ROUTING ALGORITHMS IN WSN : A SURVEY

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1 VSRD International Journal of Computer Science & Information Technology, Vol. 3 No. 2 February 2013 / 59 e-issn : , p-issn : VSRD International Journals : REVIEW ARTICLE ROUTING ALGORITHMS IN WSN : A SURVEY Neha Khurana Research Scholar, Department of Computer Science & Engineering, Integral University, Uttar Pradesh, INDIA. Corresponding Author : neha.khurana02@gmail.com ABSTRACT Sensor Network has emerged as a promising tool for monitoring the physical world, utilizing self organizing networks of battery powered wireless sensors that can sense. Sensor network nodes are limited with respect to energy supply, restricted computational capacity and communication bandwidth. To enhance the lifetime of the sensor nodes, designing efficient routing algorithm is important. As sensor networks are primarily designed for monitoring and reporting events, i.e. they are application dependent. The goal of our survey is to present a comprehensive review of routing algorithm. In this paper, we give a survey of routing protocols for Wireless Sensor Network and their applications. Keywords : Wireless Sensor Network (WSN), Sensor Nodes, Fault Tolerance, Scalability, Transmission Media, Quality of Service. 1. INTRODUCTION Recent advances in micro-electro-mechanical systems (MEMS) technology, wireless communications, and digital electronics have enabled the development of low-cost, lowpower, multifunctional sensor nodes that are small in size and communicate within short distances or in other words we may say that MEMS 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. These tiny sensor nodes, which consist of sensing, data processing, and communicating components, leverage the idea of sensor networks based on collaborative effort of a large number of nodes. The sensing circuitry measures surrounding conditions related to environment surrounding and transform them into an electric signal. Processing such a signal reveals 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 centre (sink) either directly or through a data concentration centre (a gateway). The decrease in the size and cost of sensors, resulting from such technological advances, has fuelled 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 wireless sensor network typically consists of a large number of low cost, low power and multifunctional sensor nodes that are deployed in the region of interest. These sensors are small in size but are equipped with sensor, embedded microprocessors, and radio transceivers.therefore they have not only sensing but also data processing and communicating capabilities. They communicate over a short distance via a wireless medium and collaborate to accomplish a common task. For Example Environment monitoring, military surveillance and industrial process control. The definition from Smart Dust program of DARPA is: A sensor network is a deployment of massive numbers of small, inexpensive, self powered devices that can sense, compute, and communicate with other devices for the purpose of gathering local information to make global decisions about a physical environment The definition from National Research Council of USA is: Sensor networks are massive numbers of small, inexpensive, self-powered devices pervasive throughout electrical and mechanical systems and ubiquitous throughout the environment that monitor (i.e., sense) and control (i.e., effect) most aspects of our physical world 2. NETWORK CHARACTERISTICS AND DESIGN ISSUES As compared to the traditional wireless communication networks such as mobile ad hoc network (MANET) and cellular systems, wireless sensor networks have the following unique characteristics and constraints: Dense sensor node deployment: Sensor nodes are usually densely deployed and can be several orders of magnitude higher than that in a MANET.

