Network Structure Based Protocols for Wireless ... - Semantic Scholar

IEEE International Conference on Advances in Engineering &Technology Research (ICAETR - 2014), August 01-02, 2014, Dr. Virendra Swarup Group of Institutions, Unnao, India

Network Structure Based Protocols for Wireless Sensor Networks Dr. Madhumita Panda Lecturer in Computer Science Sambalpur University,Jyoti Vihar, Burla [email protected]

Abstract—

The Wireless Sensor Network (WSN) is a wireless network consisting of ten to thousand small nodes with sensing, computing and wireless communication capabilities. WSN are generally used to monitor activities and report events, such as fire, overheating etc. in a specific area or environment. It routs data back to the Base Station (BS). Data transmission is usually a multi-hop from node to node towards the BS. Sensor nodes are limited in power, computational and communication bandwidth. Primary goal of researchers is to find the energy efficient routing protocol. This study highlights the recent routing protocols for sensor networks and presents a classification for the various approaches pursued. The three main categories explored in this paper are data-centric, hierarchical and location-based. Each routing protocol is described and discussed under the appropriate category with advantages and limitations. The paper concludes with issues open for research.

Prabira Kumar Sethy Lecturer in Electronics Sambalpur University, Jyoti Vihar, Burla [email protected]

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. 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. 1.1 Related Work

KEYWORDS Wireless Sensor Networks, Routing Protocols, Energy Efficient Protocols, Flat Routing protocols, Hierarchical Protocols and Location Based Protocols.

1.

Introduction

Wireless sensor networks are widely considered as one of the most important technologies. WSN has provided a small and low cost sensor node with the capability of sensing various types of environmental phenomena and wireless communication [1, 2].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 [3][4][5][6][7]. 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

Although there are some previous efforts for surveying the characteristics, applications, and communication protocols in WSNs [8, 9], the scope of the survey presented in this paper is distinguished from these surveys in many aspects. The surveys in [8] and [9] addressed several design issues and techniques for WSNs describing the physical constraints on sensor nodes, applications, architectural attributes, and the protocols proposed in all layers of the network stack. The goal of [10] is to make a comprehensive survey of design issues and techniques for sensor networks describing the physical constraints on sensor nodes and the protocols proposed in all layers of network stack. Possible applications of sensor networks are also discussed. That survey is a good introductory for readers interested in the broad area. Although a number of routing protocols for sensor networks are covered, the paper does not make a classification for such routing protocols and the list of discussed protocols is not meant to be complete given the scope of the survey. However, these surveys were not devoted to routing only. Due to the importance of routing in WSNs and the availability of a significant body of literature on this topic, a detailed survey becomes necessary and useful at this stage. Our work is a dedicated study of the network layer, describing and categorizing the different approaches for data routing. Researchers have been designed a number of different routing protocols. This paper discus about Network Structure Routing protocols for sensor networks listing some important protocols of each category and giving their advantages and disadvantages. The three main classes analyze in this paper are flat, hierarchical and location based routing

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IEEE International Conference on Advances in Engineering &Technology Research (ICAETR - 2014), August 01-02, 2014, Dr. Virendra Swarup Group of Institutions, Unnao, India protocols. Each protocol is depicted and covered under the appropriate category. The rest of the paper is structured as follows. Section 2 exhibits routing techniques in WSN. In the section 3, Network Structure Based protocols are described in detail. Section 4 Presents an analysis of Flat and Hierarchical Routing Protocols and Finally section 5 concludes the paper with future research directions on routing in WSNs.

based, negotiation-based, QoS-based, or coherent-based routing techniques depending on the protocol operation.

3. Network Structure Based Protocols. The underlying network structure can play significant role in the operation of the routing protocol in WSNs. In this section, we survey in details most of the protocols that fall below this category.

2. Routing Techniques in WSN The growing interest in WSN and the emergence of new architectural technique is the reason for studying of routing protocols. Routing protocols for wired networks and ad-hoc networks are not applicable to wireless sensor networks. It should be energy conserving, scalable, robust, fault tolerant and self-organizing. Based on the underlying network structure routing techniques are classified into three categories: flat, hierarchical and location based routing. Based on the protocol operation it can be classified into Negotiation based, Multi-path, Query based, QoS based and Coherent based routing. Routing Techniques classification is shown in the following figure1.

