WIRELESS SENSOR NETWORKS

WIRELESS SENSOR NETWORKS
` WIRELESS SENSOR NETWORKS Author: Aleksandar Crnjin, 00/17 Supervised by: Dr Veljko Milutinović,

Topics Localization and Positioning Synchronization and Security
Introduction What are WSNs? An example: ESB WSN applications Energy conservation Ad-hoc routing Basic principles Overview of routing protocols Data-centric protocols Rumor Routing Geographical protocols Localization and Positioning Synchronization and Security The TinyOS operating system The TinyDB query system Proto programming language

Introduction: What are WSNs?
Sensors, connected into wireless networks, are: Very small computer systems With very high interconnection ability

Introduction: What are WSNs? 2
Overview of networking technologies

The idea of wireless sensor networks was first popularized by scientists from UC Berkeley They have developed: series of sensor nodes (so-called mica nodes) open-source software TinyOS operating system TinyDB query system for efficient manipulation of sensed data An interesting fact: one of the first applications for small interconnected autonomous systems was in submarine warfare Integrated Undersea Sound Survelliance System (IUSS)

Sensor network (def): a set of small autonomous systems (sensor nodes) working together to solve at least one common problem their tasks always include some way of perception of the physical environment Sensor nodes: basic units in a sensor network they are usually powered by batteries and they have some kind of a radio transceiver Alternative names: motes (most often) smart dust

Architecture of a sensor node
Block diagram of a typical sensor node (mote)

An example: ESB, FU-Berlin
Source: [1]

WSN Applications Treatment of ill people: wearable computing
under-skin implants – measurement of blood parameters, early detection of some illnesses already marketed – capsules with 24 hours of battery life which move through the patient’s body and relay images to the doctor through another device worn externally by the patient Embedded systems: home automation temperature measurement alarm systems Traffic – sensors in cars, early traffic congestion warning Military applications Many other; new applications are constantly devised.

One application: Camalie Vineyards
One mote in the vineyards: Crossbow mica2dot mote, NiMH batteries, solar panel, 3V voltage regulator. Source: [12].

One application: Camalie Vineyards 2
The visitors of the Camalie Vineyards website can view graphs of ambient temperature (with respect to time) in all points where sensors are present. Slightly colder temperatures come from sensors in the wine cellar; dysfunctional motes report absolute zero. Source: [12].

Energy conservation Careful use of available energy is of paramount importance in WSNs Batteries, in most cases, cannot be recharged! Inefficient energy use shortens battery life considerably! Some of the methods for reduced energy consumption and prolonged battery life: Passive conservation methods Use of sophisticated energy sources Placement of sensors into energy efficient topologies Active conservation methods Hardware solutions (watchdog timer, reduced clock frequency...) Energy aware routing protocols; energy efficient medium access (MAC layer) The choice of an energy-saving operating system

Energy sources for motes
Rechargeable macro-batteries as a secondary energy source Micro-batteries on a wafer Ultracapacitors They not only hold electrical charge in the dielectric, they also hold ionic charge in the double electrical layer Energy density larger than in ordinary capacitors by an order of magnitude Micro Fuel Cells Microturbines Radioactive power sources Solar cells Energy of the human body Wearable computing Hydrogen micro-fuel cell 4mm micro turbine Source: [13]

Energy conservation: hardware solutions
Watchdog timers deliberately turn the power down if the software is stuck in an infinite loop Sleep states Variable voltage processing Deliberate performance degradation by dropping the voltage and reducing the clock frequency so the same task could be done with smaller energy consumption, at the expense of performance. Variable Voltage Processing Same task can be performed with smaller energy consumption if voltage and frequency are reduced (shaded area). Source: [13]

MAC energy aware protocols
Energy aware protocols are used at the MAC layer (energy efficient medium access). Basic principle: Energy required for signal transmission is proportional to d α d – distance between two nodes α – attenuation factor (medium dependent)

MAC energy aware protocols 2
For optimum α = 2, transmitting a signal to ½ of distance requires ¼ of energy: (½ d)2 = ¼ d2 → If a protocol can find a node on ½ distance to target, which can supply additional ¼ of energy to transmit the signal through the remaining half, ½ of energy is saved! (½ d)2 + (½ d)2 = ¼ d2 + ¼ d2 = ½ d2

