Smart Sensors and Sensor Networks

1. Smart Sensors and Sensor Networks Lecture7 Routing techniques

2. Smart Sensors and Sensor Networks Design issues in WSN routing • Routing requirements are different in WSNs than in traditional wireless networks and MANETs: • The destination in WSNs is known and communication is normally carried from multiple data to the BS (i.e. many to one); thus, the basic topology desired in data-gathering is a spanning tree; in MANETs, communication is generally on a peer-to-peer basis (one to one); • Data collected by many sensors in WSNs are based on common phenomena, so it is a high probability that these data have some redundancy; • In MANETs, node are highly mobile, while in WSNs the sensors are generally static, thus the nature of dynamics is different; even when sensors are mobile their mobility is low; • Mobile nodes in MANETs can have their energy sources (e.g. batteries) renewed, replaced or recharged; sensors have limited energy resources, generally not replaceable or renewable, and they must be managed carefully for prolonging the network lifetime;

3. Smart Sensors and Sensor Networks • Sensors are expected to perform sensing and communication without continual maintenance or human attendance and battery replenishment; this limits the amount of sensor’s energy available; • In many applications sensors are randomly deployed so they have to organize themselves and collaborate; • Current routing protocols designed for traditional networks cannot be used directly in a sensor network because: • Sensors should be self-organizing because their random deployment; the operation of the sensor networks is unattended, so network organization and configuration should be performed automatically; • In most applications, sensors are stationary; some movements may be allowed but, usually, with low mobility; • Sensor networks are application specific, that is design requirements change with the application; • Data collected by many sensors are based on common phenomena; it is highly probable that there is data redundancy; data aggregation is needed to avoid replicated transmissions which consume energy;

4. Smart Sensors and Sensor Networks • Sensor networks are data-centric networks; in traditional networks, data are requested from a specific node while in sensor networks, data are requested based on certain attributes; the sensors can remain in the sleep state, with the data reported from the few remaining sensors providing lower quality; once an event of interest is detected, the system should be able to configure so as to obtain high-quality results; • WSNs have large number of sensors, potentially on the order of thousands; therefore, sensor nodes need not have a unique ID because the overhead of ID maintenance is high; in data centric networks, data can be more important than knowing which nodes sent the data; • WSNs use attribute-based addressing; a user issues an attribute-based address composed of a set of attribute-value pair query; an example of query is: temperature > 600F; only sensors that sense temperature > 600F need to respond; • Position awareness of sensor nodes is important because data collection is based on the location; currently, it is not feasible to use global positioning system hardware (GPS); there are other methods, such as triangulation;

5. Smart Sensors and Sensor Networks Routing challenges in WSNs • Routing challenges are given by WSNs specificities, such as: • Ad hoc deployment: the system should be adaptive to changes in network connectivity as a result of node failure; • Energy consumption without losing accuracy: energy-conserving forms of communication and computation are essential; the malfunctioning of some sensor nodes because of power failure can cause significant topological changes and might require rerouting packets and reorganizing the network; • Computation capabilities: sensors may not be able to run sophisticated network protocols; • Communication range: a route will generally consist of multiple wireless hops; • Fault tolerance: some sensors may fail because of power lack, physical damage or environmental interference; this should not affect the overall task of the sensor network; MAC and routing protocols must establish new links; this may require actively adjusting transmit powers and signaling rates to reduce energy consumption or reroute packets through regions of the network where more energy is available;

6. Smart Sensors and Sensor Networks • Scalability: changes in network size, node density and topology should not affect the task and operation of the sensor network; routing protocols should be scalable enough to respond to events in the environment; • Hardware constraints: despite the limited hardware resources, a sensor should adapt to the environment; • Transmission media: traditional problems associated with wireless channels (e.g. fading, high error rate) may also affect the operation of sensor networks; in general, the required bandwidth of sensor data will be low, on the order of 1 – 100 kb/s; sensors use frequently the ZigBee technology for transmission; it is based upon low-cost, low-complexity and short range radio communication; related to the transmission media is the design of medium access control (MAC); • Connectivity: sensor nodes are expected to be highly connected, in order to preclude their complete isolation from each other; this, however, may not prevent variability of the network topology and network size due to sensors’ failures for different reasons; • Control overhead: control packet overhead increases with node density; • Quality of service: bounded latency for data delivery is another condition for time-constrained applications;

