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Microgrids are essential in ensuring responsible energy consumption and production patterns to support and advance the United Nations' sustainable development goals. However, due to the limited supply in the off-grid microgrids, an increase in the integration of electric vehicles (EVs) into electrical networks for their charging operation is expected to create a void between the energy supply and demand. Demand response (DR) programs provide a better solution to the energy management problem of microgrids. However, the difference in the objectives of the stakeholders of the off-grid microgrid can create an issue in providing optimal solutions to its energy scheduling problem. This paper focuses on the multi-objective energy scheduling strategy of the off-grid microgrid to manage its limited energy supply optimally using an incentive-based DR program. A fast heuristic approach is also presented in this work to reconfigure off-grid microgrid optimally during its hourly operations. Furthermore, Hong's (2m+1) point estimation method is used in this work to consider the uncertainties in the EVs and other DR participants. The proposed work is analyzed on the 33-bus and 69-bus off-grid microgrid consisting of droop-controlled distributed generations, EV charging stations, and consumers with curtailable loads. For the day-ahead operation of the off-grid microgrids, the analysis shows that the proposed work reduces the operational cost of the two off-grid microgrids by 2.3% and 4.56%, respectively. [Display omitted] • Priority-ordered incentive-based DR program for the EV and non-EV loads is proposed. • Fast dynamic reconfiguration of the off-grid microgrid is presented. • Multi-objective energy scheduling problem for the off-grid microgrid is modeled. • Utopia-tracking approach is used to solve the conflicting multi-objective problem. [ABSTRACT FROM AUTHOR]

• Proposing a novel reversible QCA-based half-adder structure based on the coplanar layout and simple quantum cells, focusing on low occupied area and energy consumption;. • Recommending a single-layer reversible QCA-based full adder design based on the coplanar layout and simple quantum cells with emphasis on the low-occupied area and low latency;. • Suggesting the reversible QCA-based multiplier design using the suggested structures for IoT devices such as printed devices and microcontroller units;. • Designing the proposed reversible QCA-based multiplier using a powerful quantum tool for evidence of its near zero-energy dissipation. An enormous variety of items and things are connected via wired or wireless connections and specific addressing schemes, which is known as the Internet of Things (IoT). However, IoT devices that adopt aggressive duty-cycling for high power efficiency and prolonged lifespan necessitate the incorporation of ultra-low power consumption always-on blocks. The multiplier plays a crucial role in enhancing the capabilities of low-power IoT devices, particularly those operating with energy-efficient batteries that offer extended battery life. The previous multipliers have a struggling speed, enormous occupied area, and high energy consumption; therefore, all prior flaws must be fixed by implementing it in a suitable technology, like the quantum computing. Therefore, this paper examines the ultra-low power circuit for nano-scale IoT platforms. It also suggests novel quantum-based adders for multiplier structure. The proposed designs are simulated using the QCADesignerE 2.2 tool by focusing on energy-efficient and occupied areas for miniaturizing IoT systems. [Display omitted] [ABSTRACT FROM AUTHOR]

Internet Protocol version 6 (IPv6) address configuration is needed to meet the needs of current portable devices and wearable applications, where each device needs its own unique identification number. High complexity in Wireless Sensor Network (WSN) and Internet of Things (IoT) environments has increased challenges, such as low energy consumption and high overhead in IPv6 address configuration. The proposed IPv6 generation is based on a hybrid mathematical approach that retains the IP address's confidentiality and prevents Denial of Service (DoS) attacks from new and existing nodes in the network. In this paper, the proposed hybrid protocol for DoS attacks from new and existing nodes is analyzed. It is evident from the results that the rate of successful configuration, overhead, and energy consumption is found to be better than the existing approach, namely, Programming Protocol-independent Packet Processors Duplicate Address Detection (P4DAD), match prevention technique, etc. • Focuses on IPv6 using a hybrid Duplicate Address Detection (DAD). • Reduces the energy consumption of the IPv6 as compared to P4DAD, etc. • Reduces the long wait time compared to EUI-64. • ASR is investigated for the Hybrid DAD under three different scenarios [ABSTRACT FROM AUTHOR]

