A Novel Energy-efficient Data Dissemination Protocol for Arbitrary Sink Mobility

Abstract

A number of data dissemination techniques have been proposed for wireless sensor networks recently, however, they fail to address the following challenges effectively: (1) Propagation of data interest and reinforcement of dissemination paths is accomplished by way of broadcast, which is inefficient. (2) Current approaches either require the sinks to forward their locations throughout the network or are only suitable for localized sink mobility.

This paper describes EASM, an energy-efficient arbitrary sink mobility data dissemination scheme to address these challenges. Sink and source nodes in EASM communicate through rendezvous points. Thus a sink interested in a particular event directly queries and gets a response from corresponding source node using the rendezvous point as a mediator. This eliminates the need to flood the network with interest or advertisement messages. We also propose a novel directed response approach for sending the reply based on the instantaneous sink location at the time of the query, which ensures a response even for fast moving sinks. The system replicates rendezvous points to reduce hot-spot usage complexity. Our results show that EASM effectively reduces energy consumption. Results also indicate that EASM efficiently handles and is preferred scheme for arbitrary degrees of sink mobility.

keywords: Wireless sensor networks, Data dissemination protocols, performance.

1 Introduction

Disseminating data efficiently from source nodes to sink nodes is one of the main functions in wireless sensor networks (WSNs). The unique characteristics of wireless sensor networks described below makes the problem of disseminating the data generated by source nodes non-trivial:

1. To ensure that the mobile phenomenon is accurately tracked, the WSNs are usually large and spread out over a wide geographical terrain.

2. The computational power, storage capacity and battery life of sensor nodes is limited. Furthermore, the nodes themselves cannot be reconfigured (to add extra resources, for example) once deployed.

3. Frequent topology changes due to node failures or sensor node movement is the norm.

4. Sink nodes may be mobile and move randomly, however, the source nodes may not have knowledge of the sink movement pattern.

Since a WSN is made up of a large number of low power radio devices that are distributed throughout the sensing space it follows that the datadissemination protocols must be scalable, efficient, fault-tolerant and adaptable [8, 12]. A number of data-dissemination protocols have been suggested in the literature, however, they fail to address the following challenges effectively:

• Overhead of network broadcast and resulting power consumption – Protocols, such as Directed Diffusion (DD) [9], rely on the sink nodes to flood the network with an interest message, followed by the source node sending a data response. The Two-tier Data Dissemination (TTDD) [18] protocol alleviates this overhead to some extent by dividing the sensing space into cells and limiting the data query flooding by a sink to its own cell. However the advertisement of availability of data at each source is still done by flooding the network. The sinks can then query the source directly for the data of interest. Both approaches result in excessive power consumption.

• Overhead of sink mobility – In DD the mobile sink nodes have to propogate their location information continuously throughout the WSN, which is inefficient. On the other hand, TTDD is only suited for intra-cell sink mobility and cannot handle sinks moving faster than their local (inside cell) forwarding states getting updated. Furthermore, the cell size cannot be increased on-the-fly to accomodate fast-moving sinks since the cell sizes are statically determined. The Ring-based Index [19] scheme also addresses localized sink mobility patterns, however, it is not suitable for fast moving sinks.

We propose EASM – Energy-efficient Arbitrary Sink Mobility – data-dissemination scheme for WSNs. It is a rendezvous-based scheme [5], where the sink and source nodes communicate through well-known rendezvous points. In our scheme we exploit the fact that sensor nodes are aware of their own location through the use of schemes, such as GPS [13]. Sink nodes indicate the type of data (or topic) they are interested in to the rendezvous points and thereafter send their requests to these rendezvous points. Each sink node uses a hash function to map a data type of interest with an appropriate rendezvous point. This rendezvous point acts as the source node for the sink thereby servicing its requests. This approach involves sinks directly making requests to their rendezvous points, and thereby avoid expensive network-level broadcasts of data request (or interest). The rest of the paper is organized as follows: Section 2 describes the EASM protocol architecture and its fault tolerant capabilities; Section 3 describes experimental results; Section 4 compares our work with related research; and Section 5 provides concluding remarks.

