Smart Sensors and Sensor Networks

1. Smart Sensors and Sensor Networks Lecture6 Coverage and topology control

2. Smart Sensors and Sensor Networks Coverage • The purpose of deploying a WSN is to collect relevant data for processing and reporting; • The two important questions are: • Given a sensor deployment, that is a particular placement of sensors over a certain geographical area, which points of this area are close enough to sensors such that an event taking place at this point can be sensed? Or, which points are covered? • Given an area to be observed, some coverage requirements and some constraints (cost, presence of obstacles, availability of different types of sensors and so on), what number of sensors is needed and where should they be placed? • The covering and deployment concepts are a measure of QoS of the sensing function; • An other important implication: if there are many sensors, it might be possible to switch some sensors into sleep mode without affecting coverage; this offers energy saving and prolongs the lifetime of the overall network;

3. Smart Sensors and Sensor Networks • Deployment and coverage are close concepts but in many applications coverage is more important; • For example in applications like wildfire detection or habitat monitoring, large areas have to be observed with large number of sensors and, in such situations, deployment is often a loosely controlled process; • Design choices: • Subject to be covered: area vs. discrete points; • Sensor deployment method: deterministic vs. random; • Sensing and communication ranges: nodes may have different sensing ranges; communication range can be equal or different to the sensing range; • Additional critical requirements: energy efficiency and connectivity, referred to as energy – efficient coverage and connected coverage; • Algorithm characteristics: centralized vs. distributed/ localized; • Objective: maximize network lifetime or minimize the number of sensors;

4. Smart Sensors and Sensor Networks • Final goal: to have each location in the targeted physical space within sensing range of at least one sensor; • Problems: • The quality of the signals: depend, among other factors, on the distance; for ex. the amplitude of the sound waves decreases quadratically with the distance; • Directionality: ideally, the sensitivity of a sensor is the same in all directions; in practice, certain directions may be preferred; • A same sensor can generate different outputs for the same stimulus at different times; the signal generated by a sensor for an external event at a certain distance is a distance-dependent random variable; • Coverage will be approached in static WSNs; • The coverage model: sensors have omnidirectional antennae and can monitor a disk whose radius is referred to as sensing range; • Three main directions: area coverage, point coverage and barrier coverage;

5. Smart Sensors and Sensor Networks • Area coverage • The main objective is to cover (monitor) an area (region); • Energy – efficient random coverage: • Mechanisms that conserve energy resources are highly desirable because they have a direct impact on network lifetime; • A frequently used mechanism is to schedule the sensor node activity and allow redundant nodes to enter the sleep mode as often and as long as possible; questions: • Which rule should each node follow to determine whether to enter sleep mode? • When should nodes make such a decision? • How long should a sensor remain in the sleep mode? • One solution: • A large number of sensors is considered, deployed randomly for area monitoring; • The goal is to achieve an energy – efficient design that maintains area coverage; • Because the number of deployed sensors is greater than the optimum required for maintaining area coverage, the solution is to divide the sensor nodes into disjoint sets so that every set can individually cover the area; • The sets are activated successively, one being active and the others in sleep state; • The goal is to determine a maximum number of disjoint sets;

6. Smart Sensors and Sensor Networks • Connected random coverage: • A network is connected if any active node can communicate with any other active node, possibly using intermediate nodes as relays; • Once the sensors are deployed, they organize into a network that must cover the area and must be connected; • A frequently addressed objective is to determine a minimal number of working sensors required to maintain the initial coverage area as well as connectivity; • Selecting a minimal set of working nodes reduces power consumption and prolongs network lifetime; • An intuitive result shows that: • If the communication range RC is at least twice the sensing range RS , a complete coverage of a convex area implies connectivity of the working nodes; • If the communication range is too large, radio communications may interfere; • If, however, RC> RS,, a subset of working nodes can be optimally chosen for full coverage; • Some applications may require different degrees of coverage still maintaining working node connectivity: • A network has a coverage degree k, k – coverage, if every location is within the sensing range of at least k sensors; • Networks with a higher coverage degree can obtain higher sensing accuracy and be more robust to sensor failure; • If RC≥ RS,, a k – covered network will result in a k – connected network.