2 Neha Khurana VSRDIJCSIT, Vol. III (II), 2013 / 60 Battery-powered sensor nodes: Sensor nodes are usually powered by battery and are deployed in a harsh environment where it is very difficult to change or recharge the batteries. Severe energy, computation, and storage constraints: Sensors nodes are having highly limited energy, computation, and storage capabilities. Self-configurable: Sensor nodes are usually randomly deployed and autonomously configure themselves into a communication network. Unreliable sensor nodes: Since sensor nodes are prone to physical damages or failures due to its deployment in harsh or hostile environment. Data redundancy: In most sensor network application, sensor nodes are densely deployed in a region of interest and collaborate to accomplish a common sensing task. Thus, the data sensed by multiple sensor nodes typically have a certain level of correlation or redundancy. Application specific: A sensor network is usually designed and deployed for a specific application. The design requirements of a sensor network change with its application. Many-to-one traffic pattern: In most sensor network applications, the data sensed by sensor nodes flow from multiple source sensor nodes to a particular sink, exhibiting a many-to-one traffic pattern. Frequent topology change: Network topology changes frequently due to the node failures, damage, addition, energy depletion, or channel fading. Design factors: The sensor nodes are usually scattered in a sensor field. Each of these scattered sensor nodes has the capabilities to collect and route data back to the sink. The design factors are addressed by many researchers. None of these studies has a fully integrated view of all the factors driving the design of sensor networks and sensor nodes. These factors are important because they serve as a guideline to design a protocol or an algorithm for sensor networks. Initially WSNs was mainly motivated by military applications. Later on the civilian application domain of wireless sensor networks have been considered, such as environmental and Species monitoring, production and healthcare, smart home etc. These WSNs may consist of heterogeneous and mobile sensor nodes, the network topology may be as simple as a star topology; the scale and density of a network varies depending on the application. To meet this general trend towards diversification, the following important design issues of the sensor network have to be considered. These are as follows: Fault tolerance: Some sensor nodes may fail or be booked due to lack of power or have physical damage or environmental interface. The failure of sensor nodes should not affect the overall task of the sensor network. Fault tolerance is the ability to sustain sensor network functionalities without any interruption due to sensor node failure. Scalability: The number of sensor nodes deployed in studying a phenomenon may be the order of hundreds or thousands.depending on the application the number may reach an extreme value of millions. New schemes must be able to work with this number of nodes. They must also utilize the high density of the sensor networks. The density can range from few sensor nodes to few hundred sensor nodes in a region. Production Cost: As we all know that sensor networks consists of a large number of sensor nodes, the cost of a single node is very important to justify the overall cost of the network. If the cost of the network is more expensive than deploying traditional sensors, the sensor network is not cost justified. As a result the cost of each sensor node has to be kept low. Hardware Constraints: A sensor node is made up of four basic components, these are : A Sensing unit A Processing unit A Transceiver unit A Power unit They may also have additional application-dependent components such as a location finding system, power generator, and mobilize. Sensing units are usually composed of two subunits: Sensors and Analog and Digital converters (ADCs). Environment: Sensor nods are densely deployed either very close or directly inside the phenomenon to be observed. Therefore, they usually work unattended in remote geographic areas. They may be working in the large machinery, at the bottom of an ocean, in a biologically or chemically contaminated field, in a battlefield beyond the enemy lines and in a home or large building. Transmission Media: In a multihop sensor network, communicating nodes are linked by a wireless medium.to enable global operation of these networks the chosen transmission medium must be available worldwide. Most of the current hardware for sensor nodes is based on RF (Radio-Frequency) circuit design. The Wireless Integrated Network Sensor (WINS) architecture also uses radio links for communication. Another possible mode of internodes communication in sensor network is by infrared. Infrared communication is license free and robust to interference from electrical devices. Infrared based transceivers are cheaper and easier to build. Power Consumption: The wireless sensor node being a microelectronic device can only be equipped with a limited power source. In some application scenarios, replenishment of power resources might be impossible. Sensor node lifetime, therefore, shows a strong dependence on battery

