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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]

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]

This paper presents an energy-efficient technique for resolving intra-cluster contentions in cluster-based wireless sensor networks. The proposed technique, bit map – binary countdown (BM–BCD), provides collision-free medium access by exchanging short signal tones between the head node and member nodes of the cluster. The BM–BCD improves existing tone-based contention resolution schemes in the following two aspects. Firstly, BM–BCD overcomes the hidden terminal problem by actively involving head node into the contention resolution process. Secondly, BM–BCD introduces a novel group-splitting algorithm which improves energy efficiency of member nodes by spreading the contention resolution over all available elimination rounds. The paper also introduces a simple intra-cluster MAC protocol, named STAR–TONE, which incorporates the proposed BM–BCD, and provides an analytical model for dimensioning main protocol parameters. Our analysis, verified by simulations, demonstrates that BM–BCD reduces the power consumption during contention for up to 44.5% compared to the baseline countdown protocol under bursty traffic conditions. [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]

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]

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]

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]

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]

Abstract Cache partitioning is a promising technique to improve cache management mechanism. To explore the advantages of cache partitioning, most existing schemes either consider performance improvement or address energy reduction. These techniques focus on specific application domain or single optimization goal, which limit their extensive use. To address these issues, this paper proposes R euse l O cality aware C ache p A rtitioning scheme (ROCA) to reserve more high reuse locality blocks in the last level cache. ROCA uses a reuse locality reservation algorithm to maintain the cache fraction. Meanwhile, reuse locality guided block placement policy is proposed to further improve cache efficiency. Evaluation results show that the proposed technique improves energy efficiency as well as system performance with reduced hardware cost, compared to the state-of-the-art approach. [ABSTRACT FROM AUTHOR]

Highlights • A Low complexity, energy efficient image compression suitable for WSNs. • The integration of DTT in lossy image compression. • A ROI coding for WSNs preserving energy and improving quality. • A considerable reduction in energy consumption and data transferred. • Helping development of smart surveillance systems using low-power cameras. Abstract Reducing the algorithmic complexity of image compression techniques is a major challenge in wireless image sensor networks (WISNs). Many image compression standards, such as JPEG and JPEG2000, are unsuitable for implementation in WISNs because of their high energy consumption. In this paper, a solution to this problem is proposed. It consists of a region-of-interest (ROI) based image compression using the discrete Tchebichef transform (DTT).The main idea is about compressing only the ROI instead of the whole image. The DTT is used as an alternative to the discrete cosine transform (DCT) due to its low complexity and good energy compaction. Simulation results have shown that the proposed method reduces the number of arithmetic operations, the processing/transmission energy consumption and the amount of transmitted data. The savings obtained generally exceed 50%. Furthermore, it has a competitive compression efficiency compared with the state-of-the-art image compression techniques. Graphical abstract Image, graphical abstract [ABSTRACT FROM AUTHOR]

Abstract In wireless communication systems, energy efficiency (EE) and spectral efficiency (SE) tradeoff is a challenging issue as they are inversely proportional to each other. Most previous work has provided the communication between two points that may lead to the inefficient use of network resources. It is crucial to study optimal power allocation (PA) including relay nodes in order to improve power saving and data rate. This paper proposes a PA optimization method for multi-user, based on EE and SE in Long Term Evolution-Advanced (LTE-A). The optimization problem is formulated as multi-objective optimization (MOO), considering both downlink and uplink transmissions. The mathematical expression of optimal power for EE and SE (OPES) is derived for Transparent and Non-Transparent modes with amplify-and-forward (AF) relay. Numerical results confirm the effectiveness of the proposed OPES formula compared to previously used techniques. [ABSTRACT FROM AUTHOR]

Abstract Wireless sensor networks consist of a set of connected devices deployed to report sensitive environmental data. Key management in wireless sensor networks remains a challenging issue due to the limited resource capacity of devices. Most existing solutions focus only on the key storage and updating optimization giving less attention to the mobility, which is more needed in the nowadays applications. In this paper, we propose a secure and efficient key management system with mobility support. The proposed scheme is based on hybrid key establishment to meet both the robustness and efficiency requirements. The sensor nodes can be mobile, where they could leave, rejoin their cluster, or join other ones. We incorporate lightweight techniques for sensor node integration, departure, revocation and key updating. Its efficiency is evaluated by comparison with other concurrent schemes, where it demonstrates the best results. [ABSTRACT FROM AUTHOR]

