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Algorithms in sensor networks By: Raghavendra kyatham.

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Presentation on theme: "Algorithms in sensor networks By: Raghavendra kyatham."— Presentation transcript:

1 Algorithms in sensor networks By: Raghavendra kyatham

2 What are sensor networks

3 A sensor network is a collection of some (sometimes even hundreds & thousands) smart sensor nodes which collaborate among themselves to form a sensing network. Smart sensors are wireless computing devices that sense information in many variety of environments to provide a multidimensional view of the environment. ex: some sensors can sense light, some can sense temperature simultaneously. The main task of a sensor network can be divide into three categories. Sensing, processing and acting. After sensing the environment based on the query provided by the user, a sensor node can process the sensed data, may even sometimes aggregate it with other nodes data and send it to the base station.

4 What are sensor networks contd: Based on the results provided by individual nodes, the network can act by providing the results to the user or to a node connected to the internet. Are smart sensors possible ? Recent advances in MEMS technology have led to the development of a new class of computing devices with wireless communication capabilities called smart sensors. That are low cost, low power, multifunctional miniature devices. A single smart sensor is limited in its capabilities, like restricted memory, restricted battery power etc…. But when formed as a network with many sensors it can do some high computational tasks.

5 What are sensor networks contd:


7 sensor nodes are distributed randomly over an object of interest, to from a sensing network and monitor the environment. These nodes can group and self organize. Sensor network can provide access to information anywhere and at anytime by collecting, processing, analyzing and disseminating the data. what should be the number of nodes in the network? Dissemination of data in sensor networks is of two types, query driven and continuous update. Smart environments and ubiquitous computing is possible with sensor networks.

8 Examples of sensor networks Vigilant surveillances like security in a shopping mall, an air passengers behavior. Predetermining Environmental hazards, providing precision agriculture. Monitoring of computer server rooms. Monitoring of manufacturing plants.

9 Sensor network challenges The challenges faced by a sensor network depend on the transient nature of the nodes and the number of nodes in the network. The transient nature of the nodes is due to there limited capableness. And because of that there topology changes frequently. Many situation require ad hoc deployment of sensor nodes. As sensor nodes are limited in power, capacity and memory extending the lifetime of the system is difficult. The large number of sensor nodes in a network will also lead to many different challenges like, avoiding collisions, optimal routing etc…

10 Requirements of the sensor network The requirements of a sensor network depends on the type of the application and the number of sensor nodes in the network. But almost all the networks have some common requirements. Aggregating its own sensed data with other nodes data. Self organization of the network. Provide queriying ability. Maximizing the lifetime of the system by appropriate utilization of the energy.

11 Sensor node architecture A general sensor node may consist of the following five components. A sensing hardware, Memory, Processor, Power supply, Transceiver.

12 Routing in sensor networks one basic operation of sensor networks is to gather the sensed data and transmit it to the base station, for further processing or as result to a given query. The general scenario in these networks is, during data gathering the intermediate nodes can aggregate the data in order to avoid redundant transfers. The order in which the data or the aggregated data is transmitted from the source node to the base station is the problem of routing. Why can’t we apply the standard routing algorithms? Tree based routing is used when there are few number of nodes and hierarchical routing is used for large number of nodes.

13 Routing in sensor networks contd: All the routing protocols should respect the energy constraints of the nodes. All the routing algorithms mentioned below consider sensor nodes to be static, homogeneous and energy constrained. Almost all the algorithms mentioned below will try to maximize the lifetime of the sensor network. The lifetime of the network can be described as the time till data can be transferred, before a sensor node gets completely drained of its energy. The effective utilization of energy is the typical measure of performance in sensor networks.

14 Sensor network topology with routing tree overlays The most common way of routing in a sensor networks is routing trees (multi hop routing). A routing tree is a collection of sensor nodes with the base station as the root of the tree. Sensor A is the parent for sensors B and C.. Sensor nodes transmit all there results to there parent nodes only. It is the responsibility of the parent node for forwarding them to the base station. A child can keep track of several parent nodes, and depending on the power levels or the quality of the communication links a child node can change its parent node.

15 The Maximum Lifetime Data Aggregation Problem (MLDA) ” Given a collection of sensors and a base station, together with their location and the energy of each sensor, find a data gathering schedule, where sensors are permitted to aggregate incoming data packets, with maximum lifetime”. Routing structures such as routing trees is well suited when there are only a few number of nodes in the network. Managing the routing trees in such case will become infeasible and the overlaps in the routing trees can not be effectively utilized. A data gathering schedule is a way the data packets are collected from all the sensors and routed to the base station with maximum lifetime.

16 The Maximum Lifetime Data Aggregation Problem (MLDA) The main assumption of this algorithm is that the location of the sensors, base station and energy values of the sensor nodes are known priori. In this model the lifetime of the system is intrinsically connected to the data gathering schedule. During each round a sensor will collect its own, neighbor’s data and possibly aggregate it and send it to the base station. If there is T such rounds and ‘f ‘be the total number of packets a sensor node i transmits to sensor node j.By respecting the energy constraints at each node, the data transferring schedule can be viewed as flow network G (V,E). Schedule S induces a flow network G = ( V, E).