3 Neha Khurana VSRDIJCSIT, Vol. III (II), 2013 / 61 lifetime. The main task of a sensor node in a sensor field is to detect events, perform quick local data processing and then transmit the data. Power consumption can hence be divided into three domains: Sensing, Communication and Data processing. grid are considered equivalent in terms of the cost of packet routing. Quality of Service: In some applications, data should be delivered within a certain period of time from the moment it is sensed; otherwise the data will be useless. Therefore bounded latency for data delivery is another condition for time-constrained applications. As the energy gets depleted, the network may be required to reduce the quality of the results in order to reduce the energy dissipation in the nodes and hence lengthen the total network lifetime. Data Latency and Overhead: These are considered as the important factors that influence routing protocol design. Data aggregation and multi-hop relays cause data latency. In addition, some routing protocols create excessive overheads to implement their algorithms, which are not suitable for serious energy constrained networks. 3. CLASSIFICATION OF ROUTING PROTOCOLS Research Methodology Matter : A routing protocol is considered adaptive if certain system parameters can be controlled in order to adapt to the current network conditions and available energy levels. In order to streamline this survey, we use a classification according to the network structure and protocol operation (routing criteria). Location Based protocol Data Centric protocols Heirarical protocol Multipath based protocols Network flow and Qos aware protocol Location Based protocol: Most of the routing protocols for sensor networks require location information for sensor nodes.the location information-based routing algorithm uses location information to guide routing discovery and maintenance as well as data forwarding, enabling directional transmission of the information and avoiding information flooding in the entire network. In location based protocol of routing, sensor nodes are addressed by means of their locations. The distance between neighboring nodes can be estimated on the basis of incoming signal strengths. Relative addresses of neighboring nodes can be obtained by exchanging such information between them. 1. Geographic Adaptive Fidelity (GAF): GAF conserves energy by turning off unnecessary nodes in the network without affecting the level of routing fidelity. It forms a virtual grid for the covered area. Each node uses its GPSindicated location to associate itself with a point in the virtual grid. Nodes associated with the same point on the Such equivalence is exploited in keeping some nodes located in a particular grid area in sleeping state in order to save energy. Thus, GAF can substantially increase the network lifetime as the number of nodes increases. The state transition diagram of GAF has three states : Discovery, Active Sleeping When a sensor enters the sleeping state, it turns off its radio for energy savings. In the discovery state, a sensor exchanges discovery messages to learn about other sensors in the same grid. Even in the active state, a sensor periodically broadcasts its discovery message to inform equivalent sensors about its state. The main objective of GAF is to maximize the network lifetime by reaching a state where each grid has only one active sensor based on sensor ranking rules. Thus, a sensor with a higher rank will be able to handle routing within their corresponding grids. 2. Geographic and Energy-Aware Routing (GEAR): Geographic and Energy Aware Routing (GEAR) technique uses energy aware and geographically informed neighbor selection heuristics to route a packet towards the target region. Within a region, it uses a recursive geographic forwarding technique to disseminate the packet. Although the energy balancing design of GEAR is motivated by sensor net applications, our protocol is generally applicable to ad-hoc networks. The idea is to restrict the number of interests in Directed Diffusion by only considering a certain region rather than sending the interests to the whole network. GEAR compliments Directed Diffusion in this way and thus conserves more energy. There are two phases in the algorithm: 1. Forwarding the packets towards the target region: GEAR uses a geographical and energy aware neighbor selection heuristic to route the packet towards the target region.

