Retraction Note: An Efficient Energy Saving Sink Selection Scheme with the Best Base Station Placement Strategy Using Tree Based Self Organizing Protocol for IoT

Model predictive control for energy-efficient optimization of radiant ceiling cooling systems

Network function virtualization (NFV) allows for implementing network functions as softwares running on standard servers, called virtual network functions (VNFs). This raises availability/reliability issues such as hard- and soft-ware failures. Thus, merely mapping primary VNFs is not enough to ensure high availability/reliability. As a result, additional protection schemes are required to improve the availability/reliability of services. With a limited resource allocated for each improvement, selecting the most potential VNFs for improving which can help maximize the reliability improvement with an appropriate cost is desirable. This paper proposes a metric to measure the improvement potential per a unit cost for VNFs. An efficient VNF selection scheme is designed based on the metric. Obtained numerical results show that the proposed selection scheme achieves higher cost efficiency and better system reliability within a limited resource.

With time division duplexing (TDD), radio resources can be flexibly divided between uplinks and downlinks. This makes TDD suitable for handling the increasingly asymmetric traffic generated by data and multimedia services. When asymmetries are different in neighbouring cells, adverse interference conditions can arise, however. The paper investigates how base station (BS) placement affects the performance of a TDD mode system with asymmetric links in a Manhattan like environment. In particular, the influence of BS placement on inter-base station interference (IBI) and inter-mobile interference (IMI) is assessed. Results show that, similar to conventional systems with symmetric traffic, it is preferable to place BSs somewhere between intersections rather than in intersections. Though the interference mechanisms are different, the studied asymmetric systems seem to be no more vulnerable to an in-intersections placement than is a symmetric system.

A distributed and spectrally efficient link selection scheme for multiuser multirelay cooperative networks with transmit beamforming is proposed and analyzed in terms of outage probability and mean spectral efficiency. More specifically, we consider a network composed by a base station equipped with M antennas which is communicating with one out of L single-antenna mobile stations under the aid of one out of N single-antenna, variable-gain amplify-and-forward relay stations. Either the direct link or the dual-hop relaying link is selected for each communication process by following a distributed link selection scheme. Single-integral expressions for upper and lower bounds of the outage probability and mean spectral efficiency are obtained and validated by Monte Carlo simulations. Furthermore, closed-form expressions derived from the asymptotic analysis of the outage probability reveal that the proposed system achieves full diversity order, which is equal to ML+N. Importantly, it is shown that the mean spectral efficiency is always higher than half of that one attained by direct transmission.

Wireless Sensor Network (WSN) consists of small low-cost, low-power multifunctional nodes interconnected to efficiently aggregate and transmit data to sink. Cluster-based approaches use some nodes as Cluster Heads (CHs) and organize WSNs efficiently for aggregation of data and energy saving. A CH conveys information gathered by cluster nodes and aggregates/compresses data before transmitting it to a sink. However, this additional responsibility of the node results in a higher energy drain leading to uneven network degradation. Low Energy Adaptive Clustering Hierarchy (LEACH) offsets this by probabilistically rotating cluster heads role among nodes with energy above a set threshold. CH selection in WSN is NP-Hard as optimal data aggregation with efficient energy savings cannot be solved in polynomial time. In this work, a modified firefly heuristic, synchronous firefly algorithm, is proposed to improve the network performance. Extensive simulation shows the proposed technique to perform well compared to LEACH and energy-efficient hierarchical clustering. Simulations show the effectiveness of the proposed method in decreasing the packet loss ratio by an average of 9.63% and improving the energy efficiency of the network when compared to LEACH and EEHC.

The increasing demand for higher resolution of images and communication bandwidth requires the streaming applications to deal with ever increasing size of datasets. Further, with technology scaling the cost of moving data is reducing at a slower pace compared to the cost of computing. These trends have motivated the proposed micro-architectural reorganization of stream processors by dividing the stream computation into functional computation, address constraints computation and address generation and deploying independent, distributed micro-threads to implement them. This scheme is an alternative to parallelizing them at instruction level. The proposed scheme has two benefits: a more efficient sequencer logic and energy savings in address generation and transportation. These benefits are quantified for a set of streaming applications and show average percentage improvement of 39 in silicon efficiency of the sequencer logic and 23 in total computational efficiency.