2 Design of EASM

This section describes the design of the Energyefficient Arbitrary Sink Mobility (EASM) data dissemination protocol for wireless sensor networks. We first describe the design principles and architecture of EASM. We then describe the fault tolerant design characteristics of EASM.

In designing EASM we have made the following assumptions about a wireless sensor network [14, 19, 7].

1. Nodes other than sink nodes are stationary – Mobility of sink nodes in a sensor network is a desired feature for many applications since it enhances data collection and querying efficiency, and improves sensor network management by identifying and/or reprogramming sensor nodes. On the other hand it is desirable to have the sensor nodes stationary to alleviate challenges of route changes.

2. Nodes are aware of their own location – It is expected that sensor nodes will be location aware by leveraging schemes such as GPS [13]. Even if every node may not have GPS capability, it is conceivable that GPS capabilities could be provided to groups of sensor nodes.

3. Data aggregation – EASM assumes that data kept at source node is in the form of a summary. Aggregation is thus not required [19] for our scheme.

2.1 EASM Architecture and Protocol Interactions

Figure 1 illustrates the architecture of the sensing space and the application of the EASM protocol for data dissemination. EASM divides the sensing space into a virtual grid. This is done to identify the grid center location for a sink originating a data request. Grid center is the center of the cell from which sink makes a request, as shown in Figure 1. Grid center location is used in directed response which is explained in the Data Response section later.

1. Grid – is the entire sensor network, which we assume to have been partitioned into a grid.

2. Sink node – is a node, which may be mobile, that is interested in receiving notifications of particular event types it is interested in.

3. Source node – is a node, which we assume to be stationary, that senses the phenomenon of interest and is capable of storing the sensed data, which ultimately must be disseminated to sink nodes interested in the data. We also assume that the source node has aggregate data for a given event type. Thus, we do not address the issue of data aggregation in this paper.

4. Rendezvous node – is a node, also assumed to be stationary, that acts a mediator responsible for relaying the event subscription by the sink to the source.

5. Interceptor node – is a node, which is stationary, in the vicinity of the rendezvous node. Interceptors are used to improve the fault tolerance and scalability of the EASM protocol as described in Section 2.2.

6. Forwarder node – is a node, which is stationary, and has been elected as a forwarder to relay information between source and sink nodes.

7. Home cell – Virtual grid divides sensing space into cells, home cell for a sink is the cell from which it originated a request.

8. Grid/cell center node – is a centrally located sensor node in each cell area to which a sink node belongs. Source nodes use these grid centers as the destination to directly route data information. Grid centers subsequently relay this information to the sink node.

Rendezvous point in EASM denotes a point or a location within sensing space. Thus, each event type has a corresponding location in the sensing space called the rendezvous point. Note that rendezvous point receives the sink requests and forwards them to the source node. A node at the rendezvous point called rendezvous node is used for carrying out the above functionality. Often times a node may not be present at the exact location. However, since GPSR delivers a packet to a node geographically closest to the destination location [11] (if a node is not present at the destination location) we may use a node which is geographically closest to the rendezvous point. For the rest of the paper, we use the terms rendezvous point and rendezvous node interchangeably. Similarly, a grid center is a location as well as a node

Figure 1: EASM Architecture

responsible for delivering response to the respective sinks.

For every event type that a sink node is interested in, EASM uses a hash function for determining a rendezvous point. Use of hash table for data location has been successfully demonstrated in Geographic Hash Table (GHT) [15], Ring-Based Index [19] and peerto-peer systems [16].

In EASM, when an event to be tracked is first detected by a sensor node in the geographical terrain, the node elects itself as the source node for that event. If the initial sensing node does not have sufficient residual energy, it may elect a neighbor as the source node. This is essential since the source has to respond to data requests from, possibly several, sink nodes. The source node registers its location information with its rendezvous point. The source node thus acts as the end-point for all queries for the event it is sensing.

Once the event discovery and source node registration with the rendezvous point is done, the sink nodes interested in an event can send queries towards the rendezvous point location. The rendezvous point, which acts as the mediator, forwards the data queries to appropriate source nodes. On receving data queries, the source node sends the data response directly toward the sink node querying for data.

