*3.2.7 Cross-layered approach*

When compared to layered approaches, cross-layered approach in WSN is energy efficient. The protocol stack is considered as a single system instead of individual layers in the cross-layered approach. For interaction among the protocol layers, state information of the protocols is shared among all layers. Cross-layered protocol implementations significantly affect the system efficiency with respect to the energy and lifetime.

energy. However, the probabilistic CH selection process prompts unequal clusters

Periodic, event-driven, and query-based protocol (PEQ) and its variation, CPEQ, were proposed by Boukerche et al. [35] in 2006. PEQ is designed for achieving the following: low latency, high reliability, and broken path

reconfiguration. CPEQ is a cluster-based routing protocol. The publish/subscribe

Genetic algorithm-based clustering approach (LEACH-GA) was introduced in literature [36] to predict the optimal probability for electing an optimal number of CHs. This approach improved the network lifetime by achieving energy-efficient

Artificial bee colony (ABC)-based algorithm [37] has been proposed, where the CH selection is performed by adopting the ABC algorithm. ABC algorithm improves the clustering process by employing efficient and fast search feature to select the CHs. Both cluster members to CH, and CH to BS communication is performed by direct data communication. However, this protocol does not consider the coverage

Ant colony algorithm for data aggregation (DAACA) has been introduced by Chi

Lusheng Miao et al. [39] have introduced network coding to resolve the issues in gradient-based routing (GBR) scheme, such as broadcasting of interest messages by sink node which prompts duplication of packets, which causes more energy dissipation, and point-to-point message delivery forces more data retransmissions due to the unstable network environment in WSNs. The authors have proposed network coding for GBR (GBR-NC) to implement energy-efficient broadcasting algorithm which reduces network traffic. Further, the authors have presented two competing

In 2012, Rashmi Ranjan Rout et al. [40] proposed an energy-efficient triangular (regular) deployment strategy with directional antenna (ETDDA), where 2-connectivity pattern has been utilized. This pattern is accomplished by aligning the directional antenna beam of a sensor node in a specified direction toward the sink. Data forwarding depends on network coding for many-to-one traffic flow from sensor nodes to sink. The proposed approach ensures energy efficiency, robustness,

Ming Ma et al. [41] have put forward a mobility-based data-gathering mechanism for WSNs. A mobile data collector (M-collector), perhaps a mobile robot or a vehicle, is implemented with a transceiver and battery. The M-collector travels through a specific path and determines the sensor nodes, which comes within its communication range while traversing. Then, it collects the data from the sensor nodes in the single-hop communication and forward the data to the base station without delays. Hence, this mechanism improves the lifetime of the sensor nodes. The authors have primarily focused to reduce the length of each data-gathering tour

Lin et al. [38]. This approach comprises of three phases: initialization, packets transmissions, and operations on pheromones. In the transmission phase, the next hop is dynamically selected by determining the number of pheromones of neighbor nodes and the residual energy. Pheromones' adjustments are accomplished for every specified number of rounds of data transmissions. Besides, various pheromones' adjustment strategies such as basic-DAACA, elitist strategy-based DAACA (ES-DAACA), maximum- and minimum-based DAACA (MM-DAACA), and ant colony system-based DAACA (ACS-DAACA) are utilized to enhance the network lifetime. However, duplication packets are transmitted from sink nodes to initialize

the network, which causes higher energy depletion in the network.

algorithms such as GBRC and auto-adaptable GBR-C to minimize the data

and better connectivity in communicating data to the sink.

called as single-hop data-gathering problem (SHDGP).

mechanism is used to broadcast requests throughout the network.

of the CH and it prompts more energy dissipation.

which leads to more energy dissipation.

*Data Collection Protocols in Wireless Sensor Networks DOI: http://dx.doi.org/10.5772/intechopen.93659*

clustering.

retransmissions.

**91**

## **4. Existing routing techniques**

In WSN, so many techniques are proposed to achieve energy efficiency, longer lifetime, fault tolerance. Low latency by different researchers are briefly explained in this section. Most of these solutions are designed based on different techniques such as clustering, network coding, duty cycling, aggregation, directional antennas, sink mobility, and cross-layer solutions.