7. Smart Sensors and Sensor Networks • Point coverage • The objective is to cover a set of points; • Random point coverage: scenario: • A limited number of points with a known location that need to be monitored is considered; • A large number of sensors are dispersed randomly in close proximity to the targets; • The sensors send the monitored information to a central processing node; • The requirement is that every target must be monitored at all times by at least one sensor, assuming that every sensor is able to monitor all targets within its sensing range; • Deterministic point coverage: • Given a set of n points, the objective is to determine a minimum number of sensor nodes and their locations so that the given points are covered and the sensors deployed are connected; • For the case in which all sensors have the same sensing range and this equals the communication range, an algorithm was proposed: it begins by constructing the minimum spanning tree over the targeted points and then successively selects sensor node locations on the tree;

8. Smart Sensors and Sensor Networks • Barrier coverage • The goal is to minimize the probability of undetected penetration through the barrier (sensor network); • Barrier coverage model 1: • The following problem is addressed: given a field instrumented with sensors and the initial and final locations of an agent that needs to move through the field, determine a maximal breach path (MBP) and the maximal support path (MSP) of the agent; • The MBP (MSP) corresponds to the worst (best) case coverage and has the property that, for any point on the path, the distance to the closest sensor is maximized (minimized); • The model assumes homogeneous sensor nodes, known sensor locations, with sensing effectiveness decreasing as the distance increases; • Also important is the determination of the number of sensor nodes to be deployed randomly in the field so that the probability of a penetration path is close to zero; • A solution was proposed for grid – based SNs and random SNs: a critical density for a given SN based on percolation theory was proposed; if the density is lower, a penetrating path that will not be detected almost surely exist, and for a higher density any crossing object is almost surely detected;

9. Smart Sensors and Sensor Networks • Barrier coverage model 2: • It is called the exposure – based model, in which the sensing abilities of the sensors diminish as the distance increases; • Another important factor is the sensing time (exposure): the longer the exposure time, the greater the sensing ability; • The two dimensional sensing model is defined as: d(s,p) = the Euclidean distance between the sensor s and the point p λ and k are sensor technology – dependent parameters • Another characteristic is the intensity of the sensor field; all – sensor field intensity for a point p, field F and n sensors s1, s2, …, sn is defined as: • The exposure of an object moving in the sensor field during the interval [t1, t2] along the path p(t) is defined as: • Given a field instrumented with sensors and the initial and final points of the object, the problem of determining the minimal exposure path, which corresponds to the worst – case scenario is considered;

10. Smart Sensors and Sensor Networks • A grid – based approach was used: • Step 1: the minimal exposure path in each grid square is restricted to line segments connecting any two vertices; • Step 2: the grid is transformed into a weighted graph in which the weight (exposure) of an edge is approximated using numerical techniques; • Another aspect of the exposure – based model is: to estimate sensor node deployment density, one should consider the sensor characteristics as well as target specifications; • For example, detection of an enemy tank requires fewer nodes due to the strong acoustic signal compared with soldier detection that might require more sensors; • A solution was proposed assuming the target moves in a straight line, with constant speed, between two points; two radii were associated for a given sensor: • Radius of complete influence: defined as the distance from the sensor so that all targets originating within this radius are detected; • Radius of no influence: any target originating beyond it cannot be detected; • Using the sensing and exposure model and knowing the threshold energy, required to detect a target, a solution was proposed for calculating the influence radii as well as the sensor nodes’ deployment density; • To cover an area A, the deployment of O(A/r2) nodes, where r is the radius of no influence, achieves a probability of detection of at least 98%.

