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Routing Techniques In Wireless Sensor Networks Information Technology Essay

WSNs usually contain a large number of nodes typically with highly correlated collected data. Wireless sensor networks are formed by small sensor nodes communicating over wireless links without using a fixed networked infrastructure. Each sensor node comprises sensing, processing, transmission, mobilizer, position finding system, and power units (some of these components are optional, like the mobilizer) as shown in fig. 1, [1].

The rapid deployment, self-organization, and fault-tolerance characteristics of WSNs because of which WSNs has various profound effects on military and civil applications such as target field imaging, intrusion detection, weather monitoring, security and tactical surveillance, distributed computing, detecting ambient conditions such as temperature, movement, sound, light or the presence of certain objects, inventory control, and disaster management. Deployment of a sensor network in these applications can be in random fashion (e.g. dropped from an airplane in a disaster management application) or manual (e.g., fire alarm sensors in a facility or sensors planted underground for precision agriculture). The sensor nodes are scattered in field in which every node has capability to collect the data or route the data to another sensor node which may be act as Base Station (BS). BS can be connected to a communication channel or Internet from which user can access the data.

Challenges to WSNs includes limited functional capabilities, including problems of size, Power factors, Node costs, Environmental factors, Transmission channel factors, Topology management complexity and node distribution [2], Scalability concerns [3]. Power consumption and Bandwidth are two main constrains which effect the life time of WSNs. We need to apply various routing technique which can use power and bandwidth efficiently so as to improve the network lifetime. So in this article we survey energy efficient routing technique of WSNs. [4] gave the various routing protocols and we further add energy efficient protocols into it.

II. ENERGY EFFICIENT ROUTING PROTOCOLS IN WIRELESS SENSOR NETWORK

In this section we survey the routing protocols for WSNs. In general, routing in WSNs can be divided into flat-based routing, hierarchical-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, nodes will play different roles in the network. In location-based routing, sensor nodes’ positions are exploited to route data in the network. A routing protocol is considered adaptive if certain system parameters can be controlled in order to adapt to current network conditions and available energy levels.

Flat Routing — the first category of routing protocols are the multihop flat routing protocols. In flat networks, each node typically plays the same role and sensor nodes collaborate 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 [5]) were shown to save energy through data negotiation and elimination of redundant data. These two protocols motivated the design of many other protocols that follow a similar concept. In the rest of this subsection, we summarize these protocols, and highlight their advantages and performance issues.

Sensor Protocols for Information via Negotiation: Heinzelman et al. in [6, 7] proposed a family of adaptive protocols called Sensor Protocols for Information via Negotiation (SPIN) that disseminate all the information at each node to every node in the network assuming that all nodes in the network are potential BSs. This enables a user to query any node and get the required information immediately. These protocols make use of the property that nodes in close proximity have similar data, and hence there is a need to only distribute the data other nodes do not posses. The SPIN family of protocols uses data negotiation and resource-adaptive algorithms bases on metadata.

The SPIN family is designed to address the deficiencies of classic flooding by negotiation and resource adaptation, thus achieving a lot of energy efficiency. SPIN is a three-stage protocol as sensor nodes use three types of messages, ADV, REQ, and DATA, to communicate. ADV is used to advertise new data, REQ to request data, and DATA is the actual message itself. The protocol starts when a SPIN node obtains new data it is willing to share. It does so by broadcasting an ADV message containing metadata. If a neighbor is interested in the data, it sends a REQ message for the DATA and the DATA is sent to this neighbor node. The neighbour sensor node then repeats this process with its neighbors. As a result, the entire sensor area will receive a copy of the data.

Advantages of SPIN are that topological changes are localized since each node need know only its single-hop neighbors. Sensor nodes operate more efficiently and conserve energy by sending data that describe the sensor data instead of sending all the data. Metadata negotiation almost halves the redundant data.

However, SPIN’s data advertisement mechanism cannot guarantee delivery of data. To see this, consider the application of intrusion detection where data should be reliably reported over periodic intervals, and assume that nodes interested in the data are located far away from the source node, and the nodes between source and destination nodes are not interested in that data; such data will not be delivered to the destination at all.