We use Greedy Perimeter Stateless Routing (GPSR) [11] as the underlying routing protocol for our scheme. GPSR essentially consists of two basic methods for forwarding packets: (a) Greedy Forwarding, where each forwarding node greedily sends a packet to that neighbor, which is closest to the final destination. Thus with each hop the packet moves successively closer to destination node, and (b) Perimeter Forwarding, which is used when greedy forwarding fails i.e., when the current forwarding node does not have a neighbor closer to the destination than itself [11]. In this case, the packet is forwarded using right-hand rule to its neighbor (which in this case is farther from the destination than itself). The same forwarding scheme is followed till a node can again forward packets using greedy forwarding.

Locations of rendezvous points, which are geographic cartesian coordinates, are used to send packets. This eliminates the need for network-level broadcast for propogation of interest (data request) and availability (data response). Further source nodes respond to data queries using a directed response approach to ensure that sinks receive a response even if they have moved from their query location. We explain the EASM approach in detail in the remainder of this section.

Data Requests We assume that the rendezvous points are populated with source node location information before any sink makes a data request in the sensor network. In EASM a sink fills in a grid center field in all the requests it sends, which is the center of the cell from where the request originates. The sensing space can be assumed to be divided into cells as shown in Figure 1, where the dimension of the cell is a configuration parameter. The grid center is an indication of the query location and is used as a hint when source node responds to the query. Depending on the event type, a data request is routed to appropriate rendezvous points.

Data Response Sinks are mobile and may move after making a request, thus sink location can not be used to send data response. EASM uses grid center field contained in the query as a hint to the query location to generate a response to the query. We call this approach directed response, since the response is sent in the direction of the cell from where sink originated request as shown in Figure 1.

In EASM the responsibility of eventually delivering a data response to sink nodes is delegated to grid centers. Grid center works similar to rendezvous nodes in that it is aware of the instantaneous location of sink nodes. Thus on receiving a data response it can be routed by the grid center to appropriate sink.

Data Delivery A sink node is given an expected turn-around time for every data query it generates, after which it polls its grid center for data availability. The polling message is used by sink node to indicate its instantaneous location to grid center for data delivery. Alternately, it may send a location update message each time it moves. Initially all the data responses are stored locally with the grid center since the instantaneous sink location may not be known. On receiving a polling message from a sink, grid center sends the response to sink if the data is available locally. Here we assume that sink node does not update its one hop neighbors after it sends a poll message to its grid center such that data packets are delivered to sink nodes sucessfully.

2.2 Fault-tolerance in EASM

Till now we have considered that for an event type a sink sends requests to a single rendezvous point which in turn forwards it to a source node which contains the data. This approach has following problems:

• Hot-spot complexity: Hot-spot complexity is the number of maximum of packets a single node handles in a WSN. If we have a single rendezvous node handling data requests for a specific event type, the hot-spot complexity increases.

• Rendezvous node energy depletion: Like any other sensor node, rendezvous nodes are energy-constrained and may not be able to carry out routing functions if its energy level falls below a threshold.

• Node failure: Rendezvous nodes might fail, data requests can not be routed to source node in this case.

We describe replication of rendezvous points that addresses each of the above problems next. In replication we (1) identify the nodes which can serve as rendezvous points, and (2) replicate the forwarding information to the newly elected rendezvous points. A rendezvous point replication decision is taken by the current rendezvous point, which we call initial rendezvous point. It is based on its residual energy level e, when the energy falls below e/k (where k is a configuration parameter) the rendezvous point should be replicated since the current point may not be able to perform the forwarding functions.

1. Node identification: The initial rendezvous point sends an overload message to all its one hop neighbors, indicating the start of replication. Each one hop neighbor conducts an election for identification of a node, which will be used as a new rendezvous point. The election is an n hop broadcast starting from the node that initiated it, the nodes involved in the election respond with their residual energy level. Each node participates in only one election to ensure one result per election. Node with the highest energy level is elected as a rendezvous point, we call this node forwarder. All the other nodes that participated in the election are called interceptors. The election result (the node which was elected) is forwarded to the initial rendezvous point as well as all the participating nodes. A participating node, which we call an interceptor stores the election result (location of forwarder).