To achieve the carbon neutrality goal, the industrial parks with heavy multi-energy consumption are gradually promoting green and low-carbon transitions. This transition is a long-term dynamic evolution process, which is affected by the strategies of different stakeholders and unpredictable events. It will lead to the industrial parks blindly promoting carbon emission reduction tasks. Therefore, it is necessary to analyze the stakeholders' strategy evolution trajectories based on the evolutionary game theory under changing scenarios to provide correct policy guidance for their carbon emission reduction process. In this paper, firstly, an evolutionary game model of carbon emission reduction in industrial parks is constructed, including an energy supplier, an industrial park operator, and the government. Secondly, the dynamic behavior characteristics of different stakeholders and the factors affecting the stability of the strategy equilibrium are analyzed thoroughly. Finally, different scenarios are formulated to simulate and analyze industrial parks' long-term dynamic evolutionary game law of carbon emission reduction. Furthermore, discussions and suggestions are provided to guide industrial parks' future carbon emission reduction transitions. [ABSTRACT FROM AUTHOR]

With the rapid development of global informatization, data center computing tasks are increasing, and the resulting energy consumption problems are particularly prominent in the context of carbon emission reduction, and affect the safe and green operation of data centers. Aiming at the problem of high and large fluctuation energy load of data center servers, this paper presents a method of optimizing data center servers load considering spatial-temporal load distribution of computing resources. Co-optimized allocation of computing resources and load is studied with examples from the property that computing resources is adjustable in time and space dimensions. The results show that the proposed method is able to reduce the peak electric and cooling loads of the data center room by 10.7 %, reduce the total annual electric consumption by 2.66 % and the total annual cooling consumption by 2.46 %, and reduce the economic and environmental costs of energy supply for the data center in the case study, which comprehensively shows that the load optimization of data center servers has been achieved through the optimal allocation of computing resources. [ABSTRACT FROM AUTHOR]

Buoyant airborne turbines (BATs) are powerful, energy-efficient, adaptive, and environment-friendly. They can be utilized as aerial base stations to expand the wireless network coverage, increase capacity and reliability, improve energy efficiency, and provide a higher line of sight (LoS) probability as they work at higher altitudes. The paper bears the question, what if we equipped a BAT with a complete wireless network base station stack and considered it a new beyond fifth generation/sixth generation (B5G/6G) wireless network device? Therefore, this article discusses enabling BATs as new B5G/6G wireless network equipment. We provide the preliminaries that make the BATs suitable for wireless network applications. We propose the expected configuration and architecture for BATs-based B5G/6G wireless networks. We discuss the opportunities, desired applications, and deployment challenges of BATs in B5G/6G wireless networks. Using simulations, we present the evidence and viability of enabling BATs for extending wireless networks. [Display omitted] • What if we use BATs as a new B5G/6G wireless network device? • BATs are efficient wind turbines with adjustable heights and a mobile ground station. • BATs can serve in B5G/6G wireless networks as a base station. • The simulation results show the BAT gNB capability to serve approximately 120 users (UEs). • The reasonably sized BAT rotor can sufficiently power the 5G gNB base station. [ABSTRACT FROM AUTHOR]

Securing Internet-of-Medical Things (IoMT) devices is a multidomain task that involves analysis of attack patterns from data & control packets. Existing security models either work on pre-emptive analysis, real-time analysis or post-processing analysis of different packet types. These operations add to the complexity of data processing, which reduces Quality of Service (QoS) levels of IoMT deployments. To overcome these issues, this paper proposes design of a sharded chain based high efficiency IoMT security model that uses bioinspired optimizations. The proposed model initially deploys a novel single chained Proof-of-Medical-Trust (PoMT) based consensus technique, which assists in identification of high-trust miners. The QoS levels are continuously monitored for each block request, and a Teacher–Learner based Optimization (TLbO) model is deployed to make sharding decisions. These decisions are reconfirmed by a Particle Swarm Optimization (PSO) model, which assists in estimation of shard length under different attack types. These attacks include Spoofing, Spying, and Flooding attacks, which affects the authenticity of data samples from IoT devices under real-time scenarios. The PSO Model also recommends splitting & merging decisions based on the context of IoMT deployments. The PoMT consensus is modified to support sharded chains, and assists in reducing mining delays without compromising miner trust levels. The modified PoMT consensus is capable of selecting overlapping miner nodes, while incorporating load balancing for optimal energy consumption during the mining process. Due to these enhancements, the proposed model is able to reduce mining delay by 8.5%, while reducing energy consumption by 3.2%, and reducing access delay by 4.9% under real-time clinical scenarios. • An innovative Proof-of-Medical-Trust (PoMT) based consensus mechanism. • The Quality of Service (QoS) levels for each block request are continually monitored. • A Particle Swarm Optimization (PSO) model verifies these selections and aids in estimating shard length. • The PSO Model also advises dividing and combining IoMT installations. • The improved PoMT consensus is capable of selecting overlapping miner nodes. [ABSTRACT FROM AUTHOR]

