A. Rajesh

Bio: A. Rajesh is an academic researcher from VIT University. The author has contributed to research in topics: Wireless sensor network & Node (networking). The author has an hindex of 7, co-authored 40 publications receiving 336 citations. Previous affiliations of A. Rajesh include Pondicherry Engineering College.

Implementation of an Efficient Artificial Bee Colony Algorithm for Node Localization in Unmanned Aerial Vehicle Assisted Wireless Sensor Networks

Abstract: Node localization is a fundamental task in wireless sensor networks as it is useful for several localization based protocols and applications. Node localization using Global Poisoning System (GPS) employed fixed terrestrial anchor nodes suffers from high deployment cost and poor localization accuracy in GPS denied locations. These issues can be easily handled by deploying movable Unmanned Aerial Vehicles (UAVs). A movable UAV equipped with a single GPS module virtually increases number of anchor nodes and localizes a node at different locations. Hence, UAVs are cost effective and also provides high localization accuracy. As the flying altitude of UAV greatly influence localization accuracy, the present work firstly optimizes the flying height and then the node localization is defined as least square optimization problem using this optimal height. Since the classical received signal strength indicator based multilateration results high localization error, the least square localization using optimization techniques is found to be better alternative. The recently proposed Artificial Bee Colony (ABC) algorithm is a powerful optimization technique that can be applied for this optimization problem to achieve high accuracy. Thus, this paper aims at designing an ABC localization technique using UAV anchors to achieve minimum localization error. Further, we provide detailed simulation analysis to support the proposed ABC localization scheme.

Implementation of self adaptive mutation factor and cross-over probability based differential evolution algorithm for node localization in wireless sensor networks

Abstract: Node localization or positioning is essential for many position aware protocols in a wireless sensor network. The classical global poisoning system used for node localization is limited because of its high cost and its unavailability in the indoor environments. So, several localization algorithms have been proposed in the recent past to improve localization accuracy and to reduce implementation cost. One of the popular approaches of localization is to define localization as a least square localization (LSL) problem. During optimization of LSL problem, the performance of the classical Gauss–Newton method is limited because it can be trapped by local minima. By contrast, differential evolution (DE) algorithm has high localization accuracy because it has an ability to determine global optimal solution to the LSL problem. However, the convergence speed of the conventional DE algorithm is low as it uses fixed values of mutation factor and cross-over probability. Thus, in this paper, a self-adaptive mutation factor cross-over probability based differential evolution (SA-MCDE) algorithm is proposed for LSL problem to improve convergence speed. The SA-MCDE algorithm adaptively adjusts the mutation factor and cross-over probability in each generation to better explore and exploit the global optimal solution. Thus, improved localization accuracy with high convergence speed is expected from the SA-MCDE algorithm. The rigorous simulation results conducted for several localization algorithms declare that the propose SA-MCDE based localization has about (40–90) % more localization accuracy over the classical techniques.

An unmanned aerial vehicle-aided node localization using an efficient multilayer perceptron neural network in wireless sensor networks

Abstract: Localization of sensor node is decisive for many localization-based scenarios of wireless sensor networks (WSNs). Node localization using fixed terrestrial anchor nodes (ANs) equipped with global positioning system (GPS) modules suffers from high deployment cost and poor localization accuracy, because the terrestrial AN propagates signals to the unknown nodes (UNs) through unreliable ground-to-ground channel. However, the ANs deployed in unmanned aerial vehicles (UAVs) with a single GPS module communicate over reliable air-to-ground channel, where almost clear line-of-sight path exists. Thus, the localization accuracy and deployment cost are better with aerial anchors than terrestrial anchors. However, still the nonlinear distortions imposed in propagation channel limit the performance of classical RSSI and least square localization schemes. So, the neural network (NN) models can become good alternative for node localization under such nonlinear conditions as they can do complex nonlinear mapping between input and output. Since the multilayer perceptron (MLP) is a robust tool in the assembly of NNs, MLP-based localization scheme is proposed for UN localization in UAV-aided WSNs. The detailed simulation analysis provided in this paper prefers the MLP localization scheme as they exhibit improved localization accuracy and deployment cost.