3.1 Flat Routing The first category of routing protocols is the multihop flat routing protocols. In flat networks, each node typically plays the same role and sensor nodes collaborate together to perform the sensing task. Due to the large number of such nodes, it is not feasible to assign a global identifier to each node. This consideration has led to data centric routing, where the BS sends queries to certain regions and waits for data from the sensors located in the selected regions. Since data is being requested through queries, attribute-based naming is necessary to specify the properties of data. Early works on data centric routing, e.g., SPIN and directed diffusion [11] was shown to save energy through data negotiation and elimination of redundant data. These two protocols motivated the design of many other protocols which follow a similar concept. In the rest of this subsection, we summarize these protocols and highlight their advantages and their performance issues. 

Fig. 1. Classification on Routing Techniques. In this section, we survey the state-of-the-art routing protocols for WSNs. In general, routing in WSNs can be divided into flat-based routing, hierarchic al-based routing, and location-based routing depending on the network structure. In flat-based routing, all nodes are typically assigned equal roles or functionality. In hierarchical-based routing, however, nodes will play different roles in the network. In location-based routing, sensor nodes' positions are exploited to route data in the network. Furthermore, these protocols can be classified into multipath-based, query-

TinyOS beaconing

The TinyOS embedded sensor network platform [12] employs a very simple ad-hoc routing protocol. The base station periodically broadcasts a route update beacon message to the network. The beacon message is received by a few nodes that are in the vicinity of the base station. These nodes mark the base station as their parent and rebroadcast the beacon to their neighbours. The algorithm proceeds recursively with nodes progressively propagating the beacon to their neighbours; each node marks the first node that it hears from as its parent. The beacon is thus flooded throughout the network, setting up a breadth-first spanning tree rooted at the base station. This process is repeated at periodic intervals known as epochs. Each network node periodically reads its sensor data and transmits the data packet to its parent in the spanning tree. The parent node in turn forwards the packet to its parent and so on. This process is repeated until the data finally reaches the base station. The attractive feature of TinyOS beaconing is its simplicity– nodes do not have to maintain large routing tables or other complicated data structures. Each node needs to remember only its parent node in the path to the base

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IEEE International Conference on Advances in Engineering &Technology Research (ICAETR - 2014), August 01-02, 2014, Dr. Virendra Swarup Group of Institutions, Unnao, India station. By combining the beaconing with a MAC layer scheduling scheme such as TDMA, the nodes can conserve power by keeping their radio off most of the time. In spite of its attractive features, the beaconing protocol suffers from one main disadvantage: it is not resilient to node failures. If a parent node fails, then its entire subtree is cut off from the base station during the current epoch. Moreover, the protocol results in uneven power consumption across network nodes. The nodes nearer to the base station consume a lot of power in forwarding packets from all the nodes in their subtree, whereas the leaf nodes in the spanning tree do not have to perform any forwarding at all and consume the least power.

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Sensor Protocols for Information via Negotiation:

Fig. 2: SPIN Protocol. Node A starts by advertising

(SPIN) [13]

is among the early work to pursue a data-centric routing mechanism. The idea behind SPIN is to name the data using high-level descriptors or metadata. Before transmission, meta-data are exchanged among sensors via a data advertisement mechanism, which is the key feature of SPIN. Each node upon receiving new data, advertises it to its neighbors and interested neighbors, i.e. those who do not have the data, 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 is no standard meta-data format and it is assumed to be application specific, e.g. using an application level framing. 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 and DATA message that carry the actual data. Fig. 2, redrawn from [13], summarizes the steps of the SPIN protocol. One of the advantages of SPIN is that topological changes are localized since each node needs to know only its single-hop neighbours. SPIN gives a factor of 3.5 less than flooding in terms of energy dissipation and meta-data negotiation almost halves the redundant data. However, SPIN’s data advertisement mechanism cannot guarantee the delivery of data. For instance, if the nodes that are interested in the data are far away from the source node and the nodes between source and destination are not interested in that data, such data will not be delivered to the destination at all. Therefore, SPIN is not a good choice for applications such as intrusion detection, which require reliable delivery of data packets over regular intervals.

its data to node B (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(ef). 