Abstract In a tree-based Wireless Sensor and Actor Network (WSAN), data acquisition can be done through a mobile actor to reduce the traffic congestion. However, packet dropping at the resource-limited (such as, buffer space and energy) intermediate nodes leads to poor data delivery and energy efficiency. In this paper, a Markov Decision Process (MDP) based buffer management mechanism has been proposed in tree-based WSAN to reduce the data delivery delay and to improve the energy efficiency. A mathematical model for a mobile actor has been presented to analyze buffer occupancy and energy consumption with real-time traffic. The state of a node is represented based on buffer levels which may change with dynamic traffic. The buffer states are analyzed at which packets will be dropped to minimize the average delivery delay and energy consumption. Extensive simulations have been performed to show the efficacy of proposed mechanism in terms of average end-to-end delay and energy consumption. [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]

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]

• Whether it is a homogeneous WSNs or a heterogeneous WSNs, the cluster head selection scheme is extremely important and will affect the performance of the entire network. • The traditional LEACH clustering scheme and DEEC clustering scheme only consider the remaining energy of the node or the initial energy of the node when selecting the cluster head. This paper comprehensively considers the remaining energy of the node, the relative density of the node, and the relative distance between the node and the base station to generate the probability of the node being elected as the cluster head. • This paper considers an improved low-energy dynamic clustering scheme based on fuzzy logic to balance the energy consumption of the network and prolong the lifetime of network. • A low-power dynamic clustering scheme based on fuzzy logic provides an effective method for multi-layer clustering networks. It is inconvenient to replace the sensor nodes with limited energy in wireless sensor networks (WSNs) when they are deployed in remote or dangerous monitoring area, that makes energy consumption become the main issue to limit wide application of WSNs. Existing clustering methods only consider heterogeneity of node energy for head selection, irrespective of differences in network structure and node density. Fuzzy logic scheme is applied in this paper to consider multiple factors clustering with the purpose of prolonging network lifetime. In addition to the initial energy, the relative density of nodes and the relative distance between nodes and base station are considered to select the cluster head dynamically. Experimental results have demonstrated that the proposed dynamic clustering scheme can balance the energy consumption of the nodes in both homogeneous and heterogeneous networks, effectively prolong the lifetime of the network, maintain the accuracy of data aggregation. [Display omitted] [ABSTRACT FROM AUTHOR]

Cloud computing has emerged as the most revolutionary technology in the field of computing. The cloud service providers (CSPs) have high computational facilities called data centers (DCs) at their disposal. CSPs provide services to the users through virtual machines (VMs). VM placement is the mapping of VMs onto physical machine called hosts. In this paper, we propose a two-tier virtual machine placement algorithm. Firstly, we propose a queueing structure to manage and schedule a large set of VMs. Secondly, a multi-objective VM placement algorithm called crow search based VM placement (CSAVMP) is proposed to reduce the resources wastage and power consumption at the data centers. VM migration is an indispensable part of any cloud platform for activities like maintenance, load balancing, fault tolerance etc. Three different migration strategies namely serial, parallel, improved serial have been tested and a comparative result has been produced. [ABSTRACT FROM AUTHOR]

Advancements in cloud computing has transformed it into the most promising computing paradigm for business organizations. In a dynamic cloud environment, Virtual Machine (VM) migration is a critical step in resource management, especially in large scale datacenters. Most of the VM migration and load balancing policies are based on power consumption and response time, with little attention on the interferences caused due to VM migrations. Such interferences degrade the overall performance of the system, and consequently violates the service level agreement (SLA) between the customer and the provider. This paper introduces an interference aware prediction mechanism for VM migration, with auto scaling. The automatic scaling policies help to handle sudden load changes with precise prediction and minimum VM migration. The experimental results and comparative analysis show that the proposed model is capable of predicting the interference more accurately. It also provides an optimum threshold range for VM migration. [ABSTRACT FROM AUTHOR]