Directed diffusion: In [9], C. Intanagonwiwat et al. proposed a popular data aggregation paradigm for WSNs called directed diffusion. Directed diffusion is a data-centric (DC) and application-aware paradigm in the sense that all data generated by sensor nodes is named by attribute-value pairs. The main idea of the DC paradigm is to combine the data coming from different sources en route (in-network aggregation) by eliminating redundancy, minimizing the number of transmissions, thus saving network energy and prolonging its lifetime. Unlike traditional end-to-end routing, DC routing finds routes from multiple sources to a single destination that allows in-network consolidation of redundant data.

In directed diffusion, sensors measure events and create gradients of information in their respective neighborhoods. The BS requests data by broadcasting interests. An interest describes a task required to be done by the network. An interest diffuses through the network hop by hop, and is broadcast by each node to its neighbors. As the interest is propagated throughout the network, gradients are set up to draw data satisfying the query toward the requesting node (i.e., a BS may query for data by disseminating interests and intermediate nodes propagate these interests). Each sensor that receives the interest sets up a gradient toward the sensor nodes from which it receives the interest. This process continues until gradients are set up from the sources back to the BS. More generally, a gradient specifies an attribute value and a direction. The strength of the gradient may be different toward different neighbors, resulting in different amounts of information flow. At this stage, loops are not checked, but are removed at a later stage. Fig. 3 shows an example of the working of directed diffusion (sending interests, building gradients, and data dissemination).

Fig. 3 An example of interest diffusion in a sensor network.

When interests fit gradients, paths of information flow are formed from multiple paths, and then the best paths are reinforced to prevent further flooding according to a local rule. In order to reduce communication costs, data is aggregated on the way. The goal is to find a good aggregation tree that gets the data from source nodes to the BS. The BS periodically refreshes and resends the interest when it starts to receive data from the source(s). This is necessary because interests are not reliably transmitted throughout the network.

All sensor nodes in a directed-diffusion-based network are application-aware, which enables diffusion to achieve energy savings by selecting empirically good paths, and by caching and processing data in the network. Caching can increase the efficiency, robustness, and scalability of coordination between sensor nodes, which is the essence of the data diffusion paradigm. The performance of data aggregation methods used in the directed diffusion paradigm is affected by a number of factors, including the positions of the source nodes in the network, the number of sources, and the communication network topology.

Directed diffusion differs from SPIN in two aspects. First, directed diffusion issues data queries on demand as the BS sends queries to the sensor nodes by flooding some tasks. In SPIN, however, sensors advertise the availability of data, allowing interested nodes to query that data. Second, all communication in directed diffusion is neighbor to neighbor with each node having the capability to perform data aggregation and caching. Unlike SPIN, there is no need to maintain global network topology in directed diffusion. However, directed diffusion may not be applied to applications (e.g., environmental monitoring) that require continuous data delivery to the BS. This is because the query-driven on-demand data model may not help in this regard.

REEP (Reliable and energy efficient protocol) --F. Zabin et al. [8] propose a new energy-aware WSN routing protocol, reliable and energy efficient protocol (REEP), which is proposed, makes sensor nodes establish more reliable and energy-efficient paths for data transmission. The performance of REEP has been evaluated under different scenarios, and has been found to be superior to the popular data-centric routing protocol, directed-diffusion. REEP differs from the other data-centric routing protocols in sensor networking in the following respects. First, REEP is an interactive on-demand protocol, in which path establishment can be done based on the choice of any user or an application. Secondly, each node maintains an energy threshold value and participates in path setup with adequate energy for data transmission. Finally, the request priority queue is used for loop prevention and alternate path setup in case of failed path, without invoking periodic flooding.

Both REEP and DD have some similarities and differences between them. Similarities include the following: both of these two protocols are application-aware and diffusion based; use the same naming scheme, the data-centric approach and negative events in case of path failure; construct single path for data transmission based on the local interactions and operate mainly in three steps until the path has been established.

Differences include the following: paths in DD are created according to their interest where, as in REEP, paths are created after selecting a task. This is also energy-efficient, because in case a large number of sources report several events towards the sink node, it does not send request for all events. Instead, requests are sent only for those events that are requested by the application. DD uses a gradient setup technique by flooding the exploratory events, which incurs more energy consumption, whereas in REEP, the gradient setup technique is not required.

