Data Dissemination and Fusion in Sensor Networks

1. Data Dissemination and Fusion in Sensor Networks

2. The Need for Data Dissemination and Fusion • Energy efficiency is an essential factor; therefore, short-range hop-by-hop communication is preferred over direct long-range communication to the destination • Since sensor network contains large amount of data for the end user, methods of combining or aggregating data into small set of information is necessary and contributes to energy savings • Data aggregation (aka data fusion) can combine unreliable data readings to produce accurate signal by improving the common signal and reducing the noise

3. Taxonomy of Data Delivery Models in Wireless Sensor Networks • Wireless sensor networks are classified according to their data delivery model into the following categories [Kulik+ 2002]: • Continuous • LEACH [Heinzelman+ 2000, 2002] is designed for routing data to base stations in static wireless sensor networks • TEEN (Threshold sensitive Energy Efficient sensor Network Protocol) [Agrawal+ 2001] and PEGASIS (Power Efficient GAthering in Sensor Information Systems) [Lindsey+ 2001] are both proposed as improvements to LEACH • Observer-initiated • In Directed Diffusion [Intanagonwiwat + 2000], data are named using attribute-value pairs and sensed information in the network can be associated with such a pair. The sensor nodes send queries expressing their interest for sensed information satisfying a specific criteria

4. Taxonomy of Data Delivery Models in Wireless Sensor Networks • Event-driven • SPIN (Sensor network Protocols via Information Negotiation) [Kulik+ 2002] are set of protocols designed to disseminate data to all nodes in the network • Hybrid • The above three approaches can coexist in the same network

5. Directed Diffusion[Intanagonwiwat+ 2000] • Motivated by scaling, robustness and energy efficiency requirements • Directed diffusion is data-centric in that all communication is for named data • Data generated by sensor nodes is named using attribute-value pairs • All nodes in the network are application-aware • A node requests data by sending interests for named data • A sensing task is disseminated via sequence of local interactions throughout the sensor network as an interest for named data • Nodes diffusing the interest sets up their own caches and gradients within the network to which channel the delivery of data • During the data transmission, reinforcement and negative reinforcement are used to converge to efficient distribution • Intermediate nodes fuse interests, aggregate, correlate or cache data

6. Directed Diffusion[Intanagonwiwat+ 2000] • Assumes that sensor networks are task-specific – the task types are known at the time the sensor network is deployed • An essential feature of directed diffusion is that interest, data propagation and data aggregation are determined by local interactions • Focused on design of dissemination protocols for tasks and events • Naming • Task descriptions are named (specifies an interest for data matching the list of attribute-value pairs) and also called as interest • Example task: “Every I ms, for the next T seconds, send me a location of any four-legged animal in subregion R of the sensor field.” • task = four-legged animal // detect animal location • interval = 20 ms // send back events every 20 ms • duration = 10 seconds // … for the next 10 seconds • rect = [-100, 100, 200, 400] // from sensors within rectangle

7. Directed Diffusion[Intanagonwiwat+ 2000] • Naming • A sensor detecting an animal may generate the following data: • task = four-legged animal // type of animal seen • instance = horse // instance of this type • location = [150, 200] // node location • intensity = 0.5 // signal amplitude measure • confidence = 0.85 // confidence in the match • timestamp = 01:30:45 // event generation time • Interests and Gradients • Interest is generally given by the sink node • For each active task, sink periodically broadcasts an interest message to each of its neighbors (including rect and duration attributes) • Sink periodically refreshes each interest by re-sending the same interest with monotonically increasing timestamp attribute for reliability purposes

8. Directed Diffusion[Intanagonwiwat+ 2000] • Interests and Gradients • Every node maintains an interest cache where each item in the cache corresponds to a distinct interest (different type, interval attributes with disjoint rect attributes) • Interest entries in the cache do not contain information about the sink • In some cases, definition of distinct interests allows interest aggregation • The interest entry contains several gradient fields, up to one per neighbor • When a node receives an interest, it determines if the interest exists in the cache • If no matching exist, the node creates an interest entry • This entry has single gradient towards the neighbor from which the interest was received with specified data rate • Individual neighbors can be distinguished by locally unique identifiers • If the interest entry exists, but no gradient for the sender of interest • Node adds a gradient with the specified value • Updates the entry’s timestamp and duration fields