With the increasing consumption of energy in data centers, the demand for energy efficient multiprocessor job scheduling is growing dramatically. Besides flow time, energy conservation has become a significant issue and drawn enormous interest. In this paper, an online non-clairvoyant scheduling algorithm Significance-based Multiprocessor Round Robin (SbMRR) is proposed. SbMRR utilizes the unbounded speed model, where the range of the speed of any processor is from zero to infinity. To validate the effectiveness of the algorithm, mathematical and simulation-based analysis are conducted which demonstrates that SbMRR provides the minimum sum of significance-based flow time and energy consumed. SbMRR is O(α)-competitive, more precisely ((α + 1)⁄((1–1⁄αβ)))-competitive, where β ≥ 4 a constant. The competitive ratio of SbMRR is least to date. SbMRR provides the minimum sum of energy consumed and significance-based flow-time. [ABSTRACT FROM AUTHOR]

Cloud computing infrastructures are designed to support the accessibility and availability of various services to consumers over the Internet. Data centers hosting Cloud applications consume massive amount of power, contributing to high carbon footprints to the environment. Hence, solutions are needed to minimize the energy consumption. This paper focuses on the development of a dynamic task scheduling algorithm by proposing an Integer Linear Programming (ILP) model that minimizes the energy consumption in a Cloud data center. Furthermore, an Adaptive Genetic Algorithm (GA) is proposed to reflect the dynamic nature of the Cloud environment and to provide a near optimal scheduling solution that minimizes the energy consumption. The proposed adaptive GA is validated by simulating the Cloud infrastructure and conducting a set of performance and quality evaluation study in this environment. The results demonstrate that the proposed solution offers performance gains with regards to response time and in reducing energy consumption. [ABSTRACT FROM AUTHOR]

The Internet-of-Things (IoT) represents the next groundbreaking change in information and communication technology (ICT) after the Internet. IoT is concerned with making everything connected and accessible through the Internet. However, IoT objects (things) are characterized by constrained computing and storage resources. Therefore, the Cloud of Things (CoT) paradigm that integrates the Cloud with IoT is proposed to meet the IoT requirements. In CoT, the IoT capabilities (e.g., sensing) are provisioned as services. Unfortunately, the two-tier CoT model is not efficient in the use cases sensitive to delays and energy consumption (e.g., in healthcare). Consequently, Fog Computing is proposed to support such IoT services and applications. This paper reviews the most relevant Fog-enabled CoT system models and proposes an energy-aware allocation strategy for placing application modules (tasks) on Fog devices. Finally, the performance of the proposed strategy is evaluated in comparison with the default allocation and Cloud-only policies, using the iFogSim simulator. The proposed solution was observed to be more energy-efficient, saving approximately 2.72% of the energy compared to Cloud-only and approximately 1.6% of the energy compared to the Fog-default. [ABSTRACT FROM AUTHOR]

This paper studies the optimization problem of energy efficiency in multi-cell networks with multiple users such as 5G network. However, the spectrum allocation and inter-cell interference (ICI) in multi-cell networks are the important challenges. The Soft Frequency Reuse (SFR) scheme which manages the spectrum and reduces ICI is used to build SFR-cellular networks. We take the global energy efficiency of the SFR-cellular network as the objective function of the optimal problem to obtain the maximum energy efficiency. Unfortunately, the objective function is a non-concave function, which is significantly difficult to be solved directly. Therefore, we utilize the fractional program, successive convex approximation, Lagrange dual, and Karush–Kuhn–Tucker (KKT) conditions to transform the objective function into a concave function. In such case, we could solve the problem with the convex optimization method. Finally, based on SFR, we propose a global energy efficiency optimization algorithm to search for the optimal energy efficiency of networks. Simulation results show that the proposed algorithm holds the better performance. [ABSTRACT FROM AUTHOR]

In traditional IEEE 802.11 protocols, the RSSI is usually taken as the criterion for AP selection, by which the AP with the greatest signal strength may be heavily burdened. In this paper, A SDN-based AP selection scheme called TPR is proposed. Through centralized control from SDN controller which has a full view of the network, our TPR enables AP selection more reasonable and efficient. In addition, the OpenFlow protocol embedded in the AP firmware is revised to efficiently collect the measurement data for AP selection. Besides, since the data processing task is shifted to the SDN controller, the burden of each STA is greatly alleviated thus resulting in fewer energy consumption and overheads at STAs. Experimental results demonstrate the ability of our TPR to increase the throughput of the WLAN system in multi-AP scenario and to reduce the number of handoffs among different APs. [ABSTRACT FROM AUTHOR]