Constrained anisotropic diffusion routing (CADR):

Two routing techniques, information-driven sensor querying (IDSQ) and constrained anisotropic diffusion routing (CADR), were proposed in [10]. CADR aims to be a general form of directed diffusion. The key idea is to query sensors and route data in the network such that information gain is maximized while latency and bandwidth are minimized. CADR diffuses queries by using a set of information criteria to select which sensors can get the data. This is achieved by activating only the sensors that are close to a particular event and dynamically adjusting data routes.

The main difference from directed diffusion is the consideration of information gain in addition to 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. Estimation theory was used to model information utility. In IDSQ, the querying node can determine which node can provide the most useful information with the additional advantage of balancing the energy cost. Simulation results showed that these approaches are more energy-efficient than directed diffusion where queries are diffused in an isotropic fashion and reach nearest neighbors first.

COUGAR: Another data-centric protocol called COUGAR [11] views the network as a huge distributed database system. The key idea is to use declarative queries in order to abstract query processing 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 BS is responsible for generating a query plan that specifies the necessary information about the data flow and in-network computation for the incoming query, and sends 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 provides a network-layer-independent method for data query. However, COUGAR has some drawbacks. First, the addition of a query layer on each sensor node may add 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).

EADD (Energy Aware Directed Diffusion)--The most important element of Wireless Sensor Networks technologies is energy efficiency. Directed Diffusion is one of the energy efficient routing protocols. It selects one reinforced path from source to destination and then forward data packets through that route instead of forwarding broadcast packets. But it doesn’t worry about available energy of each sensor nodes. Sensor nodes have not enough energy. It allows nodes stop more quickly as using only the fastest path. It cause unbalanced life cycle of the nodes. Consequently, we need to consider available energy of sensor nodes. J. Choe and K. Kim [34] Propose EADD (Energy Aware Directed Diffusion) for Wireless Sensor Networks. This scheme changes the node’s forwarding moment that depends on each node’s available energy. EADD allows the nodes to response more quickly than the nodes which have lower avail ble energy. This scheme is very simple so that it can be adapted to any forwarding strategies for routing protocols of wireless sensor networks. EADD is helpful to achieve balanced nodes energy distribution and extension of network life cycle. EADD solves the protocol problems of DD.

Hierarchical Routing

Hierarchical routing is an efficient way to lower energy consumption within a cluster, performing data aggregation and fusion in order to decrease the number of transmitted messages to the BS. Hierarchical routing is mainly two-layer routing where one layer is used to select cluster heads and the other for routing.

LEACH protocol: Heinzelman, et al. [12] introduced a hierarchical clustering algorithm for sensor networks, called Low Energy Adaptive Clustering Hierarchy (LEACH). LEACH is a cluster-based protocol, which includes distributed cluster formation. LEACH randomly selects a few sensor nodes as cluster heads (CHs) and rotates this role to evenly distribute the energy load among the sensors in the network. In LEACH, the CH nodes compress data arriving from nodes that belong to the respective cluster, and send an aggregated packet to the BS in order to reduce the amount of information that must be transmitted to the BS. The operation of LEACH is separated into two phases, the setup phase and the steady state phase. In the setup phase, the clusters are organized and CHs are selected. In the steady state phase, the actual data transfer to the BS takes place. The duration of the steady state phase is longer than the duration of the setup phase in order to minimize overhead. During the setup phase, a predetermined fraction of nodes, p, elect themselves as CHs as follows. A sensor node chooses a random number, r, between 0 and 1. If this random number is less than a threshold value, T (n), the node becomes a CH for the current round. The threshold value is calculated based on an equation that incorporates the desired percentage to become a CH, the current round, and the set of nodes that have not been selected as a CH in the last (1/P) rounds, denoted G. It is given by

where G is the set of nodes that are involved in the CH election. All elected CHs broadcast an advertisement message to the rest of the nodes in the network that they are the new CHs.

Although LEACH is able to increase the network lifetime, there are still a number of issues about the assumptions used in this protocol, the protocol assumes that all nodes begin with the same amount of energy capacity in each election round, assuming that being a CH consumes approximately the same amount of energy for each node. The protocol should be extended to account for non-uniform energy nodes (i.e., use an energy-based threshold). An extension to LEACH, LEACH with negotiation, was pro-posed in [12]. The main theme of the proposed extension is to precede data transfers with high level negotiation using meta-data descriptors as in the SPIN protocol discussed earlier. This ensures that only data that provides new information is transmitted to the CHs before being transmitted to the BS. Table 1 compares SPIN, LEACH, and directed diffusion according to different parameters. It is noted from the table that directed diffusion shows a promising approach for energy-efficient routing in WSNs due to the use of in-network processing.

