• Energy Research

Wireless body sensor networks (WBSNs) monitor the changes within the human body by having continuous interactions within the nodes in the body network. Critical issues with these continuous interactions include the limited energy within the node and the nodes becoming isolated from the network easily when it fails. Moreover, when the node’s burden increases because of the failure of other nodes, the energy utilization as well as the heat dissipated increases much more, causing damage to the network as well as human body. In this paper, we propose a threshold-based fail proof lifetime enhancement algorithm which schedules the nodes in an optimal way depending upon the available energy level. The proposed algorithm is experimented with a real time system setup and the proposed algorithm is compared with different routing mechanisms in terms of various network parameters. It is inferred that the proposed algorithm outperforms the existing routing mechanisms.

The world is advancing to a new era where a new concept is emerging that deals with “wirelessness”. As we know, renewable energy is the future, and this research studied the integration of both fields that results in a futuristic, powerful, and advanced model of wireless body area networks. Every new emerging technology does have some cons; in this case the issue would be the usage of excess energy by the sensors of the model. Our research is focused on solving this excessive usage of energy to promote the optimization of energy. This research work is aimed to design a power-saving protocol (PSP) for wireless body area networks (WBANs) in electronic health monitoring (EHM). Our proposed power-saving protocol (PSP) supports the early detection of suspicious signs or sporadic elder movements. The protocol focuses on solving the excessive energy consumption by the body attached to IoT devices to maximize the power efficiency (EE) of WBAN. In a WSNs network, the number of sensor nodes (SNs) interact with an aggregator and are equipped with energy harvesting capabilities. The energy optimization for the wireless sensor networks is a vital step and the methodology is completely based on renewable energy resources. Our proposed power-saving protocol is based on AI and DNN architectures with a hidden Markov model to obtain the top and bottom limits of the SN sources and a less computationally challenging suboptimal elucidation. The research also addressed many critical technical problems, such as sensor node hardware configuration and energy conservation. The study performed the simulation using the OMNET++ environment and represent through results the source rate to power critical SNs improves WBAN’s scheme performance in terms of power efficiency of Sporadic Elder Movements (SEM) during various daily operations.

The goal of today’s technological era is to make every item smart. Internet of Things (IoT) is a model shift that gives a whole new dimension to the common items and things. Wireless sensor networks, particularly Low-Power and Lossy Networks (LLNs), are essential components of IoT that has a significant influence on daily living. Routing Protocol for Low Power and Lossy Networks (RPL) has become the standard protocol for IoT and LLNs. It is not only used widely but also researched by various groups of people. The extensive use of RPL and its customization has led to demanding research and improvements. There are certain issues in the current RPL mechanism, such as an energy hole, which is a huge issue in the context of IoT. By the initiation of Grid formation across the sensor nodes, which can simplify the cluster formation, the Cluster Head (CH) selection is accomplished using fish swarm optimization (FSO). The performance of the Graph-Grid-based Convolution clustered neural network with fish swarm optimization (GG-Conv_Clus-FSO) in energy optimization of the network is compared with existing state-of-the-art protocols, and GG-Conv_Clus-FSO outperforms the existing approaches, whereby the packet delivery ratio (PDR) is enhanced by 95.14%.

One of the primary challenges that wireless technology in the present generation is facing is always best connected (ABC) service. This is possible only when the wireless overlay networks follow a cooperative and coordinated process. Vertical handoff is one such process. Concerning this process, the main challenge is to develop algorithms that take care of optimal connection management with proper resource utilization for uninterrupted mobility. In this paper, we develop a new hybrid cuckoo search (CS) and genetic algorithm (GA) that maximizes the performance of heterogeneous wireless systems in terms of minimizing latency, handover failure probability, and enhancing the throughput. We focus on an optimized simulation framework to demonstrate the advantage of our hybrid model. It can be discerned from the simulation analysis that the proposed hybrid technique increases throughput by 17% and 8% compared to the cuckoo search and genetic algorithms applied individually. The performance of the proposed scheme is promising for applications wherein the handoff mechanisms have to be optimized to control frequent handoffs to further reduce the power consumption of user equipment.

Wireless sensor networks (WSNs) and the Internet of Things (IoT) are increasingly making an impact in a wide range of domain-specific applications. In IoT-integrated WSNs, nodes generally function with limited battery units and, hence, energy efficiency is considered as the main design challenge. For homogeneous WSNs, several routing techniques based on clusters are available, but only a few of them are focused on energy-efficient heterogeneous WSNs (HWSNs). However, security provisioning in end-to-end communication is the main design challenge in HWSNs. This research work presents an energy optimizing secure routing scheme for IoT application in heterogeneous WSNs. In our proposed scheme, secure routing is established for confidential data of the IoT through sensor nodes with heterogeneous energy using the multipath link routing protocol (MLRP). After establishing the secure routing, the energy and network lifetime is improved using the hybrid-based TEEN (H-TEEN) protocol, which also has load balancing capacity. Furthermore, the data storage capacity is improved using the ubiquitous data storage protocol (U-DSP). This routing protocol has been implemented and compared with two other existing routing protocols, and it shows an improvement in performance parameters such as throughput, energy efficiency, end-to-end delay, network lifetime and data storage capacity.

Organic food consumption has increased during the last years as a consequence of its direct impact on consumer health, life style, and social convenience as well as on the environment and sustainable development. Compared to the European level, the consumption of organic food products is quite low in Romania. This paper investigates the perception and attitudes of the organic food consumers from the North-West Development Region of Romania. Consumers’ perception towards organic food products was measured using 30 items. The data were collected from 568 respondents and analyzed using descriptive and inferential statistics. A factor-cluster approach was used to identify consumer groups. The findings indicated that health concerns, sensory appeal, sustainable consumption and weight concerns are the main reasons for consuming organic food products. Three main groups of organic food consumers were identified: “gourmand”, “environmentally concerned” and “health concerned”.