Energy solutions in the ESB
Microcontroller TI MSP340: energy consumption 3V, 1mA (3mW) total energy consumption is 12mA at 4.5 V, plus additional 12mA during data transmission program memory: 8KB EEPROM only 2KB of conventional RAM memory; 60KB Flash RAM for special purposes only (high energy consumption!) watchdog timer: uses an NMI interrupt to shut the node down immediately, if software gets stuck in an infinite loop sleep mod, consumes only mA (practically equivalent to battery self-discharge) Printed circuit of the ESB

Ad-hoc routing Wireless networks: computer networks with wireless communication links Ad-hoc networks: in ad-hoc nets every node can forward packages the decision whether each node will or will not forward packages is made dynamically, with regard to current connectivity matrix. Comparison: in conventional computer networks, there are dedicated nodes (routers, switches, hubs...) which forward packages. Such approach is impossible in ad hoc networks – all nodes are constantly on the move, connectivity changes all the time so there are no good candidates for dedicated routers.

Ad-hoc routing In ad-hoc networks, individual nodes have no a priori knowledge of network topology when connected, they gradually discover other nodes Basic principle: a newly connected nodes advertises it’s presence reads broadcast messages from neighboring nodes In this way, a node gradually discovers: the positions of other nodes in a network, ways to reach each one of them.

Ad-hoc routing When enough time has passed, routing protocol convergence is established. When this happens, each node “knows” about all the other nodes in the net, and how to reach each one of them. Convergence time is total time elapsed from when the network topology is changed (for example, when a communication link goes down) to when all nodes have restructured their routing tables in accordance with the change.

Ad-hoc routing Conventional networks use data-centric routing: each node has an unique data identifier which serves as its address on the network (IP-address, MAC-address...) Some ad-hoc networks use different concepts For example, it might not be necessary for information to be routed from a specific starting point to a specific endpoint, but only to be propagated and diffused through the network. This is called Rumor Routing. Or, geographical coordinates could be used as means for addressing (Geographical Routing).

Overview of ad hoc routing protocols

Data-centric protocols 1
Proactive protocols: maintain consistent knowledge of network topology through routing tables which are exchanged between nodes periodically Drawbacks: network bandwidth is wasted: routing tables are an overhead which decreases available bandwidth for useful data some paths are never used, and yet they are kept in the tables simulations show very long convergence time some proactive protocols never converge in large wireless networks! Example: WRP – Wireless Routing Protocol

Reactive protocols: information about paths is not kept; a path is discovered on demand only, by flooding the network with Route Request packages Drawbacks: on demand path discovery introduces significant delays flooding the network can lead to severe network congestion Examples: can be found, in detail, in [10] AODV – Ad-hoc On-demand Distance Vector DSR – Dynamic Source Routing TORA – Temporally Oriented Routing Algorithm

Hybrid protocols: locally proactive, globally reactive e.g. ZRP – Zone Routing Protocol The network is divided into routing zones the zone is usually an sphere with a specified diameter, measured in hop counts the nodes positioned at the maximum distance from central node X are called peripheral nodes for the routing zone centered at X. When package needs to be sent from node A to node B: if B is within the same routing zone as A, local routing table is looked up to find the path to B (proactive behaviour), if this is not the case, Route Request packages are sent to all peripheral nodes each peripheral node repeats this step (checks whether the destination B is inside its routing zone).

ZRP example: A sphere centered at S: is a zone of radius ρ = 2 J, G, H, I: peripheral nodes If a package is sent from node S to node G: Destination is within the same zone, routing table is looked up. If a package is sent from node S to node T: Destination is in a different zone, Route Request packages are sent to nodes G, H, I, J. Node I discovers that T is within its routing zone (d = 2), It looks up its routing table, and sends the package to L.

Rumor Routing first proposed by Braginsky and Estrin (UCLA) in their paper [4]. All nodes are divided into two groups: nodes that perceive events, nodes which seek information about events, from the nodes of the preceding group There is no network topology There is no coordinate system The protocol doesn’t try to find an optimum route, it seeks only to relay the information end-to-end sub-optimum routes are satisfactory for this purpose

Rumor Routing 2 Rumor Routing protocol: 1
Events A and B are perceived on some nodes An agent is sent from both groups (A and B) to “spread rumors” about the whereabouts of possible sources of information for events A and B When agent B meets agent A on its way, it goes on to spread information about both events A and B 1 1 2 3 2 Illustration of Rumor Routing protocol (Source: [1])

