Introduction to Wireless Sensor Networks -Research Problems Clustering

Introduction to Wireless Sensor Networks -Research Problems (Clustering, Routing, etc)

Energy-Efficient Communication Protocol Architecture for Wireless Microsensor Networks (LEACH Protocol) [Heinzelman+ 2000, 2002] – LEACH (Low-Energy Adaptive Clustering Hierarchy) is a clustering-based protocol that utilizes the randomized rotation of local cluster base stations to evenly distribute the energy load within the network of sensors – It is a distributed, does not require any control information from base station (BS) and the nodes do not need to have knowledge of global network for LEACH to function – The energy saving of LEACH is achieved by combining compression with data routing – Key features of LEACH include: Ø Localized coordination and control of cluster set-up and operation Ø Randomized rotation of the cluster base stations or clusterheads and their clusters Ø Local compression of information to reduce global communication

LEACH [Heinzelman+ 2000, 2002] – Considered microsensor network has the following characteristics: Ø The base station is fixed and located far from the sensors Ø All the sensor nodes are homogeneous and energy constrained – Communication between sensor nodes and the base station is expensive and no high energy nodes exist to achieve communication – By using clusters to transmit data to the BS, only few nodes need to transmit for larger distances to the BS while other nodes in each cluster use small transmit distances – LEACH achieves superior performance compared to classical clustering algorithms by using adaptive clustering and rotating clusterheads; assisting the total energy of the system to be distributed among all the nodes – By performing load computation in each cluster, amount of data to be transmitted to BS is reduced. Therefore, large reduction in the energy dissipation is achieved since communication is more expensive than computation

LEACH [Heinzelman+ 2000, 2002] Algorithm Overview – The nodes are grouped into local clusters with one node acting as the local base station (BS) or clusterhead (CH) – The CHs are rotated in random fashion among the various sensors – Local data fusion is achieved to compress the data being sent from clusters to the BS; resulting the reduction in the energy dissipation and increase in the network lifetime – Sensor elect themselves to be local BSs at any given time with a certain probability and these CHs broadcast their status to other sensor nodes – Each node decided which CH to join based on the minimum communication energy – Upon clusters formation, each CH creates a schedule for the nodes in its cluster such that radio components of each non-clusterhead node need to be turned OFF always except during the transmit time – The CH aggregates all the data received from the nodes in its cluster before transmitting the compressed data to BS

LEACH [Heinzelman+ 2000, 2002] Algorithm Overview – The transmission between CH and BS requires high energy transmission – In order to evenly distribute energy usage among the sensor nodes, clusterheads are self-elected at different time intervals – The nodes decides to become a CH depending on the amount of energy it has left – The decisions to become CH are made independently of the other nodes – The system can determine the optimal number of CHs prior to election procedure based on parameters such as network topology and relative costs of computation vs. communication (Optimal number of CHs considered is 5% of the nodes) – It has been observed that nodes die in a random fashion – No communication exists between CHs – Each node has same probability to become a CH

LEACH [Heinzelman+ 2000, 2002] Algorithm Details – The operation of LEACH is achieved by rounds – Each round begins with a set-up phase (clusters are selected) followed by steadystate phase (data transmission to BS occurs) 1. Advertisement Phase: – Initially, each node need to decide to become a CH for the current round based on the suggested percentage of CHs for the network (set prior to this phase) and the number times the node has acted as a CH – The node (n) decides by choosing a random number between 0 and 1 – If this random number is less than T(n), the nodes become a CH for this round – The threshold is set as follows: P T(n) = 1 – P * (rmod 1 ) P 0 If n C G Otherwise P = desired percentage of CHs r = current round G = set of nodes that have not been CHs in the last 1/P rounds