9. Directed Diffusion[Intanagonwiwat+ 2000] • Interests and Gradients • If there exists both entry and a gradient, • The node updates the entry’s timestamp and duration fields • When a gradient expires, it is removed from its interest entry • When all gradients for an interest entry have expired, the interest entry is removed from the cache • After receiving an interest, a node may re-send the interest to subset of its neighbors • To the neighbors, it may seem that interest originated from the sending node even though it may have been generated a distant sink. This represents a local interaction • This way, interest diffuse throughout the network and not each interest have been sent to all the neighbors if a node sent matching interest recently • Gradient specifies data rate (value) and a direction in directed diffusion, whereas the values can be used to probabilistically forward data in different paths in other sensor networks

10. Directed Diffusion[Intanagonwiwat+ 2000] • Data propagation • Data message is unicast individually to the relevant neighbors • A node receiving a data message from its neighbors checks to see if matching interest entry in its cache exists according the matching rules described • If no match exist, the data message is dropped • If match exists, the node checks its data cache associated with the matching interest entry • If a received data message has a matching data cache entry, the data message is dropped • Otherwise, the received message is added to the data cache and the data message is re-sent to the neighbors • Data cache keeps track of the recently seen data items, preventing loops • By checking the data cache, a node can determine the data rate of the received events

11. Directed Diffusion[Intanagonwiwat+ 2000] • Reinforcement • After the sink starts receiving low data rate events, it reinforces one neighbor in order to “draw down” higher quality (higher data rate) events • This is achieved by data driven local rules • To enforce a neighbor, the sink may re-send the original interest with higher data rate • When the data rate is higher than before, the node node must also reinforce at least one neighbor • Reinforcement can be carried out from neighbors to other neighbors in a particular path (i.e., when a path delivers an event faster than others, sink attempts to use this path to draw down high quality data) • In summary, reinforce one path, or part of it, based on observed losses, delay variances, and so on • Negative reinforce certain paths because resource levels are low

12. Directed Diffusion[Intanagonwiwat+ 2000] [Figure adapted from Intanagonwiwat+ 2000]

13. Directed Diffusion[Intanagonwiwat+ 2000] • Advantages: • Data-centric dissemination • Robust multi-path delivery • Reinforcement-based adaptation to the empirically best network path • Energy savings with in-network data aggregation and caching • Gives designers the freedom to attach different semantics to gradient values • Reinforcement can be triggered not only by sources but also by intermediate nodes

14. Directed Diffusion[Intanagonwiwat+ 2000] • Disadvantages: • It may consume memory since all the attribute list is being sent • Suggestions/Improvements/Future Work: • Exploration of possible naming schemes

15. Negotiation-Based Protocols for Disseminating Information in Wireless Sensor Networks (SPIN Protocols) [Kulik+ 2002] • SPIN (Sensor Protocols for Information via Negotiation) is a family of negotiation-based information dissemination protocols which is designed to address the deficiencies of classic flooding by negotiation and resource-adaptation • SPIN disseminates each sensor readings to all sensors in the network, treating all sensors as potential sink nodes • Nodes using SPIN protocols names their data using high-level data descriptors, called meta-data and usage of meta-data negotiations eliminate transmission of redundant data in the network • Communication decisions can be based upon both application-specific knowledge of the data and knowledge of the resources available to nodes

16. SPIN [Kulik+ 2002] • SPIN has two basic ideas: • Operate efficiently and conserve energy: communicate with each other about the sensor data received already and the data needed still • Monitor and adapt changes in their own energy resources: extend the lifetime of the system • Four difference SPIN protocols: • SPIN-PP • SPIN-EC • SPIN-BC • SPIN-RL Meta Data • Used to uniquely and completely describe the data being collected by sensors • If two pieces of actual data are distinguishable, then their meta-data should also be distinguishable • Since the format of meta-data is application-specific, each application needs to interpret and synthesize its own meta-data