2. Node Replication: On receiving the election result from each one-hop neighbor, the initial rendezvous point sends the forwarding information to the elected forwarders. Thus each forwarder can route the data requests it receives to appropriate source node. Further, for each neighbor it sends the election results of all the other neighbors which in turn is forwarded to the interceptor nodes.

Thus, we have interceptor nodes, which can intercept and send the data requests to their forwarder, and forwarders which have the forwarding information of source node they serve. When an interceptor receives a packet (data request), the packet is forwarded to its forwarder instead of the earlier initial rendezvous point. Since every node is an interceptor or a forwarder within n - 1 hop radius of the initial rendezvous point a packet is always intercepted and forwarded before it can reach rendezvous point. If a forwarder fails, the interceptor chooses a new forwarder from its list making the scheme robust and fault-tolerant. The same approach can be used to handle overloading of each forwarders. Thus the system can adapt scalably for increased load conditions.

Replication reduces hot-spot complexity as the total load gets divided between as many newly elected forwarders. By ensuring that multiple forwarders are maintained with each interceptor the system handles forwarder failures by choosing a different forwarder. The same replication scheme may be used for replication of grid centers.

3 Experimental Evaluation of EASM

This section describes results our experimental evaluation of EASM. We evaluate EASM’s performance during normal operations and showcase its fault tolerant behavior. Moreover, we compare its performance with the Directed Diffusion and Two-tier Data Dissemination approaches.

3.1 Evaluation Methodology

This section describes the methodology we used to evaluate EASM. We implemented the EASM scheme in the NS-2 [17] discrete event simulator We conduct this study to (1) verify that the scheme works by way of simulation (2) compare and contrast it against the existing schemes and 3. understand the effect of sink movement on EASM.

The underlying MAC layer was IEEE 802.11, the physical radio characteristics of each node’s network interface, such as transmit power, receiver sensitivity, data load and antenna type were chosen so as to approximate the Lucent WaveLan direct sequence spread spectrum radio. The evaluations were based on the simulation of sensor nodes which were distributed and moving within a square flat space of 1000m X 1000m. The mobility model used was the random waypoint model [10, 1].

Reports were generated by source nodes every 0.25 seconds. The simulations were run with movement patterns generated for pause times including 10, 25, 40, 50 and 75 seconds. The maximum sink speed values used for simulation were 1, 2, 5, 10, 20 and 30 meters/second. The metrics chosen for analyzing EASM behavior were: (1) Aggregate Energy Depletion - Overall reduction in energy level during simulation. (2) Average Delay - Latency seen by the sink node in obtaining reports of interest from the source node, averaged for all sink-source pairs. (3)

Routing Overhead - Total number of routing packets that a node transmits in a network for the duration of simulation. We average this value for all the nodes in simulation. (4) Packet Delivery Fraction - Fraction of number of packets received at the sink to the number of packets being sent from the source nodes. The fraction is averaged for all the sink-source pairs for each network density in the simulation.

Simulations were run for 100 seconds and has 4 source and 4 sink nodes.

3.2 Performance Results

The scheme we proposed mainly addresss the sink mobility issue. As such in the first set of experiments we study EASM behavior varying the following parameters:

1. pause time: In the sensor network, each mobile node remains initially stationary for a fixed period of time, known as the pause time. A destination is then selected from the flat simulation space defined and then the node starts moving towards the destination and the process is repeated for simulation time. Pause time is thus a measure of sink mobility, since lower pause time indicates highly mobile sinks and vice versa. stationary for longer periods.

2. Maximum sink speed: The maximum speed at which a sink may move. This is another measure of sink mobility.

Aggregate Energy Depletion variation against sink speeds and pause times is shown in Figures 2a and 2b respectively. As the sink speed increases the energy consumption increases. Increased sink movement means sink crosses and moves out of its current cell frequently. The queries for outstanding sink requests consume more energy as the distance between the sink and the home grid center increases. Similarly for sink speed 10 m/sec energy consumption reduces as pause times increase, as shown in Figure 2b.