A low cost IoT based crowd management system for public transport

Abstract: With the ever growing global population, crowding in public transport is becoming an increasing menace. Public transport systems around the world have remained largely the same over the past several decades although the population they serve has burgeoned. This paper aims to demonstrate a low cost IoT based solution to the crowding problem by using smart seats that can detect and display the seat occupancy status in real time over an internet or mobile application. The feasibility of the project was assessed and simulated using the NETSIM simulation software. The results of the software simulation showed promise and hence a hardware prototype was built using the IEEE 802.15.4 standard on the Arduino — Raspberry Pi — nRF platform. The prototype results are positive and show a fully functional IoT system that can be implemented in buses and trains.

Comprehensive review for energy efficient hierarchical routing protocols on wireless sensor networks

Abstract: In recent years, wireless sensor networks (WSNs) have played a major role in applications such as tracking and monitoring in remote environments. Designing energy efficient protocols for routing of data events is a major challenge due to the dynamic topology and distributed nature of WSNs. Main aim of the paper is to discuss hierarchical routing protocols in order to improve the energy efficiency and network lifetime. This paper provides a discussion about hierarchical energy efficient routing protocols based on classical and swarm intelligence approach. The routing protocols belonging to both categories can be summarized according to energy efficiency, data aggregation, location awareness, QoS, scalability, load balancing, fault tolerance, query based and multipath. A systematic literature review has been conducted for hierarchical energy efficient routing protocols reported from 2012 to 2017. This survey provides a technical direction for researchers on how to develop routing protocols. Finally, research gaps in the reviewed protocols and the potential future aspects have been discussed.

A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking

Abstract: Due to its simplicity and efficiency, a recently proposed optimization algorithm, Sine Cosine Algorithm (SCA), has gained the interest of researchers from various fields for solving optimization problems. However, it is prone to premature convergence at local minima as it lacks internal memory. To overcome this drawback, a novel Hybrid SCA-PSO algorithm for solving optimization problems and object tracking is proposed. The P b e s t and G b e s t components of PSO (Particle Swarm Optimization) is added to traditional SCA to guide the search process for potential candidate solutions and PSO is then initialized with P b e s t of SCA to exploit the search space further. The proposed algorithm combines the exploitation capability of PSO and exploration capability of SCA to achieve optimal global solutions. The effectiveness of this algorithm is evaluated using 23 classical, CEC 2005 and CEC 2014 benchmark functions. Statistical parameters are employed to observe the efficiency of the Hybrid SCA-PSO qualitatively and results prove that the proposed algorithm is very competitive compared to the state-of-the-art metaheuristic algorithms. The Hybrid SCA-PSO algorithm is applied for object tracking as a real thought-provoking case study. Experimental results show that the Hybrid SCA-PSO-based tracker can robustly track an arbitrary target in various challenging conditions. To reveal the capability of the proposed algorithm, comparative studies of tracking accuracy and speed of the Hybrid SCA-PSO based tracking framework and other trackers, viz., Particle filter, Mean-shift, Particle swarm optimization, Bat algorithm, Sine Cosine Algorithm (SCA) and Hybrid Gravitational Search Algorithm (HGSA) is presented.

A Low Power IoT Sensor Node Architecture for Waste Management Within Smart Cities Context.

Abstract: This paper focuses on the realization of an Internet of Things (IoT) architecture to optimize waste management in the context of Smart Cities. In particular, a novel typology of sensor node based on the use of low cost and low power components is described. This node is provided with a single-chip microcontroller, a sensor able to measure the filling level of trash bins using ultrasounds and a data transmission module based on the LoRa LPWAN (Low Power Wide Area Network) technology. Together with the node, a minimal network architecture was designed, based on a LoRa gateway, with the purpose of testing the IoT node performances. Especially, the paper analyzes in detail the node architecture, focusing on the energy saving technologies and policies, with the purpose of extending the batteries lifetime by reducing power consumption, through hardware and software optimization. Tests on sensor and radio module effectiveness are also presented.