Energy – Efficient routing and scheduling using clustering in geographic routing protocol

Abstract

In the resource-constrained wireless sensor network (WSN) geographic routing has been considered as an attractive method where it exploits the location data instead of global topology to transmit the data. The geographic routing protocol faces the routing issues when it is used by a heterogeneous device and utilizes high energy during the propagation of data. The lifespan of the sensor network depends on the efficiency of energy and capacity of the battery. Hence, successful data transmission, enrichment of battery capacity and energy utilization is necessary for WSN. To attain this requirements an effective change is made in the data transmission environment and network topology. In this paper proposed a dynamic cluster based duty cycle scheduling is initiated for the data transmission. The cluster-based scheduling and routing in geographic routing protocol (CSRGR) utilize the clustering mechanism which in turn reduces the consumption of energy and maximizes the throughput. The objective function of the proposed approach provides a scheduling and routing strategy. The demonstration of simulation results shows the effective cluster size balancing with data transmission range dynamically. The proposed algorithm is compared with the existing approach and from the results, the energy consumption is minimum for diverse scenarios.

Keywords

Duty cycle schedule throughput energy efficiency routing scheduling cluster and geographic routing

1 Introduction

The wireless sensor network (WSN) is a schema-less and self-configured network, which records and monitors the physical scenario of the atmosphere [1]. The emergence of WSN has made the perception of communication to the subsequent level and it is also applied in several standard applications [2, 3]. Energy preservation and routing are the two significant aspects considered in designing protocols for WSN [4]. The WSN consists of abundant sensor nodes and it is generally powered by batteries where the replacement or recharging of battery is a complicated process but they are expected to work for a long duration [5, 6]. The sensor nodes in the WSN utilize more energy during data transmission rather than calculation [7]. Prolonging the working of battery and WSN is a primary intent in designing the protocol. The geographic routing protocols [8] are localized routing or position-based [9, 10] and it is a fascinating method in the resource-constrained WSNs [11].

In the process of delivering information, it uses the local location of data in the place of global topology. The reduction of energy utilization often initiates the supplementary latency of data delivery. The energy preserving mechanism in WSN has initiated the minimal throughput which results in the ineffective data transmission. Effective routing and scheduling are needed to achieve effective data transmission with high throughput and minimal latency. In this paper, a cluster-based scheduling and routing in geographic routing protocol (CSRGR) is introduced. This approach highly reduces the energy utilization which in turn accomplished minimum latency for WSN. In the sensor node, energy utilization is affected by various aspects namely clustering the topologies, duty cycle schedule across the sensor node, transmission range at the dynamic rate, and retransmission. The CSRGR highly minimizes the end to end delay and the significant contribution of the proposed scheduling approaches is,

Heterogeneous Network: Balancing and enriching the energy usage during diverse states of the transmission network namely battery capacity at the initial stage, the capability of the network, range of transmission, bandwidth, and computation.

Construction of Cluster: Enhancing the lifetime and balancing of energy utilization within the cluster by optimizing the sensor nodes. The residual power of the battery acquires a constant cluster head (CH) count.

Effectiveness of Energy across Cross-Layer: Controlling of transmission range optimizes the physical layer function and duty cycle scheduling enhances the medium access control (MAC) and the function of the network layer is improved by the reversed multicast routing scheme.

Data Transmission Mechanism: The sink node propagates the resultant solution, duty cycle scheduling, and range of transmission to all the sensor nodes using a high power signal, which minimizes the consumption of energy.

The available scheduling approaches in the WSN namely self-learning based scheduling method [12], lightweight scheduling [13], and sleep scheduling policy [14]. These approaches face certain shortcomings such as collision, high latency, and complicated routing. To rectify the issues, a cluster-based scheme is introduced for routing and scheduling the transmission across the WSN. The simulation examination of the CSRGR scheme is compared with the existing algorithms. The simulation outcome represents that the proposed scheme CSRGR can minimize the latency and enriches the energy usage.

This research article is emphasized as follows: The clustering and scheduling approaches are given in the related work section, the framework of cluster-based scheduling and routing in geographic routing protocol (CSRGR) is given in section 3, the diagrammatical depiction of SSA, LWS, SSP, and CSRGR is demonstrated via a graph in section 4 and the conclusions, as well as the future suggestions, are given in section 5.