Rumor Routing 3 1 Handling the information requests:
A node requests information about event A It’s request “moves blindly” through the network until it stumbles upon a node visited by the information spreading agent. While moving, it leaves traces so it can backtrack when information is found. When a node visited by the agent is found, a route is followed to the source of information Information is retrieved and brought back to the source of the request. 2 4 3 Illustration of Rumor Routing protocol (Source: [1])

Geographical Protocols: An Overview
Routing relies on geographical position information (as opposed to data centric routing) Destination for a package is a specific area e.g. a city, a section of a highway, or, at the micro level, a part of the conference room recipient cannot determine exactly who is the sender, it can only determine roughly from where the package came from Routing decision are made with respect to real, spatial coordinates For that to be possible, positioning information is necessary one way of obtaining position information is GPS (Global Positioning System)

An example: GeoCast Proposed by Navas and Imielinski, 1997.
Three-tiered architecture: GeoHosts are endpoints of the network Client processes (applications) are run on them GeoHosts initiate message transfer On receipt of the package, they check if their geographical region is the destination of the package. GeoGateways are the network’s entry and exit points GeoHosts communicate with GeoRouters through broadcast messages a GeoGateway is responsible for a given area specified by a radius GeoRouters perform the actual routing they are aware of the coordinates of neighboring GeoRouters and GeoGateways they route the package through neighboring GeoRouters to the destination GeoGateway so that it can reach the destination geographical area.

GeoCast routing Ilustracija koncepata GeoHost, GeoGateway, GeoRouter

GeoCast routing 3 Source: [5] GeoCast communication:
Client Process GeoCast communication: A node on Net A perceives the event It sends a message to its GeoGateway Gateway forwards the message to a neighboring GeoRouter Routing is performed At some moment, destination Gateway is direct neighbor to a router; the router hands the message to destination Gateway. Destination Gateway delivers the message to Net B. Event GeoRouter GeoGateway GeoHost Direct message Client Process Source: [5]

GeoCast routing With classical IP networks, next hop address is determined by looking up a routing table With GeoCast, destination addresses are areas specified by necessary parameters if the area is a circle, location of center and length of radius are required if the area is a polygon, locations of all angles are required Routers are organized in a hierarchical manner routers responsible for smaller geographical areas are lower in the hierarchy routers responsible for larger geographical areas are lower in the hierarchy

GeoCast routing A client process, running on a GeoHost, delivers a message to its GeoRouter through its network’s GeoGateway GeoRouter consults lower-level GeoRouters and determines: Is there any overlap between the zone of responsibility of the lower level router, and the destination area? If there is an overlap, the message is forwared to the lower-level GeoRouter. When this is repeated for all lower level routers, was the destination area covered completely? If it was, the procedure ends here. If it wasn’t, the message is unicast to a higher level router, responsible for wider geographical area, which repeats the procedure starting at 2.

GeoCast routing In order to implement the described procedure, another procedure is necessary, one that will determine if the two areas overlap. If the two areas are both circles, the procedure is simple if the distance between centers is smaller than the sum of the radiuses, the circles indeed overlap

GeoCast routing If the two areas are a circle and a polygon, or they are both polygons, things get complicated Geographical calculations of higher complexity are necessary Comparison with IP nets: In IP networks, a simple query on the routing table is all that is necessary In GeoCast networks, routing decision complexity is several orders of magnitude higher!

Routing Protocols: A Conclusion
Rough classification of protocols: Data-centric: an address is some kind of datum (e.g. an IP address – 4 bytes of data) Diffusion: it is not necessary to address individual nodes, only to diffuse the information through the network Geographical: an address is a geographical area, described by geographical parameters Most uses for data centric protocols are outside of wireless sensor networks Diffusion protocols are simple but they are suitable only for a small number of use cases Geographical protocols are conceptually most suitable for wireless sensor networks but making a routing decision can be very complex

Localization and Positioning
Measurements by sensor networks are substantially more valuable if the location of measurement is known Therefore, a sensor has to be aware of its position in the real world Two approaches: Localization Positioning B C A

Localization and Positioning 2
Localization: location of the sensor is given as relative to some local point it is then possible to calculate distances and angles between individual nodes but it is not possible to determine the sensor’s global position Positioning: a sensor is given absolute coordinates on the world map e.g. coordinates can be longitude and latitude

The simplest solution to the problem of positioning is to put a GPS device in each sensor node. GPS receivers are still too expensive and too big! The remaining possibilities can be divided into 4 groups (picture). 1 Some nodes know their position, and distances between nodes are known. 2 No node knows its position, but distances between nodes are known. 3 Some nodes know their position, but distances between nodes are not known. 4 No node knows its position and distances between nodes are not known.