Smart and personalized ventilation systems have been demonstrated with high performance in creating a healthy and energy-efficient indoor environment, but they have been rarely comprehensively summarized and explored in previous studies. With the progressive development of various terminal devices and control technologies, personalized ventilation based on intelligent control is potentially a promising way to achieve efficient control and energy savings in human micro-environments. This study comprehensively summarizes and analyzes the recent studies and common utilization forms of smart ventilation and PV systems that are based on CO2 concentration control, to pave path and provide some guidelines for their integration application for reducing energy consumption and improving indoor thermal comfort. Research shows that the combination of personalized ventilation and smart ventilation is an essential development for ventilation systems. Smart ventilation with demand control logic based on CO2 concentration has been mature enough to effectively improve the effectiveness and comfortable performance of personalized ventilation. However, switching from traditional air conditioning systems to personalized ventilation still requires improved sensors and intelligent control algorithms. In addition, this paper also summarizes the exploratory studies and potential application analysis of machine-learning theories to improve intelligent control of personalized ventilation. To this end, this paper identifies future tendencies for advanced theories, integrated systems, and devices in personalized ventilation systems.

Due to development in cloud services, lots of data is transferred between users and servers of the cloud. This transmission of data consumes huge amounts of energy. This energy consumption occurs during the operation of network infrastructure, the conversion of electrical to optical signals to travel long distances, and signal amplification. As Green computing is the use of computing devices in an environmentally friendly way, i.e., using electrical energy efficiently as Data centers require a significant amount of electricity to operate and cool the servers, leading to carbon emissions from the burning of fossil fuels. Green computing in cloud services is about optimizing energy consumption and by incorporating deep learning algorithms, we can enhance the energy efficiency of cloud infrastructure. These algorithms can analyze real-time data from sensors, optimize resource allocation, and dynamically adjust power usage. Through intelligent workload scheduling, server consolidation, and power management, deep learning enables the reduction of energy waste and carbon emissions. The integration of deep learning in cloud services not only improves energy efficiency but also enhances performance and cost-effectiveness. Here we are using a deep learning model which can be used for workload prediction and resource provisioning. By analyzing historical workload patterns and user behavior, deep learning algorithmscan predict future resource demands and allocate resources accordingly, leading to more efficient resource utilization and energy savings

The emergence of Internet of Things (IoT)-based heterogeneous wireless sensor network (HWSN) technology has become widespread, playing a significant role in the development of diverse human-centric applications. The role of efficient resource utilisation, particularly energy, becomes further critical in IoT-based HWSNs than it was in WSNs. Researchers have proposed numerous approaches to either increase the provisioned resources on network devices or to achieve efficient utilisation of these resources during network operations. The application of a vast proportion of such methods is either limited to homogeneous networks or to a single parameter and limited-level heterogeneity. In this work, we propose a multi-parameter and multi-level heterogeneity model along with a cluster-head rotation method that balances energy and maximizes lifetime. This method achieves up to a 57% increase in throughput to the base station, owing to improved intra-cluster communication in the IoT-based HWSN. Furthermore, for inter-cluster communication, a mathematical framework is proposed that first assesses whether the single-hop or multi-hop inter-cluster communication is more energy efficient, and then computes the region where the next energy-efficient hop should occur. Finally, a relay-role rotation method is proposed among the potential next-hop nodes. Results confirm that the proposed methods achieve 57.44%, 51.75%, and 17.63% increase in throughput of the IoT-based HWSN as compared to RLEACH, CRPFCM, and EERPMS, respectively.