LEACH [Heinzelman+ 2000, 2002] Algorithm Details 1. Advertisement Phase: – Assumptions are (i) each node starts with the same amount of energy and (ii) each CHs consumes relatively same amount of energy for each node – Each node elected as CH broadcasts an advertisement message to the rest – During this “clusterhead-advertisement” phase, the non-clusterhead nodes hear the ads of all CHs and decide which CH to join – A node joins to a CH in which it hears with its advertisement with the highest signal strength 2. Cluster Set-Up Phase: – Each node informs its clusterhead that it will be member of the cluster 3. Schedule Creation: – Upon receiving all the join messages from its members, CH creates a TDMA schedule about their allowed transmission time based on the total number of members in the cluster

LEACH [Heinzelman+ 2000, 2002] Algorithm Details 4. Data Transmission: – Each node starts data transmission to their CH based on their TDMA schedule – The radio of each cluster member nodes can be turned OFF until their allocated transmission time comes; minimizing the energy dissipation – The CH nodes must keep its receiver ON to receive all the data – Once all the data is received, the CH compresses the data to send it to BS Multiple Clusters – In order to minimize the radio interference between nearby clusters, each CH chooses randomly from a list of spreading CDMA codes and it informs its cluster members to transmit using this code – The neighboring CHs radio signals will be filtered out to avoid corruption in the transmission

An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks [Bandyopadhyay+, 2003] – Distributed, randomized clustering algorithm to organize the sensors in a wireless sensor network into clusters to minimize the energy used to communicate information from all nodes to the processing center – Hierarchy of clusterheads leads to the energy savings – In the clustered environment, the data gathered by the sensors is communicated to the data processing center through a hierarchy of clusterheads – The processing center determines the final estimates of the parameters using information communicated by the clusterheads – The processing center can be a specialized device or one of the sensors – Sensor data communicated over smaller distances, the energy consumed in the network will be much lower than the energy consumption when every sensor communicates directly to the information processing center

An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks [Bandyopadhyay+, 2003] A New, Energy-Efficient, Single-Level Clustering Algorithm – Each sensor becomes a clusterhead (CH) with probability p and advertises itself as a clusterhead to the sensors within its radio range – these clusterheads are called volunteer clusterheads – This advertisement is forwarded to all the sensors that are no more than k hops away from the clusterhead – Any sensor node that is not clusterhead itself receiving such advertisement joins the cluster of the closest clusterhead – Any sensor node that is neither a clusterhead nor has joined any cluster itself becomes a clusterhead – called forced clusterheads – Since the advertisement forwarding has been limited to k hops, if a sensor does not receive a CH advertisement within time duration t (where t is the time required for data to reach the CH from any sensor k hops away), it means that the sensor node is not within k hops of any volunteer CHs

An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks [Bandyopadhyay+, 2003] A New, Energy-Efficient, Single-Level Clustering Algorithm – Therefore, the sensor node becomes a forced clusterhead – The CH can transmit the aggregated information to the processing center after every t units of time since all the sensors within a cluster are at most k hops away from the CH – The limit on the number of hops allows the CH to reschedule their transmissions – This is a distributed algorithm and does not demand clock synchronization between the sensors – The energy consumed for the information gathered by the sensors to reach the processing center will depend on the parameters p and k – Since the objective of this work is to organize sensors in clusters to minimize the energy consumption, values of the parameters (p and k) must be found to ensure the goal

An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks [Bandyopadhyay+, 2003] A New, Energy-Efficient, Single-Level Clustering Algorithm Assumptions made for the optimal parameters are as follows: – The sensors are distributed as per a homogeneous spatial Poisson process of intensity λ in 2 -dimensional space – All sensors transmit at the same power level – have the same radio range r – Data exchanged between two communicating sensors not within each others’ radio range is forwarded by other sensors – A distance of d between any sensor and its CH is equivalent to – Each sensor uses 1 unit of energy to transmit or receive 1 unit of data – A routing infrastructure is in place; when a sensor communicates data to another sensor, only the sensors on the routing path forward the data – The communication environment is contention- and error-free; sensors do not have to retransmit any data d / r hops