11. Smart Sensors and Sensor Networks Topology control • Topology control means to deliberately restrict the set of nodes that are neighbors of a given node, that is to deliberately restrict the topology of a network; • The topology of a network is determined by the subset of active nodes and the set of active links along which direct communication can occur; • Advantages of the high deployment density: • Ensures sufficient coverage of an area; • Redundancy to protect against node failures; • Disadvantages of the high deployment density: • Many nodes interfere with each other; • There are a lot of possible routes; • Nodes might needlessly use large transmission power to talk to distant nodes directly (also limiting the reuse of wireless bandwidth); • Routing protocols might have to recompute routes even if only small node movements have happened;

12. Smart Sensors and Sensor Networks • Options of topology control: • The set of active nodes can be reduced by periodically switching off nodes or introducing nodes in sleep state; • The set of active links (the set of neighbors) for a node can be reduced by disregarding some of them and restricting communication only to crucial links; • To arrange the nodes in certain topology types; • Topology types: • Flat topology: all the nodes are directly connected to each other; is appropriate for homogeneous networks; • Hierarchical (tiered) topology: the nodes are divided in groups and the communication between the nodes from different groups is done only through selected nodes: • Backbone hierarchy: some nodes are selected to form a backbone for the network and the communications are done only through the links within this backbone and direct links from other nodes to the backbone; • Cluster hierarchy: the network is divided in clusters;

13. Smart Sensors and Sensor Networks • Tiered topology is appropriate for heterogeneous networks; • In a tiered network, the functions of sensing, computation and data delivery are divided unequally among nodes: • These functions may be divided across the tiers, with the lowest tier performing all sensing, the middle tier performing all computation and the top tier performing all data delivery; • Alternatively, a particular function may be divided unequally among layers: for instance, each layer could perform a specialized role in computation;

14. Smart Sensors and Sensor Networks • Functional decomposition of a sensor network can reflect physical characteristics of nodes or it can simply be a logical distinction: • A subset of nodes with a long – range communication capability may form a physically hierarchical overlay network topology; • A subset of nodes may perform a specific service; such services might include data aggregation, communication over a backbone, route aggregation on behalf of a cluster of nodes; these logical role assignments can form a logically hierarchical network; logical roles can be periodically rotated for fairness; • In some cases only nodes with particular physical resources are suited for a given task;

15. Smart Sensors and Sensor Networks • Advantages of tiered topologies: • Cost-effectiveness: • Tiered architectures can reduce the cost of a sensor network by allocating resources where they can be most effectively utilized; • Sensing typically requires a large number of nodes but few resources at each node; • Data analysis requires more processing and storage resources than sensing: • The minimum resources depends on the latency that can be tolerated; • The per-node storage requirements will be, at best, inversely proportional to the number of nodes involved in data analysis and limited to the degree to which the algorithm can be distributed; • Tasks such as localization and time synchronization may also require specialized hardware, such as a GPS receiver; • If homogeneous hardware were deployed, each node would need to meet the minimum resource requirements for all tasks; because the number of nodes required will be determined by the desired sensor coverage, the overall cost of the network will be unnecessarily high; • If a large number of inexpensive nodes were allocated for sensing and a small number of more expensive nodes were allocated to data analysis, localization and time synchronization, the overall cost of the network would be reduced;

16. Smart Sensors and Sensor Networks • Longevity: • Sensor nodes cannot always be placed near a source of power and must instead be powered by batteries; • Prolonging sensor network lifetime is a critical issue because of the limits of slowly improving battery technology, physical size requirements and cost; • The lifetime requirements of applications can vary greatly: for example, habitat monitoring applications typically require a lifetime of 6 to 9 months; • For sensing tasks, that require operation over a long period of time, a low – power node, that meets the minimum processing requirements, is more effective; • For significant computation, a faster processor can be more energy efficient than a slower one, due to the short time required to perform the calculation; • A tiered architecture that partitions functions among appropriate hardware may increase network lifetime; • Scalability: • A sensor network must scale with the required number of nodes in terms of bandwidth and lifetime; • However, it is known that bandwidth in a flat topology does not scale; thus, as the size of the network increases, per-node throughput decreases toward 0;

17. Smart Sensors and Sensor Networks • In tiered architectures, the bandwidth can scale; • One approach is to use a single channel in a hierarchical structure, in which nodes on the lower tier forms clusters around regularly deployed base stations; each base station acts as a bridge to the upper tier, which provides intercluster communication across a wired infrastructure; it was shown that the network capacity grows linearly with the number of clusters; • Another approach was to use different channels at different levels of the network hierarchy; the capacity of each layer in the tiered architecture and the capacity of each cluster in a given layer scale independently; • Scaling flat WSNs in the physical dimension may lead to low density and poor connectivity; it may make sense to introduce an overlay of nodes capable of long-distance, or even fully-connected, communication; in case of tiered architectures, scalability may improve connectivity but only for a limited number of tiered architecture types; • Scaling of services can be affected by tiered architectures; for example, address-lookup services; they can be fully distributed to all nodes, partially distributed to a subset of nodes or centralized; assuming that nodes are mobile and must change their address periodically, the balance between these choices depends on the relative frequency of update and lookup operations;