Low-energy adaptive clustering hierarchy (LEACH) routing strategy was proposed by Heinzelman et al. [29]. It is a cluster-based routing algorithm to decrease energy consumption and improve the network lifetime. In this protocol, the network is divided into clusters; each cluster contains a set of CMs and a leader called CH. The CMs send the data to its respective CH; CHs communicate the collected data to the BS and are elected in a random and distributed manner. Subsequently, LEACH was altered to LEACH-C [30], a centralized approach. The process of CH selection is performed based on the residual energy of the sensor nodes. However, due to dynamic cluster formation, the distance between CH and BS is faraway and some of the cluster nodes are also faraway from the CHs; it increases the communication cost. Later, a lot of modified LEACH protocols have been proposed to enhance the network lifetime and have been reviewed in [17].

LEACH protocol has been improved as power-efficient gathering in information systems (PEGASIS) [31], a multi-hop chain-based protocol, where every node aids in transmitting and/or receiving the data from its neighbor node by forming the chain. The collected data are aggregated and carried from node to node. One of the nodes in the chain is selected as a leader; the leader node transfers data to the BS. PEGASIS performs better than LEACH by minimizing the number of transmissions from sensor nodes to BS and clustering overhead. However, data transmission delay is higher due to the large chain length.

Threshold-sensitive energy-efficient sensor network protocol (TEEN) [32] is a homogenous reactive routing protocol. In this approach, the process of CH selection is performed similar to LEACH; the data transmission varies from LEACH. The workings of TEEN are based on the thresholds, namely, Hard threshold (*H*T) and soft threshold (*S*T). However, the CH selection process is random and the size of the clusters is unequal; it causes an unbalanced energy consumption among the clusters. Network throughput is also decreased due to the threshold mechanism.

Hybrid energy-efficient distributed (HEED) protocol [33] has been proposed by Younis and Fahmy. It is a homogenous cluster-based routing protocol; CH selection is accomplished based on the probability function of residual energy and node degree. Later, HEED protocol is extended as the heterogeneous HEED to manage the routing in the heterogeneous network field. This protocol utilizes fuzzy logic model for the CH selection process; the parameters considered in the fuzzy logic model are node degree, distance, and remaining energy. Finally, direct data transmission is carried out between the CM and CH and between the CH and BS.

Qing et al. [34] have presented distributed energy-efficient clustering scheme (DEEC), a heterogeneous data collection protocol. The sensor nodes possess varied energy levels. The selection of CHs is done based on the probability ratio between the residual energy of the nodes and average energy of the whole network. The possibility of evolving a CH is higher for the nodes which possess more residual

#### *Data Collection Protocols in Wireless Sensor Networks DOI: http://dx.doi.org/10.5772/intechopen.93659*

layers, state information of the protocols is shared among all layers. Cross-layered protocol implementations significantly affect the system efficiency with respect to

*Wireless Sensor Networks - Design, Deployment and Applications*

In WSN, so many techniques are proposed to achieve energy efficiency, longer lifetime, fault tolerance. Low latency by different researchers are briefly explained in this section. Most of these solutions are designed based on different techniques such as clustering, network coding, duty cycling, aggregation, directional antennas,

Low-energy adaptive clustering hierarchy (LEACH) routing strategy was proposed by Heinzelman et al. [29]. It is a cluster-based routing algorithm to decrease energy consumption and improve the network lifetime. In this protocol, the network is divided into clusters; each cluster contains a set of CMs and a leader called CH. The CMs send the data to its respective CH; CHs communicate the collected data to the BS and are elected in a random and distributed manner. Subsequently, LEACH was altered to LEACH-C [30], a centralized approach. The process of CH selection is performed based on the residual energy of the sensor nodes. However, due to dynamic cluster formation, the distance between CH and BS is faraway and some of the cluster nodes are also faraway from the CHs; it increases the communication cost. Later, a lot of modified LEACH protocols have been proposed to

LEACH protocol has been improved as power-efficient gathering in information systems (PEGASIS) [31], a multi-hop chain-based protocol, where every node aids in transmitting and/or receiving the data from its neighbor node by forming the chain. The collected data are aggregated and carried from node to node. One of the nodes in the chain is selected as a leader; the leader node transfers data to the BS. PEGASIS performs better than LEACH by minimizing the number of transmissions from sensor nodes to BS and clustering overhead. However, data transmission delay

Threshold-sensitive energy-efficient sensor network protocol (TEEN) [32] is a homogenous reactive routing protocol. In this approach, the process of CH selection is performed similar to LEACH; the data transmission varies from LEACH. The workings of TEEN are based on the thresholds, namely, Hard threshold (*H*T) and soft threshold (*S*T). However, the CH selection process is random and the size of the clusters is unequal; it causes an unbalanced energy consumption among the clusters. Network throughput is also decreased due to the threshold

Hybrid energy-efficient distributed (HEED) protocol [33] has been proposed by Younis and Fahmy. It is a homogenous cluster-based routing protocol; CH selection is accomplished based on the probability function of residual energy and node degree. Later, HEED protocol is extended as the heterogeneous HEED to manage the routing in the heterogeneous network field. This protocol utilizes fuzzy logic model for the CH selection process; the parameters considered in the fuzzy logic model are node degree, distance, and remaining energy. Finally, direct data transmission is carried out between the CM and CH and between the CH and BS.