An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks [Bandyopadhyay+, 2003] A New, Energy-Efficient, Hierarchical Clustering Algorithm – This algorithm is extension of the previous one by allowing more than one level of clustering in place – Assume that there are h levels in the clustering hierarchy with level 1 being the lowest level and level h being the highest – The sensors communicate the gathered data to level-1 clusterheads (CHs) – The level-1 CHs aggregate this data and communicate the aggregated data to level-2 CHs and so on – Finally, level-h CHs communicate the aggregated data or estimates based on this aggregated data to the processing center

An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks [Bandyopadhyay+, 2003] A New, Energy-Efficient, Hierarchical Clustering Algorithm – The cost of communicating the information from the sensors to the processing center is the energy consumed by the sensors to communicate the information to level-1 CHs, plus the energy consumed by the level-1 CHs to communicate the aggregated data to level-2 CHs, …. , plus the energy consumed by the level-h CHs to communicate the aggregated data to the information processing center Algorithm Details – The algorithm works in a bottom-up fashion – First, it elects the level-1 clusterheads, then level-2 clusterheads, and so on

An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks [Bandyopadhyay+, 2003] A New, Energy-Efficient, Hierarchical Clustering Algorithm Details – Level-1 clusterheads are chosen as follows: o Each sensor decides to become a level-1 CH with certain probability p 1 and advertises itself as a clusterhead to the sensors within its radio range o This advertisement is forwarded to all the sensors within k 1 hops of the advertising CH o Each sensor receiving an advertisement joins the cluster of the closest level-1 CH; the remaining sensors become forced level-1 CHs – Level-1 CHs then elect themselves as level-2 CHs with a certain probability p 2 and broadcast their decision of becoming a level-2 CH – This decision is forwarded to all the sensors within k 2 hops

An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks [Bandyopadhyay+, 2003] A New, Energy-Efficient, Hierarchical Clustering Algorithm Details – The level-1 CHs that receive the advertisement from level-2 CHs joins the cluster of the closest level-2 CH; the remaining level-1 CHs become forced level-2 CHs – Clusterheads at level 3, 4, 5, …, h are chosen in similar fashion with probabilities p 3, p 4, p 5, . . . , ph respectively to generate a hierarchy of CHs, in which any level-i CH is also CH of level (i-1), (i-2), …, 1.

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

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

Directed Diffusion [Intanagonwiwat+ 2000] Naming – A sensor detecting an animal may generate the following data: task = four-legged animal // type of animal seen instance = horse // instance of this type location = [150, 200] // node location intensity = 0. 5 // signal amplitude measure confidence = 0. 85 // confidence in the match timestamp = 01: 30: 45 // event generation time Interests and Gradients – Interest is generally given by the sink node – For each active task, sink periodically broadcasts an interest message to each of its neighbors (including rect and duration attributes) – Sink periodically refreshes each interest by re-sending the same interest with monotonically increasing timestamp attribute for reliability purposes

Directed Diffusion [Intanagonwiwat+ 2000] Interests and Gradients – Every node maintains an interest cache where each item in the cache corresponds to a distinct interest (different type, interval attributes with disjoint rect attributes) – Interest entries in the cache do not contain information about the sink – In some cases, definition of distinct interests allows interest aggregation – The interest entry contains several gradient fields, up to one per neighbor – When a node receives an interest, it determines if the interest exists in the cache 1. If no matching exist, the node creates an interest entry Ø Ø 2. This entry has single gradient towards the neighbor from which the interest was received with specified data rate Individual neighbors can be distinguished by locally unique identifiers If the interest entry exists, but no gradient for the sender of interest Ø Node adds a gradient with the specified value Ø Updates the entry’s timestamp and duration fields

