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Routing protocols in sensor networks
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Directed Diffusion EAD Youssef et. al Rumor
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Figure 6.1. Categories of sensor routing protocols.
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important design goal of any sensor network. Routing algorithms are also datacentric and employ attribute-based addressing strategies along with location awareness. These can be used with various clustering and hierarchical approaches to make an ef cient routing algorithm for sensor networks. A robust and scalable stategy is required in designing a routing protocol that is also energy-ef cient with minimal control overhead. Different routing protocols in sensor networks can be divided into six categories based on their underlying architectural framework. These different categories are as follows (see Figure 6.1). r r r r r r Attribute-based (Section 6.4.1) Flat (Section 6.4.2) Geographical (Section 6.4.3) Hierarchical (Section 6.4.4) Multipath (Section 6.4.5) QoS-based (Section 6.4.6)
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Some prior studies discussing the various sensor network routing protocols have been published [3, 7 9]. We list the classi cations of sensor network routing protocols mostly based on the type of deployment of these networks. This classi cation helps the advent of newer ideas with special focus on the application being serviced by the sensor network and hence develops ef cient algorithms to facilitate better routing techniques in these networks. 6.4.1 Attribute-Based Protocols Attribute-based routing protocols in sensor networks concentrate on routing data packets based on the content of the packets and are not device-speci c. Hence, these are also known as data-centric routing approches. Since each node within the network is engaged in the routing mechanism, these nodes can make any decision and apply any routing rules to packets that is, either forward or drop the packets. In this class of routing algorithms, contents of the transmitted data are evaluated at each hop in the network.
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Event Source Interests Sink Gradients Sink
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Figure 6.2. Directed diffusion [10].
Directed Diffusion [10]. Directed diffusion (Figure 6.2) is a data-centric approach, which means that all communication is for named data. It can be categorized under both attributed-based routing and at routing protocols. Data generated by the application-aware sensor nodes is named using attribute value pairs. A node requests data by sending interests for named data. A sensing task is disseminated via sequence of local interactions throughout the network as an interest for named data. Nodes diffusing the interest set up their own caches and gradients within the network to which the data delivery is carried out. During the data transmission, reinforcement and negative reinforcement techniques are used to converge to ef cient distribution. Intermediate nodes fuse interests and aggregate, correlate, or cache data. Algorithm 1 presents a pseudocode of the directed diffusion algorithm.
ALGORITHM 1. Pseudocode Describing the Directed Diffusion Algorithm Setup phase: 1. The base station broadcasts its set of interests 2. Do 3. ForEach network node N receiving an interest from node M 4. N forwards the received interest to its neighbors (other than M) 5. N sets up a gradient with M 6. EndFor 7. Until all gradients are set up 8. Check for loops in the paths and remove them Operating phase: 1. ForEach node 2. Colect sensor data. 3. Receive messages containing sensor data readings. 4. Aggregate, correlate or fuse data (if necessary) 5. If data maches an interest 6. Forward the data according to the gradient associated with the interest 7. EndIf 8. EndFor
Energy-Aware Data-centric Routing (EAD) [11]. This protocol proposes to build a virtual backbone which contains all the active sensors in the network to facilitate energy aware routing. A routing heuristic allows us to build a broadcast tree rooted at a gateway to enable the data-centric approach. The tree spans all sensors within the network and has a large number of leaves that save power by turning off their radios. The active sensors continue working as relays for traf c generated in the network. In data-centric routing, backbone senders are in charge of data processing and information dissemination throughout the network. At each individual sensor, the local raw data is initially combined/aggregated with data from other sensors located farther away from the sink. This aggregated data are then sent to a sensor closer to the sink or to the sink itself. EAD consists of two main components: the neighboring broadcast scheduling and the distributed competition among neighbors. These components ensure that the nal tree has many leaves, and sensors with relatively higher residual power also have a greater chance of being part of the virtual backbone. EAD basically makes certain the formation of a specially rooted broadcast tree designed for data-centric routing. This protocol is suitable for applications requiring frequent queries and events.
Constrained Shortest-Path Energy-Aware Routing [12]. The distance from a source to a destination can be used as a metric for energy consumption and estimation of the propagation delay between them. It is shown that by changing the transmission power level and thereby changing the network topology graph, the algorithm can be optimized to ensure higher throughput and energy ef ciency while maintaining lower end-to-end delay. The nodes are grouped into clusters with each cluster having a clusterhead or gateway node. Routing decisions are determined and maintained at the clusterhead. Such a centralized approach is more ef cient than a distributed approach since it entails less control packet overhead maintainance. The network operates in two main cycles: data and routing. The data cycle consists of the nodes sending data to the gateway nodes, whereas during the routing cycle the routing state of each node is determined by the clusterhead and the routing information is sent to all the nodes accordingly. The constrained shortest-path algorithm uses the distance between any two nodes to determine transmission power required to send packets from one to another. The transmission energy varies inversely with d n , where d is the distance between the transmitter and receiver and n is a value based on the system and application in use. The connectivity between nodes in a cluster can be maintained by this energy parameter, making the network topology dynamic. If no constraints are enforced for the transmission energy, then each node uses its maximum energy to transmit directly to the destination. This is certainly not maintainable in the long run; therefore, a constraint has to be put in place. Rerouting is carried out by the clusterhead if (i) the sensors within the clusters are reorganized, (ii) the battery level of the active nodes falls under a certain threshold, or (iii) there are some changes required in the energy model of the nodes.