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Wireless Body Area Network (WBAN) is an interconnection of tiny biosensors that are organized in/on several parts of the body. The developed WBAN is used to sense and transmit health-related data over the wireless medium. Energy efficiency is the primary challenges for increasing the life expectancy of the network. To address the issue of energy efficiency, one of the essential approaches i.e., the selection of an appropriate relay node is modelled as an optimization problem. In this paper, energy efficient routing optimization using Multiobjective-Energy Centric Honey Badger Optimization (M-ECHBA) is proposed to improve life expectancy. The proposed M-ECHBA is optimized by using the energy, distance, delay and node degree. Moreover, the Time Division Multiple Access (TDMA) is used to perform the node scheduling at transmission. Therefore, the M-ECHBA method is used to discover the optimal routing path for enhancing energy efficiency while minimizing the transmission delay of WBAN. The performances of the M-ECHBA are analyzed using life expectancy, dead nodes, residual energy, delay, packets received by the Base Station (BS), Packet Loss Ratio (PLR) and routing overhead. The M-ECHBA is evaluated with some classical approaches namely SIMPLE, ATTEMPT and RE-ATTEMPT. Further, this M-ECHBA is compared with the existing routing approach Novel Energy Efficient hybrid Meta-heuristic Approach (NEEMA), hybrid Particle Swarm Optimization-Simulated Annealing (hPSO-SA), Energy Balanced Routing (EBR), Threshold-based Energy-Efficient Routing Protocol for physiological Critical Data Transmission (T-EERPDCT), Clustering and Cooperative Routing Protocol (CCRP), Intelligent-Routing Algorithm for WBANs namely I-RAW, distributed energy-efficient two-hop-based clustering and routing namely DECR and Modified Power Line System (M-POLC). The dead nodes of M-ECHBA for scenario 3 at 8000 rounds are 4 which is less when compared to the dead nodes of EBR. [ABSTRACT FROM AUTHOR]

In wireless-sensing networks (WSNs), the energy economy has lately emerged as the main problem. Since sensor networks run on batteries, they eventually run out of power. To increase the packet transmission ratio for sensing devices, it becomes more difficult to enhance data loss in an energy-efficient manner. In WSNs, the mobile drain causes high network energy usage and data delay. This paper suggests an Improved Ant Colony Clustering-Based Data Transmission Algorithm (EACODT) that first develops the network nodes' energy density function before allocating sensing nodes with higher residual energy as cluster leaders using the energy density function. The EACODT is thoroughly modeled for different WSN situations with variable numbers of sensing nodes and CHs, and the findings are contrasted with some recently developed meta-heuristic algorithms. As a consequence, it is discovered that EACODT gets 34% of energy usage, 98.8% of network lifespan, 95% of packet delivery ratio, 854 kbps of transmission, and a 98% convergence rate. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) is a collection of numerous independent sensor nodes that can sense, process, and manipulate data. WSN is grouped into clusters for energy-efficient data collection. A clustering and aggregation technique automatically extends the lifetime of a WSN by collecting data within the cluster to the cluster head, reduces the amount of data through processing, and transmitting. WSN routing protocols are also required for completing all types of operations in a Internet of things (IOT) environment, such as sensing, controlling, and transmitting packets. In this paper, a novel Fuzzy Clustering and Optimal Routing (FCOR) method is proposed in order to lessen the energy consumption, delay, and improve network lifetime and node density. The proposed FCOR method is executed in two stages. The initial stage consists of clustering and cluster head selection using modified Fuzzy c-means algorithm (MFCM). This algorithm will efficiently cluster the nodes and select the optimal cluster head. The second phase consists of optimal routing using a normalized whale optimization algorithm (NWOA), that select the optimal route and thus improve the lifetime of the nodes. The efficiency of the proposed FCOR approach has been determined using the evaluation metrics such as energy efficiency, packet delivery, and network lifetime. The experimental results reveals that the proposed FCOR model achieves less energy consumption of 67.8%, 54.4%, 60% and 6.67% than existing FRNSEER, E-ALWO, ACI-GSO and CRSH respectively. [ABSTRACT FROM AUTHOR]

In the resource-constrained wireless sensor network (WSN) geographic routing has been considered as an attractive method where it exploits the location data instead of global topology to transmit the data. The geographic routing protocol faces the routing issues when it is used by a heterogeneous device and utilizes high energy during the propagation of data. The lifespan of the sensor network depends on the efficiency of energy and capacity of the battery. Hence, successful data transmission, enrichment of battery capacity and energy utilization is necessary for WSN. To attain this requirements an effective change is made in the data transmission environment and network topology. In this paper proposed a dynamic cluster based duty cycle scheduling is initiated for the data transmission. The cluster-based scheduling and routing in geographic routing protocol (CSRGR) utilize the clustering mechanism which in turn reduces the consumption of energy and maximizes the throughput. The objective function of the proposed approach provides a scheduling and routing strategy. The demonstration of simulation results shows the effective cluster size balancing with data transmission range dynamically. The proposed algorithm is compared with the existing approach and from the results, the energy consumption is minimum for diverse scenarios. [ABSTRACT FROM AUTHOR]

Reasonably allocating the micro factor investment in the industry and trying to improve the energy efficiency are very important for the green development of industrial economy in Hebei Province, so as to the achievement of its double carbon target. This paper takes industrial energy consumption, assets and labor in Hebei Province as micro-input elements. Based on the panel data of 34 industrial industries in Hebei Province from 2005 to 2016, we identify the correlation of micro-input factors in different industries and conduct Granger causality test. The study shows that: (1) About half of the industry's combined energy consumption is correlated with the number of employees and assets; About two-thirds of the industry's asset workers are relevant; (2) There is a two-way causal relationship between the total energy consumption and assets of most industries and the annual average number of employees. (3) The rational allocation of capital, labor and energy consumption depends on the deepening reform of the factor market. The high energy consumption industry should actively improve and innovate the energy technology in this process to realize the coordinated promotion of energy saving and carbon reduction, and thus to promote the healthy and sustainable development of the industry steadily. [ABSTRACT FROM AUTHOR]