TABLE 1 COMPARISON BETWEEN SPIN, LEACH AND DIRECTED DIFFUSION.

Parameter

SPIN

LEACH

Directed diffusion

Optimal route

No

No

Yes

Network lifetime

Good

Very Good

Good

Resource awareness

Yes

Yes

Yes

Use of metadata

Yes

No

Yes

Power-Efficient Gathering in Sensor Information Systems (PEGASIS): S. Lindsey in [13], an enhancement over the LEACH protocol was proposed. The protocol, called PEGASIS, is a near optimal chain-based protocol. The basic idea of the protocol is that in order to extend network lifetime, nodes need only communicate with their closest neighbors, and they take turns in communicating with the BS. When the round of all nodes communicating with the BS ends, a new round starts, and so on. This reduces the power required to transmit data per round as the power draining is spread uniformly over all nodes.

Hence, PEGASIS has two main objectives. First, increase the lifetime of each node by using collaborative techniques. Second, allow only local coordination between nodes that are close together so that the bandwidth consumed in communication is reduced. Unlike LEACH, PEGASIS avoids cluster formation and uses only one node in a chain to transmit to the BS instead of multiple nodes.

Simulation results showed that PEGASIS is able to increase the lifetime of the network to twice that under the LEACH protocol. Such performance gain is achieved through the elimination of the overhead caused by dynamic cluster formation in LEACH, and decreasing the number of transmissions and reception by using data aggregation. Although the clustering overhead is avoided, PEGASIS still requires dynamic topology adjustment since a sensor node needs to know about the energy status of its neighbors in order to know where to route its data. Such topology adjustment can introduce significant overhead, especially for highly utilized networks. Moreover, PEGASIS assumes that each sensor node is able to communicate with the BS directly. In practical cases, sensor nodes use multihop communication to reach the BS. Also, PEGASIS assumes that all nodes maintain a complete database of the location of all other nodes in the network. The method by which the node locations are obtained is not outlined. In addition, PEGASIS assumes that all sensor nodes have the same level of energy and are likely to die at the same time. Note also that PEGASIS introduces excessive delay for distant nodes on the chain. In addition, the single leader can become a bottleneck. Finally, although in most scenarios sensors will be fixed or immobile as assumed in PEGASIS, some sensors may be allowed to move and hence affect the protocol functionality.

Threshold-Sensitive Energy Efficient Protocols:

Two hierarchical routing protocols called Threshold-Sensitive Energy Efficient Sensor Network Protocol (TEEN) and Adaptive Periodic TEEN (APTEEN) are proposed in [15, 16]. These protocols were proposed for time-critical applications. In TEEN, sensor nodes sense the medium continuously, but data transmission is done less frequently.

Important features of TEEN include its suitability for time-critical sensing applications. Also, since message transmission consumes more energy than data sensing, the energy consumption in this scheme is less than in proactive networks.

APTEEN, on the other hand, is a hybrid protocol that changes the periodicity or threshold values used in the TEEN protocol according to user needs and the application type. Simulation of TEEN and APTEEN has shown that these two protocols outperform LEACH. The experiments have demonstrated that APTEEN’s performance is somewhere between LEACH and TEEN in terms of energy dissipation and network lifetime. TEEN gives the best performance since it decreases the number of transmissions. The main drawbacks of the two approaches are the overhead and complexity associated with forming clusters at multiple levels, the method of implementing threshold-based functions, and how to deal with attribute-based naming of queries.

SPEAR (Sensor Protocol for Energy Aware requirements) -- Vaidehi.V et al. [35] propose a hierarchical clustering protocol SPEAR, that presents an adaptive and conceptually novel paradigm, for the election of cluster heads based on energy as well as spatial distribution Due to its heterogeneous aware nature, SPEAR yields longer stability periods and consequently a higher average throughput and longer network lifetime compared to heterogeneous oblivious such as LEACH.

In [35] SPEAR protocol, propose a similar clustering scheme. However the cluster head election process is made energy aware leading to scalability in terms of node heterogeneity. Further the protocol also ensures a uniform distribution of cluster heads in the deployment area as cluster head election is based on a threshold distance. The protocol maintains a minimum threshold distance between any two cluster head nodes leading to a uniform energy load distribution among the nodes. The network model assumptions made are same as in LEACH protocol architecture [6], [12].