Directed diffusion

A data-centric communication protocol for sensor networks has been proposed in [1 4 ]. All sensor data are characterized by attribute-value pairs. A node that requires data sends out interests for named data; interests are diffused through the network towards the nodes that are capable of responding. Data are in turn drawn towards the requesting node via gradients established along the reverse path of interest propagation. This style of data-centric communication is fundamentally different from the node-centric endto-end communication mechanism of traditional IP networks. An interest for data may contain several fields such as type, interval, duration, time stamp and the coordinates of the target region. The duration refers to the time period for which data is desired, and the interval refers to the data rate. The sink broadcasts interests to its neighbours; due to the unreliable nature of broadcast networks, interests are refreshed periodically with updated timestamp values. The initial interest specifies a large interval value; when the path to the event source is established, a higher data rate is requested. Each node maintains an interest cache that contains several fields. O ne of the fields is called a gradient that specifies the node’s downstream neighbour. The gradients in each node are used to set up the reverse path for information flow from the source to the sink. A gradient also specifies the data rate requested by the neighbouring node. Whenever an interest is received, the node looks up its interest cache. If there is no matching entry in the cache, a new interest entry is created. If

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IEEE International Conference on Advances in Engineering &Technology Research (ICAETR - 2014), August 01-02, 2014, Dr. Virendra Swarup Group of Institutions, Unnao, India a matching entry exists already, its timestamp is updated. The node further broadcasts the interest to its neighbours, and thus the interest is flooded throughout the network, ultimately reaching the source. When the source node detects an event, it searches its interest cache for matching event entries. If a matching interest entry is found, the node starts relaying its readings at the highest requested data rate among all its outgoing gradients. Intermediate nodes that receive a data message from their neighbours also check their interest caches for matching entries. If no matching entry is found, the data packet is silently discarded. O therwise, the node searches its data cache associated with the matching interest entry. If there is no recently seen data item corresponding to the interest, a new entry is created and the data is forwarded to the neighbouring nodes; if the data is already present in the cache, the data packet is silently dropped. This mechanism helps in preventing the formation of loops in data dissemination. The sink may finally receive low-rate event data from several paths. It reinforces one of its neighbours to draw high-rate events. Reinforcement is done by sending out an interest with a higher data rate (smaller interval). The same procedure is adopted by all the upstream nodes to reinforce one or more paths that deliver high-quality event data. This finally results in an empirically lowdelay path between the source and the sink. In case multiple paths are created and some paths are found to perform consistently better, an option is available to negatively reinforce the other paths. The reinforcement rules can also be applied by intermediate nodes along previously reinforced paths to enable local repair of failed or degraded paths. Figure 3 illustrates the working of the directed diffusion algorithm. Directed diffusion has been a pioneering work in the area of data-centric routing in sensor networks. It has introduced several new features such as path reinforcement, caching and in-network data aggregation. There is adequate scope for further research in each of these areas. Several routing protocols such as rumor routing [1 5 ] and highly resilient multipath routing [1 6 ] inspiration from directed diffusion.

have

drawn

Fig 3. Directed Diffusion.

 COUGAR: Another data-centric protocol called COUGAR [17] views the network as a huge distributed database system. The key idea is to use declarative queries in order to abstract query pro-cessing from the network layer functions such as selection of relevant sensors and so on. COUGAR utilizes in-network data aggregation to obtain more energy savings. The abstraction is supported through an additional query layer that lies between the network and application layers. COUGAR incorporates architecture for the sensor database system where sensor nodes select a leader node to perform aggregation and transmit the data to the BS. The architecture is depicted in Fig.4, which is redrawn from [18].The BS is responsible for generating a query plan, which specifies the necessary information about the data flow and innetwork computation for the incoming query and send it to the relevant nodes. The query plan also describes how to select a leader for the query. The architecture provides in-network computation ability that can provide energy efficiency in situations when the generated data is huge. COUGAR provided network-layer independent methods for data query. However, COUGAR has some drawbacks. First, the addition of query layer on each sensor node may add an extra overhead in terms of energy consumption and memory storage. Second, to obtain successful in-network data computation, synchronization among nodes is required (not all data are received at the same time from incoming sources) before sending the data to the leader node. Third, the leader nodes should be dynamically maintained to prevent them from being hotspots (failure prone).