17. SPIN [Kulik+ 2002] Meta Data • SPIN applications must define a meta-data format for representing data that concerns with the costs of storing, retrieving and managing the meta-data • SPIN nodes uses three types of communication messages: • ADV (new data advertisement) • REQ (request for data) • DATA (data message) • ADV and REQ messages contain only meta-data that is smaller than the DATA message • SPIN Resource Management • SPIN applications are resource-aware and resource-adaptive • By knowing the resources at hand, the nodes makes informed decisions about using their resources effectively • SPIN specifies an interface that applications can use to find out their available resources rather than specifying a specific energy management protocols

18. SPIN [Kulik+ 2002] The Problem • In conventional classic flooding, the source nodes sends data to all its neighbors and the neighbors check their record of already sent data to see if they have forwarded the data to their neighbors. If not, they forward the data and update the record • This requires small amount of protocol state at any node, disseminates data quickly in the network where neither the bandwidth is scarce and the links are error prone • The problems include: implosion, overlap and resource blindness Implosion:A node always sends data to its neighbors without being concerned about if the same data has been received by the neighbors from other nodes Overlap:The nodes waste energy and bandwidth by sending the overlapping data Resource Blindness:Nodes do not make decisions based on the energy available

19. SPIN [Kulik+ 2002] The Solution • SPIN provides solution to the problems of implosion and overlap by negotiating with each other before transmitting data eliminates the transmission of redundant data • Nodes poll their resources before transmitting or processing data by probing the resource manager which keeps track of the resource consumption • Nodes can make efficient decisions based on the available energy level • The use of meta-data descriptors eliminates the possibility of overlap since the nodes can name the part of the data the nodes are interested in receiving • Resource-awareness of local resources allow sensors to make meaningful decisions to extend longevity

20. SPIN [Kulik+ 2002] SPIN Protocols 1. SPIN-PP: A Three–stage handshake protocol for point-to-point media • This protocol works in three stages (ADV-REQ-DATA) with each stage corresponding to one of the messages • The node sends ADV message to its neighbors • Neighbors check to see if they already have received or requested this data • If not, the neighbors respond by sending REQ message to the sender • The sender responds to the REQ message sent by sending the actual DATA to the neighbors requesting the data • If the neighbor already has the advertised data, it does not send any message • Simplicity is the main strength, meaning that nodes make simple decisions, resulting in usage of small energy in computation • Each node only needs to know about its one hop neighbors

21. SPIN [Kulik+ 2002] SPIN Protocols 2. SPIN-EC: SPIN-PP with low-energy threshold • Adds simple energy-conservation heuristic to the SPIN-PP protocol • When energy is abundant, SPIN-EC acts as SPIN-PP protocol • Whenever energy comes close to low-energy threshold, it adapts by reducing its participation • The node will only participate in the full protocol if it believes that it has enough energy to complete the protocol without reaching below the threshold value • It does not prevent nodes from receiving messages such as ADV or REQ below its low-energy threshold, but prevents the nodes to handle a DATA message below the threshold

22. SPIN [Kulik+ 2002] SPIN Protocols 3. SPIN-BC: A Three–stage handshake protocol for broadcast media • Improves upon SPIN-PP for broadcast networks by using cheap, one-to-many communications, meaning that all messages are sent to broadcast address and processed by all the nodes that are within transmission range of the sender • This approach is often called broadcast-message-suppression • SPIN-BC has three main differences from SPIN-PP are: • All SPIN-BC nodes send their messages to the broadcast address such that all nodes within the transmission range of sender will receive message • Upon receiving ADV message, each node checks to see if they already have the data. If not, node sets a random timer to expire, uniformly chosen from a predetermined interval. After timer expires, the node sends an REQ message to the broadcast address, including the original advertiser in the header of message. When the nodes who are not original advertiser receive the REQ, they cancel their own request timers, preventing from sending out redundant copies of the same REQ • The nodes will send out the requested data to the broadcast address only once to get the data all its neighbors. It will not respond to multiple requests of the same data