7. Smart Sensors and Sensor Networks Routing protocols in WSNs • The main requirement is energy conservation; • According to the network structure, routing protocols are divided in: • Flat routing: all nodes are assigned equal roles; • Hierarchical routing: nodes play different roles in the network; • According to the routing strategy: • Cooperative routing: in these protocols nodes send data to a central node which aggregate data and may process it; • Adaptive routing: certain system parameters are controlled in order to adapt in the network’s current conditions and available energy levels; • Depending on the protocol operation: • Multipath-based; • Query-based; • Negotiation-based; • Because the topology is static, it is preferable to have a table-driven routing protocol because a lot of energy is used in route discovery;

8. Smart Sensors and Sensor Networks • Flat routing: • Directed diffusion • It is a data-centric and application-aware paradigm; • It is data-centric (DC) in the sense that all the data generated by sensor nodes are named by attribute-value pairs; DC performs in-network aggregation of data to yield energy-efficient data delivery; DC eliminates redundancy, minimizes the number of transmissions and thus saves network energy and prolongs its lifetime; • DC differs from address-centric (AC); in AC routing, the problem is to find short routes between pairs of addressable mobile nodes (end-to-end routing);

9. Smart Sensors and Sensor Networks • The query is disseminated or flooded throughout the network and gradients are set up to draw data satisfying the query toward the requesting node; that is, a sink may query for data by disseminating interests and intermediate nodes propagate these interests; • More generally, a gradient specifies an attribute value and a direction; • Events (i.e. data) start flowing toward the requesting node from multiple paths; a small number of paths can be reinforced so as to prevent further flooding according to a local rule; then an empirically low delay path is selected to be reinforced; • The strength of the gradient may be different toward different neighbors, resulting in different amounts of information flow;

10. Smart Sensors and Sensor Networks • Interest describes a task required to be done by the sensor network; interest is injected at some point, normally at BS; the source is unknown at this point; interest diffuses through the network hop by hop and is broadcast by each node to its neighbors; • All the sensors in a directed diffusion-based network are application-aware, which enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in the network; • In a SN based on direct diffusion, each sensor names data that it generates with one or more attributes; • The sink broadcasts the interest, which is a named task descriptor, to all sensors; the task descriptors are named by assigning attribute-value pairs that describe the task; • Each sensor stores the interest entry in its cache; the interest entry contains a time stamp field and several gradients fields; as the interest is propagated throughout the network, the gradients from the source back to the sink are set up;

11. Smart Sensors and Sensor Networks • Caching can increase the efficiency, robustness and scalability of the coordination between sensors; • Locally cached data may be accessed by other users with lower energy consumption than if the data were to be resent end to end; • When the source has data for the interest, the source sends the data along the interest’s gradient path; as the data propagates, data may be transformed locally at each node; • The sink periodically refreshes and resends the interest when it starts to receive data from the source; this is necessary because interests are not reliably transmitted throughout the network; • The main goal of this protocol is to compute a path robustly from the source to sink through the use of attribute-based naming and gradient paths; • The performance of data aggregation methods used in directed diffusion paradigm is affected by the position of the source nodes in the network, the number of sources and the communication network topology; • Event radius (ER) model and random source (RS) model:

12. Smart Sensors and Sensor Networks • Hierarchical routing • In an hierarchical architecture, higher energy nodes can be used to process and send the information while low energy nodes can be used to perform the sensing in the proximity of the target; • Creation of clusters and assigning special tasks to cluster heads can greatly contribute to overall system scalability, lifetime and energy efficiency; • LEACH protocol • LEACH = Low Energy Adaptive Clustering Hierarchy • Is a cluster-based protocol that includes distributed cluster formation; • The protocol allows for a randomized rotation of the cluster head’s role in the objective of reducing energy consumption and to distribute the energy load evenly among the sensors in the network; • LEACH uses localized coordination to enable scalability and robustness for dynamic networks and incorporates data fusion into the routing protocol in order to reduce the amount of information that must be transmitted to the BS; • A TDMA/CDMA MAC was used for reducing inter- and intracluster collisions;

13. Smart Sensors and Sensor Networks • Because data collection is centralized and performed periodically, this protocol is most appropriate when constant monitoring by the sensor network is needed; • A user may not need all the data immediately; thus, periodic data transmissions, which may drain the limited energy of the sensor nodes are unnecessary; • LEACH introduces adaptive clustering, i.e. reclustering after a given interval with a randomized rotation of the energy-constrained cluster head so that energy dissipation in the network is uniform; it was shown, using simulation, that only 5% of nodes need to act as cluster heads; • The operation of LEACH is done in two phases: the setup phase and the steady state phase; • In the setup phase, the clusters are organized and cluster heads are selected; • In the steady state phase, the actual data transfer to the BS takes place; • The duration of the steady state phase is longer than the duration of the setup phase in order to minimize overhead; • During the setup phase, a predetermined number of nodes, p, elect themselves as cluster heads as follows: • A sensor node chooses a random number, r Є [0,1];

