Several protocols strive to improve network longevity but fail to ameliorate the uneven overhead imparted upon the sensor nodes that lead to temporal deaths. The proposed work uses a metaheuristic approach that promotes load balancing and energy-efficient data transmission using the fruit fly optimization algorithm (FFOA). The approach combines the LEACH protocol with differential evolution (DE) to select an optimum cluster head in every cluster. The algorithm is designed to provide energy-efficient data transmissions based on the smell and vision foraging behavior of fruit flies. The approach considers the compactness of nodes, energy capacity, and the distance of sensor nodes from the base station and geocentric location, and other factors to select an optimal cluster head. It provides an optimal solution for the nodes in overlapping cluster heads and the energy problem that occurs due to uneven clustering. The metaheuristic approach implements multi-hop routing by finding an optimal path and allows the cluster head re-election strategy when the data transmission is intense. Simulations prove that FFOA-based LEACH increases the network lifetime through energy-efficient clustering and routing when compared with LEACH and DE-LEACH.

Wireless sensor networks (WSNs) have attracted interest in recent years for many applications where human intervention is not possible. Application-oriented sensor nodes are positioned in a geographical location referred to as a sensing field to perceive the changes in the surroundings. These sensor nodes are of low cost, use less power, and have limited transmission capability. Hence, prolonging the lifetime of WSNs is a major challenge in addition to security, reliability, and scalability [

Several protocols exist to perform energy-efficient clustering and routing [

The proposed paper depicts a blend of differential evolution (DE) with a metaheuristic approach using the fruit fly optimization algorithm (FFOA) to choose the cluster heads. The study also strives to render optimal path selection of cluster heads to the base station and analyzes multiple ways to discover an energy-efficient network. The cluster heads are re-elected whenever the residual energy goes below the threshold to promote the network lifetime and to thus reduce the premature death of nodes.

Section 3 describes fruit fly behavior and the mathematical model of the way the flies locate their food. This Section hybridizes DE with optimization of the fruit fly algorithm to perform clustering in the LEACH protocol. Section 4 portrays multi-hop routing using the Floyd-Warshall algorithm. Implementation results are discussed in Section 5, and the final conclusion and future scope are presented in Section 6.

LEACH, an improved energy-efficient communication model [_{i}(

where

An improved version of the LEACH protocol curtailed the energy utilization for data transmission in WSNs [

A biologically inspired optimal method [

The well-known particle swarm optimization (PSO) clustering [

An evolutionary-based optimization algorithm was presented based on the prey chasing behavior of grey wolves [

The selection of cluster head plays a predominant role in establishing the WSN and reducing the extent of the temporal death of nodes. This can be achieved by combining the DE and FFOA [

The main motivation behind the proposed work is to examine the existing methods and develop a more energy-efficient WSN. Usually, proactive protocols are applied for intra-cluster communication and reactive protocols are used for inter-cluster communications. Routing enables connectivity between sensor nodes and improves the mobility of data. The following assumptions and network models are implemented to improve the residual energy of the nodes:

The cluster heads and member nodes are kept with the least mobility, and the base station is kept static.

Communication between the member nodes and the cluster heads are bidirectional.

The network consists of cluster heads, member nodes, base station, and helper cluster head to assist the cluster heads if needed.

Multi-hop routes should be established from the cluster head. Routes cannot be established by member nodes.

Routes are given with an identification number (ID) to perform message exchanges.

Channel capacity may vary from node to node according to link strength.

Least hop count makes the data packets reach the base station quickly.

Time-division multiple access with collision detection can be implemented.

A member node can sense and send the data and sleep if no data are sensed or exchanged. A node selected as cluster head or helper cluster head will not sleep.

The steps for LEACH-DE-FFOA are as follows:

Step A: Initial selection of cluster heads using the energy model of LEACH.

Step B: Assess the fitness function (ffn) using DE.

Step C: Find the initial positions of fruit flies using DE.

Step D: Selection of cluster heads using FFOA with the following steps:

Handle nodes that are common to two or more clusters.

Find an optimal path using multi-hop transmission.

Step E: Calculate the residual energy of cluster heads:

If cluster heads consume more energy, repeat step 4.

Or if cluster heads consume less energy, Stop.

The nodes in the network are considered to be static in the network. Messages are exchanged between the neighboring nodes to share the location and energy information. The cluster head is selected for the first time based on two important facets: the node has more energy capacity and the node is nearer to the base station. The cluster heads have the role of aggregating data from member nodes using their limited memory and power and need to send the same information to the base station through the least hops. Hence, the cluster heads nearer to the base station can transmit data faster without spending much more energy than those that are farthest.

The proposed protocol considers the geometric center (GC) of the network based on the density of the nodes. The cluster heads nearer to the GC are considered to be super cluster heads and possess more neighbors nearby. The super cluster heads relay the data transferred by the cluster heads that are farthest from the base station. If a cluster is dense, it is likely to aggregate data from its member nodes with lesser energy as the distance is smaller between member nodes and the cluster head. _{temp} is formulated as in

Here,

IE_{m} Inceptive Energy of a node,

RE_{m} Residuary Energy of node

d_{toBS} Euclidean distance of node

d_{fartoBS} Euclidean distance of the farthest node to BS.

d_{toGC} Euclidean distance between a node to the geometric center.

d_{fartoGC}Euclidean distance from the farthest node to the geometric center.

