Routing in a Wireless Sensor Network

A project report submitted to

The Department of Computer Science and Engineering, Indian School Mines

Dhanbad

In partial fulfillment of the requirement for the award of degree of

Bachelor of Technology in Computer Science and Engineering

By

Mohammad Kafee Uddin (2009JE0651)

Aloukik Mishra(2009JE0640)

Under the guidance of

Prof P. K. Jana

Dept. of Computer Science and Engg

ISM, Dhanbad

Department of Computer Science and Engineering Indian School of Mines, Dhanbad-826004

Department of Computer Science and Engineering

Indian School of Mines, Dhanbad

Dated: 3-12-2012

CERTIFICATE

This is to certify that the dissertation entitled “ Routing in a Wireless Sensor

Network” is being submitted to Indian School of Mines, Dhanbad by Mohammad

Kafee Uddin(2009Je0651) and Aloukik Mishra(2009JE0640) and in partial

fulfillment of their Bachelor of Technology degree in Computer Science &

Engineering of the same institution incorporates the result of his own work,

carried out under my supervision and guidance. This dissertation has not been

submitted for any other degree, elsewhere to the best of my knowledge.

Prof P. K. Jana

HOD, CSE ISM Dhanbad

Abstract Recent advances in wireless sensor networks have led to many new protocols specifically designed for sensor networks where energy awareness is an essential consideration. Most of the attention, however, has been given to the routing protocols since they might differ depending on the application and network architecture. This paper surveys recent routing protocols for sensor networks and presents a classification for the various approaches pursued. We have classified routing protocols according to three different parameters, namely Mode of Functioning and Type of Target Applications, Participation style of the Nodes, and the Network Structure. Each routing protocol is described and discussed under the appropriate category.

Acknowledgement

First and foremost, we would like to express our sincere gratitude

to our Project guide, Prof P. K. Jana, Head of the Department of

Computer Science and Engineering for providing us with such an

opportunity and permitting this project in the department.

We were privileged to experience a sustained and involved

interest on the part of our mentors. This encouraged us to boldly step

into what was an unexplored expanse before us. Exploring new realms

in the field of Computer Science and Engineering helped unveil new

insights into the field expanding our knowledge of the same.

We would also like to thank our friends who were ready with a

positive comment all the time, whether it was an off-hand comment to

encourage us or a constructive piece of criticism.

Last but not least, we would like to thank the faculty members

and the Department, in general, for extending a helping hand at every

juncture of need.

Mohammad Kafee Uddin Aloukik Mishra

Location: Location:

Date: Date:

Contents Certificate Abstract Acknowledgements 1 Introduction to WSN 2 Classification Of Routing Protocols 2.1 Based on Mode of Functioning and Type of Target Applications 2.1.1 Proactive :- 2.1.2 Reactive :- 2.1.3 Hybrid :- 2.2 According to the Participation style of the Nodes 2.2.1 Direct Communication :- 2.2.2 Flat :- 2.2.3 Clustering Protocols :- 2.3 Depending on the Network Structure 2.3.1 Data Centric :- 2.3.2 Hierarchical :- 2.3.3 Location Based :- 3 Data Dissemination Protocols 3.1 Flooding 3.2 Gossiping 3.3 Rumor Routing 3.4 Sequential Assignment Routing 3.5 Direct Diffusion 3.6 Sensor Protocol for Information via Negotiation 3.7 Geographic Hash Table 4 Data Gathering Protocols 4.1 Direct Transmission 4.2 Power Efficient Gathering for Sensor Information Systems 4.3 LEACH

1. Introduction to WSN

Recent advances in micro-electro-mechanical systems and low power and highly integrated digital electronics have led to the development of micro-sensors. Such sensors are generally equipped with data processing and communication capabilities. The sensing circuitry measures the ambient conditions related to the environment surrounding the sensor and transforms them into an electric signal. Processing such a signal reveals some properties about objects located and/or events happening in the vicinity of the sensor. The sensor sends such collected data, usually via radio transmitter, to a command center (sink) either directly or through a data concentration center (a gateway). The decrease in the size and cost of sensors, resulting from such technological advances, has fueled interest in the possible use of large set of disposable unattended sensors. Such interest has motivated intensive research in the past few years addressing the potential of collaboration among sensors in data gathering and processing and the coordination and management of the sensing activity and data flow to the sink. A natural architecture for such collaborative