18. Smart Sensors and Sensor Networks • Sensor network hardware types: • Small sensor nodes: • They have small dimensions and low consume; • An example is the Berkeley mote: is integrates a 8 bit microcontroller, a low-power radio and various sensors; the TinyOs was added; • Subsequent generations are Mica and Mica2 motes, based on an ATMega 128L microcontroller; they have connectors for extension boards with different sensors; • A newer version is the Spec mote with the size of 2 x 2,5 mm; it reduces size, cost and power consumption but offers less capabilities; • A more advanced solution is Imote: it integrates a powerful ARM processor core, including Flash and SRAM memories, and a Bluetooth radio on the same chip; the size is 3 x 3 cm; the main board can be extended with sensing and actuating boards; it also runs TinyOs, being compatible with the applications for Berkeley motes; • Large sensor nodes: • They have significant computing power, large memories and more I/O peripherals, such as Ethernet or PCMCIA connectors; • They consume more power and are not easy to deploy with battery power;

19. Smart Sensors and Sensor Networks • Nodes are roughly classified into this group if they have a high-speed 32 bit microcontroller/ microprocessor, large memories and high power consumption; • An example is Stargate; it has a 400 MHz Intel XScale processor, 32 MB flash memory and 64 MB SDRAM; it includes a PCMCIA slot, a compact flash slot and a connector for a Mica or Mica2 mote; it can be expanded with Ethernet, serial and USB ports; • Another example is WINS NG 2.0; it employs a Hitachi SH-4 processor, which is a 32 bit RISC with 300 MIPS and 1.1 GFLOPS FPU; it has a GPS receiver, two radios, sensor connectors, Ethernet and 2 PCMCIA slots; • The highest end are embedded PCs (PC/104 and PC/104 plus); they are fully compatible with PCs at a much smaller size ( 90 x 96 mm); they have GPIO lines for connecting sensors but do not have integrated radios; these modules can be attached;

20. Smart Sensors and Sensor Networks • Task decomposition and allocation • A designer should decompose a complex application into different tasks and assign them to appropriate hardware in the tiered network; the goal is to match different task requirements with different node capabilities; • There are three basic types of tasks in a SN: • Sensing: is the process of collecting data from the physical world; • Processing: data from different sensors are processed; • Communication: enables collaborative signal and data processing from multiple sensors and delivery of results to interested users; • Sensing • Sensing uses different types of sensors to capture signals from the physical world, such as temperature, light, acoustic and seismic; • All signals decay with the distance; as a result, the signal-to-noise ratio (SNR) decreases with distance; • SNR is one of the fundamental factors that decide the quality of signal processing; high density improves the quality of sensed values; • Small nodes are recommended; they are easy to deploy and power efficient, thus increasing network lifetime;

21. Smart Sensors and Sensor Networks • Different sensing tasks have different hardware requirements, based on the sampling rate and accuracy; • In an environmental monitoring application, ambient temperature may change slowly, potentially allowing a sampling rate as low as one sample every 10 minutes (1.67 x 10-3 Hz); small nodes are very suited for such tasks; • In an application that recognize bird calls, the acoustic sampling rate could be as high as 22 KHz; most small nodes cannot perform such a sensing task; • Processing • This task can be as simple as detecting abnormal temperature changes in a fire alarm system or as complex as tracking a target moving through the network or estimating the direction of a bird call; • Processing often combines multiple sensor outputs from local neighboring nodes and it is thus referred to as collaborative signal and data processing; • This has two advantages: • By combining multiple sensor outputs, the processing result is more reliable and accurate; • Only the aggregate result needs to be sent to a user across the network and through gateway nodes, thus saving a significant amount of energy; • Small nodes are suited only for lightweight processing, for example to obtain the average, minimum and maximum value from different sensor readings or to perform simple filtering in sound applications;

