Outline. Wireless Ad Hoc & Sensor Networks (Wireless Sensor Networks III) Localisation and Positioning. Localisation and Positioning properties

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1 Wireless Ad Hoc & Sensor Networks (Wireless Sensor Networks III) Outline Localisation and Positioning Topology Control Routing Summary WS 2009/2010 Prof. Dr. Dieter Hogrefe/Prof. Dr. Xiaoming Fu Dr. Omar Alfandi 2 Localisation and Positioning In many circumstances it is useful or even necessary for a node in WSN to be aware of its position e.g. tracking, event-detection etc. Sometimes used in protocols such as localised routing protocols Not an option Manually configuring location information too much effort Equipping each node with GPS receiver too expensive deployment limitations (not working indoor) Localisation and Positioning properties Determine physical vs. logical location Numeric coordinate system vs. living room Absolute vs. relative coordinates Absolute coordinates via anchors, i.e. special nodes that know the exact position (3 anchors are needed for 2-dimensions) Localised vs. centralised computation Accuracy and precision How close is estimated position to real position? How often is accuracy achieved? Scale Indoor, outdoor or global deployment Limitationsit ti e.g. GPS works only outdoor; some systems have limited range Costs Time, energy, price of deployment 3 4

2 Localisation and Positioning basic approaches Three approaches to determine a node s position Proximity-based approaches Use information about a node s neighbourhood Exploit finite range of wireless communication Triangulation and trilateration Exploiting geometric properties of a scenario Use distance (lateration) or angle (angulation) estimates + simple geometry to compute position estimates Scene analysis Trying to analyse characteristic properties of the position of a node in comparison with pre-measured properties fingerprint of location e.g. characteristic signature of a radio environment α (x=2, y=4) β (x=8, y=4) (x=5, y=1) 5 Localisation and Positioning determining distances Determining distances Received signal strength indicator (RSSI) Ptx P rcvd = c d = α d Send out signal of known strength, use received α d = distance signal strength and path loss coefficient to P tx estimate the distance P rcvd Time of Arrival (ToA) Use time of transmission, propagation speed, time of arrival to compute distance Problem: exact time synchronisation Time and Difference of Arrival (TDoA) Use two different signals with different propagation speeds Compute difference between arrival times to compute distance e.g. ultrasound and radio signal Problem: Calibration, expensive/energy-intensive hardware α cp P = path loss coefficient tx rcvd = transmission power = received signal strength 6 Localisation and Positioning determining angles Determining Angles Angle can be either angle of a connecting line between an anchor and a position- unaware node to a given reference direction ( 0 north ) or angle between two connecting lines, if no reference direction is commonly known to all nodes Traditional approach: use directional antennas Antennas that only send to/receive from a given direction Rotating axis (similar to radar station/ lighthouse) BUT: inappropriate for sensor nodes, but could be used for anchors Another approach: exploit finite propagation speed of waveforms Multiple antennas are mounted on a device at known separation measure time difference between signal s arrival at different antennas Smaller antenna separation higher precision of time differences Angulation is less frequently discussed in the context of WSNs 7 Localisation and Positioning multi-hop Problem Not every node is in the range of at least three anchors Idea: Multi-hop communication First idea: DV-Hop Count number of hops, assume length of one hop is known start counting hops between anchors, divide known distance Second idea: DV-Distance If range estimates between neighbours exist, use them to improve total length of route estimation in previous method Third idea: Iterative multilateration If not all nodes can reach at least 3 anchors, let nodes which are in range compute position estimate and spread this in the network Problem: Errors accumulate Fourth idea: Probabilistic position description Similar to previous, but accept problem that nodes positions are only probabilistically known use probability for further computations 8

3 Outline Localisation and Positioning Topology Control Routing Summary Topology Control Problem Network can be too dense, i.e. to many nodes are in the same (radio) neighbourhood no efficient operation possible High transmission power waste of energy resources Burden for the MAC protocol Many collisions/ too complex operations Burden for the routing protocol Many paths to handle, when moving continuous update necessary Idea: Make topology less complex (use topology ) Reducing/ling transmission power Deciding which links to use Turning some nodes off 9 10 Topology Control Topology Control Node Topology Control Link Flat network Topology Network topology Hierarchical network Options Control node Deliberately turn on/off certain nodes Control link Deliberately use/do not use certain links Exploit redundant deployment of nodes e.g. periodically switching off nodes with low energy reserves and activating other nodes instead Power Backbones Clustering 11 12