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IEEE International Conference on Advances in Engineering &Technology Research (ICAETR - 2014), August 01-02, 2014, Dr. Virendra Swarup Group of Institutions, Unnao, India

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Fig. 4: Query plan at a leader node: The leader node gets all the readings, calculates the average and if it is greater than a threshold sends it to the gateway (sink). 

Energy Aware Routing:

The objective of energy-aware routing protocol [19], a destination initiated reactive protocol, is to increase the network lifetime. Although this protocol is similar to directed diffusion, it differs in the sense that it maintains a set of paths instead of maintaining or enforcing one optimal path at higher rates. These paths are maintained and chosen by means of a certain probability. The value of this probability depends on how low the energy consumption of each path can be achieved. By having paths chosen at different times, the energy of any single path will not deplete quickly. This can achieve longer network lifetime as energy is dissipated more equally among all nodes. Network survivability is the main metric of this protocol. The protocol assumes that each node is addressable through a class-based addressing which includes the location and types of the nodes. The protocol initiates a connection through localized flooding, which is used to discover all routes between source/destination pair and their costs; thus building up the routing tables. The high-cost paths are discarded and a forwarding table is built by choosing neighboring nodes in a manner that is proportional to their cost. Then, forwarding tables are used to send data to the destination with a probability that is inversely proportional to the node cost. Localized flooding is performed by the destination node to keep the paths alive. When compared to directed diffusion, this protocol provides an overall improvement of 21.5% energy saving and a 44% increase in network lifetime. However, the approach requires gathering the location information and setting up the addressing mechanism for the nodes, which complicate route setup compared to the directed diffusion.

Rumor routing:

Rumor routing [20] is another variation of Directed Diffusion and is mainly intended for contexts in which geographic routing criteria are not applicable. Generally Directed Diffusion floods the query to the entire network when there is no geographic criterion to diffuse tasks. However, in some cases there is only a little amount of data requested from the nodes and thus the use of flooding is unnecessary. An alternative approach is to flood the events if number of events is small and number of queries is large. Rumor routing is between event flooding and query flooding. The idea is to route the queries to the nodes that have observed a particular event rather than flooding the entire network to retrieve information about the occurring events. In order to flood events through the network, the rumor routing algorithm employs long lived packets, called agents. When a node detects an event, it adds such event to its local table and generates an agent. Agents travel the network in order to propagate information about local events to distant nodes. When a node generates a query for an event, the nodes that know the route, can respond to the query by referring its event table. Hence, the cost of flooding the whole network is avoided. Rumor routing maintains only one path between source and destination as opposed to Directed Diffusion where data can be sent through multiple paths at low rates. Simulation results have shown that rumor routing achieves significant energy saving over event flooding and can also handle node’s failure. However, rumor routing performs well only when the number of events is small. For large number of events, the cost of maintaining agents and event-tables in each node may not be amortized if there is not enough interest on those events from the sink. Another issue to deal with is tuning the overhead through adjusting parameters used in the algorithm such as time-to-live for queries and agents.

3.2 Hierarchical and cluster-based routing protocols Hierarchical routing protocols organize the network into groups called clusters as shown in Figure 5 below.

Fig.5 Hierarchical Protocol.