In recent years, research on Unmanned Aerial Vehicles has become one of the most interesting topics for industry and academics. UAVs path planning is one of the most important issues in terms of guaranteeing good performance in real-world applications. Its main objective is to determine and ensure an optimal and collision-free trajectory (path) between two positions from a starting point (source) to a destination point (target), while dealing with some requirements (e.g. safety, environment complexity, obstacle avoidance, energy consumption, etc.). In view of this topic's complexity, an efficient path planning algorithm is required. In this paper, we propose an improvement of the meta-heuristic African Vulture Optimization Algorithm (AVOA), named Chaotic Cauchy Opposition-based AVOA (CCO-AVOA), for solving the UAVs path planning problem in a 3D environment. The effectiveness of the proposed CCO-AVOA is validated in different environments with various numbers of waypoints and threats taking into account the fitness value, path cost, height cost, obstacles cost, UAV's angle cost, and execution time metrics. Compared to ten well-known meta-heuristics, simulation results demonstrate the efficiency of the proposed CCO-AVOA approach in most cases by obtaining a short, smooth, least costly, and collision-free path with better stability for UAVs in complex environments. • An improved algorithm (CCO-AVOA) is proposed for the UAV path planning. • The UAV path planning problem aims to minimize UAV requirements' costs. • The algorithms' performance is assessed across four different scenarios. • The CCO-AVOA algorithm proves superior in comparison with ten well-known algorithms. [ABSTRACT FROM AUTHOR]

Enhancing the utilization of new energy consumption relies significantly on mid-to-long-term load forecasting. However, current studies face limitations in effectively extracting and utilizing future climate forecast information. So, in this paper, we proposed a wind power mid-long-term load forecasting method considering different wind energy characteristics for effective future climate information prediction. Further, the wind energy resources climate forecast results data is taken as input to improve the adaptability of the prediction model and realize the screening of different forecast error characteristic data sets by building a wind energy feature mining model. Then, an adaptive prediction model is constructed based on the Gray wolf optimization algorithm (GWO) and the long short-term memory network (LSTM). The result obtained by the proposed model is compared with the existing research. From, the result analysis it is clear that the performance of the proposed prediction method is better than the existing research. [Display omitted] [ABSTRACT FROM AUTHOR]

Topology control in Underwater Wireless Sensor Networks (UWSNs) has been an active area of research owing to unique challenges an underwater environment poses. Node mobility due to water currents creates problems in localization and data transfer. In this paper, we propose an energy efficient topology control for mobile UWSNs using Expectation Maximization (EM) and Vickrey–Clarke–Groves (VCG) Auction mechanism. In the first stage, followed by a water current induced topology disturbance, the designated cluster head runs the EM algorithm to find similar sensor nodes in its proximity. In the second stage, cluster head runs VCG auction mechanism to compensate sensor nodes for costs incurred during data transfer. This ensures seamless data transmission between sensor nodes of heterogeneous ownership leading to higher energy efficiency. Simulations present higher energy efficiency of the network post EM and VCG steps. Comparison with existing protocols demonstrates superior performance of the protocol proposed in this paper. [Display omitted] • Node mobility in underwater sensor networks disturbs network topology. • Extant game theory-based protocols for topology control are not flexible enough. • Our protocol uses Expectation Maximization for soft clustering of sensor nodes. • Sensor nodes are compensated for data transfer through an auction mechanism. • Our protocol gives better results in simulations-based comparisons with extant protocols. [ABSTRACT FROM AUTHOR]

With the emergence of Internet of Things (IoT) and allied applications for smart cities, sustainability goals have seen a prominent emphasis. This paper focuses on the energy efficiency aspect of such sustainable smart city goals. Although energy efficiency has been studied at different levels of a smart city's Information and Communication Technology (ICT) infrastructure, this paper specially focuses on device level energy minimization strategy by means of modelling the energy consumption while accounting for the Clusterheads (CluH) and duty cycling and thereby using evolutionary algorithms. In this paper, a Genetic Algorithm (GA) and a hybrid Artificial Neural Network based Particle Swarm Optimization (PSO), namely Feed Forward Neural Network based PSO(FFNN-PSO) has been used to solve the energy minimization problem. Simulation experiments carried out for different scenarios with varying configuration demonstrate the efficacy of the hybrid neuro evolutionary scheme. [Display omitted] • IoT-based energy optimization model for smart city scenarios. • Genetic Algorithm (GA) and a hybrid Artificial Neural Network based Particle Swarm Optimization (PSO), namely Feed Forward Neural Network based PSO(FFNN-PSO). • Several output metrics, such as the number of alive nodes, load, residual energy, and cost function, were used to pick the best cluster head nodes in IoT network clusters. • The proposed method enacts an intelligent duty cycling by predicting sleep–wake cycles. [ABSTRACT FROM AUTHOR]

