Data-Centric Power-Efficient Routing Protocols

7.3.1 Sensor Protocols for Information via Negotiation

The aim of the family of adaptive negotiation-based protocols for WSNs called SPIN, presented in [Kul], is to efficiently disseminate information among sensors in an energy-constrained environ-ment. The basic idea in SPIN is to name the data using high-level descriptors called metadata. The use of metadata negotiation reduces the transmission of redundant data throughout the network, as com-pared to the classic Flooding protocol. Hence, SPIN protocols address major problems of flooding, i.e., message implosion, overlap, and resource blindness (as SPIN protocols are energy-aware).

Thisis done by negotiating data at the sensor nodes before transmission occurs and introducing

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ADV

FIGURE . SPIN protocol. (a) Node A advertises its data to node B. (b) Node B in response sends a request to node A. (c) Node A sends the requested data to node B. (d) Node B sends an advertisement to (ADV) to its neighbors.

(e) The interested nodes respond with request messages. (f) Data is transmitted by node B. (Redrawn from Kulik, J., Rabiner, W., and Balakrishnan, H., Adaptive protocols for information dissemination in wireless sensor networks, in Proceedings of the th Annual ACM/IEEE International Conference on Mobile Computing and Networking, pp. –,

.)

resource adaptation. In SPIN each sensor node features a resource manager that keeps track of power consumption and triggers energy-aware behavior.

SPIN does not specify a standard metadata format, as it is application-specific. However, as meta-data exchange is done before meta-data transmission, in order to maintain energy efficiency the size of metadata should be significantly less than that of sensor data.

Three types of messages are introduced by SPIN protocols.

• ADV messages, which allow a sensor to advertise that a node has data to share. This message contains the metadata used to identify the sensor data.

• REQ messages, which allow a node to request specific data.

• DATA messages, which contain the actual sensor data.

The basic functioning of the SPIN protocols is represented in Figure ..

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As is shown in Figure ., SPIN works in three stages. When a node has new sensor data, it sends an ADV message to its neighbors with the relative metadata (ADV stage). When neighbors receive the ADV message, they check whether the advertised data has already been received (or requested). If not, the node sends a REQ message to the source (REQ stage). Finally, the source of the ADV message responds to the REQs with a DATA message, containing the advertised data. Upon receiving data, a node can apply data aggregation techniques and then advertise the aggregated data. Furthermore, a low-energy threshold can be used to reduce participation in the protocol, i.e., a node will participate in a stage only if its remaining energy is sufficient to complete the following stage.

As compared to classical Flooding and Gossiping protocols, SPIN features shorter dissemination times and higher reliability. As the power consumption of the nodes during the transmission of DATA packets is usually much higher than that needed for ADV/REQ packets, SPIN also provides lower energy consumption than flooding. However, the advantage in terms of energy consumption becomes less pronounced when the amount of data is small. In fact, the energy reduction technique adopted here decreases the amount of data to be transmitted. This is effective when data transmission requires more power than reception and idle states. However, as discussed in [Abi], this assump-tion is not always true for sensor nodes. For this reason, a number of cross-layer routing approaches exist, such as LEACH [Hei], which directly manage the low-power/sleep states of the nodes, thus decreasing their duty cycles.

7.3.2 Directed Diffusion Protocol

Directed Diffusion, presented in [Int], represents one of the most important data dissemination paradigms for WSNs, as it introduces a naming scheme, in which data generated by sensor nodes is named by attribute–value pairs. In order to save energy, short-range hop-by-hop communication is preferred over long-range communication to the destination, and data aggregation is performed locally to reduce the data size before transmission.

Thebasic idea of Directed Diffusion is that nodes request data by sending interests. An interest specifies a list of attribute–value pairs that describe the sensing task, i.e., type, interval, duration, sensing area, etc. Data matching the interest is drawn toward the node itself.

Both sensing tasks and data sent in response to interests are named using a similar naming scheme, based on attribute–value pair description, i.e., name and type of objects, data rate of events, duration, geographical area, etc.