14. Smart Sensors and Sensor Networks • If this random number is less than a threshold value, T(n), the node becomes a cluster head for the current round; • The threshold value is calculated based on an equation that incorporates the desired percentage to become a cluster head, the current round and the set of nodes not selected as a cluster head in the last 1/P rounds, denoted by G: • After the cluster heads have been elected: • They broadcast an advertisement message to the rest of the nodes that they are the new cluster heads; • Upon receiving this advertisement, all the other nodes decide on the cluster to which they want to belong, based on the signal strength of the advertisement; • The noncluster head nodes inform the appropriate cluster heads that they will be members of the cluster; • After receiving all the messages from the nodes that would like to be included in the cluster and based on the number of nodes in the cluster: • The cluster head node creates a TDMA schedule and assigns each node a time slot when it can transmit; • This schedule is broadcast to all nodes in the cluster; • During the steady state phase, the sensor nodes sense and transmit data to the cluster heads;

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16. Smart Sensors and Sensor Networks • The cluster head nodes aggregate data before sending them to the BS; • After a certain time, which is determined a priori, the network goes back into the setup phase and enters another round of selecting new cluster heads; • Each cluster communicates using different CDMA codes to reduce interference from nodes belonging to other clusters; • Problems: • LEACH assumes that all nodes can transmit with enough power to reach the base station if needed and that each node has computational power to support different MAC protocols; • It assumes that nodes have always data to send and nodes located near each other have correlated data; • It is not obvious how the number of predetermined cluster heads is going to be uniformly distributed through the network; it is possible that the elected cluster heads will be concentrated in one part of the network, some nodes missing cluster heads in their vicinity; • The protocol assumes that all nodes begin with the same amount of energy capacity, supposing that a cluster head removes approximately the same amount of energy for each node; • LEACH with negotiation: is an extension to LEACH; • The main idea is that high-level negotiation using metadata descriptors precede data transfers; • This ensures that only data that provide new information are transmitted to the cluster heads before being transmitted to the BS;

17. Smart Sensors and Sensor Networks • Virtual grid architecture (VGA) routing • The solution is based on data aggregation and in-network processing; • The data aggregation is performed at two levels: local and then global; • Nodes are arranged in a fixed topology due to their stationary or very low mobility; • Fixed, equal, adjacent and no overlapping clusters with regular shapes are selected to obtain a fixed rectilinear virtual topology; • Inside each zone, a node is optimally selected to act as cluster head; the set of cluster heads, also called local aggregators (LA), performs the local aggregation; • Several heuristics have been proposed to allocate a subset of the cluster heads, called the master aggregators (MA), in order to perform near optimal global data aggregation so that the routing cost form the source nodes to the BS is minimized; • Most of the heuristics start with the first node in the VGA architecture and proceed sequentially the whole topology left to right and then right to left in a top-down fashion; • There is no any restriction regarding the position of the BS and the VGA; BS can be located at an arbitrary place;

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19. Smart Sensors and Sensor Networks • Hierarchical vs. flat topology routing • Adaptive routing • A family of adaptive protocols is called Sensor Protocols for Information via Negotiation (SPIN): • These protocols disseminate all the information at each node to every node in the network, assuming that all nodes in the network are potential BSs; • This enables a user to query any node and get required information immediately; • The protocols use the property that nearby nodes have similar data and thus distribute only data that the other nodes do not have;

20. Smart Sensors and Sensor Networks • The SPIN family of protocols uses data negotiation and resource-adaptive algorithms; • Nodes running SPIN assign a high-level name, called metadata, to describe their collected data and perform metadata negotiations before any data are transmitted; thus, no redundant data are sent throughout the network; • The format of metadata is application specific and is not specified in SPIN; for example, sensors might use their unique IDs to report metadata if they cover a certain known region; • In addition SPIN has access to the current energy level of the node and adapts the protocol it is running on the remaining energy level; • SPIN protocols address the deficiencies of classic protocols based on flooding and gossiping, which waste energy and bandwidth by sending extra and unnecessary copies of data by sensors covering overlapping areas; for that, negotiation and resource adaptation is used; • Sensor nodes use 3 types of messages: ADV, REQ and DATA; • ADV advertises new data, REQ request data and DATA is the actual message; • The protocol starts when a SPIN node obtains new data that it is willing to share; • If a neighbor is interested in the data, it sends a REQ and it will receive the DATA; • The sensor node repeats this process with its neighbors until the entire sensor area will receive a copy;