In recent years, cloud computing has emerged as a promising paradigm providing various resources in the Cloud Data Center (CDC), including computations, storage, and even platform as a service. The virtualization technology plays a pivotal role in CDC resources management and provides an enchanting feature of virtual machine (VM) migration which provides several benefits in terms of VM scheduling, fault tolerance, load balancing, energy efficiency, power management, and security. To achieve efficient VM migration, a plethora of VM migration schemes have been proposed in the literature aiming to serve the quality of service-driven user requirements. This paper surveys the most recent and state-of-the-art Artificial Intelligence (AI) based and conventional load balancing, energy-aware, SLA aware, and network-aware live VM migration schemes. Through an extensive literature review, this paper investigates the most critical aspects of conventional and AI-driven live VM migration schemes. Finally, a few open research challenges that require further consideration from the research community are highlighted. [Display omitted] • Overview of cloud computing, data centers, virtualization, and VM migration. • Conventional and AI-based Live VM migration schemes categorization. • Live VM migration survey of load, energy, SLA, and network schemes. • VM migration analysis based on migration cost and AI benefits and drawbacks. • Live VM migration research challenges and future directions. [ABSTRACT FROM AUTHOR]

Wireless Body Area Networks (WBANs) is a specialized field with applications both in medical and non-medical domains. In WBAN, nodes' (both wearable and implanted) may vary in capabilities, hence, carrying data at various rates. Propagating such data reliably and efficiently is challenging and results in increased delay and poor service. In sensitive healthcare monitoring applications, communicating sensitive data reliably with less delay is highly desirable. In WBANs, unwanted traffic is often generated, which wastes network bandwidth and causes delay. To address this issue, in this paper, we have proposed an efficient QoS-based Multi-Path Routing (MPR) scheme for WBAN. In MPR, the incoming traffic is categorized into normal and emergency. Emergency traffic has been given high priority and routed on the best path within the network that improves the reliability and throughput of a WBAN. The proposed scheme outperforms state-of-the-art approaches in terms of energy efficiency, network throughput, packet drop ratio, packet delivery ratio, and end-to-end delay. For 1000 data packets, collecting eight bio-sensor nodes yields a maximum throughput of 11,000 packets transmitted, and successively, in 10000 data collecting, with less packet drop ratio to the MS (Medical Server). Lastly, a fuzzy logic-based evaluation of the proposed scheme further testifies that the proposed work outperforms state-of-the-art approaches. [Display omitted] [ABSTRACT FROM AUTHOR]

Energy consumption prediction is critical to intelligent power dispatching and smart grid optimization. However, the task remains challenging due to big data fluctuation and the low accuracy of a single feature. This paper proposes a novel short-term energy consumption prediction method for electric vehicle charging stations based on attention feature engineering and stacked GRU (Gated Recurrent Unit). First, we select several handcrafted features from historical energy consumption data. Second, we assign weights to these handcrafted features using the attention mechanism. Third, we propose a multi-sequence stacked GRU in parallel architecture as the network core to extract the correlations between the base and handcraft features. We mitigate the data fluctuations by modifying the prediction resolution and evaluate our method by comparing it with RNN, stacked-GRU, and DeepDeff GRU. The experiment results demonstrate that our method outperforms the state-of-the-art method with a prediction accuracy improvement of 27.2%. [Display omitted] • A feature processing method based on feature engineering and attention mechanism is proposed. • A multi-sequence stacking GRU training structure is proposed. • The proposed method was tested on the energy consumption dataset of charging stations and compared with other methods. [ABSTRACT FROM AUTHOR]

Smart grids have gained popularity to manage energy resources on the consumption side. Energy management on the home side is still under consideration where the system controls the home appliances intelligently with cost-effective and manageable processes. This paper presents a Trust-aware Energy Management System for Smart Homes (TEMSH) by using smart scheduling and time management based on controllable and uncontrollable appliances management. This system is based on advanced communication technologies and security mechanisms. The trust mechanism provides the authentication services at the edge level to secure the user data from any type of unauthorized access and data leakage. The proposed system is deployed on houses to analyze the overall energy consumption and appliances. The results indicate the proposed system is more feasible for home appliances and able to manage and reduce the energy cost by around 55% cumulative Cost in the shape of bills and best for a green environment. The proposed system will be feasible to control the energy crises all over the world and reduce energy utilization and cost at the home level. [Display omitted] This paper presents a Trust-aware Energy Management System for Smart Homes (TEMSH) by using smart scheduling and time management based on controllable and uncontrollable appliances management. This system is based on advanced communication technologies and security mechanisms. The trust mechanism provides the authentication services at the edge level to secure the user data from any type of unauthorized access and data leakage. The proposed system is deployed on houses to analyze the overall energy consumption and appliances. [ABSTRACT FROM AUTHOR]