Qing et al. [34] have presented distributed energy-efficient clustering scheme (DEEC), a heterogeneous data collection protocol. The sensor nodes possess varied energy levels. The selection of CHs is done based on the probability ratio between the residual energy of the nodes and average energy of the whole network. The possibility of evolving a CH is higher for the nodes which possess more residual

enhance the network lifetime and have been reviewed in [17].

the energy and lifetime.

**4. Existing routing techniques**

sink mobility, and cross-layer solutions.

is higher due to the large chain length.

mechanism.

**90**

energy. However, the probabilistic CH selection process prompts unequal clusters which leads to more energy dissipation.

Periodic, event-driven, and query-based protocol (PEQ) and its variation, CPEQ, were proposed by Boukerche et al. [35] in 2006. PEQ is designed for achieving the following: low latency, high reliability, and broken path reconfiguration. CPEQ is a cluster-based routing protocol. The publish/subscribe mechanism is used to broadcast requests throughout the network.

Genetic algorithm-based clustering approach (LEACH-GA) was introduced in literature [36] to predict the optimal probability for electing an optimal number of CHs. This approach improved the network lifetime by achieving energy-efficient clustering.

Artificial bee colony (ABC)-based algorithm [37] has been proposed, where the CH selection is performed by adopting the ABC algorithm. ABC algorithm improves the clustering process by employing efficient and fast search feature to select the CHs. Both cluster members to CH, and CH to BS communication is performed by direct data communication. However, this protocol does not consider the coverage of the CH and it prompts more energy dissipation.

Ant colony algorithm for data aggregation (DAACA) has been introduced by Chi Lin et al. [38]. This approach comprises of three phases: initialization, packets transmissions, and operations on pheromones. In the transmission phase, the next hop is dynamically selected by determining the number of pheromones of neighbor nodes and the residual energy. Pheromones' adjustments are accomplished for every specified number of rounds of data transmissions. Besides, various pheromones' adjustment strategies such as basic-DAACA, elitist strategy-based DAACA (ES-DAACA), maximum- and minimum-based DAACA (MM-DAACA), and ant colony system-based DAACA (ACS-DAACA) are utilized to enhance the network lifetime. However, duplication packets are transmitted from sink nodes to initialize the network, which causes higher energy depletion in the network.

Lusheng Miao et al. [39] have introduced network coding to resolve the issues in gradient-based routing (GBR) scheme, such as broadcasting of interest messages by sink node which prompts duplication of packets, which causes more energy dissipation, and point-to-point message delivery forces more data retransmissions due to the unstable network environment in WSNs. The authors have proposed network coding for GBR (GBR-NC) to implement energy-efficient broadcasting algorithm which reduces network traffic. Further, the authors have presented two competing algorithms such as GBRC and auto-adaptable GBR-C to minimize the data retransmissions.

In 2012, Rashmi Ranjan Rout et al. [40] proposed an energy-efficient triangular (regular) deployment strategy with directional antenna (ETDDA), where 2-connectivity pattern has been utilized. This pattern is accomplished by aligning the directional antenna beam of a sensor node in a specified direction toward the sink. Data forwarding depends on network coding for many-to-one traffic flow from sensor nodes to sink. The proposed approach ensures energy efficiency, robustness, and better connectivity in communicating data to the sink.

Ming Ma et al. [41] have put forward a mobility-based data-gathering mechanism for WSNs. A mobile data collector (M-collector), perhaps a mobile robot or a vehicle, is implemented with a transceiver and battery. The M-collector travels through a specific path and determines the sensor nodes, which comes within its communication range while traversing. Then, it collects the data from the sensor nodes in the single-hop communication and forward the data to the base station without delays. Hence, this mechanism improves the lifetime of the sensor nodes. The authors have primarily focused to reduce the length of each data-gathering tour called as single-hop data-gathering problem (SHDGP).