21. Smart Sensors and Sensor Networks • Multipath routing • The resilience of a protocol is measured by the likelihood that an alternate path exists between a source and a sink when the primary path fails; • This can be increased by maintaining multiple paths between the source and the sink at the expense of increased energy consumption and keeping these alternate paths alive by sending periodic messages; • The resilience of the network should be increased while keeping the maintenance overhead of these paths low; • An algorithm was proposed that route data through a path whose nodes have the largest residual energy; • The path is changed whenever a better path is discovered; • The primary path will be used until its energy falls below the energy of the backup path at which the backup path is used; • In this way, the nodes in the primary path will not deplete their energy resources through continual use of the same route, thus achieving longer life; • The path-switching cost was not quantified;

22. Smart Sensors and Sensor Networks • Query-based routing • The destination nodes propagate a query for data (sensing task) from a node through the network and a node having these data sends them back to the node which initiated the query; • These queries are described in natural language or in high-level query languages; ex.: “Are there moving vehicles in region I1?”; • All the nodes have tables consisting of the sensing task queries received and send data that match these queries when they receive them; • Directed diffusion is an example of this type of routing; • Negotiation-based routing • High-level data descriptors are used in order to eliminate redundant data transmissions through negotiation; negotiation is usually done through metadata; communication decisions are also taken based on the resource available to them; • The motivation is that flooding consumes more energy and more processing by sending the same data by different sensors; • An example is the SPIN protocols;

23. Smart Sensors and Sensor Networks • Forwarding packets can be done in many ways; the extremes are: • Flooding: • An incoming packet is sent to all neighbors; • As long as source and destination nodes are in the same connected component of the network, the packet is sure to arrive at the destination; • To avoid packets circulating endlessly, a node should only forward packets it has not yet seen; • Packets usually carry some form of expiration date (time to live, maximum number of hops) to avoid needless propagation of the packet; • Gossiping: • The packet is forwarded to an arbitrary node: • It results in the packet randomly traversing the network in the hope of eventually finding the destination node; • In gossiping, the packet delay is larger than in flooding; • Alternatively, the source could send out more than a single packet on a random walk or each node could forward an incoming packet to a subset of its neighbors, for example as determined by a topology-control algorithm, equivalent to flooding on a reduced topology;

24. Smart Sensors and Sensor Networks • For measuring the performance of forwarding rules, some metrics must be defined: • One of them is the cost for sending a packet from a source to a destination via a certain neighbor; the cost can be measured by the minimal number of hops or the minimal total energy required to reach the destination; • Each node maintains the costs in routing tables; • The routing tables are the responsibility of routing algorithms and protocols; • The routing protocols should be distributed, have low overhead, be self-configuring and be able to cope with frequently changing network topologies; • From this point of view, protocols are classified in: • Table-driven or proactive protocols: they try to keep accurate information in the routing tables as long as possible, preferably all the time; • On-demand protocols: they construct routing tables when a packet is to be sent to a destination for which no routing information is available; • Important aspects of routing protocols are: • Energy efficiency; • Routing table sizes; • Resiliency: for ex., when nodes rely on energy scavenging for their operation, they might have to power off at unforeseeable moments until enough energy has been harvested again; consequently, it may be desirable to use multiple paths; they will offer redundancy but can also be used for load balancing;

25. Smart Sensors and Sensor Networks • Forwarding can also be done without routing tables, either because the overhead to create these tables is prohibitive or because these tables are to be constructed in the first place; • The simplest solution is flooding and restricted flooding (topology control); • Randomly choosing forwarding nodes is another option; there is one advantage of wireless communication over wired communication: a single transmission can be received by all neighboring nodes in radio ranges, thus incurring transmission costs only once for many neighbors; an example is a group of protocols called Random walks (examples are Rumor routing, Random walks with known destination): • The data packet is assimilated as an “agent” that wanders through the network in search of its destination; • In the simplest form it is a purely random walk, where a packet is randomly forwarded to an arbitrary network; gossiping is such a form; • Instead of a single agent, several of them can be sent into the network by the source to shorten the time to arrival by parallelism; flooding is an extreme such form;