23. SPIN [Kulik+ 2002] SPIN Protocols 4. SPIN-RL: SPIN-BC for lossy networks • Reliable version of SPIN-BC which disseminates data through a broadcast network even in the cases of network loses packets or communication is asymmetric • Adds two adjustments to SPIN-BC to achieve reliability: • Each node maintains a record of which advertisements it hears from which nodes, and if does not receive the data within a set time after request, node rerequests the data • Nodes limit the frequency with which they will resend the data, meaning that it will wait for a set time before responding to any additional requests for the same data

24. SPIN [Kulik+ 2002] • Advantages: • Meta-data negotiation and resource adaptation • Maintains only local information about the nearest neighbors • Suitable for mobile sensors since the nodes base their forwarding decisions on local neighborhood information • Disadvantages: • It cannot isolate the nodes that do not want to receive information; unnecessary power may be consumed

25. SPIN [Kulik+ 2002] • Suggestions/Improvements/Future Work: • Study SPIN protocols in mobile wireless network models • Develop more sophisticated resource-adaptation protocols to use available energy well • Design protocols that make adaptive decisions based not only on the cost of communicating data, but also the cost of synthesizing it

26. DIFS: A Distributed Index for Features in Sensor Networks [Greenstein+ 2003] • This work considers searches over semantically rich high-level events, and presents the design, analysis, and numerical simulations of a spatially distributed index that provides for efficient index construction and range searches • The conventional approach to storing time series data is to have all sensing node sending their data to a central repository external to the environment • While obtaining the flexibility of processing the data, sending every sensor reading to external site incurs high energy consumption • In addition, the links near a gateway or an external storage repository can become communication bottlenecks as the network size and the sensed data increase • As a result, it may be advisable to store data locally at or near the location of the generation of the sensed data

27. DIFS: A Distributed Index for Features in Sensor Networks [Greenstein+ 2003] • One approach to retrieve this stored data is to flood a query to all nodes that may have suitable data and have those nodes send their response to the querying node • In this approach, data is sent when and where it is required • If some queries are originated within the sensor network, it is not advisable to send the data to an external site instead of sending it to the internal querying data • If more data is collected than required, this local storage approach increase energy savings • There are two extensions to this approach for further energy savings: • Data can be processed, aggregated, and/or pruned while propagating towards the query sink

28. DIFS: A Distributed Index for Features in Sensor Networks [Greenstein+ 2003] • There are two extensions to this approach for further energy savings: • The developers of Directed Diffusion [Intanagonwiwat + 2000],TAG [Madden+ 2002], and others describe specific forms of in-network aggregation and pruning of data that can select relevant data and produce statistics. This approach uses “data-centric” routing that queries are not directed towards individual nodes, but they are stated only in terms of desired data • The data can be processed locally to identify high-level “events” that of interest. These events can refer directly to sensor readings. The queries are directly for such events, and the responses comprised of summarized data about those events. Here, the routing is also data-centric, but queries and responses interact with higher-level abstractions

29. DIFS: A Distributed Index for Features in Sensor Networks [Greenstein+ 2003] • These energy savings approaches reduce the energy required to respond to queries, but do not deal with the cost of basic “flood-then-respond” approach in that cost of flooding each query to all possible nodes • “Data-centric storage” (DCS) approach [Shenker+ 2002] avoids the flooding of queries -- all events are named and stored at a network location based on the name and queries for an event are routed to appropriate network node where the relevant data can be accessed • Storing data by name allows creation of a mechanism between data and queries such that queries need not be flooded • GHT [Ratnasamy + 2002] proposes a specific solution to achieve DCS in which event names are hashed to geographic locations and stored at the node closest to the hashed location