4 Neha Khurana VSRDIJCSIT, Vol. III (II), 2013 / 62 retrieve the data by sending a request message. SPIN s meta-data negotiation solves the classic problems of flooding such as redundant information passing, overlapping of sensing areas and resource blindness thus, achieving a lot of energy efficiency. There are three messages defined in SPIN to exchange data between nodes. These are: ADV message to allow a sensor to advertise a particular meta-data, REQ message to request the specific data. DATA message that carry the actual data. There are two cases to consider: (a) When a closer neighbor to the destination exists: GEAR picks a next-hop node among all neighbors that are closer to the destination. (b) When all neighbors are further away: In this case, there is a hole. GEAR picks a next-hop node that minimizes some cost value of this neighbor 2. Disseminating the packet within the region: If the packet has reached the region, it can be diffused in that region by either recursive geographic forwarding or restricted flooding. Restricted flooding is good when the sensors are not densely deployed. Data-Centric Protocols: Data centric protocols are query based and they depend on the naming of the desired data, thus it eliminates much redundant transmissions. The base station 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. 1. Flooding and gossiping: Flooding and gossiping are two classical mechanisms to relay data in sensor networks without the need for any routing algorithms and topology maintenance. In flooding, each sensor receiving a data packet broadcasts it to all of its neighbors and this process continues until the packet arrives at the destination or the maximum number of hops for the packet is reached. In Gossiping is a slightly enhanced version of flooding where the receiving node sends the packet to a randomly selected neighbor, which picks another random neighbor to forward the packet to and so on. 2. SPIN (Sensor protocols for information via negotiation): The idea behind SPIN is to name the data using high-level descriptors or meta-data. Before transmission, metadata are exchanged among sensors via a data advertisement mechanism, which is the key feature of SPIN. Each node upon receiving new data, broadcasts it to its neighbors and interested neighbors, i.e. those who do not have the data, (a) Node B responds by sending a request to node A (b) After receiving the requested data (c) Node B then sends out advertisements to its neighbors (d) Who in turn send requests back to B (e f). 3. Directed Diffusion: Directed Diffusion is an important milestone in the data-centric routing research of sensor networks. The idea aims at diffusing data through sensor nodes by using a naming scheme for the data. The main reason behind using such a scheme is to get rid of unnecessary operations of network layer routing in order to save energy. Direct Diffusion suggests the use of attributevalue pairs for the data and queries the sensors in an on demand basis by using those pairs. 4. Gradient-based routing (GBR): A slightly changed version of Directed Diffusion, called Gradient-based routing (GBR). The idea is to keep the number of hops when the interest is diffused through the network. Hence, each node can discover the minimum number of hops to the sink, which is called height of the node. The difference between a node s height and that of its neighbor is considered the gradient on that link. A packet is forwarded on a link with the largest gradient. On the other hand, three different data spreading techniques have been presented: Stochastic scheme: When there are two or more next hops with the same gradient, the node chooses one of them at random. Energy-based scheme: When a node s energy drops below a certain threshold, it increases its height so that other sensors are discouraged from sending data to that node. Stream-based scheme: The idea is to divert new

5 Neha Khurana VSRDIJCSIT, Vol. III (II), 2013 / 63 streams away from nodes that are currently part of the path of other streams. 5. Constrained Anisotropic Diffusion Routing (CADR): Constrained anisotropic diffusion routing (CADR) is a protocol, which strives to be a general form of Directed Diffusion. The idea is to query sensors and route data in a network in order to maximize the information gain, while minimizing the latency and bandwidth. This is achieved by activating only the sensors that are close to a particular event and dynamically adjusting data routes. The major difference from Directed Diffusion is the consideration of information gain in addition to the communication cost. In CADR, each node evaluates an information/cost objective and routes data based on the local information/cost gradient and end-user requirements. The information utility measure is modeled using standard estimation theory. 