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IEEE International Conference on Advances in Engineering &Technology Research (ICAETR - 2014), August 01-02, 2014, Dr. Virendra Swarup Group of Institutions, Unnao, India Each cluster selects a node that serves as the clusterhead. The cluster-head is responsible for collecting the sensor data from all the cluster members, aggregating them and transmitting a summary to the base station. This results eliminating a large number of redundant messages from the nodes, thereby reducing the overall power consumption in the network. It also avoids many MAC layer collisions that waste the available bandwidth. This enables the sensor network to scale to a large number of nodes. The disadvantage of cluster-based algorithms is that the base station should be reachable from all the cluster-heads. This drains the power reserves of the cluster-heads quickly, thereby disconnecting the corresponding clusters from the network. It is possible to avoid this problem by periodically rotating the cluster heads among the nodes to ensure uniform energy consumption. LEACH [21] is one of the first hierarchical routing approaches for sensors networks. The idea proposed in LEACH has been an inspiration for many hierarchical routing protocols [22][23][24][25], although some protocols have been independently developed [26][27]. We explore hierarchical routing protocols in this section. 

LEACH:

Low-Energy Adaptive Clustering Hierarchy (LEACH) [21] 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. 

PEGASIS & Hierarchical-PEGASIS( PowerEfficient Gathering In Sensor Information Systems)

(PEGASIS) [23] 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. As shown in Fig. 6 node c0 passes its data to node c1. Node c1 aggregates node c0’s data with its own and then transmits to the leader. After node c2 passes the token to node c4, node c4 transmits its data to node c3. Node c3 aggregates node c4’s data with its own and then transmits to the leader. Node c2 waits to receive data from both neighbors and then aggregates its data with its neighbors’ data. Finally, node c2 transmits one message to the base station.

Fig 6. Chaining in PEGASIS. The difference from LEACH is to use multi-hop routing by forming chains and selecting only one node to transmit to the base station instead of using multiple nodes. PEGASIS has been shown to outperform LEACH by about 100 to 300% for different network sizes and topologies. Such performance gain is achieved through the elimination of the overhead caused by dynamic cluster formation in LEACH and through decreasing the number of transmissions and reception by using data aggregation. However, PEGASIS introduces excessive delay for distant node on the chain. In addition the single leader can become a bottleneck. Hierarchical-PEGASIS [24] is an extension to PEGASIS, which aims at decreasing the delay incurred for packets during transmission to the base station and proposes a solution to the data gathering problem by considering energy delay metric. In order to reduce the delay in PEGASIS, simultaneous transmissions of data messages are pursued. To avoid collisions and possible signal interference among the sensors, two approaches have been investigated. The first approach incorporates signal coding, e.g. CDMA. In the second

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IEEE International Conference on Advances in Engineering &Technology Research (ICAETR - 2014), August 01-02, 2014, Dr. Virendra Swarup Group of Institutions, Unnao, India approach only spatially separated nodes are allowed to transmit at the same time. The chain-based protocol with CDMA capable nodes, constructs a chain of nodes, that forms a tree like hierarchy, and each selected node in a particular level transmits data to the node in the upper level of the hierarchy. This method ensures data transmitting in parallel and reduces the delay significantly. Since the tree is balanced, the delay will be in O(lg N) where N is the number of nodes. For instance, in Fig.7 redrawn from [24], node c3 is the designated leader for round 3. Since, node c3 is in position 3 (counting from 0) on the chain, all nodes in an even position will send to their right neighbor. Nodes that are receiving at each level rise to next level in the hierarchy.

Fig 7.Data gathering in a chain based Binary Scheme. Now at the next level, node c3 is still in an odd position (1). Again all nodes in an even position will aggregate its data with its received data and send to their right. At the third level, node c3 is not in an odd position, so node c7 will aggregate its data and transmit to c3. Finally, node c3 will combine its current data with that received from c7 and transmit the message to the sink.The non-CDMA based approach creates a three-level hierarchy of the nodes and interference effects is reduced by carefully scheduling simultaneous transmissions. Such chain-based protocol has been shown to perform better than the regular PEGASIS scheme by a factor of about 60. Although the PEGASIS approaches avoid the clustering overhead of LEACH, they still require dynamic topology adjustment since sensor’s energy is not tracked. For example, every sensor needs to be aware of the status of its neighbor so that it knows where to route that data.Such topology adjustment can introduce significant overhead especially for highly utilized networks.