2 Related works

In the sensor network with wireless multimedia [15] routing the data and discovering the shortest path is a mandatory process. The multimedia intensity causes traffic during data transmission and it makes the energy of the node to exhaust the power faster. This makes the breakage of the routing link and makes the halt in the data transmission. The node in the idle state also uses energy that also results in the usage of energy unnecessarily. To rectify the issues of energy-awareness, a simple synchronized sleep scheduling mechanism (EA-TPGF-SS) is initiated, which has the capability of feedback passing. EA-TPGF-SS is developed based on the geographic routing protocol and it has degradation in the rate of Packet Delivery Ratio (PDR).

The duty-cycled approach is used in the industrial sensor network [16] and the sleep schedule is incorporated with the duty cycle method, which enriches the lifetime of the network. Two-Phase Greedy Forwarding (TPGF) is effective in data transmission due to the advantages namely bypassing of a hole, multipath, and shortest path. The irregularity in the radio network causes a delay in the transmission and the rate of sleep is also minimized, which makes the transmission ineffective.

A sleep scheduling mechanism for the multicast routing geographic approach is developed to schedule the process of routing and data transmission [17]. The general issue in the wireless network is flooding that causes due to inappropriate data transmission and it causes high energy consumption. The geographic routing protocol is computationally high cost and transmission also faces limitations namely flooding, ineffectiveness, and high energy consumption. Sleep scheduling with an energy-efficient approach is developed to transmit the data with minimum energy utilization and effective transmission [18].

The shortcoming in the existing approaches is considered in designing the proposed framework cluster-based scheduling and routing in geographic routing protocol (CSRGR). In this approach cluster scheme is introduced and the nodes are arranged with the assistance of the cluster. The duty cycle mechanism is incorporated in scheduling the data transmission process.

3 Problem statement and proposed model

The proposed protocol cluster-based scheduling and routing in geographic routing protocol (CSRGR) is discussed in this section.

3.1 Cluster construction

The cluster construction is initiated in the geographic routing protocol and the cluster head D is assigned. The cluster formation is initiated and the cluster formation is given as, $\sum_{D \in α} b_{snd} = 1, \forall sn \in SN$ (1)

The intra-routing path is initiated after the formation of the cluster and the routing path connects the source to the head of the cluster. The routing establishment is equated as follows, $b_{DSD} = \sum_{cp \in {CP}_{DSD}} t_{cp}, \forall ds \in DS; D \in α$ (2) where the decision variable in the intra-routing path is signified as t_cp and all available candidate path CP_DSD define the path cp belongs to CP_DSD for every pair from the source node DS to the cluster head D. In the solution phase, an interested path in the network is listed and the needed path is chosen from the list.

The data source (DS) set is given and the aggregated information is received at the sink node. The inter routing is established to connect the sink node with the cluster head (D) of every source node is given as follows, $b_{DSD} = \sum_{p \in P_{D}} η_{p}, \forall ds \in DS; D \in α$ (3) where η_q is the set of decision variables and if it is chosen for transmission η_q is assigned with 1, otherwise 0

In Fig. 1, G1 to G6 signifies the cluster head, k denotes the sink and 21 nodes are arranged.

Fig. 1

Construction of six cluster groups and the cluster-based routing.

3.2 Constraints in intra-routing

The conjunction in the constraint is ensured from every cluster head and it also has one intra routing path to the source node that is from every cluster. The connection establishment is given in equation 4 and the transmission constraint is given in equation 5. $\sum_{D \in α} \sum_{cp \in {CP}_{DSD}} t_{cp}, \forall ds \in DS$ (4) $\sum_{D \in α} \sum_{cp \in {CP}_{DSD}} t_{cp} δ_{cp (x, y)} ⩽ J_{(x, y)}, \forall ds \in DS; (x, y) \in M$ (5) where J(x,y) signify the link (x,y) on the tree, and the value of the intra-routing decision variable is assigned as 1.