distributed sensors is a network with wireless links that can be formed among the sensors in an ad hoc manner. A general sensor node is made up of four basic components as shown in Fig. 1: a sensing unit, a processing unit, a transceiver unit and a power unit. They may also have application dependent additional components such as a location finding system, a power generator and a mobilizer. Sensing units are usually composed of two subunits: sensors and analog to digital converters (ADCs). The analog signals produced by the sensors based on the observed phenomenon are converted to digital signals by the ADC, and then fed into the processing unit. The processing unit, which is generally associated with a small storage unit, manages the procedures that make the sensor node collaborate with the other nodes to carry out the assigned sensing tasks. A transceiver unit connects the node to the network. One of the most important components of a sensor node is the power unit. Power units may be supported by a power scavenging unit such as solar cells. There are also other subunits, which are application dependent. Most of the sensor network routing techniques and sensing tasks require the knowledge of location with high accuracy. Thus, it is common that a sensor node has a location finding system. A mobilizer may sometimes be needed to move sensor nodes when it is required to carry out the assigned tasks.

Networking unattended sensor nodes are expected to have significant impact on the efficiency of many military and civil applications such as combat field surveillance, security and disaster management. These systems process data gathered from multiple sensors to monitor events in an area of interest. For example, in a disaster management setup, a large number of sensors can be dropped by a helicopter. Networking these sensors can assist rescue operations by locating survivors, identifying risky areas and making the rescue crew more aware of the overall situation. Such application of sensor networks not only can increase the efficiency of rescue operations but also ensure the safety of the rescue crew. On the military side, applications of sensor networks are numerous. For example, the use of networked set of sensors can limit the need for personnel involvement in the usually dangerous reconnaissance missions. In addition, sensor networks can enable a more civic use of landmines by making them remotely controllable and target specific in order to prevent harming civilians and animals. Security applications of sensor networks include intrusion detection and criminal hunting. However, sensor nodes are constrained in energy supply and bandwidth. Such constraints combined with a typical deployment of large number of sensor nodes have posed many challenges to the design and management of sensor networks. These challenges necessitate energy awareness at all layers of networking protocol stack. The issues related to physical and link layers are generally common for all kind of sensor applications, therefore the research on these areas has been focused on system-level power awareness such as dynamic voltage scaling, radio communication hardware, low duty cycle issues, system partitioning, energy-aware MAC protocols. At the network

layer, the main aim is to find ways for energy-efficient route setup and reliable relaying of data from the sensor nodes to the sink so that the lifetime of the network is maximized. Routing in sensor networks is very challenging due to several characteristics that distinguish them from contemporary communication and wireless ad hoc networks. First of all, it is not possible to build a global addressing scheme for the deployment of sheer number of sensor nodes. Therefore, classical IP-based protocols cannot be applied to sensor networks. Second, in contrary to typical communication networks almost all applications of sensor networks require the flow of sensed data from multiple regions (sources) to a particular sink. Third, generated data traffic has significant redundancy in it since multiple sensors may generate same data within the vicinity of a phenomenon. Such redundancy needs to be exploited by the routing protocols to improve energy and bandwidth utilization. Fourth, sensor nodes are tightly constrained in terms of transmission power, on-board energy, processing capacity and storage and thus require careful resource management. Due to such differences, many new algorithms have been proposed for the problem of routing data in sensor networks. These routing mechanisms have considered the characteristics of sensor nodes along with the application and architecture requirements. Data-centric protocols are query-based and depend on the naming of desired data, which helps in eliminating many redundant transmissions. Hierarchical protocols aim at clustering the nodes so that cluster heads can do some aggregation and reduction of data in order to save energy. Location based protocols utilize the position information to relay the data to the desired regions rather than the whole network. We will explore the routing mechanisms for sensor networks developed in recent years. Each routing protocol is discussed under the proper category. Our aim is to help better understanding of the current routing protocols for wireless sensor networks.

2. Classification Of Routing Protocols Routing techniques are required for sending data between sensor nodes and the base stations for communication. Different routing protocols are proposed for wireless sensor network. These protocols can be classified according to different parameters. (a)Routing Protocols can be classified as Proactive, Reactive and Hybrid, based on their Mode of Functioning and Type of Target Applications. (b)Routing protocols can be classified as Direct Communication, Flat and Clustering Protocols, according to the Participation style of the Nodes. (c)Routing Protocols can be classified as Hierarchical, Data Centric and location based, depending on the Network Structure. 2.1 Based on Mode of Functioning and Type of Target Applications 2.1.1 Proactive:- In a Proactive Protocol the nodes switch on their sensors and transmitters,

sense the environment and transmit the data to a BS through the predefined route. Examples: The Low Energy Adaptive Clustering hierarchy protocol (LEACH) utilizes this type of protocol. 2.1.2 Reactive:- If there are sudden changes in the sensed attribute beyond some pre-determined threshold value, the nodes immediately react. This type of protocol is used in time critical applications. Examples: The Threshold sensitive Energy Efficient sensor Network (TEEN) is an example of a reactive protocol. 2.1.3 Hybrid:- Hybrid protocols incorporate both proactive and reactive concepts. They first compute all routes and then improve the routes at the time of routing. Examples: Adaptive Periodic TEEN(APTEEN) is an example of a reactive protocol. 2.2 According to the Participation style of the Nodes. 2.2.1 Direct Communication:-