30. DIFS: A Distributed Index for Features in Sensor Networks [Greenstein+ 2003] • DIFS extend the the data-centric storage architecture to support range queries where only events with attributes in a certain range are desired. It provides for low average search and storage communication requirements and tries to balance these requirements over participating nodes • DIMENSIONS [Ganesan+ 2002] also relies on the placement of data within the sensornet and use of data-centric rendezvous points with lower level sensor readings and produces a multiresolution index (or view) of data • High-Level Events • High-level events, such as a hot region or a target detection, a map, or a histogram can be described in many ways • The paper propose adding new data structures to store high-level data abstractions to the simple attribute types introduced by Diffusion • Such abstractions would be defined system-wide at deployment time

31. DIFS: A Distributed Index for Features in Sensor Networks [Greenstein+ 2003] • Classification of Event Properties and Relationships • Classification proposed has been designed with the consideration of attribute range and distribution queries • The goals of a system directed at binary events such as “zebra sightings” are different from the goals of providing range searches over events that are each comprised of attributes with values • The goal of a search over binary events is to determine the locations of those events and when such events are rare, it is much more energy-efficient to construct a rendezvous point where events could register and queries could search than to flood a search • Events defined by attributes with values that fall within a specified range are less common, i.e., there may be many hot regions in a network, but few with a heat gradient with a slope greater than s

32. DIFS: A Distributed Index for Features in Sensor Networks [Greenstein+ 2003] • Classification of Event Properties and Relationships • For this reason, this paper develops a new method to support range queries more efficiently and proposes mechanisms to run on top of GHT to address range queries • The high-level events are classified as follows: • Sensor value(s): • Includes raw sensor values that comprise high-level events, composite measurements and summary statistics such as average, median, etc • Examples include the peak temperature of a hot region, the speed that an animal target is moving • Sensor values can be search over a designed area and they are represented as integers or floating point numbers

33. DIFS: A Distributed Index for Features in Sensor Networks [Greenstein+ 2003] • Classification of Event Properties and Relationships • Timing parameters: • Essential to know not only a specific value for a region, but also how this value varies over time, I.e., a hot region that has been hot for some period of time • Spatial dimensions: • Refers to physical shape and location of an event, i.e., hot regions larger than a given area • Regions can described as enclosing circles, ellipses, or polygons and their points of interest can be represented as integer or floating point coordinates

34. DIFS: A Distributed Index for Features in Sensor Networks [Greenstein+ 2003] • Classification of Event Properties and Relationships • Event Interrelationships: • In the spatial domain, relationships between events translates to proximity or intersection, i.e., is an area of high CO2concentration also an area of bright sunlight? • In the temporal domain, event interrelationships translate to succession and temporal separation, i.e., did an area of high CO2 concentration happens immediately after bright sunlight? Table 1: Event Property and Relationship Classification

35. DIFS: A Distributed Index for Features in Sensor Networks [Greenstein+ 2003] • Storage and Search Architecture • Time series data generated by sensor nodes is locally processed by statistical and pattern recognition engines to generate high-level events that these events are stored locally where they are created, and information about their various attributes is inserted into indices • An interested user or an automaton poses queries to these indices • The query results are found in the indices themselves, at the storage nodes, and even at the nodes that generate time series data • In terms of event generation and search, nodes serve two functions: • all nodes may be used to store raw time series data and events • a subset of nodes serve as index nodes to facilitate search

36. DIFS: A Distributed Index for Features in Sensor Networks [Greenstein+ 2003] Storage and Search Architecture Figure 2: A storage and search architecture

37. DIFS: A Distributed Index for Features in Sensor Networks [Greenstein+ 2003] • Advantages: • DIFS efficiently supports range queries and queries related to distribution of values in space by using histograms, that direct queries to the relevant nodes • The paper builds on an already proven technique and simulation results show that DIFS outperforming GHT in query and communication costs • DIFS was designed to incorporate balancing of communication load over the network by having more than one query entry point and provision to originate search at any node in the tree • DIFS is scalable to large number of searches or stores as it eliminates the restriction of propagating every data information to the root and originating every query at the root