For each sensing task, the sink node (i.e., the node that originated the query) periodically broad-casts an interest message to its neighbors. Each node has an interest cache, where several parameters such as a time stamp, a gradient, the data rate, duration, etc. are maintained for each distinct interest.

Each gradient is a reply link to the neighbor from which the interest was received and contains a data rate field (derived from the interval attribute of the interest) and a duration field (derived from the time stamp and expiresAt attributes of the interest) indicating the approximate lifetime of the interest.

Such parameters are updated every time a node receives an interest. Interest entries do not contain information about the sink node, as data is delivered through the gradients, which specify the addresses of the neighbors from which the interest has been received. As the nodes maintain only local information and no topology information is needed, the scalability of Directed Diffusion is high. In order to spread interests throughout the network, after an interest has been received a node may decide to resend it to all or to some of its neighbors. Each node also has a data cache used to direct interests (thus avoiding flooding) and to prevent transmission loops.

When a node receives a data message from a neighbor node, it first attempts to find a matching interest in the interest cache. If a matching interest is found, the message can be forwarded to each node for which it has a gradient, otherwise it is silently dropped.

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With such a mechanism, the sink may receive the same data several times from different paths.

For this reason a reinforcement scheme has been introduced, in order to use network bandwidth more efficiently and thus to save energy. The reinforcement mechanism makes use of the data rate parameter of the interest. In fact, at the beginning, the sink node broadcasts a low data rate inter-est. When the gradient has been set up, i.e., when the sink starts receiving low data rate messages, it reinforces one particular neighbor by increasing its data rate. When a node realizes that it has been reinforced, it also has to reinforce at least one neighbor. From the sequence of reinforcements on single hops, a high data rate path is established from the source of the events to the sink node.

The functioning of the Directed Diffusion routing protocol is shown in Figure ..

A convenient rule for the selection of the node to be reinforced has to be adopted in order to select a low delay path. For example, a node might choose the neighbor from which more events have been received, or the neighbor that regularly reports events before others. However, the path selected is generally not optimum.

There is also a mechanism for negative reinforcements that change a high data rate to a low rate, which can be used when a better path is found.

Thereinforcement mechanism can also be used locally to repair failing or degraded paths. In this case, the reinforcement is not triggered by the sink node, as an intermediate node can reinforce an alternative link. However, this mechanism has to maintain alternative low data rate paths, so there is a trade-off between robustness and energy efficiency.

Event Source

Interests Sink

(a) (b)

Event

Gradients Sink Source

(c) Event

Source

Sink

FIGURE . Directed Diffusion protocol operations. (a) Interest propagation. (b) Initial gradients setup. (c) Data delivery along reinforced path. (Redrawn from Intanagonwiwat, C., Govindan, R., and Estrin, D., Directed diffusion:

A scalable and robust communication paradigm for sensor networks, in Proceedings of the th Annual ACM/IEEE International Conference on Mobile Computing and Networking, pp. –, .)

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Directed Diffusion does not directly decrease the duty cycle of the nodes (i.e., nodes do not go to sleep), so energy efficiency is only achieved by reducing the number of packets transmitted and per-forming data aggregation. This technique is efficient when the idle power consumption is much lower than that required for transmitting and receiving. However, in many real cases of devices character-ized by high power consumption in idle states (i.e., with CSMA protocols) Directed Diffusion does not achieve high energy savings. Despite this, with respect to the SPIN protocol family, Directed Diffusion features lower overheads in terms of packets sent, as SPIN needs explicit negotiation for each packet, while Directed Diffusion uses periodic interests and gradients perform implicit negotiation. Lower overheads also imply higher energy efficiency. However, in Directed Diffusion the number of data packets is higher than in SPIN, due to the existence of multiple paths, so the energy consumption of Directed Diffusion also depends on the size of data packets as compared to negotiation packets.