6. Cougar: The main idea is to use declarative queries in order to abstract query processing from the network layer functions such as selection of relevant sensors etc. and utilize in-network data aggregation to save energy. The abstraction is supported through a new query layer between the network and application layers. COUGAR proposes architecture for the sensor database system where sensor nodes select a leader node to perform aggregation and transmit the data to the gateway (sink). The architecture is depicted in Figure. The gateway is responsible for generating a query plan, which specifies the necessary information about the data flow and in-network computation for the incoming query and send it to the relevant nodes. Step 3: Forward it to another sensor. If the pre-cached information is not up-to date, the nodes gather information from its neighbors within a look-ahead of d hops. Once the query is being resolved completely, it is sent back through either the reverse or shortest-path to the sink. ACQUIRE mechanism provides efficient querying by adjusting the value of parameter d. Note that if d is equal to network size, then the protocol behaves similar to flooding. On the other hand, the query has to travel more hops if d is too small. A mathematical modeling has been derived for the energy cost of the ACQUIRE approach and been compared to both flooding and ring search, i.e. gradual increase in number of hops. An optimal value of parameter d is calculated for a grid of sensors where each node has four immediate neighbors. 8. Energy-Aware Data-Centric Routing (EAD): EAD is a novel distributed routing protocol, which builds a virtual backbone composed of active sensors that are responsible for in-network data processing and traffic relaying. In this protocol, a network is represented by a broadcast tree spanning all sensors in the network and rooted at the gateway, in which all leaf nodes radios are turned off while all other nodes correspond to active sensors forming the backbone and thus their radios are turned on. Specifically, EAD attempts to construct a broadcast tree that approximates an optimal spanning tree with a minimum number of leaves, thus reducing the size of the backbone formed by active sensors. EAD approach is energy aware and helps extend the network lifetime. The gateway plays the role of a data sink or event sink, whereas each sensor acts as a data source or event source. Hierarchical protocols: The main aim of hierarchical routing is to efficiently maintain the energy consumption of sensor nodes by involving them in multi-hop communication within a particular cluster and by performing data aggregation and fusion in order to decrease the number of transmitted messages to the sink. Cluster formation is typically based on the energy reserve of sensors and sensor s proximity to the cluster head. Here some hierarchical routing protocols are described below: 7. Acquire: The approach views the sensor network as a distributed database and is well-suited for complex queries which consist of several sub queries. The querying mechanism works as follows: Step 1: The query is forwarded by the sink and each node receiving the query. Step 2: Tries to respond partially by using its pre cached information and 1. Low-energy adaptive clustering hierarchy (LEACH): This 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

6 Neha Khurana VSRDIJCSIT, Vol. III (II), 2013 / 64 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. 2. PEGASIS and Hierarchical (PEGASIS): Power-efficient Gathering in Sensor Information Systems (PEGASIS) is an improvement of the LEACH protocol. Rather than forming multiple clusters, PEGASIS forms chains from sensor nodes so that each node transmits and receives from a neighbor and only one node is selected from that chain to transmit to the base station (sink). Gathered data moves from node to node, aggregated and eventually sent to the base station. The chain construction is performed in a greedy way. PEGASIS avoids cluster formation and uses only one node in a chain to transmit to the BS (sink) instead of using multiple nodes. 3. TEEN(Threshold sensitive Energy Efficient sensor Network protocol) : TEEN is a hierarchical protocol designed to be responsive to sudden changes in the sensed attributes such as temperature or in other words we can state that TEEN is a hierarchical clustering protocol, which groups sensors into clusters with each led by a Cluster head. Responsiveness is important for time-critical applications, in which the network operated in a reactive mode. TEEN pursues a hierarchical approach along with the use of a datacentric mechanism. The sensor network architecture is based on a hierarchical grouping where closer nodes form clusters and this process goes on the second level until base station (sink) is reached. 4. APTEEN(The Adaptive Threshold sensitive Energy Efficient sensor Network ): APTEEN is an improvement to TEEN to overcome its shortcomings and aims at both capturing periodic data collections (LEACH) and reacting to time-critical events (TEEN).APTEEN is an extension to TEEN and aims at both capturing periodic data collections and reacting to time critical events. The architecture is same as in TEEN. When the base station forms the clusters, the cluster heads broadcast the attributes, the threshold values, and the transmission schedule to all nodes. Cluster heads also perform data aggregation in order to save energy. APTEEN supports three different query types: historical, to analyze past data values; one-time, to take a snapshot view of the network; and persistent to monitor an event for a period of time. 5. Energy-aware routing for cluster-based sensor networks: Younis et al. have proposed a different hierarchical routing algorithm based on three tier architecture. Sensors are grouped into clusters prior to network operation. The algorithm employs cluster heads, namely gateways, which are less energy constrained than sensors and assumed to know the location of sensor nodes. Gateways maintain the states of the sensors and sets up multi-hop routes for collecting sensors_ data. A TDMA based MAC is used for nodes to send data to the gateway. The gateway informs each node about slots in which it should listen to other nodes transmission and slots, which the node can use for its own transmission. The command node (sink) communicates only with the gateways. 