22. Smart Sensors and Sensor Networks • Large nodes perform processing tasks that require extensive computations, such as beamforming, target recognition and classification; • The in-network processing saves a significant amount of energy by reducing the communication cost; • Communication • Is the most complex task in a SN due to its ad hoc nature and resource constraints; • It can be further divided into subtasks roughly represented by different layers; a common decomposition includes a MAC layer and a routing layer; • Communication enables collaborative processing but also interactions between a user and the sensor network; • In a tiered architecture, nodes are often organized into clusters; • If a large node exists in the cluster it is selected as a cluster head; • These nodes must use the same radio to communicate and need to run the same low – level protocols, such as the link and MAC protocols; • Within a cluster, nodes only send their data to the cluster head; • The cluster head sends aggregate data to a base station using a long-range radio; • The role of cluster head will typically rotate among cluster members; • In a tiered architectures, nodes with special communication capabilities can form a backbone to carry most of the traffic;

23. Smart Sensors and Sensor Networks • Forming tiered architectures • There are several mechanisms; they start from the hardware diversity and from the tasks assigned to the hardware; • Engineered networks • A simple way to organize a network into tiers is to engineer the network by hand; the network designer must specify which nodes participate at each tier and how nodes in each tier will be organized; • A tiered architecture can be created by manually configuring a routing topology, by specializing the software loaded on each node or by providing specialized hardware on particular nodes; • Tiered architectures can include varying degrees of manual organization; • Manual configuration of large number of nodes is too time-consuming and expensive; • Automatic organization of nodes into tiers is for minimizing the above disadvantages but it does not provides always an optimum solution; • Routing mechanisms • One way to use the resources of a subset of nodes automatically to benefit the entire network is route biasing;

24. Smart Sensors and Sensor Networks • Route biasing increases the packet forwarding load on nodes with more energy, thus increasing the lifetime of the network; • Route biasing can also be used to attract more data to nodes with greater processing power, increasing the amount of in-network processing; • Resource-biased path selection introduces a delay in forwarding packets; • This approach works best in environments with many resource-rich nodes, in which case the latency to find a route will be low; • The delay value is difficult to determine and may change as the remaining energy of the nodes changes; • Energy-aware routing is similar to the preceding approach, except that it uses a different metric to select appropriate routes; • In one implementation, each node maintains a list of neighbors and the cost of transmitting through those neighbors to a given destination; • The cost is computed using the metric advertised by that neighbor, plus a hop metric consisting of a weighted multiple of the cost of transmitting a packet and the fraction of energy remaining; • The average cost of forwarding through each neighbor is advertised to other nodes; • The paths selected tend to be those that include the least expensive links and the nodes with the most remaining capacity; • An extension of the algorithm allows reach nodes to accept disproportionate load;

25. Smart Sensors and Sensor Networks • These routing mechanisms do not form a hierarchical structure; however, they do allow resource-poor nodes to become aware of and benefit from resource-rich nodes; the benefit is modest but so is the overhead too; • Clustering mechanisms • An alternative to the routing protocols above described is to divide the network into clusters led by cluster heads; • Cluster members can utilize resources or services available at the cluster head; • Cluster heads can form clusters, so clustering is hierarchical; • Clusters can be used to form a physical hierarchy, organize a logical hierarchy in a flat topology or simply identify the set of nodes that will use a particular specialized resource, such as a GPS receiver; • Clustering algorithms can be judged on the properties of the clusters they form; • Although many algorithms are designed to form one-hop clusters, others limit the size or diameter of a cluster; • The size of the cluster controls the load on the cluster head and its diameter controls the cost of communication between each node and the cluster head; • A balance between cluster size and cluster diameter will be desirable;