The _{c} factor represents the number of nodes that a cluster head can cover. The probability of a node ‘_{temp} value:

The proposed methodology uses DE to find the positions of nodes through mutation, crossover, and selection [

Pseudo code_ Differential Evolution |
---|

BEGIN |

Define f_{p}, FV, C_{R} |

n → 0 |

DO |

n → n + 1 |

FOR EACH parameter vector PV_{i,G} |

CHOOSE r_{1}, r_{2}, r_{3} such that r_{1}≠r_{2}, r_{2}≠r_{3}, r_{3}≠r_{1} |

COMPUTE MV_{i, Gen} → PV_{r3, Gen} + FV(PV_{r1, Gen} – PV_{r2, Gen}) |

SET j=randint(0,1), k → 0 |

WHILE rand(0,1) ≤ C_{R} AND k < n-1 |

DO |

U_{k,child}((j+1) mod n) = U_{k}(1+(j+k) mod n) |

k+=1 |

GENERATE rand(0,1) |

END WHILE |

IF f(U_{i, Gen}) < f(PV_{i, Gen}) |

THEN |

PV_{i, Gen+1} → U_{i, Gen} |

ELSE |

PV_{i, Gen+1} → PV_{i, Gen} |

END IF |

i += 1 |

END FOR |

RETURN PV_{i, Gen+1} |

In the first step, the initial fitness/adaptive function of the parent generation is defined. In the subsequent production, if the appropriateness of the child is superior to the parent, then the fitness value of the child is considered and replaces the set of parents with children. If not, then the fitness function value of the parent is considered when selecting the cluster head in the next iteration. This helps to fix the primary positions of the fruit flies in the FFOA. The spots of the fruit flies are identified based on mutation value, crossover probability, and mutation probability as in

where _{c} represents the number of individuals exchanged in the group, _{cross} value if the fitness value is less than the average fitness value. Otherwise, the following applies:

Similarly, the final mutation probability will be the same as _{mut} if the fitness value is less than the average fitness value. Otherwise, the following applies:

where _{mff} represents the maximum fitness function value.

The limitation of DE is that it requires more time to find the search distance during the initial steps. Sometimes it may be inaccurate due to the failure of global search. Hence, FFOA is used to minimize the scope of the search. FFOA follows the shortest distance between the nodes to improve the search accuracy. Let _{FFtoB} be the shortest distance between the initial position of the fruit fly and the boundary. Search scope is then defined as:

_{FFtoB} * (1 –

where _{FFtoB}/_{opt}, is modeled as in

Similarly, the common nodes between clusters may lead to replication of data and cause energy loss to the cluster heads [

The replication of data transfer by a node through two distinct cluster heads must be eliminated. The common nodes therefore have an entry for the hop count and distance to reach the respective cluster heads. A node can transmit data through the corresponding cluster head to the base station based on the least hop count value maintained within it. In such a case, the energy consumption can still be optimized as in

where

When the cluster heads are elected using the LEACH-DE-FFOA protocol, they are advertised to form clusters by announcing cluster head_ADV. The nodes other than cluster heads reply JOIN_REQ to the corresponding cluster head. In turn, the cluster head accepts the member node based on its closeness and allocates the schedule using the TDMA approach. When member nodes receive the slot to transfer data, they transmit packets and return to the sense/sleep mode. The cluster head performs aggregation of data packets from all its member nodes and communicates them to the base station [

The limitation ofthe cluster head is that it may lose more energy when data packets are too numerous. In such a condition, a helper-cluster head is represented by the cluster head considering the resource capacity and nearness to the cluster head. The nominated helper-CH takes the responsibility of route discovery and transfers the packets to the base station. All of the member nodes sleep when they are not performing the sensing operation, but the helper cluster heads never sleep. Single-hop or multi-hop transmissions are used to transfer data from a node to the base station through the cluster head. When the distance between the cluster head to reach the super-cluster head is less than the threshold distance, d, then the data packets are transferred by single-hop communication. Otherwise, multi-hop communication is applied. The Floyd-Warshall algorithm is used to find the shortest distance to reach the super-cluster head by a cluster head as opposed to computing an optimal path. The algorithm shown in

The DE- and FFOA-combined LEACH protocol was tested in a simulation using NS2 to verify its performance. Sensor nodes of 100 count were randomly deployed over a region of 100 m × 100 m. The simulation parameters are shown in

Parameter | Value |
---|---|

Transmission model | Two ray ground |

MAC Protocol | IEEE 802.15.14 |

Traffic | Poisson Traffic |

Protocol | LEACH |

No. of sensor nodes | 100 |

Antenna | Omni directional antenna |

Frequency | 2.4 GHz |

Packet rate | 15 packets/sec |

Let the initial energy, IR, of the sensor nodes be 0.5J, and let a few nodes with 1J be introduced to portray the uneven energy distribution in the network. Let the geometric center be at the location (50, 50) and the transmission and reception energy be 50nJ/bit. The maximum number of rounds considered is 2000.

The number of nodes alive in each round is shown in

The residual energy of the nodes in the proposed protocol is comparatively larger than those described in previous work at each round of iteration as shown in

Delivery ratio is the number of data packets sent to the base station per unit time. The packet delivery ratio of the proposed method is analyzed as shown in

The comparison of network lifetime for LEACH-DE-FFOA compared with LEACH and DE-LEACH was studied, and the findings are shown in

Many approaches improve the lifetime of WSNs. However, the problem of uneven overhead imparted upon the sensor nodes still prevails. The DE-based FFOA hybrid optimization technique is a metaheuristic approach that prolongs the lifespan of WSNs. The uneven distribution of load on a sensor node is focused on and a scheme to distribute the load on multiple sensor nodes at each round is performed. The issue of nodes in overlapping cluster heads and the energy problem that occurs due to uneven clustering has been addressed. Multi-hop routing and a re-election strategy for cluster heads reduces the usage of energy and provides an optimal solution. Simulation results prove that the approach outperforms previousmethods and improves the overall network performance. Further, the work can be extended by applying several other nature-inspired optimization algorithms. The algorithm canbe made secure by usingother adaptive algorithms that result in secured commutation of packets.

We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.