Build wireless M2M and IoT sensor networks: Data dissemination
Sequential assignment routing
One solution to this problem is the use of a set of algorithms which performs organization and mobility management in sensor networks is proposed. This sequential assignment routing (SAR) algorithm creates multiple trees, where the root of each tree is one-hop neighbor of the sink.
Each tree grows outward from the sink and avoids nodes with low throughput or high delay. At the end of the procedure, most nodes belong to multiple trees. An instance of tree formation is illustrated in Figure 12.5 below.
The trees rooted at A and B, and two of the one-hop neighbors of the sink, are shown. Node C belongs to both trees, and has path lengths of 3 and 5, respectively, to the sink, using the two trees. Each sensor node records two parameters about each path through it: the available energy resources on the path and an additive QoS metric such as delay. This allows a node to choose one path from among many to relay its message to the sink.
The SAR algorithm chooses path with high estimated energy resources, and provisions can be made to accommodate packets of different priorities. A weighted QoS metric is used to handle prioritized packets, which is computed as a product of priority level and delay. The routing ensures that the same weighted QoS metric is maintained.
Thus, higher priority packets take lower delay paths, and lower priority packets have to use the paths of greater delay. For example, if node generates packet of priority 3, it follows the longer path along tree (B),and packet of priority 5 (higher priority) will follow the shorter path along tree A,so thatthe priority × delay QoS metric is maintained. SAR minimizes the average weighted QoS metric over the lifetime of the network. The sink periodically triggers metric update to reflect the changes in available energy resource after some transmissions.
Directed Diffusion
The directed diffusion protocol is useful in scenarios where the sensor nodes themselves generate requests/queries for data sensed by other nodes, instead of all queries arising only from a BS. Hence, the sink for the query could be a BS or a sensor node. The directed diffusion routing protocol [8] improves on data diffusion using interest gradients.
Each sensor node names its data with one or more attributes, and other nodes express their interest depending on these attributes. Attribute-value pairs can be usedto describe an interest in intrusion data as follows, where an interest is nothing but a set of descriptors for the data in which the querying node is interested.
type = vehicle /* detect vehicle location */
interval = 1 s /* report every 1 second */
rect = [0, 0, 600, 800] /* query addressed to sensors within this
rectangle*/
Timestamp = 02:30:00 /*when the interest originated*/
expiresAt= 03:00:00 */till when the sink retains this data */
The sink has to periodically refresh its interest if it still requires the data to be reported to it. Data is propagated along the reverse path of the interest propagation. Each path is associated with a gradient that is formed at the time of interest propagation.
While positive gradients encourage the data flow along the path, negative gradients inhibit the distribution of data along a particular path. The strength of the interest is different toward different neighbors, resulting in source-to-sink paths with different gradients.
The gradient corresponding to an interest is derived from the interval/data-rate field specified in the interest. For example, if there are two paths formed with gradients 0.8 and 0.4, the source may send twice as much data along the higher gradient path compared to the lower gradient one. For the interest mentioned earlier, a sensor may send data of the following kind:
type = vehicle /* type of intrusion seen */
instance = car /* particular instance of the type */
location = [200,250] /* location of node */
confidence = 0.80 /* confidence of match */
timestamp = 02:45:20 /* time of detection */
The diffusion model allows nodes to cache or locally transform (aggregate) data. This increases the scalability of communication and reduces the number of message transmissions required.
The concept of reinforcement is used to update a node’s interest along a particular path. For example, suppose the sink wants more frequent updates from the sensors which have detected an event.
It reinforces the path by sending an interest with a higher data-rate requirement, in effect increasing the gradient of that path. On the other hand, if the sink needs only fewer updates, it applies negative reinforcement by sending an interest of lower required data-rate.
The directed diffusion model uses data naming by attributes and local data transformations to reflect the data-centric nature of sensor network operations. The local operations of data aggregation are application-specific. Gradients model the network-wide results of local interactions by regulating the flow of data along different paths, depending on the expressed interest.
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