3.3 Constraints in inter-routing

The sensor node D is elected as a cluster head and an intra-routing path from the source to the sink node is established with the value of 0. The candidate path in the network value is assigned with 1 otherwise the value for η_p is 0 and there is no path, which is equated as, $\sum_{p \in P_{D}} η_{p} ⩽ 1, \forall D \in α$ (6)

The inter-routing decision variable J(x,y) is assigned with the value 1 if some path on the network is elected and the indicator of cluster head is δ_q(x,y) whereas a link is elected in the network. The constraints on these two paths are described in equation 7, $\sum_{p \in P_{D}} η_{p} δ_{p (x, y)} ⩽ J_{(x, y)}, \forall D \in α, (x, y) \in M$ (7)

If the cluster head is on the path, any sensor node among that path can be elected as an intermediary node on the inter-cluster routing path. The process of electing the node is given in equation 8, $\begin{matrix} \sum_{p \in P_{D}} η_{p} . δ_{psn} ⩽ \frac{\sum_{p \in P_{D}} η_{p} . δ_{pD} + b_{snd} + L_{1} (1 - b_{snd})}{2}, \\ \forall sn \in SN, D \in α \end{matrix}$ (8)

3.4 Constraints in link

Let us assume, all the data are distributed to the sink node and the constraint on the link is collected by every node, which is delivered by the relevant path. A sensor node is elected to acquire data sent from a source DS and the out-degree value is at least 1 where the link is established from the source node and it is given as, $\sum_{sn \in SN} J_{(ds, sn)} ⩾ 1, \forall sn \in SN$ (9)

The count of the out-degree links for every node is not greater than the value 1 and in the inter-routing region at most one link from the senor node x to y. The establishment of the scenario is given as, $\sum_{sn \in SN} J_{(x, y)} ⩽ 1, \forall x \in SN$ (10)

The intermediary nodes are forwarded and they are received by the cluster head, which assures the establishment of a link among the cluster head to the sink for the data transmission. Thus, one relay node x is available to deliver coverage to a cluster that has members with the constraint b_dsd ⩾ 1, ds ∈ DS and the in-degree link summation to the relay node x is assigned with the value 1 is given as, $\sum_{y \in SN} J_{(x, D)} ⩾ b_{dsd}, \forall ds \in DS, \forall d \in D$ (11)

After completing the data forwarding that is data delivered to the sink node and at least a single node is available to deliver the coverage to the sink node. The in-degree link summation is assigned as 1 and it is equated as, $\sum_{x \in SN} J_{(x, S)} ⩾ 1$ (12)

3.5 Constraints in node to node data transmission

The data transmission among the node x and y is represented by d(x,y) and it is given in the following equation,

$\begin{matrix} d_{(x, y)} = \\ \frac{(g^{- λ} \sum_{n \in SN} a {(n . x)}^{(DIFS)} (RTS + SIFS + CTS + \bar{B}) + DIFS + \bar{N})}{g^{- λ} \sum_{n \in SN} a {(n . x)}^{(DIFS) - (RTS + SIFS + 2 θ)} g^{- λ} \sum_{n \in SN} a (n, y) - \bar{N}, \forall (x, y) \in M} \end{matrix}$ (13) where the random backoff time is signified as $\bar{B}$ and the average of time in NAV is denoted as $\bar{N}$ . The data transmission time is denoted by the CT ACK, and RTS frames. The SIFS and DIFS signify the short inter-frame space and distributed inter-frame space respectively.

The data transmission among the node to node with the transmission time is denoted as ∅ (x, y), and for the active links, the range is assigned as 1. The acquiring of minimum energy utilization TU is l(x,y) and the transmission is given as, $l_{(x, y)} - K_{3} (l - V_{(x, y)}) ⩽ \emptyset_{(x, y)}, \forall (x, y) ɛ M$ (14)

3.6 Constraints in duty cycle scheduling

The incoming data from the node with a predetermined limit of time is received by the clustered node and it is given in the following equation $k_{x} + \emptyset_{(x, y)} + ɛ ⩽ k_{y}, \forall (x, y) \in M$ (15) where the value k_y signifies the successful transmission of data to the node y. Every node in the transmission area has a guard time ɛ and the information is gathered within the guard time. Thus, the sleep latency issue is rectified and for the duty cycle schedule, the time bias in the retransmission is minimized. The wake-up time of the node y is signified as l_y and the transmission is earlier. The total energy utilization TU is signified as follows, $minTU = \sum_{x \in SN} [(k_{x} - l_{x}) E_{r} + g_{x} (r_{x}) \sum_{y \in SN} \emptyset_{(x, y)}]$ (16)

Equation 18 comprises of utilized energy within the transmission network when the data sent and received where sensor nodes are idle.