17 The Maximum Lifetime Data Aggregation Problem (MLDA) By maintaining the flow conservation principle and the energy constraints of each sensor, an optimal admissible flow network is constructed i.e. a directed graph G having all the sensors as nodes and the base station as the root. Each directed tree rooted at the base station is considered as an aggregate tree, and schedule is a collection of such trees. The number of rounds the aggregation tree is used to transmit data is denoted by f and associating it with each one of the edges. The depth of a schedule is defined as max {depth (v): v belongs to V}.

18 The Maximum Lifetime Data Aggregation Problem (MLDA) An iterative algorithm GETTREE is used to get an aggregation tree A with life time f from the admissible flow network. The running time of the below described algorithm is of polynomial time in the number of sensors.

19 The Maximum Lifetime Data Aggregation Problem (MLDA)

20 max-min zPmin max-min zPmin is an approximation algorithm for online power aware routing. The goal of this algorithm is similar to that of the previous algorithm that we discussed, to maximize the lifetime of the network. Online routing refers to that there is no fixed schedule for routing the messages. In this algorithm the network is represented as a weighted graph G (V, E). The nodes in the network are the vertices of the graph with weights corresponding to there power levels. Edges correspond to the communication link between nodes and the edge weight as the cost of sending data between them.

21 max-min zPmin The max-min zPmin is defined as routing the data along a path with maximal minimal fraction of the remaining power in a sensor node after the data is transmitted i.e. max-min path and a path with minimal power consumption Pmin, with zPmin being the relaxed power consumption for sending the data. The algorithm runs the Dijkstra algorithm for at most | E | times to find the shortest path. The running time of this algorithm is O (|E|. (|E| + |V| log |V|)).

22 Hierarchical routing All the above discussed algorithms tried to maximize the lifetime of the system by finding a routing path that uses less energy. This type of routing is known as multi hop routing or static clustering which has very serious limitations when the number of nodes in the network becomes very large. Static or multi hop routing protocols require the knowledge of the energy levels of the sensor nodes which may be difficult to obtain in large networks. One method of obtaining such information is through broadcasting. But ? In large network networks transmitting data through intermediate nodes may sometimes consume more than routing directly to the base station. So large networks are divided into zones are clusters.

23 Leach (low energy adaptive clustering hierarchy) Leach is also an energy efficient protocol for routing in sensor networks. Leach is based on the principle of clusters and is organized into rounds. In each round a self elected cluster head collects data from all other sensors in the cluster, aggregates it and transmits it directly to the base station. During the setup phase a predetermined fraction of nodes elect themselves as cluster heads. A threshold value T(n) is used to compare the random values generated by the node wanting to be the cluster head. If the value of a particular node is less than the threshold value, then it will act as the cluster head for the current round.

24 Leach (low energy adaptive clustering hierarchy) Once a cluster head is selected it broadcasts its ‘ID’ to all other nodes in the cluster. A non cluster head may receive many broadcasts from different cluster heads; it makes a selection among them by comparing the quality of the communication link with various cluster heads. On receiving the decision of the noncluster heads the cluster head creates a schedule and informs it to nodes in the cluster. In this way each node follows the schedule and transmits the data to the cluster head, and the head after aggregating the data transmits it to the base station directly. The key feature of leach when compared to the above discussed protocols is its localized coordination for cluster setup and operation.

25 Zone based max-min zPmin Zone based routing is a hierarchical approach to the max-min zPmin. The algorithm groups the nodes in the network structurally into geographical zones that can overlap, and organizes zones hierarchically to control routing across zones. The algorithm is divided into three main parts, first how the nodes in a zone collaborate to estimate the energy level of the zone. Second, how data is routed within a zone and third, how data is routed across zones. The energy estimation of the zones is done relative to the direction of data transmission. The zones are assumed to be squares with their neighbors being in north, east, west, and south directions.

26 Zone based max-min zPmin There is a controller node in each zone which estimates the energy level of the zone i.e. estimating the number of messages that can flow through the zone. The controller poles each node in the zone for its energy level and then runs the max-min zPmin algorithm. Then it simulates sending proportionate amount of data units, and repeats it until a node on the path gets saturated.

27 Zone based max-min zPmin

28 After estimating the power level of each zone with respect to the directions of the other zones, the next thing is estimating a global path to route the data. The zones are represented as a K+1 graph, where k vertices correspond to each data direction through the zone. The zone label vertex is connected to all the data direction vertices and the data direction vertices are connected to neighboring zone vertices if data can be transmitted in that way.

29 Zone based max-min zPmin The edges in this zone graph do not have weights, and a global route for sending data can be found as the max-min path in the zone graph. The path that is selected should be the path that goes through zones with maximum power levels i.e. a slight modification to the max-min zPmin algorithm. After a global path through the zones is found the next task is to find routes within a zone. For each node in the overlap region, the number of paths that can be locally routed through each node is computed during the energy level estimation. Finally only those nodes that have maximum data weight is selected to maximize the global flow between zones i.e. choosing nodes which can be useful in both local and global routing.

30 Zone based max-min zPmin The algorithm to find global path to route the data.

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