The increasing use of workflows in various fields of science has led to the development of the Workflow as a Service (WaaS) platform, which provides workflow execution as a service to the users. The WaaS platform must use appropriate resource provisioning, sharing, and scheduling, to properly allocate the workflow tasks to the Cloud resources and fulfill the scheduling objectives. In this paper, we have proposed a novel workflow scheduling approach for the WaaS platform that reduces the average makespan of the workflows, improves energy efficiency, and reduces the resource renting cost of the Cloud resources. Our scheduling model consists of containers, virtual machines (VMs), and hosts. We have also proposed a suitable scaling policy for the Cloud resources to improve resource utilization. Extensive simulations with real-world workflows and comparison with some state-of-the-art algorithms show the efficacy of our approach in improving performance and energy efficiency while reducing the monetary cost. [Display omitted] • Multi-criteria scheduling approach for the Workflow as a Service platform. • Optimal MIPS for tasks decides sharing of virtual machine resources with containers. • Resource scaling policy to improve the schedulability of the workflows. • Results show improvement in performance and energy-efficiency while reducing cost. [ABSTRACT FROM AUTHOR]

In this paper, CMOS technology-based neural session key generation is proposed for integration with the Internet-of-Things (IoT) to enhancing security. Recent technological developments in the IoT era enable improved strategies to exacerbate the maintenance of energy efficiency and stability issues. The existing security solutions do not properly address IoT's security. A small logic area ASIC implementation of a re-keying enabled Triple Layer Vector-Valued Neural Network (TLVVNN) using CMOS architectures with measurements of 65 and 130 nanometers are proposed for integration with IoT. The paper aims to defend IoT devices using TLVVNN synchronization to enhance security. For a 20% weight misalignment in the re-keying phase, the synchronization period may be decreased from 1.25 ms to less than 0.7 ms, according to behavioral simulations. Experiments to verify the proposed technique's performance are conducted, and the findings demonstrate that the proposed method has greater performance benefits than the existing related techniques. [ABSTRACT FROM AUTHOR]

Operation on demand flexibility in cloud computing services has resulted in great popularity and wide adoption. These services integrate thousands of computers, storage and communication networks, which implies a high consumption of electrical energy. Therefore, renewable energy-based cloud data centers are replacing traditional energy power grids. In this regard, the workload could be transferred to different nodes among different cloud data centers geographically distributed regarding renewable energy availability. In this sense, this paper presents a framework based on Cloudsim for virtual machine migrations among cloud data centers regarding sustainability optimization. Moreover, an approach for migrations among datacenters based on an expert system has been tested in several scenarios with renewable energy dynamically available. Experimental results show the improvements of the proposed framework and how expert systems can take advantage of renewable energy availability in terms of sustainability while preserving the QoS in terms of execution time. [Display omitted] [ABSTRACT FROM AUTHOR]

Due to the long distance between satellites and the earth, uplink users need considerable energy to achieve satellite communication. Therefore, the energy efficiency is very important for user terminals. In addition, multibeam Low Earth Orbit (LEO) satellite networks may cause cochannel interference between beams because of full frequency reuse. Moreover, power competition among users will easily occur if the users begin to privately increase the transmission power. Power competition limits capacity and reduces energy efficiency. To solve these problems, this paper proposes a power control strategy balancing capacity and energy efficiency. A dual-objective optimization model is established based on the maximum transmission power constraint and the minimum signal-to-interference ratio (SIR) constraint. The model is solved by a back propagation (BP) neural network based on the fast nondominated sorting genetic algorithm (NSGAII). The neural network can adjust the optimization degree of the energy efficiency and capacity by preference factors. The simulation experiment results show that, while the user minimum transmission rate increases, the EE optimization of the NSGAII-BP neural network is better than that of the traditional weighting method. [ABSTRACT FROM AUTHOR]