Roja Chandanala et al. [42] have presented a mechanism to preserve energy in flood-based WSNs by applying two techniques: network coding and duty cycling. Initially, the authors have proposed DutyCode, a cross-layer technique, where Random Low Power Listening MAC protocol was devised to implement packet streaming. The authors have applied flexible intervals for randomizing sleep cycles. Further, an enhanced coding scheme was proposed, which selects appropriate network coding schemes for nodes to remove redundant packet transmissions.

aggregates sensed data from the sensor nodes using compressive sensing. A random projection root node with compressive data-gathering aids to achieve a balanced energy consumption all over the network. Besides, eMSTP has been introduced which is the extended version of MSTP; the sink node in the eMSTP behaves like a

Ahmad et al. [49] proposed a protocol called Away Cluster Heads with Adaptive

A genetic algorithm-based approach [50] has been applied for binding the sensor nodes to the sink nodes, considering the balanced load among the sink nodes. The authors have presented a fitness function which takes into account the communication cost between the sensor node and sink node and the processing cost of the sink node. This approach dealt with the nodes which do not have any sink node in

In 2015, energy-aware routing (ERA) [51] has been proposed, where the residual

PSO-based approach for energy-efficient routing and clustering has been proposed in literature [55]. Routing path between the gateway to BS is determined using the PSO technique. This approach provides energy-efficient routing and energy-balanced clustering. This approach is fault tolerant when CHs failed. But, nodes that are not reachable to any gateway are left out from communication. Gravitational search algorithm for cluster head selection and routing (GSA-CHSR) [56] has been proposed. The authors have used GSA algorithm for deciding the optimal number of CH nodes and finding the optimal route between CH and BS. This approach improves performance parameters such as network lifetime, residual energy, and the number of packets received at BS. However, this approach incurs

Guravaiah and Leela Velusamy [57] proposed a routing protocol titled hybrid cluster communication using RFD (HCCRFD) based on clustering using river formation dynamics-based multi-hop routing protocol (RFDMRP) [58]. This protocol increases the network lifetime. However, load balancing among CHs is not considered and clustering overhead exists due to periodic CH selection. Further, the authors have proposed a balanced energy and adaptive cluster head selection algorithm (BEACH) [59]. They considered the parameters such as degree of the node, remaining energy of the node, the distance from BS to the sensor node, and the average transmission

energy of the CHs and the intra-cluster distance are the parameters taken into account for the process CH selection. However, the parameters such as the optimal number of CHs, network density, and cluster coverage are not considered in the CH selection process; hence this causes uneven energy consumption in every cluster. A GSA-based approach titled GSA-based energy-efficient clustering (GSA-EEC) was presented by literature [52]. For the fitness value calculation, the parameters considered are the distance between the sensor nodes and gateways, the distance between gateways and sink, and residual energy of gateways. This approach improves the network lifetime and total energy consumption. Further, they introduced a routing strategy titled gravitational search algorithm-based multi-sink placement (GSA-MSP) for placing multiple sinks on the sensor network [53]. Priority-based WSN clustering of multiple sink scenario using artificial bee colony [54] has been proposed. The fitness function in this approach considers the energy of the sink node and the sensor node, the distance between the sensor node

network lifetime. However, global node information is required for communicating data and the size of the clusters is also unequal. As the node distribution among the clusters is unequal, this approach prompts to variation in energy depletion ratio

) and this mechanism has been utilized for enhancing

root node for all MST.

Clustering Habit (ACH<sup>2</sup>

among clusters in the network.

*Data Collection Protocols in Wireless Sensor Networks DOI: http://dx.doi.org/10.5772/intechopen.93659*

their communication range.

to the sink node, and the priority of each sink.

clustering overhead for selecting the optimal set of CHs.

distance to its neighbors for achieving the load-balanced clustering.

**93**

Meikang Qiu et al. [43] have introduced informer homed routing (IHR), which is a novel energy-aware cluster-based fault-tolerance mechanism for WSN. IHR is the foremost variant of dual homed routing (DHR) fault-tolerance mechanism. In this mechanism, each sensor node is attached with two cluster heads called primary cluster head (PCH) and backup cluster head (BCH). Sensor nodes deliver the data to PCH rather than sending simultaneously to both PCH and BCH. In each round, BCH probes the PCH to identify whether the PCH is active or not using the beacon message. In three continuous rounds, if BCH cannot receive any beacon message from PCH, then BCH will declare that the PCH has failed and it informs to sensor nodes to transmit data to BCH. Hence, IHR provides an energy-efficient faulttolerance mechanism to prolong the lifetime of the network. However, cluster head selection process is containing more overhead.