In this type of protocols, any node can send information to the Base Station(BS) directly. When this is applied in a very large network, the energy of sensor nodes may be drained quickly. Its scalability is very small. Examples: SPIN is an example of this type of protocol. 2.2.2 Flat:-

In this protocol, if any node needs to transmit data, it first searches for a valid route to the BS and then transmits the data. Nodes around the base station may drain their energy quickly. Its scalability is average. Examples: Rumor Routing is an example of this type of protocol. 2.2.3 Clustering Protocols:- According to the clustering protocol, the total area is divided into numbers of clusters. Each and every cluster has a cluster head (CH) and this cluster head directly communicates with the BS. All nodes in a cluster send their data to their corresponding CH. Examples: TEEN is an example of this type of protocol. 2.3 Depending on the Network Structure 2.3.1 Data Centric:- Data centric protocols are query based and they depend on the naming of the desired data, thus it eliminates much redundant transmissions. The BS sends queries to a certain area for information and waits for reply from the nodes of that particular region. Since data is requested through queries, attribute based naming is required to specify the properties of the data. Depending on the query, sensors collect a particular data from the area of interest and this particular information is only required to transmit to the BS and thus reducing the number of transmissions. Examples: SPIN was the first data centric protocol. 2.3.2 Hierarchical:-

Hierarchical routing is used to perform energy efficient routing, i.e., higher energy nodes can be used to process and send the information; low energy nodes are used to perform the sensing in the area of interest. Examples: LEACH, TEEN, APTEEN. 2.3.3 Location Based:-

Location based routing protocols need some location information of the sensor nodes. Location information can be obtained from GPS (Global Positioning System) signals, received radio signal strength, etc. Using location information, an optimal path can be formed without using flooding techniques. Examples: Geographic and Energy-Aware Routing(GEAR)

3.Data Dissemination Protocols Data dissemination is the process by which queries or data are routed in the sensor network. The data collected by sensor nodes has to be communicated to the BS or to any other node interested in the data. The node that generates data is called a source and the information to be reported is called an event. A node which is interested in an event and seeks information about it is called a sink. Traffic Models have been developed for sensor networks such as the data collection and data dissemination (diffusion) models. In the data collection model, the source sends the data it collects to a collection entity such as the BS. This could be periodic or on demand. The data is processed in the central collection entity. Data diffusion, on the other hand, consists of a two-step process of interest propagation and data propagation. An interested is a descriptor for a particular, intrusion or presence of bio-agents. For every event that a sink is interested in, it broadcasts its interest to its neighbors and periodically refreshes its interest. The interest is propagated across the network and every node maintains an interest cache of all events to be reported.

3.1 Flooding In Flooding, Each node which receives a packet broadcasts it, if the maximum hop count of the packet is not reached and node itself is not the destination of the packet. This technique does not require complex topology maintenance or route discovery algorithms. Flooding has following disadvantages: Implosion: This is situation when duplicate messages are sent to the same node. This occurs when a node receives copies of the same message from many of its neighbours. Overlap: The same event may be sensed by more than one node due to overlapping of regions of coverage. This results in their neighbors receiving duplicate reports of the same event. Resource Blindness: The flooding protocol does not consider the available energy at the nodes and results in many redundant transmissions. So, it reduces the network lifetime.

3.2 Gossiping Gossiping is modified version of flooding, where the nodes do not broadcast a packet, but send packets to a randomly selected neighbor. This avoids the problem of Implosion. It takes a long time for a message to propagate throughout the network. Though gossiping has considerably lower overhead than flooding, it does not guarantee that all nodes of the network will receive the message. It relies on the random neighbor selection to eventually propagate the message throughout the network. 3.3 Rumor Routing Rumor Routing is an agent based path creation algorithm. Agents are long-lived entities created at random by nodes. These are basically packets which are circulated in the network to establish shortest path to events that they encounter. They can also perform path optimizations at nodes they visit. When agent finds a node whose path to an event is longer than its own, it updates the nodes routing table.