(a) Sink Speeds

(b) Pause Times

Figure 2: Energy Depletion

Figures 3a and 3b plots the routing load variation with sink speed, for a network size of 200 nodes. In line with the intution from EASM design, routing load increases with sink speed since faster sinks lead to more nodes participating in routing. EASM scales well as the sink speed, since there is only a 10% increase in the routing load when the sink speed is increased from 1 m/sec to 30 m/sec.

For the same increase in speed, the latency increases by almost 33% as shown in Figures 4a and 4b. A sink has to indicate its current location to a

(a) Sink Speeds

(b) Pause Times

Figure 3: Routing Load

grid center before data response can be sent to it. As the distance between grid center and sink increases (since sink is faster, it changes cells frequently), the time to indicate its location and get data response also increases thereby adding to the delay perceived by sink node.

Figures 5a and 5b inllustrate that packet delivery fraction more or less remains constant. Thus, increased sink speed does not affect the success ratio in EASM.

(a) Sink Speeds

(b) Pause Times

Figure 4: Average Delay

3.3 Fault Tolerent Behavior of EASM

In this section we study how random node failures affect the working of EASM. Packet delivery fraction for differnt values of node failure is plotted in Figure 6a. Note that the replication uses a system parameter n, indicating the depth of replication. Delivery fraction reduces gradually as more nodes are failed in the network.

However, as n is increased the success ratio increases. Even if some rendezvous points fail during the simulation, the replication allows choosing an alternate rendezvous point thereby successfully forwarding the packet to its destination. As failure

(a) Sink Speeds

(b) Pause Times

Figure 5: Packet Delivery Fraction

rate is increased, we expect that packets take alternate routes to their destination thereby increasing the delays. This is corroborated by the results in Figure 6b. It shows the variation of average delay when rate of node failure is increased. For example when a forwarder node fails for an interceptor, it chooses an alternate forwarder to send the packets to which geographically may not be the closest choice thus adding to the overall delay. Since more and more packets have to take alternate routes to their destination nodes the routing load increases.

Replication adds to the routing load only marginally, as shown in Figure 6c. Thus EASM is able to handle high failure rates and route packets to their

(a) Packet Delivery Fraction  (b) Average Delay  (c) Routing Load

Figure 6: Performance in Presence of Failures

respective destinations with marginal overheads.

3.4 Comparison of EASM Performance

We compare EASM with TTDD and DD in this section. The same metrices described in Section 3.1 are used in these comparisons. TTDD alpha size is set to 50, for the same experimental setup used for EASM. We study the impact of network size and sink mobility (in terms of sink speed) on each of the schemes.

Figure 7 shows the routing load for each of the protocols as network size is increased. TTDD has the highest load, followed by DD and EASM. Each source in TTDD has to construct and maintain a grid for dissemination of data, which is a costly operation [2].

Variation of average delay against network size is shown in Figure 8a. The delays experienced by all the schemes are comparable. We also observe the average delay for different sink speeds for TTDD and EASM as shown in Figure 8b. Note that TTDD and EASM performance is similar for smaller sink speeds ( <10 m/sec). However, TTDD delay starts increasing when sink speed was increased beyond 10 m/sec. Recall that TTDD is suitable for localized sink mobility patterns and hence experiences in-

Figure 7: Comparison of Routing Load

creased delays as speed is increased.

Aggregate energy consumption is plotted for various network sizes in Figure 9a. EASM and TTDD graphs are comparable, while DD has slightly higher consumption than each EASM and TTDD. However, note that when sink speed is increased TTDD localized flooding (which is done when a sink enters a new cell) increases leading to more energy consumption at higher speeds. This can be observed in Figure 9b.

(a) Network Size

(b) Sink Speeds

Figure 8: Comparison of Average Delay

3.5 Discussion

This section summarizes the EASM performance compared to other data dissemination schemes we have studied.

1. Sink movement pattern: EASM handles localized mobility pattern by sending the data response towards the grid center as explained in Data Response section in the architecture. Further, the grid center temporarily stores data response it received for a sink. It forwards the data when sink sends a query message (containing its instantaneous data) to that grid center. Both TTDD and Index Based schemes are suit-

(a) Network Size

(b) Sink Speeds

Figure 9: Comparison of Average Energy Consumption

able only for localized mobility patterns i.e. if the sink is within the cell from which it originated the request in the first place. Thus in EASM, even for increased speeds the data is delivered successfully to the sink that requested it.