Indoor PM2.5 must be effectively controlled to minimize adverse impacts on public health. Cooking is one of the main sources of PM2.5 in residential areas, and indoor air quality (IAQ) management methods such as natural and mechanical ventilation, range hood, and air purifier are typically used to reduce cooking-generated PM2.5 concentrations. However, studies on the combined effects of various IAQ management methods on indoor PM2.5 reduction and energy consumption are limited. In this study, a theoretical model was established to estimate the performance of various IAQ management methods for controlling indoor PM2.5 concentrations and energy consumption. The model was verified by comparative experiments in which, various IAQ management methods were operated individually or combined. Seasonal energy consumption was calculated through the verified model, and energy consumption saving scenarios were derived for maintaining indoor PM2.5 concentrations less than 10 μg/m3, a World Health Organization annual guideline, under fair and poor outdoor PM2.5 concentrations of 15 and 50 μg/m3, respectively. Based on our results, we found that energy consumption could be reduced significantly by applying natural ventilation in spring, autumn, and summer and mechanical ventilation in winter. Our study identified efficient energy saving PM2.5 management scenarios using various IAQ management methods by predicting indoor PM2.5 concentration and energy consumption according to the annual life patterns of typical residents in South Korea.

Review for "Ultraviolet durable and recyclable radiative cooling covering for efficient building energy savings"

Wireless sensor networks (WSNs) are formed using a cluster of sensor nodes (SNs), deployed randomly to perform sensing operations in an area under observation. Due to the unavailability of an external power source, the energy efficiency considered as one of the critical issues in WSNs. Selection of a sensor node (SN) from a wireless sensor network (WSN) cluster to serve as an aggregator or cluster head (CH), considered as an efficient method to increase the lifetime of wireless sensor network (WSN). In this paper, we have proposed an energy efficient CH selection scheme for WSN, to enhance the lifetime and average residual energy of a single WSN cluster. The proposed strategy nominates a group SNs as CHs, based on their channel condition with the base station (BS) and their residual energy. The proposed algorithm is helpful in solving the problem of unbalanced energy consumption in WSNs. Furthermore, the mechanism of using mobile sink during the hand-off stage helps to overcome the delay in data transmission. Moreover, the incorporation of energy harvesting significantly increases the lifetime of WSN. In comparison to a state-of-art technique available in the literature, our scheme shows a 33% increase in lifetime and presents a steady decrease in residual energy for the same rounds of data transmission.

Wireless sensor networks are energy constraint networks. Energy efficiency, to prolong the network for a longer time is critical issue for wireless sensor network protocols. Clustering protocols are energy efficient approaches to extend the lifetime of network. Intra-cluster communication is the main driving factor for energy efficiency of clustering protocols. Intra-cluster energy consumption depends upon the position of cluster head in the cluster. Wrongly positioned clusters head make cluster more energy consuming. In this paper, a simple and efficient cluster head selection scheme is proposed, named Smart Cluster Head Selection (SCHS). It can be implemented with any distributed clustering approach. In SCHS, the area is divided into two parts: border area and inner area. Only inner area nodes are eligible for cluster head role. SCHS reduces the intra-cluster communication distance hence improves the energy efficiency of cluster. The simulation results show that SCHS has significant improvement over LEACH in terms of lifetime of network and data units gathered at base station.

In the future, the wireless network environment may suffer due to the unavailability of new spectrum bands. Cognitive radio research considers the current spectrum underutilization and provides a better model for the next-generation wireless environment. Since the implementation of the cognitive radio and its policies is a bigger challenge under static spectrum allocation, i.e., current wireless networks policy, many issues are in front of us to accomplish a better cognitive radio wireless environment. One of the major challenges is a secure transmission and efficient free channel selection. In this research, the authors considered an efficient free-channel selection scheme as objective and derived an integrated approach for free-channel selection with techniques, Dynamic weighted-VIKOR.

As the use of unmanned aerial vehicles (UAVs) in various commercial, civil, and military applications increases, it becomes important to study the design of aerial drone corridors that can support multiple simultaneous UAV missions. In this work, we study the placement of base stations (BSs) to serve aerial drone corridors while satisfying specific UAV mission requirements, such as the geometrical waypoints for the UAV to fly through and the minimum data rate to be supported along the mission trajectory. We develop a mathematical model of the drone corridor and propose a brute force algorithm that leverages A* search to meet the quality of service (QoS) requirements of the corridor by choosing the minimal set of BS locations from a pre-determined initial set. Using raytracing simulations, BS placement results are presented for various antenna array sizes in a dense urban region in East Manhattan. It was found that, for the scenario under consideration, a single BS equipped with an 8x8 antenna array is sufficient to satisfy the given QoS requirements of the corridor, while two BSs are required when using 4x4 antenna arrays.