Global coordinates are available for the complete topology, Only local coordinates are known, It is possible only to roughly estimate the position of the whole system, Only the network’s connectivity matrix is available. 1 Some nodes know their position, and distances between nodes are known. 2 No node knows its position, but distances between nodes are known. 3 Some nodes know their position, but distances between nodes are not known. 4 No node knows its position and distances between nodes are not known.

Since only some nodes need to be aware of their position in order to establish positioning for the complete network, only some nodes need to be equipped with a GPS device, because of high energy consumption, GPS could be turned on only occasionally, in those intervals, remaining nodes perform localization with respect to the GPS-equipped nodes. Alternate solution: instead of equipping them with a GPS device, some nodes could have their position manually input by an operator (based on measurements from his GPS device). This solution is sometimes neither practical nor possible! For mobile networks, public positioning stations are usually used.

Positioning Positioning of every node is possible in Case 1 from the table that is the case when positions of some nodes are known, and distances between nodes are known, too. Three already positioned nodes are enough to determine position of another node if the distances between nodes are known. one way of measuring distance is measuring the attenuation of the radio signal If some of the 3 nodes is not positioned with sufficient precision, the error propagates quickly!

Positioning (2D) pi, pj – already positioned nodes n – newly added node We consider two spheres, with radiuses dn, i i dn, j, around their centers pi i pj They intersect in two points: n i n’ The final step in positioning is to choose one of these points In order to do this, we need the third already positioned node Mutual visibility is checked with the third node, in order to discount one of the points n i n’. In satellite positioning (3D) the final step is usually unnecessary; the object has to be on Earth’s surface→ points in space or below surface are instantly discarded

Localization In case no node is aware of it’s position, but distances between nodes are known, localization is the only possible approach. Capkun, Hamdi, Hubaux, “GPS-free Positioning in Mobile Ad-hoc Networks” Describes the procedure which establishes a global coordinate system (CS) based on measurements of distances between nodes

Localization Basic procedure for establishment of a global coordinate system (CS), in a two-dimensional environment, is as follows: Each node searches for its immediate neighbors in this way, “immediate neighborhood”, consisting of all nodes one hop away, is formed. The distance table obtained in (1) is sent to all neighbors In each node a local coordinate system is established, with that node in the center For each node n, two additional nodes p and q are chosen from the immediate neighborhood, in order to define x and y axes x-axis is a line drawn from the circle, through node p, oriented outwards y-axis is always perpendicular to the x-axis, node q is necessary only to determine its orientation

Localization In each node, the remaining nodes’ positions are expressed in the local coordinate system One of the local coordinate systems is chosen (for example, CS of node i); origins of all other CS’s have their locations in the CS of node i For each node j, j ≠ i: Axes are rotated so that they become parallel to axes of CS i Coordinates of the origin of CSj, with respect to CSi, are added to coordinates of all pointsd in CSj. In this way, all local coordinate systems are unified into a global coordinate system, in which node i holds the position (0,0).

Localization and Positioning: Conclusion
For the measurements to be truly useful, it is necessary to know the location of the measurement. When some of the nodes are aware of their global position, it is possible to establish positioning information for each node in the network. If there are no such nodes, it is still possible to construct a local coordinate system.

Introduction What are WSNs? Primer čvora senzora WSN applications Energy conservation Ad-hoc routing Basic principles Overview of routing protocols Data-centric protocols Rumor Routing Geographical protocols Localization and Positioning Synchronization and Security The TinyOS operating system The TinyDB query system Proto programming language

Synchronization Time synchronization of events and activities is essential in all distributed systems In wireless sensor networks: even more precise synchronization is needed and it has to obtained with scarce critical resources (battery power, communication channel availability, etc.) Comparison with conventional distributed systems: each node can operate only as long as its batteries last therefore, clocks in all nodes cannot be maintained from a central place (so called master clock) as that node may run out of power each use of the CPU and the communication medium comes with a great price in energy therefore, existing systems aren’t good enough!