26. Smart Sensors and Sensor Networks • Routing can be: unicast, multicast or broadcast; • Energy-efficient unicast • At a first glance it appears to be a simple problem: take the network graph, assign to each link a cost value that reflects the energy consumption across this link and pick any algorithm that computes least-cost paths in a graph; • In fact there are various aspects how energy or power efficiency can be conceived of in a routing context; fig. shows a scenario for a communication between nodes A and H including link energy costs and available battery capacity per node:

27. Smart Sensors and Sensor Networks • Minimize energy per packet (or per bit): • The most straightforward formulation is to look at the total energy required to transport a packet over a multihop path from source to destination, including all overheads; the goal is to minimize, for each packet, this total amount of energy by selecting a good route; • Minimizing the number of hop counts will typically not achieve this goal as routes with few hops might include hops with large transmission power to cover large distances; • In fig., the minimum energy route is A-B-E-H, requiring 3 units of energy; the minimum hop count route would be A-D-H, requiring 6 units of energy; • Maximize network lifetime: • A WSN’s task is not to transport data but to observe and, possible, control; • The network should be able to fulfill its duty for as long as possible; • It is not yet clear when the network lifetime ends; options: • Time until the first node fails; • Time until there is a spot that is not covered by the network (loss of coverage); • Time until network partition (when there are two nodes that can no longer communicate with each other); • While these aspects are related, they require different solutions;

28. Smart Sensors and Sensor Networks • Routing considering available battery energy: the finite energy supply in nodes’ batteries is the limiting factor to network lifetime; as a consequence it makes sense to use information about battery status in routing decisions; possibilities: • Maximum Total Available Capacity: • Choose that route where the sum of the available battery capacity is maximized without taking needless detours; • In fig., route A-B-E-G-H has a total available capacity of 6 units but it contains a detour and should be discarded; route A-C-F-H will be selected; • Minimum Battery Cost Routing (MBCR): • Instead of looking directly at the sum of available battery capacities along a given path, MBCR instead looks at the reluctance of a node to route traffic; this reluctance increases as its battery is drained; • Reluctance or routing cost can be measured as the reciprocal of the battery capacity; then, the cost of a path is the sum of this reciprocals and the rule is to pick that path with the smallest cost; • Since the reciprocal function assigns high costs to nodes with low battery capacity, this will automatically shift traffic away from routes with nodes about to run out of energy; • In fig., route A-C-F-H is assigned a cost of 1/1 + ¼ = 1.25, but route A-D-H only has cost 1/3; this route will be chosen protecting node C from needless effort;

29. Smart Sensors and Sensor Networks • Min-Max battery Cost Routing (MMBCR): • The goal is to protect nodes with low energy resources; • Instead of using the sum of reciprocal battery levels, simply the largest reciprocal level of all nodes along a path is used; then, the path with the smallest cost is used; in this sense, the optimal path is chosen by minimizing over a maximum; • The same effect is achieved by using the smallest battery level along a path and then maximizing over these path values; this is then a maximum/ minimum formulation; • In fig., route A-D-H will be selected; • Conditional Max-Min Battery Capacity Routing (CMMBCR): • The approach is to conditionalize upon the actual battery power levels available; • If there are routes along which all nodes have a battery level exceeding a given threshold, then select the route that requires the lowest energy/ bit; • If there is no such route, then pick that route which maximizes the minimum battery level; • Minimize variance in power levels: • To ensure a long network lifetime, one strategy is to use up all the batteries uniformly to avoid some nodes prematurely running out of energy and disrupting the network; • Hence, routes should be chosen such that the variance in battery levels between different routes is reduced; • However, it is not obvious that thus in fact maximizes network lifetime; other factors like deployment patterns, event patterns and battery discharge/ recharge mechanisms have also to be considered;

30. Smart Sensors and Sensor Networks • Broadcast and multicast • Broadcast means when a node distributes information to all nodes in the network; the main problem is how to restrict the set of forwarding nodes as much as possible while still ensuring that all nodes receive the data; • Multicast means when a node distributes information to a subset of all nodes in the network; the main problem is how to construct routing structures; possibilities: • Source-based tree: the first idea is to construct, for each source, a tree, rooted at the given source, that contains all the destinations for this source and, if necessary, additional nodes to ensure that the tree can be constructed; which tree to select is determined by the optimization goal, which reflects the link costs: • For each source, minimize the total cost: try to find a tree for which the sum of all link costs is minimal, over all possible trees rooted at the source; Steiner tree problem; • For each source, minimize the maximum cost to each destination: instead of minimizing the total cost of the tree, one can minimize the costs to each individual destination separately; this maps the multicast problem to repeated unicast shortest path problems, which can be solved with any routing algorithm; • Shared, core-based tree: • Constructing and maintaining a dedicated tree for each source incurs considerable overhead; this overhead can be reduced if only a single tree is maintained; this is a solution when the destination sets for all sources are identical;