Directed Diffusion [Intanagonwiwat+ 2000] Interests and Gradients 3. If there exists both entry and a gradient, Ø The node updates the entry’s timestamp and duration fields – When a gradient expires, it is removed from its interest entry – When all gradients for an interest entry have expired, the interest entry is removed from the cache – After receiving an interest, a node may re-send the interest to subset of its neighbors – To the neighbors, it may seem that interest originated from the sending node even though it may have been generated a distant sink. This represents a local interaction – This way, interest diffuse throughout the network and not each interest have been sent to all the neighbors if a node sent matching interest recently – Gradient specifies data rate (value) and a direction in directed diffusion, whereas the values can be used to probabilistically forward data in different paths in other sensor networks

Directed Diffusion [Intanagonwiwat+ 2000] Data propagation – Data message is unicast individually to the relevant neighbors – A node receiving a data message from its neighbors checks to see if matching interest entry in its cache exists according the matching rules described 1. If no match exist, the data message is dropped 2. If match exists, the node checks its data cache associated with the matching interest entry Ø If a received data message has a matching data cache entry, the data message is dropped Ø Otherwise, the received message is added to the data cache and the data message is re-sent to the neighbors – Data cache keeps track of the recently seen data items, preventing loops – By checking the data cache, a node can determine the data rate of the received events

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

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

Stealth Routing [Turgut+ 2009] Intruder Tracking Sensor Network Sensor networks used to detect and track intruders in a geographic region – “Interest area” – Observations are disseminated to the sink by hop transmission Performance metric: tracking error – Difference between the model maintained by the sink and the real location of intruders See demo here…

Stealth Routing [Turgut+ 2009] The problem of stealth The intruders belong to malicious and resourceful adversary – If the adversary knows the locations of the nodes: it can avoid sensors, find and exploit blind spots, introduce fake observations, capture or compromise nodes A node is: – Stealthy: if the adversary doesn’t know its existence – Disclosed: if the adversary can accurately locate the node – Have various levels of stealth between these two extremes How can a node loose stealth: – Accidentally – Through wireless transmission in the presence of the intruder – Routing / dissemination protocols did not previously consider the issue of stealth – The issue of stealth has been at best marginally addressed in the field of sensor networks. Our objective: – Develop a dissemination algorithm which optimizes stealth without sacrificing the other performance parameters

Stealth Routing [Turgut+ 2009] Quantifying stealth Stealth level σ(t) as probability at time t that the node is not disclosed to the opponent – Non disclosed node: σ(t) = 1 – Disclosed node: σ(t) = 0 – Stealth level decreases in response to transmission events. – Probability of disclosure at transmission

Stealth Routing [Turgut+ 2009] Try and Bounce (TAB) A dissemination algorithm designed from the ground up to take into consideration stealth. It is based on the creation and forwarding of reports about intruder location – All the transmissions are semantically meaningful to every node (there is no “blind” transmission) The local nodes maintain a local model of their environments – Updated through a series of inferences We will now investigate three aspect of TAB – A. The maintenance of the local model – B. The forwarding path – C. The decision to make a transmission

Stealth Routing [Turgut+ 2009] A. Maintenance of the local model (1) TAB agent maintains a local model of environment represented by the triplet <N, I, R> – N: set of node models lists the series of sensor nodes known to agent, whether within tx_range, active/inactive, or under threat – I: set of intruder models contains the list of the intruder nodes believed to be in the area of sensor network, their last known position and potentially other observed properties – R: set of report models contains the list of reports about intruder nodes to the sink For each report, model maintains the intruder node, its location, time when the observation was made, and path record of the report The model also keeps track of whether the node is responsible forwarding of the report or if it is responsible in checking its forwarding

Stealth Routing [Turgut+ 2009] A. Maintenance of the local model (2) Series of inferences triggered every time an agent makes an observation, receives, transmits or overhears a message – – Inferences are also triggered by passage of time Inferences has the complexity of O(1) TAB agent never maintains any historical information The number of reports do not exceed those of the active intruders Bookkeeping inferences – Occlusion – Obsoleting Inferences concerning intruder nodes – Sighting – Report received – Inference from silence

