Nitin Mittal

Modified Grey Wolf Optimizer for Global Engineering Optimization

Nature-inspired algorithms are becoming popular among researchers due to their simplicity and flexibility. The nature-inspired metaheuristic algorithms are analysed in terms of their key features like their diversity and adaptation, exploration and exploitation, and attractions and diffusion mechanisms. The success and challenges concerning these algorithms are based on their parameter tuning and parameter control. A comparatively new algorithm motivated by the social hierarchy and hunting behavior of grey wolves is Grey Wolf Optimizer GWO, which is a very successful algorithm for solving real mechanical and optical engineering problems. In the original GWO, half of the iterations are devoted to exploration and the other half are dedicated to exploitation, overlooking the impact of right balance between these two to guarantee an accurate approximation of global optimum. To overcome this shortcoming, a modified GWO mGWO is proposed, which focuses on proper balance between exploration and exploitation that leads to an optimal performance of the algorithm. Simulations based on benchmark problems and WSN clustering problem demonstrate the effectiveness, efficiency, and stability of mGWO compared with the basic GWO and some well-known algorithms.

A stable energy efficient clustering protocol for wireless sensor networks

Sensor networks comprise of sensor nodes with limited battery power that are deployed at different geographical locations to monitor physical events. Information gathering is a typical but an important operation in many applications of wireless sensor networks (WSNs). It is necessary to operate the sensor network for longer period of time in an energy efficient manner for gathering information. One of the popular WSN protocol, named low energy adaptive clustering hierarchy (LEACH) and its variants, aim to prolong the network lifetime using energy efficient clustering approach. These protocols increase the network lifetime at the expense of reduced stability period (the time span before the first node dies). The reduction in stability period is because of the high energy variance of nodes. Stability period is an essential aspect to preserve coverage properties of the network. Higher is the stability period, more reliable is the network. Higher energy variance of nodes leads to load unbalancing among nodes and therefore lowers the stability period. Hence, it is perpetually attractive to design clustering algorithms that provides higher stability, lower energy variance and are energy efficient. In this paper to overcome the shortcomings of existing clustering protocols, a protocol named stable energy efficient clustering protocol is proposed. It balances the load among nodes using energy-aware heuristics and hence ensures higher stability period. The results demonstrate that the proposed protocol significantly outperforms LEACH and its variants in terms of energy variance and stability period.

A boolean spider monkey optimization based energy efficient clustering approach for WSNs

Wireless sensor network (WSN) consists of densely distributed nodes that are deployed to observe and react to events within the sensor field. In WSNs, energy management and network lifetime optimization are major issues in the designing of cluster-based routing protocols. Clustering is an efficient data gathering technique that effectively reduces the energy consumption by organizing nodes into groups. However, in clustering protocols, cluster heads (CHs) bear additional load for coordinating various activities within the cluster. Improper selection of CHs causes increased energy consumption and also degrades the performance of WSN. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for long run operation of WSN. Clustering a network with proper load balancing is an NP-hard problem. To solve such problems having vast search area, optimization algorithm is the preeminent possible solution. Spider monkey optimization (SMO) is a relatively new nature inspired evolutionary algorithm based on the foraging behaviour of spider monkeys. It has proved its worth for benchmark functions optimization and antenna design problems. In this paper, SMO based threshold-sensitive energy-efficient clustering protocol is proposed to prolong network lifetime with an intend to extend the stability period of the network. Dual-hop communication between CHs and BS is utilized to achieve load balancing of distant CHs and energy minimization. The results demonstrate that the proposed protocol significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.

A Novel Energy Efficient Stable Clustering Approach for Wireless Sensor Networks

Wireless sensor network (WSN) consists of densely distributed nodes that are deployed to observe and react to events within the sensor field. In WSNs, energy management and network lifetime optimization are major issues in the designing of routing protocols. Clustering is an efficient data gathering technique that effectively reduces the energy consumption by organizing nodes into groups. However, in clustering protocols, cluster heads (CHs) bear additional load for coordinating various activities within the cluster. Improper selection of CHs causes increased energy consumption and also degrades the performance of WSN. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for the long run operation of WSN. Clustering a network with proper load balancing is an NP-hard problem. To solve such problems having vast search area, optimization algorithm is the preeminent possible solution. In this paper, differential evolution based clustering algorithm for WSNs named threshold-sensitive energy-efficient delay-aware routing protocol (TEDRP), is proposed to prolong network lifetime. Dual-hop communication between CHs and BS is utilized to achieve load balancing of distant CHs and energy minimization. The paper also considers stability-aware model of TEDRP named stable TEDRP (STEDRP) with an intend to extend the stability period of the network. In STEDRP, energy aware heuristics is applied for CH selection in order to improve the stability period. The results demonstrate that the proposed protocols significantly outperform existing protocols in terms of energy consumption, system lifetime and stability period.