Synchronization Possible distortions of measured time, as the clock propagates through the sensor network: Jitter: “podrhtavanje” časovnika usled nepreciznosti. Skew: the clock becomes faster or slower than normal (frequency distortion) Drift: measured time differs by a constant offset (phase distortion) this is a problem only if different nodes have different offsets Source: [1]

Synchronization There is no optimum solution with satisfies all criteria (preciseness, lifespan, availability) Different approaches are often combined Some commonly applied solutions: Explicit synchronization instead of keeping all clocks in synch all the time events are recorded with respect to local time when needed, these time marks are converted to another scale on demand only Peer-to-peer synchronization amount of synch related errors between two nodes is proportional to distance between them therefore, keeping a centralized clock is not a good approach instead, only neighboring nodes exchange synch related information

Security Sensor networks usually consist of a large number of nodes
To supervise each node is practically impossible Therefore, sensor networks are: highly susceptible to logical and physical attacks and communication interception a node could be seized, reprogrammed, then put back into the network by means of reverse engineering, nodes could be designed to trick the network into treating them as authentic Various forms of abuse are then possible intercepting confidential information (sensed data) falsifying sensor readings Distributed Denial of Service (DDoS) attacks.

Security To protect every single node from reprogramming is economically unfeasible Other approaches are used: node-to-node authentication: nodes in the network have to prove their identity to each other node revocation: when an intruding node is discovered, it is forbidden to access the network any further Applied protocols have to be made resilient Meaning, the network has to be able to continue functioning properly, even if some nodes are compromised.

Security 3 Privacy of sensed data is kept by encryption
Conventional approach – large keys Unsuitable for sensor networks – because of limited memory! Commonly applied approach – hop-to-hop encryption Messages are encrypted using short keys in every node Drawback: if one node in the chain is taken over, there is no more encryption for any messages passing through that node Multipath routing before it is sent, the message is broken into several chunks these chunks move through the network using different routes they are not reassembled until they reach the destination

Security 4 DoS attacks – another threat
through DDoS attacks, attackers can deliberately drain the batteries physical protection: spread spectrum communication frequency hopping logical protection: constantly checking and discarding messages with invalid authenticity information danger: in this way, the very protection from DDoS can drain the battery! because, power is constantly spent on authenticity checks for incoming messages

Security Energy cost of added security through authentification: as much as 71% extra energy cost is due to increased amount of transmission!

Synchronization and Security: Conclusion
These are the problems also present in conventional networks However, because of different architecture, many traditional approaches are not suitable Solutions for sensor networks have to be designed with respect to the specific architecture of sensor nodes most of all, the scarcity of energy resources

TinyOS Because of the specific architecture of sensor networks, a suitable operating system had to be devised Conventional OSes for embedded systems VxWorks Windows CE PalmOS QNX They all require ROM of 100KB or more! The ESB, introduced in Chapter 1, has only 8 KB ROM! Because of this, TinyOS was developed.

Characteristics of TinyOS
Event-driven programming conventional context switching is impossible, there is no room for the stack! a system of components is used instead; each component has its own static area in memory (“frame”) in this way, the need for stack is eliminated programs are executed only in response to the events events are registered by components beforehand Energy conservation Interrupt polling is forbidden: if the CPU constantly checks the status register, battery power is continuously expended Interrupt masking is forbidden, too: node which requests the interrupt would spend power while it waits

TinyOS Architecture TinyOS has: Each component advertises:
a short-term task scheduler components, which usually have: command handling routines (command handlers) event handling routines (event handlers) a fixed amount of allocated memory (frame) a few simple tasks Each component advertises: which commands it can handle, which events it can report. All tasks, commands, and event handler routines are executed only within the allocated memory frame

TinyOS Architecture Memory frames are allocated statically at compile time Commands are issued by higher level components, to lower level components they place parameters on pre-defined locations within the memory frame they deposit tasks for later execution at some moment, they provide feedback to the caller component Event handling routines: respond to hardware events write information to the memory frame deposit tasks for later execution signal events to higher level components issue commands to lower level components

TinyOS Architecture 3 Tasks:
perform the actual work with respect to other tasks, they are atomic once started, they cannot be interrupted by other tasks higher level events, however, can interrupt them (pre-emption) issue commands to lower level components signal events to higher level components deposit new tasks to their component Short-term scheduler: a simple FIFO buffer