Exploiting sink mobility for balancing the energy dissipation of sensor nodes and preventing energy holes is a growing trend in the field of wireless sensor network (WSN). The mobile sink moves throughout the network and collects data in a single-hop fashion resulting in less energy consumption of the sensor nodes. Despite the advantage of this scheme, data forwarding to the mobile sink during an emergency situation is a major issue that should be dealt with. For efficient data delivery within the stipulated deadline, the sensor node which has sensed a delay-intolerant data needs to forward the data towards the mobile sink. In this paper, a Moore curve based trajectory of the mobile sink has been considered for efficient sink based data collection. This work is also motivated by anycast forwarding of delay sensitive data to the mobile sink before the sensed data lose its relevance, where the node follows a strict sleep-wake pattern. We have evaluated the effectiveness of the proposed trajectory based data collection scheme by simulation using MATLAB. The delay-intolerant data routing scheme (DRS) shows that the data reaches the mobile sink within the given deadline while restricting the energy dissipation of the sensor nodes to a minimum. This have been verified by extensive simulations which show that when compared to static sink, the usage of a mobile sink improves the network lifetime by almost 12%. This work has also been compared with an existing work (PMDD) to show its validity. The simulation results depict that DRS shows an improvement of about 25%–29% in terms of network lifetime. [ABSTRACT FROM AUTHOR]

In this paper, we propose an energy efficient approximate multiplier design obtained by truncating the input operands. In the structure, the n-bit multiplication operation is transformed to a smaller bit length multiplication plus some add and shift operations. The simple calculation core makes the multiplier a scalable yet low power structure. Also, we suggest an output quality-tunable multiplier providing ability to change the output quality during the multiplication operation. The characteristics of the proposed multiplier are compared with those of the exact and some other approximate multipliers in a 45 nm technology. The comparison reveals an average improvement of 89.2% (74.9%) in terms of the energy (area) compared to that of the exact multiplier. The utility of the proposed multiplier in a JPEG encoder application is also investigated. The results reveal that the Peak Signal to Noise Ratio (PSNR) reduction is at most 0.15 dB compared to that of the exact one. [ABSTRACT FROM AUTHOR]

The energy consumption of cloud robotics is an important design factor to be considered early in system development. Software as the main enabler of cloud robots, its energy consumption will directly influence the energy consumption level of the entire system. In this paper, we propose a green software model for cloud robotics based on energy consumption time state transition matrix (ETSTM), which can effectively integrate the logic functions, energy consumption, and execution time of software into a single model. To improve the practicability of ETSTM, we provide a software energy consumption function based on software characteristic fitting by a backpropagation (BP) neural network, which can be used to predict software energy consumption. Furthermore, we provide two types of energy consumption analysis algorithms based on explicit execution path search and bounded model checking (BMC) for ETSTM. The experiment results show that the proposed method can achieve good performance. [ABSTRACT FROM AUTHOR]

• This paper aims to provide an overview of the Internet of Energy concept in the Industrial Internet of Things paradigm. • Internet of Energy is a decentralized, smart and viable energy solution that is yet unexplored in the industrial paradigm. The concept is emphasized in close relation to the Internet of Things, Industrial Internet of Things and Industry 4.0. • Internet of Energy integration in the industry is focused to provide key requirements, applications, architecture frameworks and open challenges. The Internet of Energy (IoE) transforms energy production, supply, and consumption to fulfill high energy demands via intelligent automation of industrial energy producers and consumers. This paper emphasizes the concept of the IoE in the Industrial Internet of Things (IIoT) perspective in order to ensure productivity, control, reduced cost, real-time decision making and monitoring, customer satisfaction, and innovative experience. Furthermore, industrial applications and requirements for optimizing performance, transmission, and consumption for efficient energy utilization and increased productivity have also been discussed. Industrial services, technologies, prerequisites, and application requirements are focused on outlining the architectural framework for achieving net-zero energy efficiency, applicability, and limitations. The taxonomy of communication layer protocols is discussed, compared, and evaluated in depth along with their limitations, to help with pre-technology implementations. Moreover, open challenges such as middleware, mobility, data integrity, and scalability are also discussed in depth with their potential solutions. [ABSTRACT FROM AUTHOR]