Among the available hierarchical routing protocols, LEACH shows significant performance improvements in terms of network lifetime and throughput due to energy load distribution among the nodes achieved by a scheme called dynamic clustering. The LEACH protocol [12] however does not scale well in a heterogeneous condition due to the random cluster head election scheme. Further simulations indicate that the CHs (cluster head) are not uniformly distributed throughout the deployment region due to a stochastic election of CHs.

NSEEAR (Network Stability Enhancement by Energy Aware Routing): W.Y. Leong [33] propose NSEEAR, an adaptive routing protocol which leads to a better energy utilization of the network to prolong its stability period (the time interval before the death of the first node) and the significant network lifetime (the time interval before half of the network is dead) which is crucial for many sensing applications in which the feedback from the network should be reliable. Aggregation of data is done at all nodes so as to maintain the size of data packet small but still have the effective content. NSEEAR organizes the network into concentric tiers around the base station and routes aggregated data packets by forwarding them from one tier to another in the direction of the base station. Relaying nodes are elected by considering their distance from the transmitting node and the base station, as well as their residual energy.

NSEEAR does not adopt the usual cluster formation. Instead, the network is organized into concentric tiers around the sink. The transmission from the nodes to the sink takes place by Direct Transmission as well as multi-hopping, depending on the nodes residual energy. [33] Compare the results with LEACH and show substantial improvements in network stability period and significant network lifetime.

Small minimum energy communication network (MECN): In [17], a protocol is proposed that computes an energy-efficient subnetwork, the minimum energy communication network (MECN), for a certain sensor network utilizing low-power GPS. MECN identifies a relay region for every node. The relay region consists of nodes in a surrounding area where transmitting through those nodes is more energy-efficient than direct transmission. The main idea of MECN is to find a subnetwork that will have fewer nodes and require less power for transmission between any two particular nodes. In this way, global minimum power paths are found without considering all the nodes in the network. This is performed using a localized search for each node considering its relay region. MECN is self- reconfiguring and thus can dynamically adapt to node failure or the deployment of new sensors. The small MECN (SMECN) [18] is an extension to MECN. In MECN, it is assumed that every node can transmit to every other node, which is not possible every time. SMECN helps in sending messages on minimum- energy paths. Moreover, the subnetwork constructed by SMECN makes it more likely that the path used is one that requires less energy consumption. In addition, finding a subnetwork with a smaller number of edges introduces more overhead in the algorithm.

Two-Tier Data Dissemination: An approach in [19], called Two-Tier Data Dissemination (TTDD), provides data delivery to multiple mobile BS. In TTDD, sensor nodes are stationary and location-aware, whereas sinks may change their locations dynamically. Although TTDD is an efficient routing approach, there are some concerns about how the algorithm obtains location information, which is required to set up the grid structure. The length of a forwarding path in TTDD is larger than the length of the shortest path. Comparison results between TTDD and directed diffusion showed that TTDD can achieve longer lifetimes and shorter data delivery delays. However, the overhead associated with maintaining and recalculating the grid as network topology changes may be high. Furthermore, TTDD assumed the availability of a very accurate positioning system that is not yet available for WSNs.

The above mentioned flat and hierarchical protocols are different in many aspects. At this point, we compare the different routing approaches for flat and hierarchical sensor networks as shown in Table 2.

TABLE 2 HIERARCHICAL VS. FLAT TOPOLOGIES ROUTING

Hierarchical routing

Flat routing

Reservation-based scheduling

Contention-based scheduling

Collisions avoided

Collision overhead present

Reduced duty cycle due to periodic sleeping

Variable duty cycle by controlling sleep time of nodes

Data aggregation by clusterhead

Node on multihop path aggregates incoming data from neighbors

Simple but non-optimal routing

Routing can be made optimal but with an added complexity.

Requires global and local synchronization

Links formed on the fly without synchronization

Overhead of cluster formation throughout the network

Routes formed only in regions that have data for transmission

Lower latency as multiple hops network formed by cluster- heads always available

Latency in waking up intermediate nodes and setting up the multipath

Energy dissipation is uniform

Energy dissipation depends on traffic patterns

Energy dissipation cannot be controlled

Energy dissipation adapts to traffic pattern

Fair channel allocation

Fairness not guaranteed

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 [20, 14, 21]. Alternatively, the location of nodes may be available directly by communicating with a satellite using GPS if nodes are equipped with a small low-power GPS receiver [23]. 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 network as possible. The problem of designing sleep period schedules for each node in a localized manner was addressed in [22, 23].