Despite the rapid development of the Internet of Things technologies where more and more medical sensors and gadgets are connected to the Internet, limited energy resources due to transmission, and security are still main challenges. Most cases, patients wear multiple medical devices that transmit sensed medical data wirelessly to servers, which might cause big traffic on the communication networks, which in turn cause high energy consumption. Therefore, using the data aggregation, we can considerably reduce the energy consumption by eliminating redundant data; yet collected data must be fully protected. Secure data collection and transfer to centralized servers in healthcare applications employing IoT is quite challenging to protect against several attacks for illegal data access. For this reason, massive security measures should be taken to ensure that patients' data can only be accessed by legitimate users. This paper proposes EPPADA: Efficient Privacy‑Preserving Authentication and Data Aggregation scheme in conjunction with Homomorphic Encryption concepts to meet requirements of healthcare using IoT with green computing technologies. The main objective of this proposed scheme is to decrease the communication overhead and energy consumption while maintaining safe and secure aggregation of the healthcare data between medical sensors and cloud servers. The proposed system is experimentally developed using E-health sensor shield V2.0 platform. Based on security analysis that has the most extensive set of security features, our multi-objective approach enhances the End-to-End Delay, Computational Cost, Communication Overhead, besides maintaining security features. [ABSTRACT FROM AUTHOR]

The new era of information technologies made smart healthcare by including edge, Internet of Things (loT), Internet of Medical Things (IoMT), cloud computing etc. This adaptation has partially replaced the old medical system, making healthcare more efficient, easy, and customized. Wireless medical sensors are used in the smart healthcare sector to capture the patient's health data and transmit it to the remote server for treatment or diagnostic purposes. Every time a cloud-based solution is adopted to collect and preserve the sensitive and personal health information. This collected patient data is shared between the different stakeholders across the network. Due to this, healthcare systems are vulnerable to confidentiality, integrity and privacy threats. Secure data access is a big challenge in such systems. In this paper, a lightweight authentication technique with the privacy-preserving schema using fully homomorphic encryption is proposed. This approach can be used for a secure access of patient data through online mode, and also the data can be shared in an encrypted format for desired purposes of stakeholders. The proposed authentication scheme is for Internet of Medical Things that can resist all network attacks and is easy to implement. The performance analysis of the proposed work shows that overall execution time (80.03%), communication cost (15.62%) and energy consumption (79.19%) is reduced in comparison with existing work. [Display omitted] • A lightweight mutual authentication scheme for smart healthcare systems is designed and implemented using on login ID, password, and perceptual hash based biometric security. • A formal security analysis is introduced using Burrows–Abadi–Needham (BAN) logic that resists all well-known attacks and provide anonymity of patient identity. • A performance comparison of the proposed scheme with existing schemes is also carried out in terms of security features and communication overheads, computation overheads, storage and network throughput. [ABSTRACT FROM AUTHOR]

• A four-layer edge optimized and user-personalized framework for life-logging human activities is proposed. • A lightweight edge intelligence module requiring low computation is designed, which reduces data transmission to the cloud. • A novel Max Score Pooling (MSP) algorithm based on ensembled metaheuristic algorithms is developed for the user-specific parameter selection. Additionally, it makes the framework resilient to certain sensor failures. • The MSP optimized Decision Tree classifier is developed for real-time activity recognition in the Spark environment. • Experimental evaluation demonstrates the outperformance of the proposed model with existing ones. The fostered use of smart wearables for lifelogging daily activities has fuelled massive data generation. Lack of personalization, massive network traffic, increased latency, and high vulnerability to missing and noisy data are the significant impediments that existing frameworks face. This paper proposes a user-personalized and edge-optimized four-layer framework for lifelogging activities to address these impediments. A lightweight Edge Intelligence (EI) module with low computation requirements is designed to reduce data transmission to the cloud, lowering energy consumption. A novel Max Score Pooling (MSP) algorithm based on ensembled metaheuristic algorithms is proposed to provide a user-specific and optimized set of features. MSP optimized Decision Tree (MSP-DT) classifier is developed for real-time activity recognition in the Spark environment. The classifier's performance is calibrated regularly, making the framework resilient to sensor failure. Experiments demonstrate that the proposed framework can recognize 12 physical activities of different subjects with a mean accuracy of 97.67% and 47.66% reduction in transmitted data. [Display omitted] [ABSTRACT FROM AUTHOR]

School systems are primarily dependant on government and government-supported institutions for a country's rural education. It is not easy for rural schooling; rural children are faced with various obstacles before they are done. Either rural student access to specialized training resources such as multimedia schooling, non-school books, and digital education are inaccessible or lack access. Therefore, this study proposes a Human Machine Interaction-assisted Smart Educational System (HMI-aSES) for Rural Children. This paper includes a cyber-physical system (CPS) with optical fog computing to improvise intelligent education system monitoring. This technology uses fog devices with an optical fog layer for early decisions and activities without hesitating, minimum energy, and maximum bandwidth. The proposed architecture provides a flexible smart optical node for many applications powered by CPS through the Fog Manager in the middleware of Cloud and surveillance equipment and facilities. The Fog structure decision suggests an ideal positioning algorithm for putting CPS-based activities on a neighbouring optical fog machine. The latency in the range of 6.2–7.34 ms has been generated by the experimental analysis's proposed device. The energy usage of tracking cameras is dramatically decreased. The network utilization is even smaller than the traditional method by way of the proposed structure. Thus the experimental results shows HMI-aSES increases performance ratio (94.3%), network consumption (95.4%), smart education growth (97.7%), interaction ratio (98.6%) and energy consumption (95.4%). [ABSTRACT FROM AUTHOR]