A novel evolutionary approach for load-balanced clustering problem is presented in literature [44]. CH (gateway) formation is performed using a novel genetic algorithm. This algorithm differs from the traditional GA in the initial population and mutation phase. This approach balances the load among the gateways and it is energy efficient. However, sensor nodes that are not reachable to any gateway are left out from communication. Later, they extended a differential evolution-based approach [45] used for clustering the nodes with gateways (CHs) in a load-balanced way to ensure load balancing among the gateways and energy efficiency. But, this approach used single-hop communication between the gateway to BS and hence it may not be suitable for long-distance communication.

Flow partitioned unequal clustering (FPUC) algorithm has been proposed by Jian Peng et al. [46] to attain an enhanced network lifetime and coverage. FPUC has two phases: clustering and flow partition routing. In the clustering phase, cluster head is decided based on the higher residual energy and larger overlapping degree of sensor nodes. In the flow partition routing phase, cluster head collects the data from the member nodes and aggregates the data into a single packet; then it forwards the data to the sink through gateway nodes depending on residual energy The flow-partitioned routing phase has two subphases: dataflow partitioning phase and relaying phase. In the dataflow partitioning phase, the cluster head segments the dataflow into various smaller packets and then delivers these packets to its gateway nodes. In the relaying phase, gateways communicate the received data to the next hop with minimum cost.

An energy-efficient adaptive data aggregation strategy using network coding (ADANC) to attain improved energy efficiency in a cluster based duty-cycled WSN has been introduced by Rashmi Ranjan Rout et al. [47]. Network coding minimizes the network traffic inside a cluster and duty cycling scheme has been used in the cluster network to prolong network lifetime.

Dariush Ebrahimi and Chadi Assi [48] have presented a new compressive data gathering method. This method utilizes compressive sensing (CS) and random projection techniques to enhance the lifetime of large WSNs. The authors preferred the method to equally distribute the energy throughout the network rather than decreasing the overall network energy consumption. In the proposed data-gathering method, minimum spanning tree projection (MSTP) has been adopted. MSTP creates several minimum spanning trees (MSTs) and each root node of the tree

Roja Chandanala et al. [42] have presented a mechanism to preserve energy in flood-based WSNs by applying two techniques: network coding and duty cycling. Initially, the authors have proposed DutyCode, a cross-layer technique, where Random Low Power Listening MAC protocol was devised to implement packet streaming. The authors have applied flexible intervals for randomizing sleep cycles. Further, an enhanced coding scheme was proposed, which selects appropriate network coding schemes for nodes to remove redundant packet transmissions.

*Wireless Sensor Networks - Design, Deployment and Applications*

Meikang Qiu et al. [43] have introduced informer homed routing (IHR), which is a novel energy-aware cluster-based fault-tolerance mechanism for WSN. IHR is the foremost variant of dual homed routing (DHR) fault-tolerance mechanism. In this mechanism, each sensor node is attached with two cluster heads called primary cluster head (PCH) and backup cluster head (BCH). Sensor nodes deliver the data to PCH rather than sending simultaneously to both PCH and BCH. In each round, BCH probes the PCH to identify whether the PCH is active or not using the beacon message. In three continuous rounds, if BCH cannot receive any beacon message from PCH, then BCH will declare that the PCH has failed and it informs to sensor nodes to transmit data to BCH. Hence, IHR provides an energy-efficient faulttolerance mechanism to prolong the lifetime of the network. However, cluster head

A novel evolutionary approach for load-balanced clustering problem is presented in literature [44]. CH (gateway) formation is performed using a novel genetic algorithm. This algorithm differs from the traditional GA in the initial population and mutation phase. This approach balances the load among the gateways and it is energy efficient. However, sensor nodes that are not reachable to any gateway are left out from communication. Later, they extended a differential evolution-based approach [45] used for clustering the nodes with gateways (CHs) in a load-balanced way to ensure load balancing among the gateways and energy efficiency. But, this approach used single-hop communication between the gateway

to BS and hence it may not be suitable for long-distance communication.

