Clustering Objectives in Wireless Sensor Networks: A Survey and Research Direction Analysis

Machine learning-based coronary artery disease diagnosis: A comprehensive review.

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.

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

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

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.

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

Energy efficiency is a major concern in wireless sensor networks as the sensor nodes are battery-operated devices. For energy efficient data transmission, clustering based techniques are implemented through data aggregation so as to balance the energy consumption among the sensor nodes of the network. The existing clustering techniques make use of distinct Low-Energy Adaptive Clustering Hierarchy (LEACH), Harmony Search Algorithm (HSA) and Particle Swarm Optimization (PSO) algorithms. However, individually, these algorithms have exploration-exploitation tradeoff (PSO) and local search (HSA) constraint. In order to obtain a global search with faster convergence, a hybrid of HSA and PSO algorithm is proposed for energy efficient cluster head selection. The proposed algorithm exhibits high search efficiency of HSA and dynamic capability of PSO that improves the lifetime of sensor nodes. The performance of the hybrid algorithm is evaluated using the number of alive nodes, number of dead nodes, throughput and residual energy. The proposed hybrid HSA–PSO algorithm shows an improvement in residual energy and throughput by 83.89% and 29.00%, respectively, than the PSO algorithm.

Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks

objective of our paper is to predict the chances of diabetic patient getting heart disease. In this study, we are applying Naive Bayes data mining classifier technique which produces an optimal prediction model using minimum training set. Data mining is the analysis step of the Knowledge Discovery in Databases process (KDD). Data mining involves use of techniques to find underlying structures and relationships in a large database. Diabetes is a set of related diseases in which body cannot regulate the amount of sugar specifically glucose (hyperglycemia) in the blood. The diagnosis of diseases is a vital role in medical field. Using diabetic"s diagnosis, the proposed system predicts attributes such as age, sex, blood pressure and blood sugar and the chances of a diabetic patient getting a heart disease.

/pdf/diagnosis-of-heart-disease-for-diabetic-patients-using-naive-36tn961kvr.pdf

Diagnosis of Heart Disease for Diabetic Patients using Naive Bayes Method

Lifetime Improvement in Wireless Sensor Networks using Hybrid Differential Evolution and Simulated Annealing (DESA)

Unmanned aerial vehicles (UAVs) are a quintessential example of automation in the field of avionics. UAVs provide a platform for performing a wide variety of tasks, but in each case the concept of path planning plays an integral role. It helps to generate a pathway free of obstacles, having minimum length leading to lesser fuel consumption, lesser traversal time and helps in steering the aircraft and its corresponding antenna power signature safely around the hostile antenna to avoid detection. To optimize path planning to incorporate all the above-mentioned constraints, this paper presents two new hybrid algorithms particle swarm optimization (PSO) with harmony search algorithm and PSO with genetic algorithm. The hybrid algorithms perform both an exploratory and exploitative search, unlike the existing algorithms which are biased, towards either an exploitative search or an exploratory search. Furthermore, the hybrid algorithms are compared to the existing optimization algorithms and in all cases the hybrid algorithms give a minimum of 7% better result against PSO with up to a 40% better result against Invasive Weed optimization algorithm for a fixed computational time, suggesting better real-time applications.

/pdf/hybrid-pso-hsa-and-pso-ga-algorithm-for-3d-path-planning-in-iigxrle3re.pdf

Hybrid PSO-HSA and PSO-GA algorithm for 3D path planning in autonomous UAVs

We propose a novel, compact, dual-polarized ultra-wideband (UWB)–multiple-input multiple-output (MIMO) antenna, which consists of an F-shaped monopole which band rejects the IEEE 802.11ac frequency band from 5.1 to 5.95 GHz with microstrip line feeding. The suppression of the inevitable mutual coupling is achieved by using techniques such as orthogonal polarization, defected ground structure, and metamaterials. A split-ring resonator is placed between the antenna elements to reduce the coupling. The antenna has wideband impedance matching with S11 < −10 dB in the UWB frequency range from 3.1 to 10.6 GHz and has a low mutual coupling with |S21| < −20 dB. The antenna has very low envelope correlation coefficient with values equal to zero and low capacity loss value of 0.358, which proves that the MIMO antenna shows good diversity performance. The antenna has a bandwidth of 8.6 GHz and a fractional bandwidth of 33% in the lower band and 56% in the higher band.

A. Rajesh

Papers

Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks

Diagnosis of Heart Disease for Diabetic Patients using Naive Bayes Method

Lifetime Improvement in Wireless Sensor Networks using Hybrid Differential Evolution and Simulated Annealing (DESA)

Hybrid PSO-HSA and PSO-GA algorithm for 3D path planning in autonomous UAVs

A dual-polarized UWB–MIMO antenna with IEEE 802.11ac band-notched characteristics using split-ring resonator