6. Energy Efficient Homogenous Clustering Algorithm for Wireless Sensor Networks: A homogeneous clustering algorithm is being proposed for wireless sensor network that saves power and prolongs network life. The life span of the network is increased by ensuring a homogeneous distribution of nodes in the clusters. A new cluster head is selected on the basis of the residual energy of existing cluster heads, holdback value, and nearest hop distance of the node. The homogeneous algorithm makes sure that every node is either a cluster head or a member of one of the clusters in the wireless sensor network. In the proposed clustering algorithm the cluster members are uniformly distributed, and thus, the life of the network is more extended. Further, in the proposed protocol, only cluster heads broadcast cluster formation message and not the every node. Hence, it prolongs the life of the sensor networks. The emphasis of this approach is to increase the life span of the network by ensuring a homogeneous distribution of nodes in the clusters so that there is not too much receiving and transmitting overhead on a Cluster Head. 7. Self-organizing protocol: Subramanian and Katz not only describe a self-organizing protocol but develop taxonomy of sensor applications as well. Based on such taxonomy, they have proposed architectural and infrastructural components necessary for building sensor applications. The architecture supports heterogeneous sensors that can be mobile or stationary. Some sensors, which can be either stationary or mobile, probe the environment and forward the data to designated set of nodes that act as routers. Router nodes are stationary and form the backbone for communication. Collected data are forwarded through the routers to more powerful sink nodes. Each sensing node should be reachable to a router node in order to be part of the network. The algorithm for self-organizing the router nodes and creating the routing tables consists of four phases: Discovery phase: The nodes in the neighborhood of each sensor are discovered. Organization phase: Groups are formed and merged by forming a hierarchy. Each node is allocated an address based on its position in the hierarchy. Routing tables of size O(log N) are created for each node. Broadcast trees that span all the nodes are constructed. Maintenance phase: Updating of routing tables and energy levels of nodes is made in this phase. Each node informs the neighbors about its routing table and energy level. LML are used to maintain broadcast trees. Self-reorganization phase: In case of partition or node

7 Neha Khurana VSRDIJCSIT, Vol. III (II), 2013 / 65 failures, group reorganizations are performed. Multipath Routing Protocols: In these protocols, a source knows multiple routes to a destination. The routes can be simultaneously used or one of them can be active while the others are maintained for future needs. 1. SAR (Sequential Assignment Routing): SAR is one of the first protocols for wireless sensor networks that provide the notion of QoS routing criteria. It is based on the association of a priority level to each packet. Additionally, the links and the routes are related to a metric that characterizes their potential provision of quality of service. This metric is based on the delay and the energy cost. Then, the algorithm creates trees rooted at the one-hop neighbors of the sink. The protocol must periodically recalculate the routes to be prepared in case of failure of one of the active nodes. 2. Maximum Lifetime Routing in Wireless Sensor Networks : This algorithm combines the energy consumption optimization with the use of multiple routes. In this algorithm an active route (also called the primary route) is monitored to control its residual energy. Meanwhile other routes can be discovered. If the residual energy of the active route does not exceed the energy of an alternative route, the corresponding secondary route is then used. 3. Energy Aware Routing in Wireless Sensor Networks: Once multiple paths are discovered, this algorithm associates a probability of use to each route. This probability is related to the residual energy of the nodes that form the route but it is also considers the cost of transmitting through that route. 