Flow partitioned unequal clustering (FPUC) algorithm has been proposed by Jian Peng et al. [46] to attain an enhanced network lifetime and coverage. FPUC has two phases: clustering and flow partition routing. In the clustering phase, cluster head is decided based on the higher residual energy and larger overlapping degree of sensor nodes. In the flow partition routing phase, cluster head collects the data from the member nodes and aggregates the data into a single packet; then it forwards the data to the sink through gateway nodes depending on residual energy The flow-partitioned routing phase has two subphases: dataflow partitioning phase and relaying phase. In the dataflow partitioning phase, the cluster head segments the dataflow into various smaller packets and then delivers these packets to its gateway nodes. In the relaying phase, gateways communicate the received data to the next

An energy-efficient adaptive data aggregation strategy using network coding (ADANC) to attain improved energy efficiency in a cluster based duty-cycled WSN has been introduced by Rashmi Ranjan Rout et al. [47]. Network coding minimizes the network traffic inside a cluster and duty cycling scheme has been used in the

Dariush Ebrahimi and Chadi Assi [48] have presented a new compressive data gathering method. This method utilizes compressive sensing (CS) and random projection techniques to enhance the lifetime of large WSNs. The authors preferred the method to equally distribute the energy throughout the network rather than decreasing the overall network energy consumption. In the proposed data-gathering method, minimum spanning tree projection (MSTP) has been adopted. MSTP creates several minimum spanning trees (MSTs) and each root node of the tree

selection process is containing more overhead.

hop with minimum cost.

**92**

cluster network to prolong network lifetime.

aggregates sensed data from the sensor nodes using compressive sensing. A random projection root node with compressive data-gathering aids to achieve a balanced energy consumption all over the network. Besides, eMSTP has been introduced which is the extended version of MSTP; the sink node in the eMSTP behaves like a root node for all MST.

Ahmad et al. [49] proposed a protocol called Away Cluster Heads with Adaptive Clustering Habit (ACH<sup>2</sup> ) and this mechanism has been utilized for enhancing network lifetime. However, global node information is required for communicating data and the size of the clusters is also unequal. As the node distribution among the clusters is unequal, this approach prompts to variation in energy depletion ratio among clusters in the network.

A genetic algorithm-based approach [50] has been applied for binding the sensor nodes to the sink nodes, considering the balanced load among the sink nodes. The authors have presented a fitness function which takes into account the communication cost between the sensor node and sink node and the processing cost of the sink node. This approach dealt with the nodes which do not have any sink node in their communication range.

In 2015, energy-aware routing (ERA) [51] has been proposed, where the residual energy of the CHs and the intra-cluster distance are the parameters taken into account for the process CH selection. However, the parameters such as the optimal number of CHs, network density, and cluster coverage are not considered in the CH selection process; hence this causes uneven energy consumption in every cluster.

A GSA-based approach titled GSA-based energy-efficient clustering (GSA-EEC) was presented by literature [52]. For the fitness value calculation, the parameters considered are the distance between the sensor nodes and gateways, the distance between gateways and sink, and residual energy of gateways. This approach improves the network lifetime and total energy consumption. Further, they introduced a routing strategy titled gravitational search algorithm-based multi-sink placement (GSA-MSP) for placing multiple sinks on the sensor network [53].

Priority-based WSN clustering of multiple sink scenario using artificial bee colony [54] has been proposed. The fitness function in this approach considers the energy of the sink node and the sensor node, the distance between the sensor node to the sink node, and the priority of each sink.

PSO-based approach for energy-efficient routing and clustering has been proposed in literature [55]. Routing path between the gateway to BS is determined using the PSO technique. This approach provides energy-efficient routing and energy-balanced clustering. This approach is fault tolerant when CHs failed. But, nodes that are not reachable to any gateway are left out from communication.

Gravitational search algorithm for cluster head selection and routing (GSA-CHSR) [56] has been proposed. The authors have used GSA algorithm for deciding the optimal number of CH nodes and finding the optimal route between CH and BS. This approach improves performance parameters such as network lifetime, residual energy, and the number of packets received at BS. However, this approach incurs clustering overhead for selecting the optimal set of CHs.

Guravaiah and Leela Velusamy [57] proposed a routing protocol titled hybrid cluster communication using RFD (HCCRFD) based on clustering using river formation dynamics-based multi-hop routing protocol (RFDMRP) [58]. This protocol increases the network lifetime. However, load balancing among CHs is not considered and clustering overhead exists due to periodic CH selection. Further, the authors have proposed a balanced energy and adaptive cluster head selection algorithm (BEACH) [59]. They considered the parameters such as degree of the node, remaining energy of the node, the distance from BS to the sensor node, and the average transmission distance to its neighbors for achieving the load-balanced clustering.