Stealth Routing [Turgut+ 2009] A. Maintenance of the local model (3) Inferences concerning nodes – – Heartbeat Lack of retransmission No heartbeat Inference from path records Inferences concerning reports – – – Report from sighting Report from received message Report transmitted Report progress overheard Report progress timeout

Stealth Routing [Turgut+ 2009] A. Maintenance of the local model (4) Example inference: occlusion – A newer report concerning the same intruder has been either: Observed Received from another node Overheard in the transmission between other two nodes – The node discards the old report (the new report occluded it) Observations – Occlusion works because the sink is not interested in history, only on the most recent position of the intruder – The node prefers to overhear a new report… because the overhearing does not create a responsibility, while a received message does

Stealth Routing [Turgut+ 2009] B. Forwarding in TAB The unit of forwarding is a report At any step during forwarding, the report has someone responsible for it When forwarding to the next hop, the node passes the responsibility for the report – – But it needs to check whether the next hop forwards or not If the next hop does not forward, responsibility bounces back The node needs to try another path to send messages to the sink The path record in the message assures that the report does not retry failed paths Choosing the next hop – Preference-ordered list of next hops to sink – The first choice is identical to what you would have in DD or most other protocols

Stealth Routing [Turgut+ 2009] C. The decision to transmit a report Start by evaluating the stealth loss if it transmits How to make the decision to transmit? – We want to minimize stealth loss, but we also want good accuracy Idea: cap the average stealth loss / intruder / node – Calculate the running average of the stealth loss – Transmit is the running average is below a threshold

Stealth Routing [Turgut+ 2009] Simulation Study Intruder tracking sensor network – – – Interest area: 400 x 400 meters 64 nodes Sensing range = 50 m Transmission range = 50 m Experiment time = 2 hr (7200 seconds) 10. . 80 intruders cross the area during the experiment Experiment repeated for 4 different protocol / parameterization – – DD-10 directed diffusion, interval = 10 secs DD-25 directed diffusion, interval = 25 secs TAB-0. 001 try and bounce, stealth loss cap = 0. 001 stealth units per intruder per unit of time TAB-0. 003 try and bounce, stealth loss cap = 0. 003 Implemented all the protocols in the YAES simulator

Stealth Routing [Turgut+ 2009]

Stealth Routing [Turgut+ 2009] Conclusions: Stealth is an important aspect of the operation of many sensor networks The challenge is to balance between multiple performance criteria Try and bounce (TAB): an early algorithm towards this direction – We found that it outperforms DD on the stealth + tracking accuracy combination Future work: o Other performance metrics (e. g. power conservation) o Improve the models and inferences o Extend the range of practical applicability

References [Bandyopadhyay+ 2003] S. Bandyopadhyay and E. J. Coyle, An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks, IEEE INFOCOM 2003, San Francisco, CA, March 30 – April 3, 2003. [Heinzelman+ 2002] W. Heinzelman, A. P. Chandrakasan and H. Balakrishnan, An Application. Specific Protocol Architecture for Wireless Microsensor Networks, IEEE Transactions on Wireless Communications, Vol. 1, No. 4, October 2002, pp. 660 -670. [Heinzelman+ 2000] W. Heinzelman, A. P. Chandrakasan and H. Balakrishnan, Energy-Efficient Communication Protocol for Wireless Microsensor Networks, IEEE Proceedings of the Hawaii International Conference on System Sciences, January 4 -7, 2000, Maui, Hawaii. [Intanagonwiwat + 2000] C. Intanagonwiwat, R. Govindan and D. Estrin, Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks, In Proceedings of the Sixth Annual International Conference on Mobile Computing and Networks (Mobi. COM 2000), August 2000, Boston, Massachusetts. [Turgut+ 2009] D. Turgut, B. Turgut, and L. Bölöni, Stealthy dissemination in intruder tracking sensor networks, Proceedings of IEEE LCN, October 2009, pp. 22 -29.