4 Evaluation of simulation

In this section, the simulation result of the proposed protocol CSRGR is discussed and the results are compared with the approaches namely SSA, LWS, and SSP. The experiment is executed using Network Simulator (NS-2.34). The simulation parameters applied in the experiment are given in Table 1.

Table 1
Simulation parameters

Parameters Values

No. of sensor nodes 500

Simulation area 1000×1000m²

Routing protocol Dynamic Source Routing (DSR)

Queue type CMUPriQueue

Initial energy 100Joule

Packet size 300bits

MAC type MAC/802.11

Simulation time 65ms

Parameters	Values
No. of sensor nodes	500
Simulation area	1000×1000m²
Routing protocol	Dynamic Source Routing (DSR)
Queue type	CMUPriQueue
Initial energy	100Joule
Packet size	300bits
MAC type	MAC/802.11
Simulation time	65ms

4.1 Packet delivery ratio

Packet Delivery Ratio (PDR) is calculated from the count of the data packets delivered from the total count of the data packet transferred from the source to the destination node [19]. The data transmission algorithm with the highest packet delivery is considered the best algorithm. The construction of the cluster is initiated to attain the data delivery with the proper scheduling mechanism and the rate of PDR is high when compared with the other geographic and scheduling approaches. In the CSRGR, the data transmission schedule is initiated based on precedence that provides assistance to achieve the restrictions of the delay in the data transmission. Thus, a node with maximal density in CSRGR has a higher PDR value than the SSA, LWS, and SSP methods. The performance of the CSRGR, SSA, LWS, and SSP algorithm is depicted in Table 2 and Fig. 2.

Table 2
Comparison of packet delivery ratio in percentage

Algorithm Existing algorithm Proposed algorithm

Network densities EA-TPGF-SS TPGS SSP CSRGR

1 0.85 0.858 0.856 0.89

1.15 0.86 0.868 0.871 0.91

1.3 0.874 0.889 0.895 0.924

1.45 0.870 0.889 0.894 0.929

1.6 0.889 0.886 0.9 0.949

Algorithm	Existing algorithm	Proposed algorithm
Network densities	EA-TPGF-SS	TPGS	SSP	CSRGR
1	0.85	0.858	0.856	0.89
1.15	0.86	0.868	0.871	0.91
1.3	0.874	0.889	0.895	0.924
1.45	0.870	0.889	0.894	0.929
1.6	0.889	0.886	0.9	0.949

Fig. 2

Comparison of packet delivery ratio.

Fig. 3

Comparison of delivery delay.

4.2 End to end delay

The additional time taken to transmit the data from the source to the destination node is the end to end delay [19]. The protocol with minimum transmission delay is considered as an effective protocol. In the CSRGR protocol, effective data transmission is attained by the cluster-based scheduling strategy with minimum delay. The resultant values of end to end delay of the CSRGR, SSA, LWS, and SSP algorithm is given in Table 3 and Fig. 4. The end to an end delay time of the proposed protocol CSRGR is effectively minimized with the clustering based scheduling approach.

Table 3
Comparison of delivery delay

Algorithm Existing algorithm Proposed algorithm

Network densities EA-TPGF-SS TPGS SSP CSRGR

1 98 99 109 89

1.15 93 96 106 88

1.3 91 93 105 86

1.45 90 90 100 85

1.6 89 88 99 81

Algorithm	Existing algorithm	Proposed algorithm
Network densities	EA-TPGF-SS	TPGS	SSP	CSRGR
1	98	99	109	89
1.15	93	96	106	88
1.3	91	93	105	86
1.45	90	90	100	85
1.6	89	88	99	81

Fig. 4

Comparison of energy consumption under different transmission range.