In [Gad] the Directed Diffusion approach was compared with classical MANET routing proto-cols, such as the Ad-hoc On-demand Distance Vector protocol (AODV) [Perk] and the Optimized Link State Routing protocol (OLSR) [Jacq], and with another data-centric protocol, Two-Tier Data Dissemination (TTDD) [Ye], for both performance and energy efficiency. Simulation results show that AODV regularly outperforms others in terms of packet delivery ratio, latency, and energy effi-ciency. The lower efficiency of the Directed Diffusion protocol is probably due to the higher data redundancy. In fact, although there is only one reinforced path, multiple low data rate links are main-tained. In addition, the scenarios tested only comprise a small number of nodes producing data, so it is more suitable for classical ad-hoc networks than for WSNs. On the other hand, WSNs may comprise a very large number of sensing nodes. In such scenarios, maintaining a routing table with an entry for each data flow may be too expensive for resource-constrained nodes such as typical WNS nodes, so the scalability of localized^∗WSN protocols (such as data-centric or location-based approaches) is required [Estr]. Furthermore, WSN nodes are generally more faulty than other networks, as the network may operate in harsh environmental conditions. In such conditions, the Directed Diffusion approach can provide higher fault tolerance as multiple paths are maintained for each data flow.

The Directed Diffusion approach can definitely be useful for query-based sensor networks, as semantic information is used for forwarding. The drawback of this approach is that the naming scheme is strongly application-specific. However, a WSN is not usually a general-purpose network, so this is not a main concern.

7.3.3 Rumor Routing Protocol

Rumor Routing [Bra] is a probabilistic data-centric routing protocol for large-scale WSNs contain-ing several thousands of nodes, which is inspired by the Directed Diffusion approach. The Directed Diffusion approach disseminates queries through networks by means of (controlled) flooding. This is efficient if the amount of data to be transmitted is not small or if there are a large number of events. In some cases, i.e., when there are few events and many queries, event broadcast may be more efficient than query broadcast. Rumor Routing is something in between event broadcast and query broadcast, so it can be useful in the middle of the region depicted in Figure ..

In Rumor Routing, each node maintains a neighbor table as well as an event table. When a node notices an event, it probabilistically generates an agent. An agent is a long-lived packet that travels through the network in order to propagate information about the sensed events to distant nodes.

When a node receives such a packet, it updates the event table with the distance and the forwarding

∗Localized protocols maintain and use only information about neighbor nodes; no overall network or transmission flow knowledge is required.

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Query flooding

Rumor Routing—one possibility

Number of queries

Number of transmissions

Range of Rumor Routing under different conditions and parameters Event f looding

FIGURE . Rumor Routing compared to query flooding and event flooding. (Redrawn from Braginsky, D. and Estrin, D., Rumor algorithm for sensor networks, in Proceedings of the First Workshop on Sensor Networks and Applications (WSNA), Atlanta, GA, October .)

choice for the event. The generation probability is a parameter of the protocol, as well as the maximum hop number for an agent. When a node generates a query, if it already has a route to the source of the event it will use that route; otherwise it will forward the query in a random direction, until the query reaches a node that has observed the event (and therefore has a route) or the packet reaches the maximum number of hops allowed. With a similar approach, a query could fail to find an event.

In this case, the query could be retransmitted or can be flooded through the network. When an event is found via random forwarding, the cost of flooding is avoided; otherwise this cost has to be added to the cost of random forwarding. Because of random forwarding, latency in this protocol may be high. Moreover, if random forwarding fails, the additional delay for flooding may be needed. So this protocol is definitely not suitable for time-critical applications.

Similarly to Directed Diffusion, Rumor Routing achieves energy efficiency by reducing the number of messages exchanged and performing data aggregation along the path. However, as Directed Diffu-sion performs query flooding, when the number of queries is high Rumor Routing performs better.

Simulation results show that with an accurate selection of the protocol parameters Rumor Routing can outperform both event flooding and query flooding, but the downside is that performance is highly variable with varying parameters such as the number of agents and time-to-live values. As a result, in order to achieve high performance the design space has to be analyzed through extensive simulations, run using several possible configurations.