4. M-MPR (Mesh Multipath Routing): This protocol presents two operation modes. Firstly, in the disjoint MPR (D-MPR) with Selective Forwarding each packet is individually analyzed by the source and it is routed through different routes. Secondly, the D-MPR with data replication is based on the simultaneous emission of multiple copies of the same packet through different routes. Specifically, all the known routes that communicate the source and the destination propagate the packet. For the route discovery, information about the position of the nodes and about their residual energy is exchanged. Multipath-based Protocols: Considering data transmission between source sensors and the sink, there are two routing paradigms: single-path routing and multipath routing. In single-path routing, each source sensor sends its data to the sink via the shortest path. In multipath routing, each source sensor finds the first k shortest paths to the sink and divides its load evenly among these paths. 1. Disjoint Paths: Sensor-disjoint multipath routing is a multipath protocol that helps find a small number of alternate paths that have no sensor in common with each other and with the primary path. In sensor-disjoint path routing, the primary path is best available whereas the alternate paths are less desirable as they have longer latency. The disjoint makes those alternate paths independent of the primary path. Thus, if a failure occurs on the primary path, it remains local and does not affect any of those alternate paths. 2. Braided Paths: Braided multipath is a partially disjoint path from primary one after relaxing the disjointedness constraint. To construct the braided multipath, first primary path is computed. Then, for each node (or sensor) on the primary path, the best path from a source sensor to the sink that does not include that node is computed. Those best alternate paths are not necessarily disjoint from the primary path and are called idealized braided multipath. 3. N-to-1 Multipath Discovery: N-to-1 multipath discovery is based on the simple flooding originated from the sink and is composed of two phases, namely, branch aware flooding (or phase 1) and multipath extension of flooding (or phase 2). Both phases use the same routing messages whose format is given by {mtype, mid, nid, bid, cst, path}, where mtype refers to the type of a message. This multipath discovery protocol generates multiple node-disjoint paths for every sensor. 4. APPLICATION Home Control: Home control applications provide control, conservation, convenience, and safety, as follows : Sensing applications facilitate flexible management of lighting, heating, and cooling systems from anywhere in the home. Sensing applications automate control of multiple home systems to improve conservation, safety and convenience. Sensing applications capture highly detailed electric, water, and gas utility usage data. Sensing applications embed intelligence to optimize consumption of natural resources. Sensing applications enable the installation, upgrading, and networking of a home control system without wires. Sensing applications enable one to configure and run multiple systems from a single remote control. Sensing applications support the straightforward installation of wireless sensors to monitor a wide variety of conditions. Sensing applications facilitate the reception of automatic notification upon detection of unusual events. Building Automation: Sensing applications integrate and centralize management of lighting, heating, cooling, and security. Sensing applications automate control of multiple systems to improve conservation, flexibility, and security. Sensing applications reduce energy expenses through optimized HVAC management.

8 Neha Khurana VSRDIJCSIT, Vol. III (II), 2013 / 66 Sensing applications enable one to allocate utility costs equitably based on actual consumption. Sensing applications enable the rapid reconfiguring of lighting systems to create adaptable workspaces Industrial Automation: Sensing applications extend existing manufacturing and process control systems reliably. Sensing applications improve asset management by continuous monitoring of critical equipment. Sensing applications reduce energy costs through optimized manufacturing processes. Sensing applications help identify inefficient operation or poorly performing equipment. Sensing applications help automate data acquisition from remote sensors to reduce user intervention. Sensing applications provide detailed data to improve preventive maintenance programs. Sensing applications help deploy monitoring networks to enhance employee and public safety. 5. CONCLUSION Sensor Networks hold a lot of promise in applications where gathering sensing information in remote locations is required. In the future, this wide range of application areas will make sensor networks an integral part of our lives. It is an evolving field, which offers scope for a lot of research. However, realization of sensor networks needs to satisfy the constraints introduced by factors such as fault tolerance, scalability, cost, hardware, topology change, environment and power consumption. Since these constraints are highly stringent and specific for sensor networks, new wireless ad hoc networking techniques are required. Moreover, unlike MANETS, sensor networks are designed, in general, for specific applications. In this paper, we identified some of the important design issues of routing protocols for sensor networks and also compared and contrasted the existing routing protocols. 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