4.3 Energy consumption

In the WSN, every sensor node in the data transmission environment is instilled with the rechargeable batteries, and the minimum amount of energy where recharging the batteries also difficult [20]. The data transmission is initiated by the cluster and duty cycle scheduling mechanism. The data transmission process is accomplished without any interruption and data transmission is achieved in the shortest path with effective energy utilization. This situation reduces the exhaust of energy in the transmission nodes. The CSRGR protocol has minimized the usage of energy across various transmission range and network densities. The energy consumption value of the existing and proposed algorithm is displayed in Tables 4 and 5.

Table 4
Comparison of energy consumption vs. transmission range

Algorithm Existing algorithm Proposed algorithm

Transmission range EA-TPGF-SS TPGS SSP CSRGR

1.6 221 229 234 190

1.8 226 231 236 197

2 237 238 241 199

2.2 246 246 249 200

2.4 251 256 260 207

Algorithm	Existing algorithm	Proposed algorithm
Transmission range	EA-TPGF-SS	TPGS	SSP	CSRGR
1.6	221	229	234	190
1.8	226	231	236	197
2	237	238	241	199
2.2	246	246	249	200
2.4	251	256	260	207

Table 5

Comparison of energy consumption vs. network densities

Algorithm	Existing algorithm			Proposed algorithm
Transmission range	EA-TPGF-SS	TPGS	SSP	CSRGR
1	339	345	355	325
1.15	339	348	369	326
1.3	340	359	369	326
1.45	343	359	374	330
1.6	346	363	387	331

In Figs. 5 and 6, the utilization of energy across various transmission ranges and network densities is illustrated and from the observation, it is identified that the proposed algorithm utilizes minimum energy.

Fig. 5

Comparison of energy consumption across various network densities.

Fig. 6

Comparison of network lifetime.

4.3 Network lifetime

The sensor nodes in the network at some state will run of power or energy, which results in the loss of functionality, and packet delivery is not accomplished [21].

The increased network lifetime of the algorithm results in enhanced performance and the values are shown in Table 6. The comparison of network lifetime and the effectiveness of the proposed CSRGR algorithm are depicted in Fig. 6.

Table 6
Comparison of network lifetime

Algorithm Existing algorithm Proposed algorithm

Network densities EA-TPGF-SS TPGS SSP CSRGR

1 221 222 245 255

1.15 229 240 248 261

1.3 224 245 251 269

1.45 229 250 259 274

1.6 246 258 262 287

Algorithm	Existing algorithm	Proposed algorithm
Network densities	EA-TPGF-SS	TPGS	SSP	CSRGR
1	221	222	245	255
1.15	229	240	248	261
1.3	224	245	251	269
1.45	229	250	259	274
1.6	246	258	262	287

4.4 Communication overhead

The communication overhead is the ratio of the whole count of the data packets transferred from the source location to the destination location [23, 24]. It is the proportion of time applied in communicating within the transmission network. The comparison of communication overhead and the effectiveness of the proposed CSRGR algorithm are depicted in Table 7 and Fig. 7.

Table 7
Comparison of communication overhead

Algorithm Existing algorithm Proposed algorithm

Network densities EA-TPGF-SS TPGS SSP CSRGR

1 421 449 441 411

1.15 426 449 448 429

1.3 426 460 449 432

1.45 440 464 459 434

1.6 441 466 464 437

Algorithm	Existing algorithm	Proposed algorithm
Network densities	EA-TPGF-SS	TPGS	SSP	CSRGR
1	421	449	441	411
1.15	426	449	448	429
1.3	426	460	449	432
1.45	440	464	459	434
1.6	441	466	464	437

Fig. 7

Comparison of communication overhead.

From the observation of results in Table 7 and Fig. 7, the communication overhead is minimum in the CSRGR approach.

4.5 Throughput

Throughput is the definite quantity of data that is effectively sent/received over the communication link. Throughput is offered as kbps, Mbps or Gbps, and can vary from bandwidth due to a range of technical concerns, including packet loss, latency, jitter and more. Throughput denotes to how much data can be transferred from one location to another in a given amount of time.