2. Energy Consumption: For four sink-source pairs, our results indicate that the energy consumption is comparable for TTDD, DD and EASM. However, for high sink mobility patterns EASM scales nicely as compared to TTDD. TTDD requires a new broadcast each time a sink enters a new cell within the grid. Naturally, since the rate of broadcast is higher for fast moving sinks TTDD energy consumption increases more rapidly than EASM.

3. Routing Load: Routing load is significantly lower than both TTDD and DD. TTDD constructs and maintains one grid per source node for disseminating data, which are costly operations. In EASM on the other hand, interests (requests) and responses are always routed toward a destination (and are not broadcast).

4 Related Work

Sensor networks have been investigated actively over the past few years. SPIN [6] is a family of protocols used for efficiently disseminating data over a sensor network. It uses data negotiation and resourceadaptive algorithms to reduce energy consumption. Nodes assign a high-level name (meta-data) to their data and perform meta-data negotations before data communication to ensure redundant data is not sent.

GHT [15] is a data-centric storage based dissemination scheme which uses hash function to determine nodes where data are kept. However, GHT requires data be pushed to these nodes such that sink nodes can access it. It may thus introduce data transfers even when sink actually is not querying.

In Directed diffusion (DD) [9] a sink broadcasts interest (data request) message throughout the network. When source receives an interest message it sends data in a similar fashion. On receiving the data response ( possibly from multiple neighbors) one path is reinforced and is used for further communication. Multiple paths may be used by a sink node for redundancy.

Two-tier Data Dissemination (TTDD) [18] uses a two-tier architecture to disseminate the data to interested nodes. A grid is formed with the source node at one of the crossing points of the grid. Thus, the sensing space is divided by the grid into small cells. Only the nodes at the crossing points (called dissemination nodes) can forward the data. A sink node, which is interested in receiving a specific data, floods its local cell in order to discover the closest dissemination node. Each dissemination node contains forwarding information for each request of data it receives. On discovery of a dissemination point, the data request is forwarded to source node by the dissemination nodes, followed by data report being sent to the sink. Here, the smaller cell is the first tier, while the higher tier is made of the dissemination nodes at the grid points, which makes the two tier architecture.

Similar to TTDD EASM employs a virtual grid, however EASM uses this grid structure simply to find out the grid center location to be filled in sink queries while TTDD uses it for localizing sink mobility impact.

Ring-based index [19] is a rendezvous based data dissemination scheme. A rendezvous point is used as a node to communicate between sink and source nodes. An index ring is used for replication, where nodes are forwarding nodes which forward the data requests to next clockwise neighbor in the ring and index nodes which know the location of and hence can forward the request to source nodes. EASM is similar to ring-based index in that it also uses rendezvous node to query for source node location. However, the way replication is handled is fundamentally different. Instead of a ring, EASM uses n depth replication to elect forwarders and interceptors. Furthermore, EASM handles local as well as global sink mobility patterns while ring-based is suited for local sink movements.

Most of the already proposed schemes, which rely on broadcast (of interest or response messages) may not work well in large networks, with high number of sink-source pairs, especially when sinks exhibit high degrees of mobility. Data requests and responses are always sent to a location in the sensing space, thereby resulting in significant energy savings. Node replication reduces hot-spot message complexity and increases fault tolerance. Finally it is able to handle high sink speeds with marginal increase in energy consumption.

5 Conclusions

This paper discusses the design of a novel data dissemination scheme called EASM for supporting arbitrary degrees of sink mobility. The EASM scheme is highly scalable, especially for high sink speeds. It ensures data delivery for local as well as global sink movement patterns. The results indicate that the energy consumption is substantially reduced using EASM. It uses node replication as a means to (1) reduce hot-spot message complexity and (2) increase system fault tolerance. Our extensive ns-2 based simulations establish the efficiency and scalability of the proposed solution approach for data dissemination. In future work we propose to investigate the effects of data aggregation [4] and real-time data dissemination mechanisms [3].

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