An Optimal Tree-Based Routing Protocol Using Particle Swarm Optimization

Wireless sensor networks (WSNs) contain many sensor nodes which are deployed in the various geographical areas to perform various tasks like monitoring, data aggregation and data processing. For performing all these operations, energy is highly consumed, thus sensor nodes begin to die soon and also creates energy holes in some of the geographical locations. All the sensor nodes are powered by battery, and it is quite difficult to replace the battery, and so energy consumption is prime objective to increase the network lifetime. Clustering and tree-based routing like LEACH, PEDAP, TBC and TREEPSI solves most of the energy consumption problem as it saves energy during a lot of operations in WSNs. In this paper, we propose an optimal tree-based routing protocol (OTBRP) that is efficient in terms of stability period (time period before first node dead) and therefore offers good network lifetime. The parameters like first node dead, half node dead and last node dead are considered for the measurement of network lifetime. In order to evaluate the performance of OTBRP, the comparison is made with the GSTEB and PEGASIS. Simulation results show that there is a gain of approx. 200 and 150% in stability period in comparison with PEGASIS and GSTEB, respectively.

An energy-aware cluster-based stable protocol for wireless sensor networks

The widespread use of wireless sensor devices and their advancements in terms of size, deployment cost and user-friendly interface have given rise to many applications of wireless sensor networks (WSNs). WSNs need to utilize routing techniques to forward data samples from event regions to sink via minimum cost links. Clustering is a commonly used data aggregation technique in which nodes are organized into groups in order to reduce the energy consumption. However, in clustering protocols, cluster-head (CH) has to bear an additional load for coordinating various activities within the cluster. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for the long-run operation of WSN. In this paper, genetic algorithm (GA)-based threshold-sensitive energy-efficient routing protocol (TERP) is proposed to prolong network lifetime. Multi-hop communication between CHs and base station (BS) is utilized using GA to achieve optimal link cost for load balancing of distant CHs and energy minimization. The paper also considers stability-aware model of TERP named stable TERP (STERP) so as to extend the stability period (time interval from initial time to the death of first node) of the network. In STERP, energy-aware heuristics is applied for CH selection in order to improve the stability period. Analysis and simulation results demonstrate that the proposed methodology significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.

Mobility based application specific low power routing protocol for wireless sensor networks

Data gathering is a common but critical operation in many applications of wireless sensor networks (WSNs). Innovative techniques that improve Energy efficiency to prolong the network lifetime are highly required. The lifetime of a sensor system is the time during which it gathers information from all the sensors to the base station (BS). Given the location of sensors, BS and the available energy at each sensor, there is a requirement of an efficient manner in which the data should be collected from all the sensors and transmitted to BS, such that the system lifetime is maximized. In this paper, the researchers investigated a mobility based application specific low power routing (M-ASLPR) protocol for mobile WSNs (MWSNs). In this protocol, a sensor node gets elected as CH depending upon some parameters like residual energy, mobility and connection time etc. Simulation results show that the performance of M-ASLPR protocol is varied in terms of network lifetime according to the dynamic node densities and speed. The network lifetime gets affected on increasing the number of mobile nodes and speed which is due to high traffic in the network as mobile nodes interfere with each other.

Moth Flame Optimization Based Energy Efficient Stable Clustered Routing Approach for Wireless Sensor Networks

The widespread use of wireless sensor devices and their advancements in terms of size, deployment cost and user friendly interface have given rise to many applications of wireless sensor networks (WSNs). WSNs need to utilize routing protocols to forward data samples from event regions to sink via minimum cost links. Clustering is an efficient data aggregation method that effectively reduces the energy consumption by organizing nodes into groups. However, in clustering protocols, CH has to bear an additional load for coordinating various activities within the cluster. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for the long run operation of WSN. Clustering a network with proper load balancing is an NP-hard problem. To solve such problems having vast search area, optimization algorithm is the preeminent possible solution. In this paper, moth flame optimization (MFO) based threshold-sensitive energy-efficient clustering protocol (TECP) is proposed to extend the stability period of the network. Multi-hop communication between CHs and BS is utilized using MFO to achieve optimal link cost for load balancing of distant CHs and energy minimization. Analysis and simulation results demonstrate that the proposed methodology significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.

An approach to localize the blind node and optimizing parameters by using PSO

In this paper, an optimization technique is proposed in order to optimize various parameters such as energy, attenuation. Localization of blind node is accomplished with minimum error under different operating environment respectively. The problem of node localization is considered with the help of ML estimation technique in wireless sensor networks. In this paper, we consider the problem of high bandwidth utilization due to multilinks between sensors and parameters were not optimized according to signal strength. We introduce an optimization technique in order to localize a blind node in both homogeneous environment respectively. Based on the simulations, we compare performances of ML estimation and optimization technique in both the environments respectively.