Figure 3.1 illustrates the working of Rumor Routing algorithm. In figure 3.1(a), the agent has initially recorded a path distance 2 to event E1. Node A's table shows that it is at a distance 3 from event E1 and distance 2 from E2. When the agent visits node A, i+t updates its own path state information to include the path to event E2. The updating is with one hop greater distance than what it found in A, to account for the hop between

any neighbor of A that the agent will visit next, and A. It also optimizes the path to e1 recorded at node A to the shorter path through node B. The updated status of the agent and node table is shown in figure 3.1(b). When a query is generated at a sink, it is sent on a random walk with the hope that it will find a path leading to the required event. This is based on high probability of two straight lines intersecting on a planar graph, assuming the network topology is like a planar graph, and the paths established can be approximated by straight lines owing to high density of the nodes. If a query does not find an event path, the sink times out and uses flooding as last resort to propagate the query. For instance, as in figure 3.1(c), suppose a query for event E1 is generated by node P. Through a random walk, it reaches A, where it finds the previously established path to E1. Hence, the query is directed to E1 through node B, as indicated by A's table. 3.4 Sequential Assignment Routing :- The Sequential Assignment Routing(SAR) creates multiple trees ,where the root of each tree is a one hop neighbor of sink. Each tree grows outward from the sink and avoids nodes with low throughput or high delay. At the end of the procedure, most nodes belong to multiple trees. An instance of tree formation is illustrated in figure.

The tree rooted at A and B. Two of the one hop neighbors of the sink are shown. Node C belongs to both trees and has path length of 3 and 5 respectively to the sink, using the two trees. Each sensor node records two parameters about each path through it: 1. The available energy resources on the path. 2. An additive quality of service(QoS) metric such as delay. This allows a node to choose one path from among many to relay its message to the sink. The SAR chooses a path with the high estimated energy resources and provisions can be made to accommodate packets of different properties. A weighted QoS metric is used to handle prioritized packets which computed as a product of priority level and delay. The routing ensures that the same weighted QoS metric is maintained. Thus, higher priority packets take lower delay paths and lower priority packets have to use the paths of greater delay. e.g. If node C generates a packet of priority 3, it follows the longer path along tree B, and a packet of priority 5

will follow the shorter path along tree A. So that the priority X delay QoS metric is maintained. SAR minimizes the average weighted QoS metric over the lifetime of the network. The sink periodically triggers a metric update to reflect the changes in available energy resources after some transmissions. 3.5 Direct Diffusion

This protocol is useful in scenario where the sensor nodes themselves generate requests/queries for data sensed by other nodes, instead of all queries arising only from a BS. Hence the sink for the query could be a BS or a sensor node. The direct diffusion routing protocol improves on data diffusion using interest gradients. Each sensor node names its data with one or more attributes and other nodes express their interest depending on these attributes. Attribute value pairs can be used to describe an interest in intrusion data as follows. The sink has to periodically refresh its interest if it still requires the data to be reported it. Data is propagated along the reverse path of the interest propagation. Each path is associated with a gradient that is formed at the time of interest propagation. Each path is associated with the gradient that is formed at the time of interest propagation. While the positive gradients encourage the data flow along the path, Negative gradients inhibit the distribution of data along a particular path. The strength of the interest is different toward different neighbors, resulting into source to sink paths with different gradients. The gradient corresponding to an interest is derived from the interval/data-rate field specified in the interest. This model uses data naming by attributes and local data transformation to reflect the data centric nature of sensor network operations. The local operations of Data aggregation are application-specific gradient model. The network wide results of local interaction by regulating the flow of data along different paths depending on the expressed interest. 3.6 Sensor Protocol for Information via Negotiation(SPIN)

SPIN uses negotiation and resources and adaption to address the deficiencies of flooding. Negotiation reduces overlap and implosion, and a threshold based resource-aware operation is used to prolong network lifetime. Meta-data, or data describing data, is transmitted instead of row data. This requires fewer bytes and can be in an application-specific format.

SPIN has three types of messages: ADV, REQ, and DATA. A sensor node broadcasts an ADV containing meta-data describing actual data. If a neighbor is interested in the data, it sends REQ for the data. Then the sensor node sends the actual DATA to the neighbor. The neighbor again sends ADVs to its neighbors and this process continues to disseminate the data throughout the network. the simple version is shown in figure.