31. Smart Sensors and Sensor Networks • The downside is that for a given source the paths to its destinations can, in general, no longer be as short as with a dedicated source-based tree; • To share a tree among several sources, a representative node in the network, not necessarily a source or a destination, is selected and, from this node, a tree is constructed to contain all destination nodes; this tree is shared among all the sources; • In such a shared tree concept, the core node becomes a single point of failure; to overcome this shortcoming, multicore shared trees are also considered in the literature; • Mesh: • While trees represent the overhead-optimal routing structures, they are not redundant, failure of even a single link will disconnect the tree; adding links to the tree to obtain redundancy will alter its essential properties, in particular the absence of cycles; • The alternative is the mesh structure; it requires more complicated forwarding structures than does a simple tree;

32. Smart Sensors and Sensor Networks • The wireless multicast advantage (WMA): • Means that a node, by virtue of the broadcast nature of the wireless channel, can reach several or all of its neighbors with a single transmission; • A single transmission can spread information to many neighbors; • Whether this is realistic depends on the assumptions made about hardware, MAC protocol and sleeping cycles of nodes; • If WMA is considered, the design of protocols and of the resulting trees or meshes changes considerably as now not the sum of the costs of outgoing links has to be considered but only the cost of the most expensive link has to be invested in to transmit data to all neighbors; • Overview of possible multicast approaches:

33. Smart Sensors and Sensor Networks • Geographic routing: • For many applications, it is necessary to address physical locations, as “any node in a given area” or “the node at/ closest to a given point”; such requirements must be supported by a proper routing scheme; • When the position of source and destination is known as are the positions of intermediate nodes, this information can be used to assist in the routing process; the destination node has to be specified either geographically or as some form of mapping, a location service, between an otherwise specified destination and its current position is necessary; • The first aspect, sending data to arbitrary nodes in a given region, is usually referred to as geocasting; • The second aspect is called position-based routing, in combination with a location service; • In WSNs, usually the geocasting aspect of geographic routing is considerably more important; since nodes are considered as interchangeable and are not distinguished by external aspects, in particular their position, a location service is usually not necessary;

34. Smart Sensors and Sensor Networks • Location based multicast: • A simple way to implement geocasting is to base it on flooding but restrict the area where packets are forwarded; • The protocol defines a forwarding zone such that only nodes within the forwarding zone forward a received data packet; this zone can be defined in several ways: • Static zone: the smallest rectangle that contains both the source and the entire destination region, with its sides parallel to the axes of the coordinate system; alternative geometric definitions are possible as well, for example, the destination region and two tangents to it defined by the source node’s location; • Adaptive zone: each forwarding node recalculates the zone definition, using its own position as the source; this way, nodes that would be included in the static zone but would represent a detour once the intermediate node has been reached are excluded from forwarding; • Adaptive distances: while the previous two schemes contained the forwarding zone explicitly in each packet, this scheme recomputes it in each step, on the basis of information about the destination region and coordinates of the previous hop or the source; the idea is that a node u forwards a packet to its neighbors if its distance to the center of the destination region is smaller than the distance of the previous v to the center, meaning the packet has made progress; if not, the packet is only forwarded if the node is actually within the destination region, to ensure that all destinations receive the packet;

35. Smart Sensors and Sensor Networks • Mobile nodes: • Essential sources of mobility in WSNs: mobility of sensor nodes, mobility of data sinks and mobility of the observed event; • Mobile sinks: • An approach is a two-tiered network where data sources use a geographic mesh to broadcast their data and sinks subscribe to the data at their nearest mesh point; • Mobile data collectors: • In sparse networks where communication distances and energy required are high, it is less efficient to move data sinks or use multihop communication; • If the objective is to collect data from sensors and if this task tolerate some delays, the Mobile Ubiquitous LAN extension (MULE) concept can be used; • A MULE is a mobile device that moves around between sensors, collects and buffers their data and occasionally visits the actual data sink to off-load data; • Mobile regions: • The destination regions so far considered were static (dynamically adapted); • For applications like tracking mobile events, it would be useful to be able to specify a destination zone that changes its location and possibly shape; • Data should be delivered at time t to all nodes that are covered by the destination zone at time t; the service model is called mobicast.