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7.3.4 Constrained Anisotropic Diffusion Routing Protocol

In [Chu] two techniques for querying and routing in WSNs are introduced, i.e., information-driven sensor querying (IDSQ) and CADR. The problem addressed here is how to perform queries and route data maximizing the information gain while minimizing latency and bandwidth utilization.

Themain idea of IDSQ/CADR is to introduce an information utility measure based on the estima-tion theory that models the informaestima-tion content and the spatial configuraestima-tion of a network. In this way, a node can spread a query based on the evaluation of an objective function that considers both information and cost, and it can forward data based on the local gradient of the objective function.

IDSQ/CADR can be viewed as a generalization of the diffusion approach, in which both information gain and communication cost direct data diffusion. In particular, IDSQ is a sensor selection algo-rithm that aims to find the optimal order of sensor queries that provide maximum information gain while balancing energy cost. The CADR protocol, on the other hand, determines the optimal routing path from the querying to the queried sensor (through the gradient of the objective function). This approach allows sensors to send packets only when there is interesting data to report. Moreover, only the part of the network with a better information/cost trade-off may be active. So this approach is more energy-efficient than Directed Diffusion, where queries are diffused in an isotropic fashion with (controlled) flooding over the entire network. Also, compared with approaches that only minimize the energy consumption for a single path, CADR achieves better performance, as both energy cost and information gain are taken into account through an appropriate utility function.

7.3.5 Cougar Protocol

In [Yao] a data-centric protocol that models WSNs as a distributed database is presented. The Cougar protocol aims to define sensor tasks through declarative queries.

In order to achieve declarative queries in sensor networks, nodes have to implement a query layer between the network and application layers, which basically consists of a query proxy that performs in-network processing. In fact, unlike typical WSN applications, where data is forwarded and then analyzed, the Cougar approach moves part of the data analysis inside the WSN. In this way it is possible to reduce the amount of data to be transmitted. Since for WSN nodes local computations generally require less energy than data transmissions, this approach achieves better energy efficiency than centralized data extraction.

A query optimizer is located in the sink node, which produces an efficient query plan that reduces resource usage and so extends network lifetime. In order to generate a good plan, network conditions have to be known. For this purpose, a catalog can be created at the server, which maintains the useful information (that needs to be updated as the network parameters change). In addition to the network condition, the optimizer also considers existing query workload and tries to merge similar queries.

A special node, the leader, is elected. This is the node where the computations of the aggregate values will take place. Then two query plans are generated, one for the leader and one for the other nodes, i.e., nodes send the leader local data from sensor or partially aggregated data, and the leader waits until the required data is received and then sends the overall aggregated values. This approach can be used for long-term queries, for example the average temperature value can be monitored for a long time and only updates for significant changes could be transmitted. Thanks to such a data aggrega-tion, the Cougar approach is efficient for bandwidth and energy management, although it also has some drawbacks. For example, as a sensor has to wait to receive results to be aggregated in order to perform efficient data aggregation, it requires synchronization between sensor nodes along the communication path. This could be a problem because with high loss rates broken links may be hard to distinguish from long delays. In addition, this protocol requires a catalog that has to be updated at every change in the network (i.e., sensor position, connectivity, workload, etc.), and in a network with several thousands of nodes this is not a simple task.

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7.3.6 Two-Tier Data Dissemination Protocol

Ye et al. addresses in [Ye] the problem of data dissemination in the presence of mobile sink nodes.

Thisprotocol is designed for large-scale reactive sensor networks, where stationary and location-aware sensor nodes may detect target events, and multiple mobile sink nodes collect information while moving across the network. The TTDD protocol achieves scalable and efficient dissemination for query and data by proactively creating a grid structure when an event is detected. As sensor nodes are location-aware, a node with coordinates (x, y) that has sensed an event can set itself as a crossing

Thisprotocol is designed for large-scale reactive sensor networks, where stationary and location-aware sensor nodes may detect target events, and multiple mobile sink nodes collect information while moving across the network. The TTDD protocol achieves scalable and efficient dissemination for query and data by proactively creating a grid structure when an event is detected. As sensor nodes are location-aware, a node with coordinates (x, y) that has sensed an event can set itself as a crossing