4 Topology Control Topology Control network topology Node Topology Control Link Flat network Power Topology Network topology Backbones Hierarchical network Clustering Flat networks All nodes are considered as equal Reduce transmission range by power Hierarchical networks Backbone (spine) network Some nodes their neighbours form a minimal dominating set Each node has a ling neighbour All ling nodes are connected (backbone) Only links between normal nodes and ling nodes Construct clusters Partitioning of nodes into groups (clusters) Each node is exactly in one group (except bridging) Each group has a clusterhead Clusterheads are also dominating i set, but independent d set Topology Control Algorithm metrics Connectivity Algorithm should not disconnect a connected graph G Stretch factors Hop stretch factor: How much longer are paths in G 0 than in G Energy stretch factor: How much more energy does the most energy efficient path need in G 0? Small stretch factors are desired Graph metrics Small number of edges with a low maximum degree are desired Throughput Removing nodes/links should not reduce the amount of traffic Robustness to mobility Algorithm should minimise adoptions to react to node mobility Algorithm overhead Low number of additional messages, low computational overhead Outline Localisation and Positioning Topology Control Routing Summary 15 16

5 Routing in WSNs In a multihop network, intermediate nodes have to relay packets from the source to the destination The intermediate nodes have to decide to which neighbour to forward an incoming packet Typically routing tables are used that contain the most appropriate neighbours for a given packet destination The construction ti and maintenance of these tables will be discussed including the special cases When nodes are mobile For broadcast/ multicast requirements With energy efficiency as an optimisation metric When a nodes position is available Routing classification recap When does the routing protocol operate? Proactive/Table driven routing protocol always tries to keep its routing data up-to-date On-Demand Route is only determined when actually needed Hybrid Combine these to behaviours How is the network structured? Flat or hierarchical Which data is used to identify nodes? Arbitrary identifier? Position of a node? Structured identifier? Routing in ad hoc networks recap Routing gossiping and agent-based forwarding Routing in ad hoc networks table-driven approaches e.g. DSDV, CGSR, WRP on-demand approaches e.g. DSR, AODV, TORA Routing in WSNs has special requirements Most crucial aspect is energy efficiency Selection of energy efficient routes Overhead imposed by construction of routing tables Further hardware constraints Limited it memory (e.g. for routing table) and computational ti limitationsit ti Stability and dependability of routes Resiliency e.g. use multiple paths for redundancy and load balancing 19 Alternative approaches: forwarding schemes without routing tables Trivial: Flooding but very ineffective Randomised forwarding (gossiping) Randomly choose a node to forward incoming packets with a certain probability Critical threshold: ~65-70% if above, (almost) all nodes get the packet Random walks Think of data packets as agents that wandering through the network looking for data/events Agent initially performs random walk leaves traces in the network about data found on the random walk Later agents can use traces to find data Essentially works due to high probability of line intersections X=2 X=? 20

6 Routing energy efficient unicast There are various aspects how energy or power efficiency can be conceived of in a routing context Minimize energy per packet (or per bit) Minimize for each packet the amount of total energy required to transport a packet from a source to a destination via multiple hops Maximise network lifetime Several options exist: Time until first node fails or 50% of nodes fail lost of coverage for a certain spot in the network there is a partitioning of the network, i.e. not each pair of nodes can communicate anymore Routing energy efficient unicast Routing considering available battery energy Maximum total available battery capacity Choose route where sum of available battery levels is the largest Minimum Battery Cost Routing (MBCR) Choose route where sum of reciprocal battery levels is the smallest Min-Max Battery Cost Routing (MMBCR) Choose route where the largest reciprocal level of a path is used Conditional Max-Min Battery Capacity Routing (CMMBCR) Take only battery level into account when below a certain threshold Minimise variance in power levels Use up all batteries uniformly to avoid some nodes running out of energy Minimum Total Transmission Power Routing (MTPR) Find an assignment of transmission power values for each transmitter so that all transmissions are successful and the sum of all power values is minimised Routing Multicast unicast routing Instead of using only a single path, it can be useful to compute multiple paths between a given source/destination pair Disjoint paths Paths can be disjoint or braided Multiple paths can be used Simultaneously Alternatively Randomly Goal Increase robustness and reliability Braided d paths 23 Routing Broadcast and Multicast (energy-efficient) Broadcast: Distribute a packet to all reachable nodes Major challenge is how to restrict the set of forwarding nodes, while still ensuring all nodes receive the packet Multicast: Distribute a packet to a specified subgroup Basic Options Source-based tree Construct a tree (one for each node) to reach all other nodes minimise total cost of the tree (Steiner Tree problem) minimise maximum cost to each node (repeated unicast shortest path problem e.g. Dijkstra) Share, core-based tree Use only a single tree that includes all destination nodes for all sources Mesh Treesareonly1-connected use mesh to provide higher redundancy and robustness 24