An approach called LEACH-PSO [60] has been proposed for improving the network lifetime by selecting an optimum number of CHs in every round. In this work, the particle swarm optimization method is integrated with LEACH for

Energy efficiency,

FCR algorithm Energy efficiency Energy balancing is not ensured

reliable

**Sl. No. Algorithm Techniques used Metrics Drawbacks**

Clustering using genetic algorithm

Clustering (gateways) using

multi-hop GSA

LEACH, RFD

Cuckoo search algorithm

hop, spanning tree

PSO

23 GSA-CHSR [56] Clustering using

24 HCCRFD [57] Clustering using

28 MLBC [64] MOPSO, multi-

throughput

Clustering Energy efficiency, lifetime

25 BEACH [59] Clustering, RFD Energy efficiency CH selection overhead 26 GSA-EC [61] GSA, multi-hop Network lifetime Clustering overhead

21 GSA-EEC [52] Clustering, GSA Energy efficiency Load balancing among CHs not

Global node information for data transmission, cluster head selection overhead

Optimum number of CHs is not

any gateway are not considered

Nodes that are not reachable to any CH are not considered

Energy efficiency Single-hop communication

considered

considered

Energy efficiency Nodes that are not reachable to

Energy efficiency No load-balanced clustering

Energy efficiency Load balance among CHs not considered

Energy efficiency Clustering overhead

between sink and BS

18 ACH2 [49] Clustering Lifetime,

*Data Collection Protocols in Wireless Sensor Networks DOI: http://dx.doi.org/10.5772/intechopen.93659*

19 GA-based

20 Energy-aware routing (ERA) [51]

22 PSO-based routing [55]

27 Cuckoo and

29 Energy-efficient and delay-less routing [65]

*Existing protocols for data collection.*

harmony searchbased routing [63]

approach [50]

Energy-efficient CH-based GSA (GSA-EC) [61] for finding an optimal set of CHs using GSA has been proposed. To balance the energy consumption, one-hop clusters are formed using an optimal set of CHs. The authors have also proposed the hybrid approach of PSO and GSA. This approach increases network lifetime and network stability. However, this approach also incurs clustering overhead for selecting the optimal set of CHs. Later, Kavitha et al. [62] used GSA for assigning sensor nodes to an appropriate cluster head (CH) in a load-balanced way such that it reduces the energy consumption and hence enhances the lifetime of a network. Integrated clustering and routing protocol using cuckoo and harmony search has

been proposed in literature [63]. This approach has adopted the cuckoo search algorithm for CH selection. Residual energy, degree of a node, intra-cluster distance, and coverage ratio are the parameters for developing fitness function used in CH selection. The harmony search algorithm has been employed for routing from

forming the clusters.

**Table 2.**

**95**

*Data Collection Protocols in Wireless Sensor Networks DOI: http://dx.doi.org/10.5772/intechopen.93659*


#### **Table 2.**

**Sl. No. Algorithm Techniques used Metrics Drawbacks**

scalability

scalability

lifetime

energy

Energy, throughput, and low latency

Energy, low latency, scalability, and throughput

Fault tolerance and energy

Energy efficiency, lifetime

Energy, low latency, and lifetime

Energy, network lifetime

Fault tolerance, low latency, and energy

Network lifetime,

3 TEEN [32] Clustering Lifetime Same as LEACH, network

4 HEED [33] Clustering Lifetime Direct transmission,

Lifetime Network throughput decreased

consumption

consumption

round

calculations

collector

Energy Transition between active and

Energy efficiency Single-hop communication

Energy efficiency Single-hop communication

calculations

Traffic overhead

Not considering RE for CH selection, unbalanced energy

throughput decreased

heterogeneity is not considered

Direct transmission, unequal size of clusters, unbalanced energy

Bottleneck problem nearer to sink node, overhead in pheromones calculation at each