TinyOS: An example Component
A message transfer component Sends and receives individual packages to/from the lower level Sends and receives whole messages to/from the higher level As all components, it sends commands to the lower level: an initialization command: init, power management command: power(mode) It recieves the same components from the higher level

TinyOS: An example Component 2
It also sends the transfer initiation command: TX_packet (buf) It responds to following events (from lower level): package is transmitted TX_packet_done (success) package is received RX_packet_done (buffer) It signals the following events (to higher level): message is transmitted msg_send_done (success) message is received msg_rec (type, data)

This code is used to declare the message transfer component

An illustration of the amount of occupied memory in a typical sensor node: Our component, AM, takes up 356 bytes of ROM and 40 bytes of RAM In the list of components, we can also see the components which provide for hardware abstraction For example, RFM represents the built-in RFM transciever A fully functional sensor node needs only 3450 bytes of ROM and 226 bytes of RAM!

TinyDB A query processing system, which gathers information from sensor nodes which have TinyOS installed It uses a declarative programming language, TinySQL Queries are always performed on a unique, default, logic table: sensors The sensors table has: one row, for each measurement performed by any sensor node; one column, for each relevant attribute of the measurement: nodeID, temp, state, ... It is bundled together with TinyOS as a component which is installed on every mote in the net

TinyDB TinySQL is very similar to standard SQL, but there are some very important conceptual differences It also has aggregate functions and triggers A query, once input, is performed repeatedly performing frequency is input through the epoch parameter it has to be chosen carefully, having in mind the limited battery capacity Queries can be input: visually, through an application bundled with TinyOS through coding, in a language very similar to SQL

TinyDB 3 Basic form of the query: An example:
SELECT select-list [FROM sensors] WHERE where-clause [GROUP BY gb-list [HAVING having-list]] [TRIGGER ACTION cmd-name[(param)]] [EPOCH DURATION integer] An example: SELECT temp FROM sensors WHERE temp > thresh TRIGGER ACTION SetSnd(512) EPOCH DURATION 512 If temperature higher than specified threshold is found on any sensor, a beep 512ms long is sounded, and the query is performed again after 512ms.

TinyDB 4 Queries can be performed in response to external events, too
for example: an event which signals that the temperature has risen above some threshold Two procedures are necessary: Writing a component which detects the event and notifies TinyDB that it has indeed happened A query in TinyDB of the form: on event: SELECT...

TinyDB Example: ON evtTest: SELECT nodeid,light SAMPLE PERIOD 1024 In response to event evtTest, sensed light data is collected every 1 second.

Proto programming language
Proto: a high-level programming language used to program sensor and actuator networks Proposed by Jonathan Bachrach and Jacob Beal from the Massachusetts’ Institute of Technology Computer Science and Artificial Intelligence laboratory (MIT-CSAIL) Paper: “Programming sensor networks as an amorphous medium” [7]

Proto programming language 2
The goals of the Proto language: to relieve the programmer from worrying about the physical aspects of sensor network programming the exact way of providing fast, efficient and robust communication between nodes is below the barrier of abstraction instead, the programmer writes declarative code, such as If the temperature is high, (sensor measurement) Then, the field should be watered, every few hours (a command to actuators)

In order to accomplish this, the sensor network is imagined as an amorphous medium Continuous field of calculable material Sensed data in each sensor node is a point in the field Dimensions and physical distribution of the points is not known Each point in the amorphous field: executes the same code advertises its state to the immediate neighborhood Approximation of an amorphous medium

Proto programs manipulate over fields One field is a mapping scheme which assigns some value to a set of points in space

Primitives in Proto can be: terminals operators common arithmetic operators, in prefix notation if operator special primitives mux sense and act let and def

Primitives can be combined into complex expressions Besides the individual fields, field streams can be also used When evaluating expressions with field streams, the result has to be calculated separately for each field from the stream Therefore, the result is a field stream, too

Terminals correspond to constants and they generate fields For example, the terminal 2 generates a field with value 2 in every point Operators calculate the output field using a set of input fields for example, the expression generates a field with value 7 in every point the if operator has standard meaning

mux uses a selector field of booleans in order to generate a field in which for every point, one of two possible values is chosen: the value from the corresponding point in field #1, or, the value from the corresponding point in field #2. this is performed for each point separately, with respect to the boolean value in the corresponding point in the selector field.