26. Smart Sensors and Sensor Networks • Cluster stability is also important: • Clustering must be dynamic, in order to adapt to mobility and changes in network connectivity; • Clusters must be stable in the face of small changes, or the cost of periodic cluster reformation will reduce the potential benefit; • Cluster stability have also application-specific benefits; for instance, the computed communication delay between each cluster member and the cluster head, which is required for some beamforming algorithms, can be reused until cluster membership changes; • Clustering allows some nodes to sleep more than others; • Periodic reclustering is necessary to balance the load between the nodes in order to increase the network lifetime; • Clustering can be used to impose a hierarchical organization in an otherwise flat network: • The hierarchy can lend structure to in-network computation; • Each cluster member forwards sensor data to the cluster head, which aggregates/fuses data from multiple and, possibly, different types of sensors into a single observation; • The resulting observation can typically be transmitted more efficiently across the network to a consumer than the individually sensed values can be; • Such aggregation/fusion can occur at multiple levels of a tiered architecture, allowing multiple observations to be aggregated/fused together further;

27. Smart Sensors and Sensor Networks • Clustering can also introduce a hierarchy of data transmission; • A hierarchy of clusters forms a tree structure; • Nodes in each tier of the tree are divided into clusters in which the cluster head represents the cluster at the next higher tier of the tree; • When a single data sink is present, it will typically be the root of the tree; • When a node wishes to forward a sensed value, it can send a packet to its cluster head, which, in turn, forwards the packet to its cluster head, until the packet reaches the sink node; • Control information can be flooded in the reverse direction, from cluster heads to cluster members; • More general patterns of communication are also possible on a tree structure; • When a node wishes to send a packet to a node outside its own cluster, it does so through the cluster head; • The simplest clustering algorithms create one-hop clusters and use simple metrics to select cluster heads; • For example the lowest and highest degree algorithms elect cluster heads based on a node’s ID or the number of its neighbors; • A node becomes a cluster head if it has the best metric between itself and all of its neighbors; a node relinquishes its cluster head status if a node with a better metric becomes a neighbor; • Although simple, these approaches tend to be unstable in the presence of mobility because the IDs present in a neighborhood and the degree of a node will constantly change;

28. Smart Sensors and Sensor Networks • An alternative is to use node velocity as the metric; the nodes with lower velocity are chosen as cluster heads, so the cluster heads are stabile; if, however, the velocity increases important changes in cluster membership will take place; • Cluster heads can be selected according to their resources; • Nodes with two radios, one short-range and one long-range will be good candidates for cluster heads; • Because nodes are, generally, mobile and prone to failures, the network must contain more two-radios nodes than are necessary for being cluster heads; • The number of active cluster heads, and thus cluster size, is important in an network because it strikes a balance between inter-cluster and intra-cluster capacities; • To maximize available bandwidth as the network grows, the optimal number of clusters in such a network is: (W1/W2) x N1/2, where W1 and W2 are the respective bandwidth of the short and long range channels and N is the total number of nodes in the network; • With fewer clusters, the intra-cluster traffic is the limiting factor, while with more clusters the inter-cluster traffic is the limiting factor; • Cluster heads can be selected based on a rendezvous mechanism too; in such a network, each node has two radios and one of them is tuned on the rendezvous channel; after a short listening period, a node can decide, according to some metrics, to elect itself as a cluster head or to join an existing cluster;

29. Smart Sensors and Sensor Networks • Questions about clusters: • Are there cluster heads? • May cluster heads be neighbors? • May clusters overlap? If a node is adjacent to two cluster heads one must decide where does it belongs? A solution may be to assign the node to both clusters, resulting in overlapping clusters. If that is not desirable, some decision rule is required to unambiguously assign nodes to cluster heads; • How do clusters communicate? Through a unique or distributed gateway: • What is the maximal diameter of a cluster? • Is there a hierarchy of clusters?

30. Smart Sensors and Sensor Networks • Routing and addressing in a tiered architecture • The efficiency of network routing can be increased by organizing a network into a tiered architecture; • In a flat sensor network, route discovery usually requires packets to be flooded across the entire network; • Alternatives are: geographic routing and hierarchical routing; • Hierarchical routing can take two forms: • The process of discovering a route to a destination node can be tailored to take advantage of the hierarchical nature of the network; • Alternatively, the hierarchical location of a node can be encoded in the node’s address; this approach simplifies route discovery by introducing the problem of ever-changing node address; • Routing in a hierarchy: • In a cluster-based network, route discovery can be simplified by splitting the problem into two cases: • Routes to nodes inside the local cluster; will have low overhead if the cluster size is small; • Routes to nodes outside the local cluster;