38. DIFS: A Distributed Index for Features in Sensor Networks [Greenstein+ 2003] • Disadvantages: • No mentioning about the failure of sensor nodes at level of the hierarchy in the quad tree structure of DIFS • In case of dense deployment, a uniform distribution of data values causes the DIFS algorithm exploring all the leaves; hence not a very good option as far as energy consumption is considered • No mentioning about making the querying and event insertion resilient to packet loss • Overhead incurred while maintaining extra parent information

39. DIFS: A Distributed Index for Features in Sensor Networks [Greenstein+ 2003] • Suggestions/Improvements/Future Work: • Introduce dynamic repartitioning when the distribution changes over a time period • To handle large queries, may be they can be split into smaller sub-queries, encoding them to be identified later and process them separately, either locally or forwarding to other nodes that have lesser traffic – this will avoid energy depletion of the really busy query access nodes • Handle data corruption at index nodes • Improve DIFS search cost • route the query using hierarchical dissemination, as in structured replication, rather than sending unicast messages to each of the covering nodes • route to nodes in the highest tree level that will cover the entire query range, rather than decomposing the query range into minimal covering set

40. Power-Efficient Data Dissemination in Wireless Sensor Networks [Cetintemel+ 2003] • Introduction • This work presents a new event-based communication model • The proposed protocol called Topology-Divided Dynamic Event Scheduling (TD-DES), organizes the wireless network into a multi-hop network tree • The root of the tree creates a data dissemination schedule and propagates this schedule throughout the tree • The schedule is divided into fixed-size time slots, each indicating the type of data that are sent (or received), and whether it is for downstream (i.e., away from the root) or upstream (i.e., toward the root) communication • The schedule can be periodic or refreshed in arbitrary intervals, depending on the data traffic and applications -- the idea is that nodes can save energy by powering down their radios to standby mode when they have no data to send, and when they (and their descendants) do not wish to receive the data being transmitted

41. Power-Efficient Data Dissemination in Wireless Sensor Networks [Cetintemel+ 2003] • Introduction • The system uses the publish/subscribe model: each node has a specific subscription profile that indicates which data types the node is interested in receiving • TD-DES allows each node to selectively listen for interested data based on the its position in the network topology • Since data must be scheduled before it is sent, the main tradeoff investigated is increased power efficiency in exchange for sub-optimal message dissemination latency • This work addresses application-specific scheduling and data dissemination issues, which was not taken into consideration by the previous in this area

42. Power-Efficient Data Dissemination in Wireless Sensor Networks [Cetintemel+ 2003] • II. System Model • TD-DES is intended as application overlay to a CSMA/CA wireless MAC layer rather than a MAC/networking layer in itself • II.A Scheduling Model • TD-DES monitors when each node of a network (1) receives data, (2) transmits data, and (3) powers its radio down to a low-power standby mode • These radio modes – Tx, Rx, and standby – are cycled among as functions of time determined by the network’sdissemination schedule, generated by the root node and propagated down the tree as part of a control event • Thebase stationis considered to be the root node with higher computational, storage, and transmission capabilities than the rest of the nodes and it can serve as an entry point to the sensor network, integrating the sensor network with the external wired network where the monitoring task GUI resides

43. Power-Efficient Data Dissemination in Wireless Sensor Networks [Cetintemel+ 2003] • II.A Scheduling Model • The scheduler depends on topology information, event profiles, traffic statistics, and QoS requirements when generating dissemination schedules • The goal of the scheduler is to minimize network-wide power consumption (by minimizing the amount of time spent in the Rx and Tx modes) without sacrificing timely dissemination of data • II.B Network Model • TD-DES has an integrated network construction layer that organizes a wireless network into a tree topology • The topology is constructed by broadcasting advertisements from all nodes • First, the root node broadcasts a parent advertisement • Each node hearing this advertisement replies with a child message that indicates that the node will become a child of the root