sense and act represent the sensor readings and actuator commands, respectively. They are analogous to, for example, read and write procedures in Pascal In this way, Proto programs communicate with their environment. let assigns a value to a variable E.g. (let x 3) def defines a procedure (a macro) E.g. (def sq(x) (* x x))

Proto examples Example (a): terminal 2 generates a field which has value 2 in every point (“2”) Example (b): we add up the field “2” with field stream (“1”, “3”) the result is a field stream (“3”, “5”)

Proto examples Example (c): input parameters are fields “2”, “3” and field stream (“false”, “true”) As the selector is a field stream and not a field, the result will be a field stream too One field from the result field stream corresponds to selector field “false”: that is field “3” selector has semantics “do we choose the first field?”, and as it is false, we choose the 2nd Second field corresponds to “true”; that is field “2”

Proto examples 3 Example (d): a complex operation
Firstly, terminals 2 and 5 generate fields “2” and “5” Secondly, as a result of add operation the result field “7” is generated.

Proto examples Example (e): definition of a procedure we define sq(x) as x*x Example (f): terminal 3 generates field “3” as a result of a sq call, field “9” is generated Proto code: (def sq(x) (* x x)) sq(3)

Proto examples an input and output example: input – light is perceived output – a signalization light is emitted

Proto examples the following code turns the red light emitter on each sensor where any light is perceived

nbrs (x): neighborhood of point x
Proto: reduce-nbrs Proto also has primitives which enable it to describe behavior which depends not only on a single point in space (single sensor), but it’s immediate neighborhood, too The neighborhood of a single point is an infinite number of points Amorphous medium – continuous space! nbrs (x): neighborhood of point x

Proto: reduce-nbrs The reduce-nbrs primitive summarizes the neighborhood of a single point using some quantificator applicable to infinite sets Proto has five such quantificators: integral forall exists limsup equivalent to max, for infinite sets liminf equivalent to min, for infinite sets

Proto: reduce-nbrs In this way, implicit communication between points is established By aggregating the values in neighborhood of x, the points from the neighborhood communicate their value to x Real-life communication has a delay Proto simulates this delay using primitives delay and letfed

An example Proto application
the Threat Avoidance problem If we have: current coordinates, a means for perceiving the danger (threat sensor) a model of exponentially falling danger how can we calculate the safest route? Implementation in the nesC language (standard procedure language for sensor networks) ~ 2000 lines of code Implementation in Proto: only 22 lines of code

An example Proto application 2
In order to test the threat avoidance program, a model is required So, we describe a model with exponentially falling danger:

Now, with regard to our current location, we can calculate the cumulative probability of survival

Greedy-ascent procedure: in every point, the direction for next move is chosen in which the threat is best avoided

By combining all these procedures, a complete solution for threat avoidance is obtained

The results obtained when the threat avoidance program is run on a simulator For more information, please consult [7] and [9].

References [1] Thomas Hänselmann, Sensor Networks, [2] Jason Hill et al., System Architecture Directions for Networked Sensors, Department of EE/CS, UC Berkeley [3] Sam Madden, Joe Hellerstein, Wei Hong, TinyDB: In-Network Query Processing with TinyOS, UC Berkeley, [4] David Braginsky, Deborah Estrin, Rumor Routing in Sensor Networks, LECS-UCLA [5] Joe Polastre, Rachel Rubin, GeoMote: Geographical Network Architecture for Sensor Networks, CS Berkeley [6] Jeremy Elson, Time Synchronization in Wireless Sensor Networks, UCLA, [7] Jonathan Bachrach, Jacob Beal, Programming a Sensor Network as an Amorphous Medium, MIT-CSAIL, [8] Haowen Chan, Adrian Perrig, Security and Privacy in Sensor Networks, Carnegie Mellon University, [9] Jacob Beal, Continuous Semantics of Proto, MIT-CSAIL, 2006.

References [10] Đ. Trifunović, N.Milanović, V.Milutinović, Ad-hoc Networks: Estabilishing node-to-node communication with no infrastructure needed, [11] en.wikipedia.org, USA, [12] USA, [13] M. Ilyas, I. Mahgoub (ed.), Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems, CRC Press, [14] I. Stojmenović (ed.), Handbook of Sensor Networks: Algorithms and Architectures, Wiley, 2004.

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