7 Routing broadcast and multicast summary Minimise total cost (Steiner tree) One tree per source Broadcast Minimise cost to each node (e.g. Dijkstra) Broadcast and Multicast Multicast Single core Shared tree (core-based tree) Multiple core Mesh Routing Geographic Routing In the previous approaches, the routing tables contain the information to which next hop a packet should be forwarded d ( explicit) it) Alternative: Infer this information from the physical placement of the nodes ( implicit) If position of current node, current neighbours and destination is known, simply py send packet to the neighbour in the direction of the destination Options Geocasting: send to any node in a given area Position-based routing: use information to aid in routing Might need a location service to map node id to node position Routing position-based routing Forwarding strategies Most forward within r ( greedy forwarding) Send packet to the neighbour that is located closest to destination, i.e. minimise the remaining distance that the packet has to travel Nearest node with (any) forward progress Choose the nearest neighbour that still results in some progress towards the destination reduces the collision rate Directional routing Choose next hop that is angularly closest to the destination Dead End problem or closest to the connecting line to destination Problem: might result in loops! Problem: Dead ends Routing right-hand rule to leave dead ends Idea to get out of a dead end Put right hand to the wall, then follow the wall Does not work if on some inner wall walk in circle Need some additional rules to detect such circles Geometric Perimeter State Routing (GPSR) Use greedy, most forward routing as long as possible If no progress is possible: Switch to face routing Face = largest possible region of the plane that is not cut by any edge of the graph; can be exterior or interior Send packet around the face using right-hand rule Use position where face was entered and destination position to determine when face can be left again, then switch back to greedy routing Requires a planar graph! (Topology can ensure that) 27 28

8 Routing GPSR example Routing geographic routing without positions (GEM) A wants to route a packet to M A C, greedy forwarding C greedy forwarding fails (B and D are further away than C itself) finding face BFGCD right-hand rule: D B F FG intersects DM proceed to the next face F E I H K J Greedy routing: J L M A Enter face routing B C Leave face E I routing D F G H J K L M Contradiction: geographic, but no positions? Construct virtual coordinates that have enough neighbourhood information to be useful in geographic routing, but do not require actual position determination Use polar coordinates from a centre point (radius and Assign virtual angle range to neighbours of a node, bigger radius Angles are recursively redistributed to children nodes centre node Routing GeRaF Routing GeRaF example How to combine position knowledge with nodes turning on/off? Goal: Transmit message over multiple hops to destination node; deal with topology changes due to the nodes changing on/off-states Idea: Receiver-initiated forwarding Forwarding node simply py broadcasts a packet without specifying the next hop Some node will pick it up (ideally closest to destination) and forward it Problem: How to deal with multiple l forwarders? Position-information randomisation: the closer to the destination a forwarding node is, the shorter does it hesitate to forward the packet Use several annuli to make problem easier, group nodes according to distance (collisions can still occur use backoff) Source node A wants to transmit a packet to destination node G A sends packet to next section A 1 B is turned on and receives it B sends packet to section A 2 Ci is turned on and receives it C sends packet to section A 3 D and E are turned on due to back-off, E receives the packet E sends the packet to section A 4 F is turned on an receives the packet Finally, F can send the packet to the destination node G D C G A F E B A 2 A 1 A 3 All nodes within a section are treated as equivalent! A

9 Routing Location-based Multicast (LBM) Geocasting by geographically restricted flooding Define forwarding zone - nodes in this zone will forward the packet to make it reach the destination zone Forwarding zone can be defined in several ways Static ti zone: smallest rectangle containing i original i source and entire destination zone Adaptive zone: each forwarding node recalculates the zone, using its own position as source dead d ends possible Adaptive distances: in each step, the forwarding zone is recalculated based on information about destination region and coordinates of previous hop (or source) packet is forwarded by node u, if node u is closer to destination zone s centre than predecessor node v Outline Localisation and Positioning Topology Control Routing Summary Summary Localisation and Positioning is a common approach in WSN to fulfil localised requests and do geographic routing Due to the energy constraints the complexity of WSN must be kept low. Therefore, topology can be used to create clusters, backbone networks etc. Routing is the crucial service in WSN: mostly measured data has to be transmitted from multiple sources to one sink. Robustness, reliability and energy-efficiency y are most important for the routing in WSNs To save energy and to reduce the complexity of queries, the measured data can be aggregated 35 Summary further interesting topics in WSNs Transport Layer and Quality of Service (QoS) Coverage and deployment Reliable data transport Single packet delivery Block delivery Congestion Advanced application support Advanced in-network processing WSN Security Application specific support There are lot of interesting research topics in WSNs, so if you are looking for a topic for your research project or Master s thesis check out our webpage: t t i tik i tti / iki/i d h /Th 36