, we introduced an epidemic protocol aimed at large-scale ad hoc networks, where the complexity of achieving point-to-point communication would make data exchanges extremely expensive. We believe that increasing the size of the network does not necessarily mean that more sophisticated mechanisms for communication are needed. In fact, we proved that a simple solution can be as effective. Through simulations, we have presented the basic properties of the shuffling protocol and showed that, for the purpose of information dissemination, it performs significantly better than a random gossiping protocol. By establishing an agreement between the nodes exchanging data that each one will keep the other s entries, we ensure that the entries are efficiently propagated through the network and away from the source. As with most epidemic protocols, the shuffling protocol is faced with the problem of removing data from the network. In the example we presented, we dealt with this issue by introducing a counter that limits the lifetime of an entry. Other procedures, such as death certificates [4], could also be applied. We demonstrated that, with an added mechanism for removal of entries, the protocol can be successfully used for monitoring large-scale sensor networks. In the future, we plan to extend this work to monitoring continuous variables. The monitoring of the binary state of the nodes in the example presented in this paper is a first step towards that goal. Additionally, we intend to look into other possible applications for the protocol and techniques for optimizing the information exchange in terms of resource usage. References [1] BIRMAN, K. P. The surprising power of epidemic communication. In Future Directions in Distributed Computing: Research and Position Papers, vol. 2584/23. January 23, pp [2] CHANDRA, R., RAMASUBRAMANIAN, V., AND BIRMAN, K. P. Anonymous gossip: Improving multicast reliability in mobile ad-hoc networks. In Proceedings of the 21st International Conference on Distributed Computing Systems (ICDCS 1) (April 21), IEEE Computer Society. [3] CONSIDINE, J., LI, F., KOLLIOS, G., AND BYERS, J. Approximate aggregation techniques for sensor databases. In Proceeding of the 2th International Conference on Data Engineering (ICDE 4) (April 24), IEEE. [4] DEMERS, A., GREENE, D., HAUSER, C., IRISH, W., LARSON, J., SHENKER, S., STURGIS, H., SWINEHART, D., AND TERRY, D. Epidemic algorithms for replicated database maintenance. In Proceedings of the 6th Annual ACM Symposium on Principles of Distributed Computing (PODC 87) (Vancouver, Canada, August 1987), ACM, Ed., pp [5] GUPTA, I., BIRMAN, K. P., AND VAN RENESSE, R. Fighting fire with fire: using randomized gossip to combat stochastic scalability limits. Special Issue Journal Quality and Reliability Engineering International: Secure, Reliable Computer and Network Systems 18, 3 (May/June 22),

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