SPIN is based on data-centric routing, where the nodes advertise the available data through an ADV and wait for requests from interested nodes. SPIN-2 expands on SPIN, using an energy or resource threshold to reduce participation. A node may participate in the ADV-REQ-DATA handshake only if it has sufficient resources above a threshold. 3.7 Geographic Hash Table Geographic Hash Table is a system based on data centric storage inspired by internet scale distributed hash table systems such as chard and Tapestry, GHT hashes keys into geographic co-ordinates and stores a pair at the sensor node nearest to the hash value. The calculated hash value is mapped onto a unique node consistently, so that queries for the data can be routed to the correct node. Stored data is replicated to ensure redundancy in case of node failures and consistently protocol is used to maintain the replicated data. The data is distributed among nodes such that it is scalable and the storage load is balanced. GHT is more effective in

large network where a large number of events are detected but not all are queried. In this case data observed is stored in a distributed manner across all nodes, instead of being routed to central external storage. Queries are routed to the nearest node which contains a copy of the relevant data. This makes the storage and traffic distribution uniform.

4.Data Gathering Protocols The objective of the data-gathering problem is to transmit the sensed data from each sensor node to a BS. One round is defined as the BS collecting data from all the sensor nodes once. The goal of algorithms which implement data gathering is to maximize the number of rounds of communication before the nodes die and the network becomes inoperable. This means minimum energy should be consumed and the transmission should occur with minimum delays, which are conflicting requirements. Hence, the energy X delay metric is used to compare algorithms, since this metric measures speedy and energy efficient data gathering. A few algorithms that implement data gathering are discussed below. 4.1 Direct Transmission All sensor nodes transmit their data directly to BS. This is extremely expensive in terms of energy consumed, since the BS may be very far away from some nodes. Also, nodes must take turns while transmitting to

the BS to avoid collision , so the media access delay is also large. Hence, this scheme performs poorly with respect to the energy X delay matrix. 4.2 Power Efficient Gathering for Sensor Information Systems Power Efficient Gathering for Sensor Information Systems (PEGASIS) is a data-gathering protocol based on the assumption that all sensor nodes know the location of every other node, that is, the topology information is available to all nodes. Also, any node has the required transmission range to reach the BS in one-hop, when it is select as a leader. The goals of PEGASIS are as follows: Minimize the distance over which each node transmits. Minimize the broadcasting overhead. Minimize the number of messages that need to be sent to the BS. Distribute the energy consumption equally across all nodes.

A greedy algorithm is used to construct a chain of sensor nodes, starting from the node farthest from the BS. At each step, the nearest neighbor which has not been visited is added to the chain. The chain is constructed a priory, before data transmission begins and is reconstructed when nodes die out. At every node, data diffusion is carried out. So, that only one message is passed on from one node to next. A node which is designated as the leader finally transmits one message to BS. Leadership is transferred in sequential order and a token is passed. So that the nodes know in which direction to pass messages in order to reach the leader. A possible chain formation is illustrated in figure. The delay involved in message reaching the BS is O(N), where N is the total number of nodes in the network. 4.3. LEACH Low-energy adaptive clustering hierarchy is one of the most popular hierarchical routing algorithms for sensor networks. The idea is to form clusters of the sensor nodes based on the received signal strength and use local cluster heads as routers to the sink. This will save energy since the transmissions will only be done by such cluster heads rather than all sensor nodes. Optimal number of cluster heads is estimated to be 5% of the total number of nodes. All the data processing such as data fusion and aggregation are local to the cluster. Cluster heads change randomly over time in order to balance the

energy dissipation of nodes. This decision is made by the node choosing a random number between 0 and 1. The node becomes a cluster head for the current round if the number is less than the following threshold:

where p is the desired percentage of cluster heads (e.g. 0.05), r is the current round, and G is the set of nodes that have not been cluster heads in the last 1=p rounds. LEACH achieves over a factor of 7 reduction in energy dissipation compared to direct communication and a factor of 4–8 compared to the minimum transmission energy routing protocol. The nodes die randomly and dynamic clustering increases lifetime of the system. LEACH is completely distributed and requires no global knowledge of network. However, LEACH uses single-hop routing where each node can transmit directly to the cluster-head and the sink. Therefore, it is not applicable to networks deployed in large regions. Furthermore, the idea of dynamic clustering brings extra overhead, e.g. head changes, advertisements etc., which may diminish the gain in energy consumption.

5.References :- 1. Wireless sensor networks: a survey I.F. Akyildiz, W. Su*, Y. Sankarasubramaniam, E. Cayirci 2. Ad Hoc Wireless Networks By,C.Shiva Ram Murthy and B.S.Manoj 3. A survey on routing protocols for wireless sensor networks Kemal Akkaya *, Mohamed Younis 4.www.wikipedia.org/wiki/Wireless sensor network