31. Smart Sensors and Sensor Networks • An implementation of the hierarchical routing on a physical tiered network: • Route request messages are forwarded across the topology of a tier as well as across tiers through cluster heads; • For instance, in a two-tiered network, the lower layer would consist of clusters that elect a set of backbone nodes; • These backbone nodes use an independent, long-range radio to form a backbone on the second tier; • While route request messages flood the lower tier, they are also forwarded by gateway nodes onto the upper tier; • As route messages flood the upper tier, they are also forwarded down to the lower tier by other gateway nodes, • As a result a route can be discovered that utilizes a few hops across the backbone network as a short-cut path in place of many hops across the underlying network; • The approach extends to multiple tiers too, but would require an additional channel for every tier;

32. Smart Sensors and Sensor Networks • Hierarchical addressing • Tiered architectures can reduce the overhead of proactive route discovery without the latency of reactive route discovery; • Each node is given a unique identifier and a logical address that designates its position in a hierarchical network; • Because the node’s address indicates its location in the hierarchy, packets can be directed toward their destination without reactive route discovery and with limited table maintenance; • However, this approach also requires a mechanism to map unique identifiers into hierarchical addresses; • Landmark routing uses multilevel hierarchical addressing; • A packet is forwarded toward a successively closer sequence of landmarks until it arrives at the destination node; • A landmark is a node to which packets can be routed from nodes in a neighborhood of a given radius; • Landmarks form a hierarchy equivalent to a logically tiered cluster architecture so that each node in a cluster can route packets to the cluster head; • A small number of landmarks have a radius larger that the network radius and act as landmarks for the entire network; • Each lower level of the hierarchy has a larger number of nodes with a smaller radius;

33. Smart Sensors and Sensor Networks • Each node receives a logical address that is the concatenation of landmarks LMn,LMn-1,…,LM1,LM0 so that LMi is a landmark for node LMi-1; LMn is a landmark for all nodes; • Thus, given an address, a packet can be routed toward LMn; as it nears LMn, a route will be available to LMn-1 until the packet reaches LM0; • Although landmark routing does not provide shortest path routing, it only requires O(log N) storage space for routing tables; • The cost of route maintenance is also low because most landmarks have a small scope and only a few have a large scope;

34. Smart Sensors and Sensor Networks • Mapping unique IDs to hierarchical addresses • The hierarchical addresses reflect a node’s position in the tiered network architecture to reduce the cost of route table management and packet forwarding; • The drawback of this approach is that when a node changes its location in the tiered architecture, this hierarchical address must change; • Although each node has a unique ID that never changes, it must be mapped to a logical address in order to be useful; • Thus, a mapping mechanism must exist; • The mapping mechanism must allow each node to update its hierarchical address whenever its location in the tiered architecture changes and it must allow a node to lookup another node’s current hierarchical address, given the node’s unique ID; • The mapping mechanism can be: centralized, half distributed or distributed; • The centralized mechanism does not scale for lookups, nor does it scale for updates in a mobile network; • The distributed mechanism uses cluster heads for updating mapping tables; for example, in the landmark routing, there is a landmark-ID field and a node-ID field;

35. Smart Sensors and Sensor Networks • Drawbacks of tiered architectures • Organizing a network into tiers has a tendency to introduce hot spots near cluster heads, where one tier connects to another; • Altruistic routing tends to concentrate the packet-forwarding load on nodes adjacent to altruistic nodes; as a result, the network lifetime can be decreased; • Base stations that provide access to an upper tier backbone may cause hot spots in their neighborhood thus decreasing spatial concurrency; • The potential inefficiency of imposing a logical structure on an existing flat network; • Some of the protocols require inter-cluster communication through cluster heads; • It means that adjacent nodes that fall into different clusters cannot communicate directly; • Organizing nodes into a hierarchy typically introduces overhead into the network; • This is particularly true for clustering algorithms; • For instance, an algorithm first requires that a spanning tree be identified, followed by the execution of the clustering algorithm; • If node mobility results in frequent reclustering, the overhead may outweigh the benefit.