This paper presents a hybrid trust model, and a multifactor, depending on trust value of sensor nodes, remaining energy, and hop count, routing strategy known as Temperature-Aware Trusted Routing Scheme (TTRS). In fact, it establishes the shortest and most trusted routing path. The multifactor strategy selects trustworthy nodes to forward data and reduce energy utilization due to secure shorter routing paths. TTRS incorporates an efficient multifactor hotspot node detection algorithm (HNDA) along with route discovery as well as route maintenance mechanism to detect malicious relay nodes for consistent data delivery in an unattended environment. Experimental results express the admirable average performance gain of TTRS in terms of accurate trust evaluation, network lifetime, packet delivery rate, and average energy consumption per node, as compared to ATRP, EOSR, and SQEER. Consequently, TTRS is more accurate than the aforementioned competitive schemes under different loads in a hostile environment. [ABSTRACT FROM AUTHOR]

• PEM fuel cell is continuously backup power for hybrid power systems. • The efficiency of the inverter varies between 49.671% and 93.794%. • The efficiency of the inverter exhibits superior performance at low hydrogen pressures in stand-alone hybrid PEMFC/Wind/PV power systems. • The inverter efficiency feature of the model has been verified by international standards EN 50530-EN 61853-IEC 61683. Distributed Generation systems are made up of different power generation systems, which are wind turbines, solar panels, fuel cells, energy storage units, micro turbines, and combined heat cycle plants. An inverter is one of the most critical components of Distributed Generation systems. This paper focuses on inverter-based modeling and energy efficiency analysis of the off-grid hybrid system in Distributed Generation. The proposed system is created and simulated using MATLAB/Simulink platform. The obtained results show that the efficiency of the inverter varies between 49.671% and 93.794% under different loads. Model results comply with the inverter efficiency curve specified by the European Commission and U.S. Department of Energy procedures. In the model, the inverter energy efficiency of the hybrid system is compared according to temperature, wind speed, solar radiation, and hydrogen pressure. The inverter exhibits superior performance at low hydrogen pressures. Besides, the efficiency of the inverter is analyzed under load changes. [Display omitted] [ABSTRACT FROM AUTHOR]

• An adaptive core mapping algorithm, which dynamically recovers the failed cores in an application, helps improve system performance. • Core Mapping simulations carried out through the Noxim simulator for various routings algorithms (such as XY, WEST-FIRST, NORTH-LAST, and ODD-EVEN). • Hardware Verification is synthesized and simulated using Vivado Design Suite 2018.3 and verified on the Kintex-7 FPGA KC705 board. • The results showed a significant improvement in terms of energy consumption, delay, and throughput. The mapping analysis needs to have a complete performance assessment of Network-on-Chip (NoC) based on delay, throughput, and energy consumption measurements. This paper proposes an Adaptive Core Mapping (ACM) algorithm, which can dynamically react and recover from the failed core through spare core replacement to maintain system functionality. The core mapping methodology considered the complete performance evaluation for different routing algorithms. Mapping simulations executed on the NoC Simulator (Noxim), adjusted to perform a re-enactment for various routing algorithms, such as XY, WEST-FIRST, NORTH-LAST, and ODD-EVEN routings. A significant improvement observed in energy consumption, average delay, and throughput in the proposed work compared with existing algorithms. The proposed adaptive core mapping algorithm is synthesized and simulated using Vivado Design Suite 2018.3 and verified on the Kintex-7 FPGA KC705 board. The performance parameters, such as area, power consumption, and throughput, are obtained. The results show that the proposed algorithm is helpful for less area, power consumption, and high-speed applications in the NoC environment. [ABSTRACT FROM AUTHOR]

The rise of vehicular ad-hoc networks (VANETs) brings promise for guaranteeing road safety and improving traffic efficiency. Nevertheless, the pity is that the problems, such as messages authentication and privacy-preserving, that come with it seriously threaten the security and stability of the VANETs. For addressing these problems, in this paper, a pairing-free certificateless signature work for VANETs that supports parallel key-insulated is constructed. On the one hand, the presented work is constructed utilizing the elliptic curve rather than the costly pairing operation. In this way, each vehicle can generate or verify authentication message in a low energy consumption manner. On the other hand, the mechanism of parallel key-insulated is introduced in our proposed work where two helpers alternately assist the users for the periodical key updates, which can not only resist the key exposure but also reduce the risk of the helper's secret key exposure. Considering possible key exposure in the high dynamic VANETs scenarios, our scheme is more stable and feasible for VANETs environments compared with the existing works. Finally, our work is proved to be strongly key-insulated secure under the discrete logarithm assumption in the random oracle model. Meanwhile, the detailed performance comparisons demonstrate that our work is energy efficient and practical for VANET. [ABSTRACT FROM AUTHOR]