44. Power-Efficient Data Dissemination in Wireless Sensor Networks [Cetintemel+ 2003] • II.B Network Model • Whenever a node becomes a child, it broadcasts its own parent advertisement • The process continues until all the nodes get attached to the tree • A node that hears multiple parent advertisements chooses its parent node with the lowest hop count to the root • The tree construction layer is adaptive to topology changes due to node failures, additions, and mobility • The data events are disseminated throughout the network based on per-node event description rather than point-to-point messaging • This publish/subscribe type of event-based communication is the data dissemination model of choice since it decouples the producers and consumers of information

45. Power-Efficient Data Dissemination in Wireless Sensor Networks [Cetintemel+ 2003] • II.C Data/Event Model • Overlaying applications define predefined event types and these event types are maintained in a global event schema • For instance, a network with n different event types may publish event types e1, e2, e3,…, en • Each node maintains its own event subscription which is the set of event types that a node is interested in as well as its own effective subscription which is the union of its own subscription and the subscriptions of all its descendants • Each node subscribes to any event type of its own interest as well as any event type of a descendent node is interested in since each node is responsible for forwarding all relevant events to its descendants in the tree topology

46. (e1,e2) [e1,e2,e3] e2 e2 N1 N2 N5 N4 N3 N7 N6 N8 [e1,e2] (e1) (e1) [e1,e2,e3] e2 e2 e2 [e1,e2] (e2) (e2) (e1,e2) [e2] (e3) [e1,e2,e3] [e3] e2 (e1,e2,e3) [e1,e2,e3] Power-Efficient Data Dissemination in Wireless Sensor Networks [Cetintemel+ 2003] II.C Data/Event Model Figure 3: An example dissemination tree Subscriptions are given at the upper left corner of each node, effective subscriptions at the upper right. Arrows indicate the links over which the event is broadcast

47. Power-Efficient Data Dissemination in Wireless Sensor Networks [Cetintemel+ 2003] • II.C Data/Event Model • Figure 3 presents a dissemination tree of eight nodes and three event types e1, e2, and e3 with N1 being the root node of the tree • The subscription of each node is given in parentheses at the upper left of the node and the effective subscription is given at the upper right of each node in square brackets • Note that an event of type e2 generated at node N5 • The arrows indicate the links across which the event is broadcast to disseminate the event to all subscribing nodes • Note that the event is propagated both upstream (to the root and then downstream to the interested parties in the other sub-tree) and downstream; therefore, events do not always go through the root node

48. Power-Efficient Data Dissemination in Wireless Sensor Networks [Cetintemel+ 2003] • II.C Data/Event Model • An event is a message type with its own unique application-specific semantics • Consider a scenario where a sensor network whose purpose is to detect fires is deployed over a forested region • A sensor node may issue a fire_detected event to the network if its temperature reading is very high • This event would be disseminated through the network to all those nodes, (such as forest ranger stations, a centralized forest fire monitoring station, or a sink node which could notify the police, local fire-fighting units, and public news services) subscribing to fire_detected events • These nodes can also include any intermediate nodes which had to forward such events to interested nodes, even if themselves may not be interested

49. Power-Efficient Data Dissemination in Wireless Sensor Networks [Cetintemel+ 2003] • II.D Application-defined QoS • Besides carrying unique type semantics, event types may be associated with network-specific physical characteristics, such as minimum and maximum event payload sizes, latency constraints, and relative event priorities • The overlaying applications specify such event latency and priority values • III. Protocols • TD-DES event schedule determines the temporal partitioning of the RF medium for all of the event types by allocating time slots (or slots) for each event type • Each time slot is assumed to be wide enough for a single event to be propagated one hop; in other words, each slot should provide sufficient time to the underlying MAC layer to perform collision detection and retransmissions under contention

50. Power-Efficient Data Dissemination in Wireless Sensor Networks [Cetintemel+ 2003] • III. Protocols • Time slots are allocated for each event based on the determined or expected bandwidth requirements needed to propagate all generated events reliably throughout the network • Once the numbers of upstream and downstream time slots for each event type are determined, the ordering of the time slots must then be determined • Iterations are intervals of schedule that starts with a control event slot and it is also possible to interleave downstream and upstream slots together to fit into a single iteration