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Threshold-sensitive Energy Efficient Protocols (TEEN and APTEEN):

Two hierarchical routing protocols called TEEN (Threshold-sensitive Energy Efficient sensor Network protocol), and APTEEN (Adaptive Periodic Threshold-sensitive Energy Efficient sensor Network protocol) are proposed in [28] and [29], respectively. These protocols were proposed for time-critical

applications. TEEN is a hierarchical clustering protocol, which groups sensors into clusters with each led by a CH. The sensors within a cluster report their sensed data to their CH. The CH sends aggregated data to higher level CH until the data reaches the sink. Thus, the sensor network architecture in TEEN is based on a hierarchical grouping where closer nodes form clusters and this process goes on the second level until the BS (sink) is reached. TEEN uses a data-centric method with hierarchical approach. The main features of this protocol are as follows: 

Time critical data reaches the user almost instantaneously.



The soft threshold can be varied, depending on the criticality of the sensed attribute and the target application.



A smaller value of the soft threshold gives a more accurate picture of the network, at the expense of increased energy consumption.



At every cluster change time, the attributes are broadcast afresh and so, the user can change them as required

Adaptive Threshold Sensitive Energy Efficient Sensor Network Protocol (APTEEN): APTEEN aims at both capturing periodic data collections (LEACH) and reacting to time-critical events (TEEN). Thus, APTEEN is a hybrid clustering-based routing protocol that allows the sensor to send their sensed data periodically and react to any sudden change in the value of the sensed attribute by reporting the corresponding values to their CHs. CHs also perform data aggregation in order to save energy. APTEEN supports three different query types namely (i) historical query, to analyze past data values (ii) one-time query, to take a snapshot view of the network (iii) persistent queries, to monitor an event for a period of time. Energy dissipation will be lower and a large number of sensors alive in APTEEN.

3.3 Location based routing protocols In this kind 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 coordinates of neighboring nodes can be obtained by exchanging such information between neighbors [30], [31], [32].Alternatively, the location of nodes may be available directly by communicating with a satellite, using GPS (Global Positioning System), if nodes are equipped with a small low power GPS receiver [33]. To save energy, some location based schemes demand that nodes should go to sleep if there is no activity. More energy savings can be obtained by having as many sleeping nodes in the

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IEEE International Conference on Advances in Engineering &Technology Research (ICAETR - 2014), August 01-02, 2014, Dr. Virendra Swarup Group of Institutions, Unnao, India network as possible. The problem of designing sleep period schedules for each node in a localized manner was addressed in [34, 33]. In the rest of this section, we review most of the location or geographic based routing protocols.  Geographic Adaptive Fidelity (GAF): GAF [33] is an energy-aware location-based routing algorithm designed primarily for mobile ad hoc networks, but may be applicable to sensor networks as well. GAF works in such a way that, it turns off unnecessary nodes in the network without affecting the level of routing fidelity, this conserves energy. A virtual grid for the area that is to be covered is formed. The cost of packet routing is considered equivalent for nodes associated with the same point on the virtual grid. Such equivalence is exploited in keeping some nodes located in a particular grid area in sleeping state in order to save energy. By doing this the network lifetime is increased as the number of nodes increases. There are three states in this protocol and they are discovery, for determining the neighbors in the grid, active tells that the nodes are participating in routing and sleep when the radio is turned off. The load is balanced when nodes change states from sleeping to active in turns.GAF keeps the network connected, by keeping a representative node always in active node for each region on its virtual grid. Although GAF is a location based protocol, it can be considered as a hierarchical protocol, where the clusters are based on geographic location. 