From the observation of results in Table 8 and Fig. 8, the throughput is high in the CSRGR approach. The simulation results proves that the strategy presented by CSRGR approach is applicable in both small and large networks and the results demonstrated that the network lifetime using CSRGR approach is high when compared to other existing protocols with respect to various network densities respectively. Further, cluster based duty cycle scheduling enabled the CSRGR algorithm to decrease node energy consumption and achieved better load balancing among the nodes. From the results analysis, it has been also evidenced that the computational cost in terms of end to end delay is not improved as expected on increasing the network density. Stress testing in WSN can be considered as the process of determining that the network can continue to operate effectively under challenging circumstances or under high network densities. In this research work, the performance of the network is analyzed using various network densities which has been considered as a stress testing factor. Then, the PDR, End to End Delay, Energy Consumption, Network Lifetime, Communication Overhead and Thorughput has been analyzed and the proposed approach is found better than the existing algorithms under various network densities (stress factor).

Table 8
Comparison of throughput

Algorithm Existing algorithm Proposed algorithm

Network densities EA-TPGF-SS TPGS SSP CSRGR

1 0.88 0.891 0.889 0.91

1.15 0.89 0.893 0.891 0.93

1.3 0.894 0.898 0.895 0.937

1.45 0.897 0.899 0.896 0.939

1.6 0.899 0.9 0.9 0.943

Algorithm	Existing algorithm	Proposed algorithm
Network densities	EA-TPGF-SS	TPGS	SSP	CSRGR
1	0.88	0.891	0.889	0.91
1.15	0.89	0.893	0.891	0.93
1.3	0.894	0.898	0.895	0.937
1.45	0.897	0.899	0.896	0.939
1.6	0.899	0.9	0.9	0.943

Fig. 8

Comparison of throughput.

According to Table 9, the performance in terms of PDR, End to End Delay, Energy Consumption, Network Lifetime, Communication Overhead and Thorughput (independent variable) taken by these of the existing and proposed algorithms are significantly different with respect to the various network densities (dependent variable); the p-value concludes that the regression model is a good fit for the data.

Table 9

Significance of F (P-value) obtained using ANOVA

Algorithms	Packet delivery ratio	End to end relay	Energy consumption	Network lifetime	Communication overhead	Throughput
	Significance of F (P-value)
EA-TPGF-SS	0.04	0.03	0.002	0.01	0.04	0.03
TPGF
SSP
CSRGR

5 Conclusion

The sensor nodes in the data transmission network exploit additional energy during the transmission of data rather than the computation. Extending the working of the battery as well as improving the lifetime is a primary goal in designing the transmission protocol. The geographic routing protocols are localized or position-based routing approaches that are developed for the resource-constrained WSNs. The geographic routing has the benefits namely bypassing of the hole, multipath, and shortest path. The existing approaches EA-TPGF-SS, TPGS, and SSP have various limitations. EA-TPGF-SS is developed has high degradation in the rate of Packet Delivery Ratio (PDR). Two-Phase Greedy Forwarding (TPGF) causes the rate of data transmission delay and sleep also reduced, which makes the data transmission ineffectual. A sleep scheduling mechanism for multicast routing in a geographic-based approach is established to schedule the process of data transmission and routing. The shortcoming in the existing approaches is rectified and designed in the proposed framework that is cluster-based scheduling and routing in geographic routing protocol (CSRGR). In this approach cluster scheme is presented and the nodes are organized with the support of the formed cluster. The duty cycle mechanism is incorporated in scheduling for the effective data transmission process. The simulation results are analyzed and compared with the available approaches, from the observation of results it is identified that the proposed CSRGR is highly effective in terms of end to end delay, throughput and PDR rate.

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Energy – Efficient routing and scheduling using clustering in geographic routing protocol

Abstract

Keywords

1 Introduction

2 Related works

3 Problem statement and proposed model

3.1 Cluster construction

Table 1 Simulation parameters Parameters Values No. of sensor nodes 500 Simulation area 1000×1000m2 Routing protocol Dynamic Source Routing (DSR) Queue type CMUPriQueue Initial energy 100Joule Packet size 300bits MAC type MAC/802.11 Simulation time 65ms

Table 2 Comparison of packet delivery ratio in percentage Algorithm Existing algorithm Proposed algorithm Network densities EA-TPGF-SS TPGS SSP CSRGR 1 0.85 0.858 0.856 0.89 1.15 0.86 0.868 0.871 0.91 1.3 0.874 0.889 0.895 0.924 1.45 0.870 0.889 0.894 0.929 1.6 0.889 0.886 0.9 0.949