Geographic Adaptive Fidelity: GAF [23] is an energy-aware location-based routing algorithm designed primarily for mobile ad hoc networks, but may be applicable to sensor networks as well. The network area is first divided into fixed zones and forms a virtual grid. Inside each zone, nodes collaborate with each other to play different roles.

GAF can substantially increase the network lifetime as the number of nodes increases. There are three states defined in GAF: discovery, for determining the neighbors in the grid; active, reflecting participation in routing; and sleep, when the radio is turned off. In order to handle mobility, each node in the grid estimates its time of leaving the grid and sends this to its neighbors. The sleeping neighbors adjust their sleeping time accordingly in order to keep routing fidelity. Before the leaving time of the active node expires, sleeping nodes wake up and one of them becomes active. GAF is implemented both for non mobility (GAF-basic) and mobility (GAF-mobility adaptation) of nodes. Simulation results show that GAF performs at least as well as a normal ad hoc routing protocol in terms of latency and packet loss, and increases the lifetime of the network by saving energy. Although GAF is a location-based protocol, it may also be considered a hierarchical protocol, where the clusters are based on geographic location.

Geographic and Energy Aware Routing: Yu et al. [24] discussed the use of geographic information while disseminating queries to appropriate regions since data queries often include geographic attributes. The protocol, Geographic and Energy Aware Routing (GEAR), uses energy- aware and geographically informed neighbor selection heuristics to route a packet toward the destination region. The key 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. By doing this, GEAR can conserve more energy than directed diffusion.

GEAR not only reduces energy consumption for route setup, but also performs better than GPSR in terms of packet delivery. The simulation results show that for uneven traffic distribution, GEAR delivers 70–80 percent packets. For uniform traffic pairs GEAR delivers 25–35 percent packets.

LEGR (Load-balanced and energy-efficient geographic routing)--

L. Zhao et al. [25] propose LEGR protocol, which use energy aware metrics together with geographical information and packets reception rate to make routing decisions. A novel link estimator and real time energy estimation scheme is designed in the sub layer of the LEGR protocol to get the neighbour nodes’ PRR value and current energy level. To evaluate the efficiency and the effectiveness of the proposed protocol, we define several performance metrics. In terms of theses metrics, our comprehensive simulation results show that, compared with PRR*distance forwarding scheme, LEGR extend the lifetime of the sensor network about 20% with approximate delivery rate and energy efficiency. Compared with GEAR and GPSR, LEGR performs better in terms of almost all the performance metrics.

CONCLUSION

Routing in sensor networks is a new area of research, with a limited but rapidly growing set of research results. In this article we present a comprehensive survey of routing protocols in wireless sensor networks that have been presented in the literature. They have the common objective of trying to extend the lifetime of the sensor network while not compromising data delivery. We compare different energy efficient routing protocols in WSN and find out the power usage in them as shown in Table 3.

Overall, the routing techniques are classified into three categories: flat, hierarchical, and location-based routing protocols. We also highlight the design trade-offs between energy and communication overhead savings in some of the routing paradigm, as well as the advantages and disadvantages of each routing technique. Although many of these routing techniques look promising, there are still many challenges that need to be solved in sensor networks.

TABLE 3 CLASSIFICATIONS AND COMPARISON OF ENERGY EFFICIENT ROUTING PROTOCOLS IN WSN

Routing protocols in WSN

Flat network routing

Hierarchical network routing

Location based routing

Protocol

Power Usage

Protocol

Power Usage

Protocol

Power Usage

SPIN

Ltd.

LEACH

Max.

GAF

Ltd.

DD

Ltd.

TEEN & APTEEN

Max.

GEAR

Ltd.

REEP

Max.

PEGASIS

Max.

LEGR

Max.

CADR

Ltd.

NSEEAR

Max.

 

 

COUGAR

Ltd.

MECN & SMECN

Max.

 

 

EADD

Ltd.

TTDD

Ltd.

 

 

 

 

SPEAR

Max.

 

 

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