Overhead in optimal antenna pattern and transmission power

High control overhead to maintain the trajectory path, packet loss due to speed of data

sleep states overhead

reliability problems

between the CH to BS

between the CH and BS

CH selection overhead

Cluster maintenance overhead

Computational overhead in MST

Node unable to find CH, leads to

Transmission delays in competing algorithm

construction

*Wireless Sensor Networks - Design, Deployment and Applications*

2 LEACH [30] Clustering Lifetime,

5 DEEC [34] Clustering Lifetime,

Clustering and publish/subscribe mechanism

Network coding and multi-hop

antennas, network coding, and multi-

and single-hop

Network coding, duty cycling, and multi-hop

multi-hop

Clustering, genetic algorithm

Clustering using differential evolution

aggregation, and multi-hop

network coding, and duty cycling

using compressive

sensing

hop

7 DAACA [38] Clustering, ACA Energy, network

1 PEGASIS [31] Chain

6 PEQ and CPEQ [35]

8 GBR-NC, GBR-C, and autoadaptable GBR-C

[39]

11 DutyCode and ECS [42]

> evolutionary approach [44]

13 Novel

14 DE-based clustering algorithm [45]

**94**

9 ETD-DA [40] Directional

10 SHDGP [41] Mobile collectors

12 IHR [43] Clustering and

15 FPUC [46] Clustering, data

17 MSTP [48] Data aggregation

16 ADANC [47] Clustering,

*Existing protocols for data collection.*

An approach called LEACH-PSO [60] has been proposed for improving the network lifetime by selecting an optimum number of CHs in every round. In this work, the particle swarm optimization method is integrated with LEACH for forming the clusters.

Energy-efficient CH-based GSA (GSA-EC) [61] for finding an optimal set of CHs using GSA has been proposed. To balance the energy consumption, one-hop clusters are formed using an optimal set of CHs. The authors have also proposed the hybrid approach of PSO and GSA. This approach increases network lifetime and network stability. However, this approach also incurs clustering overhead for selecting the optimal set of CHs. Later, Kavitha et al. [62] used GSA for assigning sensor nodes to an appropriate cluster head (CH) in a load-balanced way such that it reduces the energy consumption and hence enhances the lifetime of a network.

Integrated clustering and routing protocol using cuckoo and harmony search has been proposed in literature [63]. This approach has adopted the cuckoo search algorithm for CH selection. Residual energy, degree of a node, intra-cluster distance, and coverage ratio are the parameters for developing fitness function used in CH selection. The harmony search algorithm has been employed for routing from

CH to BS. It is energy efficient and balances the energy consumption of the network. Further, it minimizes the un-cluster nodes, that is, nodes that are not within the communication range of any CH are minimized. But, load balancing among CHs is not considered.

range in large size (area) cluster and faraway nodes consume more energy in

• The sizes of the clusters formed in the existing protocols are not equal. This

• Density of network was not considered as a parameter in CH selection process. This impacts the formation of unequal sized clusters and leads to uneven

• Uneven distribution of load on CH and the intra- and inter-communication

• Security is the major parameter need to be considered in military applications.

• In recent years, more popularity gain is deterministic rather than probabilistic-

• Heterogeneous network in WSN is also an important problem due to different

In this chapter, classification of data collection routing protocols in WSN has been thoroughly discussed. Various techniques such as clustering, duty cycling, aggregation, network coding, sink mobility, and cross-layered solutions, and directional antennas have been utilized by data collection routing protocols for attaining long lifetime, energy efficiency, fault tolerance, and low latency. These techniques are reviewed briefly in this chapter. Finally, this chapter demonstrates a paramount comparison among the existing approaches applicable on data collection process in WSN. Future directions of routing protocols are presented at the end of

Considering security, energy efficiency is still challenging issues.

based clustering due to reliability. However, CH selection and other

computational complexity are still a challenging area.

communication and processing capabilities.

leads to unbalanced energy consumption among the clusters.

large size (area) cluster.

*Data Collection Protocols in Wireless Sensor Networks DOI: http://dx.doi.org/10.5772/intechopen.93659*

distribution of load to CH.

path length is more.

**6. Conclusions**

this chapter.

**97**

Multi-objective load-balancing clustering technique (MLBC) [64] has been proposed for clustering in WSN by adopting multi-objective PSO (MOPSO) strategy which is used for CH selection. The shortest-path tree (SPT) for loop-free routing is created using Dijkstra's algorithm. It is energy efficient and reliable. But, the nodes that are not reachable to any CH are not considered.

In energy-efficient and delay-less routing [65], CH selection is performed using firefly with cyclic randomization (FCR) algorithm. This approach reduces transmission delay in the network. But, this approach has not considered energy balancing.

Overall comparison of above routing protocols are shown in **Table 2** with the techniques used, metrics considered, and drawbacks of each solution.