In the Internet of Vehicles (IoV), the communication between vehicles and RoadSide Units (RSUs) usually through wireless channels. Therefore, the IoV requires a reliable and secure authentication and key agreement scheme to ensure that the exchanged data in the channel cannot be forged or modified by the adversary. In most existing authentication schemes, the vehicle can only authenticate with a Trusted Authority (TA), which results in the authentication efficiency of these centralized authentication schemes are easily affected by the TA's computational and communication bottlenecks as the increase of traffic density. Therefore, this paper proposes a secure and computationally efficient authentication and key agreement scheme for the IoV, where the RSU can authenticate with the vehicle. Under the analysis of the Real-Or-Random model and the simulation tool ProVerif, the proposed scheme is proved to be secure. Compared with existing schemes, the proposed scheme improves the authentication efficiency and reduces energy consumption. [ABSTRACT FROM AUTHOR]

• The article is focused on improving the performance of data transmission in wireless sensor network. The highlights of the research are as follows. • Active relay nodes are identified with fuzzy logic and utility factor. • Coverage distance is measured with Received Signal Strength (RSS) • The fuzzy inference system selects the next relay node according to the coverage distance, energy and utility factor and bandwidth availability factors. • By choosing relay node according to the above mentioned factors, the data transmission performance is improved. Numerous sensor nodes are getting utilized in the field of Wireless Sensor Network (WSN) for complex communication. In the recent scenario, there is a lack in the number of sensor node placement with less sensing and communication capabilities. There is less involvement with the characteristics of sensor nodes for direct communication with remote nodes. During the communication, sensor data are communicated to the sink with the help of sensor hubs. Therefore to improve the performance of QoS, routing in the integration of relay node selection plays an important role in WSN. In this paper, we propose Fuzzy based Relay Node Selection and Energy Efficient Routing (FRNSEER) make the routing more efficient by selecting the effective relay node while communicating with the sink node. For selecting the sink node, fuzzy rules are used where active relay nodes are selected as the outcome. The active relay node selection helps to determine the better energy and utility factor during the transmission process. To make better communication between the relay node and the sink node, sensor hubs are used with a lower energy expenditure schedule with expected functions of neighbor/working nodes. In the performance analysis, the proposed mechanism will perform better in terms of packet rate with low energy consumption compared to the existing algorithms such as Fuzzy-based Hyper Round Policy (FHRP) and Neural Network-based Localisation Scheme (NNBLS). [Display omitted] [ABSTRACT FROM AUTHOR]

• Only economic side is considered that means cost is optimized where emission is neglected and evaluate both cost and emission by using particle swarm optimization. • Only emission is minimized where cost is neglected and evaluate both cost and emission. • Both cost and emission are optimized simultaneously with same weighting factor and evaluate both cost and emission. • A comparison study using different PSO has been made. • A modified 24 h basis study has been proposed where both cost and emission are minimized fairly. An increasing demand in electric power consumption has clearly led to an exhaustion of alternating energy resources. Undoubtedly, it has harmful environmental effects. Hybrid energy and Micro grid can solve this kind of problem. The concept of micro grid is quite significant in cases where transmission of electric power is nither feasible nor profitable. An efficient scheduling of micro grid is able to meet load demand without shedding any load and the optimization is required to make it profitable and eco-friendly. In this regard this work implements a twenty four hours based environmental/economic scheduling of distributed generating units with renewable energy sources in a micro grid connected with main grid.This work proposes a framework for optimal scheduling of micro grid which minimize the cost of generating units as well as emission. Particle Swarm Optimization technique has been employed to solve this problem. Weighting factor is used for optimization in multi-objective framework where both cost and emission are minimized simultaneously. In this paper, a comparative study of employing different types of Particle Swarm Optimization has been made where Hierarchical Particle Swarm Optimization (HPSO) performs better incorporating different constraints. The results of proposed Particle Swarm Optimization method are compared and verified with results of others method which is recently employed. Finally, the comparative study indicates that proposed method gives superior solution than previous method in case of operating cost and emission. [Display omitted] [ABSTRACT FROM AUTHOR]