Table 3 Comparison of delivery delay Algorithm Existing algorithm Proposed algorithm Network densities EA-TPGF-SS TPGS SSP CSRGR 1 98 99 109 89 1.15 93 96 106 88 1.3 91 93 105 86 1.45 90 90 100 85 1.6 89 88 99 81

Table 4 Comparison of energy consumption vs. transmission range Algorithm Existing algorithm Proposed algorithm Transmission range EA-TPGF-SS TPGS SSP CSRGR 1.6 221 229 234 190 1.8 226 231 236 197 2 237 238 241 199 2.2 246 246 249 200 2.4 251 256 260 207

Table 6 Comparison of network lifetime Algorithm Existing algorithm Proposed algorithm Network densities EA-TPGF-SS TPGS SSP CSRGR 1 221 222 245 255 1.15 229 240 248 261 1.3 224 245 251 269 1.45 229 250 259 274 1.6 246 258 262 287

Table 7 Comparison of communication overhead Algorithm Existing algorithm Proposed algorithm Network densities EA-TPGF-SS TPGS SSP CSRGR 1 421 449 441 411 1.15 426 449 448 429 1.3 426 460 449 432 1.45 440 464 459 434 1.6 441 466 464 437

Table 8 Comparison of throughput Algorithm Existing algorithm Proposed algorithm Network densities EA-TPGF-SS TPGS SSP CSRGR 1 0.88 0.891 0.889 0.91 1.15 0.89 0.893 0.891 0.93 1.3 0.894 0.898 0.895 0.937 1.45 0.897 0.899 0.896 0.939 1.6 0.899 0.9 0.9 0.943

References

Table 1
Simulation parameters

Parameters Values

No. of sensor nodes 500

Simulation area 1000×1000m²

Routing protocol Dynamic Source Routing (DSR)

Queue type CMUPriQueue

Initial energy 100Joule

Packet size 300bits

MAC type MAC/802.11

Simulation time 65ms

Table 2
Comparison of packet delivery ratio in percentage

Algorithm Existing algorithm Proposed algorithm

Network densities EA-TPGF-SS TPGS SSP CSRGR

1 0.85 0.858 0.856 0.89

1.15 0.86 0.868 0.871 0.91

1.3 0.874 0.889 0.895 0.924

1.45 0.870 0.889 0.894 0.929

1.6 0.889 0.886 0.9 0.949

Table 3
Comparison of delivery delay

Algorithm Existing algorithm Proposed algorithm

Network densities EA-TPGF-SS TPGS SSP CSRGR

1 98 99 109 89

1.15 93 96 106 88

1.3 91 93 105 86

1.45 90 90 100 85

1.6 89 88 99 81

Table 4
Comparison of energy consumption vs. transmission range

Algorithm Existing algorithm Proposed algorithm

Transmission range EA-TPGF-SS TPGS SSP CSRGR

1.6 221 229 234 190

1.8 226 231 236 197

2 237 238 241 199

2.2 246 246 249 200

2.4 251 256 260 207

Table 6
Comparison of network lifetime

Algorithm Existing algorithm Proposed algorithm

Network densities EA-TPGF-SS TPGS SSP CSRGR

1 221 222 245 255

1.15 229 240 248 261

1.3 224 245 251 269

1.45 229 250 259 274

1.6 246 258 262 287

Table 7
Comparison of communication overhead

Algorithm Existing algorithm Proposed algorithm

Network densities EA-TPGF-SS TPGS SSP CSRGR

1 421 449 441 411

1.15 426 449 448 429

1.3 426 460 449 432

1.45 440 464 459 434

1.6 441 466 464 437

Table 8
Comparison of throughput

Algorithm Existing algorithm Proposed algorithm

Network densities EA-TPGF-SS TPGS SSP CSRGR

1 0.88 0.891 0.889 0.91

1.15 0.89 0.893 0.891 0.93

1.3 0.894 0.898 0.895 0.937

1.45 0.897 0.899 0.896 0.939

1.6 0.899 0.9 0.9 0.943