MFR, DIR, and GEDIR:

Stojmenovic and Lin [35] described and discussed basic localized routing algorithms. These protocols deal with basic distance, progress, and direction based methods. The key issues are forward direction and backward direction. A source node or any intermediate node will select one of its neighbors according to a certain criterion. The routing methods, which belong to this category, are MFR (Most Forward within Radius), GEDIR (The Geographic Distance Routing) that is a variant of greedy algorithms, 2-hop greedy method, alternate greedy method and DIR (compass routing method). GEDIR algorithm is a greedy algorithm that always moves the packet to the neighbor of the current vertex whose distance to the destination is minimized. The algorithm fails when the packet crosses the same edge twice in succession. In most cases, the MFR and Greedy methods have the same path to destination. In the DIR method, the best neighbor has the closest direction (that is, angle) toward the destination. That is, the neighbor with the minimum angular distance from the imaginary line joining the current node and the destination is selected. In MFR method, the best neighbour A will minimize the dot product ̅̅̅̅.̅̅̅̅, where S,D are the source and destination nodes,

respectively, and ̅̅̅̅ represents the Euclidian distance between the two nodes S;D. Alternatively, one can maximize the dot product ̅̅̅̅.̅̅̅̅. Each method stops forwarding the message at a node for which the best choice is to return the message back to a previous node. GEDIR and MFR methods are loop-free, while DIR method may create loops, unless past traffic is memorized or a time-stamp is enforced [35]. A comparison study [35] between these algorithms showed that the three basic algorithms had comparable performance in terms of delivery rate and average dilation. Moreover, simulations revealed that the nodes in MFR and Greedy methods select the same forwarding neighbor in more than 99% cases and the entire selected paths were identical in most of the cases.

4. ANALYSIS Flat routing is simple protocol but it suffers large amount of control packet overhead and lack of scalability. In recent year, researches are moved to Hierarchical routing. Compared with all routing protocols in WSN, Hierarchical routing protocols has many advantages like more scalability, consume less energy and more robustness [36]. This section summarizes the advantages of hierarchical routing.

1) More Scalability Scalability refers to the performance of communicating system won’t be degraded when number of nodes will increases. In WSN, there may be thousand of nodes. It can be achieved by localize the interaction among the communicating nodes, which can be done through hierarchical routing. Compare to flat routing it can be easily manageable.

2) Less Energy In hierarchical routing Cluster head performs data aggregation and data transmission. This will lead save great deal of energy compare to flat and location based routing. In addition to that clustering with inter and intra cluster communication reduces the node to communicate with node present in long distance. This will also help to consume less amount of energy.

3) More Robustness The topology of a WSN may change due to alternative state of sensor node from sleep node to active node. Hierarchal routing is highly suitable for topology control and network management. It is work well in large-scale scenario compared to flat routing which is always reactive or proactive. Moreover Hierarchical routing is reservation-based, collisions avoided, Fair channel allocation, Reduced duty cycle due to periodic sleeping of node, simple but not a optimal routing and energy dissipation is uniform. It has two main drawbacks. First overhead of cluster head formation throughout the network and second it require global and local synchronization.

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IEEE International Conference on Advances in Engineering &Technology Research (ICAETR - 2014), August 01-02, 2014, Dr. Virendra Swarup Group of Institutions, Unnao, India

5. CONCLUSION WSN is most emerging ubiquitous computing technology which can be employed in wide spectrum of application in both civilian and military scenarios [1]. Wireless Sensor Network technology extends numerous application domains and it is crucial that WSNs perform in reliable and robust manner. One of the major issues in the design of routing protocol for WSN is energy efficiency due to limited energy resources of sensors. This paper survey several different routing strategies for wireless sensor network. Therefore routing protocols designed for WSN should be energy efficient as possible to prolong the life time of individual sensors. Other possible future research for routing protocols includes the integration of sensor networks with wired networks (i.e. Internet). Most of the applications in security and environmental monitoring require the data collected from the sensor nodes to be transmitted to a server so that further analysis can be done. On the other hand, the requests from the user should be made to the sink through Internet. Since the routing requirements of each environment are different, further research is necessary for handling these kinds of situations. Another interesting is the consideration of node mobility. Most of the current protocols assume that the sensor nodes and the sink are stationary. However, there might be situations such as battle environments where the sink and possibly the sensors need to be mobile. In such cases, the frequent update of the position of the command node and the sensor nodes and the propagation of that information through the network may excessively drain the energy of nodes. New routing algorithms are needed in order to handle the overhead of mobility and topology changes in such energy constrained environment.

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