This paper proposes LBA, a lifetime balanced data aggregation scheme for the Internet of Things (IoT) under an application-specified end-to-end delay requirement. In contrast to existing aggregation schemes, LBA aims to prolong the IoT network lifetime under network heterogeneity and dynamics, while ensuring the required data delivery delay. To achieve this goal in a distributed manner, LBA adaptively adjusts the aggregation delays of neighboring devices to balance the lifetime between them. As such balancing takes place in all neighborhoods, the minimal device lifetime in the network is increased gradually, thus prolonging the lifetime of the entire network. The effectiveness of LBA is demonstrated via extensive experiments on a testbed. Generally, when the network presents a higher degree of heterogeneity and dynamics, LBA’s performance gain over a state-of-the-art non-adaptive data aggregation scheme becomes more significant, and the gap between LBA’s performance and its theoretical upper bound gets smaller. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent fuzzy logic control strategy optimized by genetic algorithm (GA) has been proposed for uniaxial parallel hybrid electric vehicle (PHEV), in the fuzzy controller, the ratio between the motor target torque and the total demand torque is the first input variable, and the state of charge (SOC) of the battery is the second input variable. The torque distribution coefficient between the engine and the motor is as the output variable. The proposed strategy is compared to the electric auxiliary control strategy. The whole vehicle is modeled based on experimental data and the strategy is verified in automotive simulation software ADVISOR, the results show that the proposed control strategy can reduce fuel consumption, reduce emissions, ensure balanced charging and discharging of the battery, effectively avoid the production of the engine peak torque, and improve the overall performance of the vehicle. [ABSTRACT FROM AUTHOR]

Green and reliable communication has great significance for Industrial Internet of Things. Unfortunately, because of the loss nature, achieving reliable transmission is challenging. In this paper, a novel hybrid transmission protocol (HTP) is proposed to maximize lifetime while the reliability is still guaranteed. The proposed protocol adopts Send-Wait automatic Repeat-Request protocol in hotspot areas to reduce the energy consumption and network coding based redundant transmission approach with adaptive redundancy level in non-hotspot areas to guarantee the reliability. The proposed protocol could improve the lifetime and shorten the delay on the premise of ensuring the reliability. Comparing with Send-Wait automatic Repeat-Request protocol, it can improve lifetime by 15%–30% under the same reliability and improve the reliability by 12%–45% under the same lifetime. Comparing with network coding based redundant transmission approach, the lifetime has increased by more than one time under the same reliability. [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 recent decades, cluster-based schemes have emerged as viable solutions for energy conservation problem in wireless sensor networks (WSN). However, most of the existing solutions incur imbalanced energy consumption and high network overheads. In addition, existing cluster-based solutions do not optimize route discovery based on the restricted resources of sensor nodes. Moreover, most of the cluster-based solutions perform periodical re-clustering for load balancing, which results in shortening network lifetime. This research paper presents a Dynamic Energy-aware Fault Tolerant Routing (DEFTR) protocol that exploits uniform-sized network partitioning based on network size and utilizes a multi-facet routing mechanism, which takes into consideration the residual energy, and position and link quality of neighbors. Furthermore, DEFTR not only offers reliable and energy efficient data routing but also supports fault tolerance. Simulation results demonstrate that DEFTR improved the network lifetime by 20.9% and throughput by 35%, also it reduced the delay by 29% and transmission cost by 46% in comparison to the existing work. [ABSTRACT FROM AUTHOR]

In recent years, resource constrained hardware makes the operating system simplicity a most crucial design criterion. The majority of research in such operating systems has focused on the reactive nature of an event-driven kernel. However, the event-based kernel may result in the latency of processing events and erroneous energy profiling. Most battery-driven wireless sensor nodes use the radio event to wake up sleeping nodes, but the radio always consumes more energy than other components during the transmission intervals. The contribution of this paper is that we present EXOS , a timer-driven operating system which can both process periodic events on time and obtain rapid responses to external signals. The system kernel can be easily ported to any other memory-constrained target platforms. EXOS is able to provide the detailed prediction of each component's energy consumption during program execution. The evaluation has demonstrated that EXOS can be practically integrated into wireless sensor networks. [ABSTRACT FROM AUTHOR]

This paper proposes a Cluster-Based Data Aggregation (CBDA) method for identifying pests in Arabica Coffee plantation using Wireless Sensor Networks (WSNs). Acoustic signals that are generated with biting sound by the pests inside stem are captured by WSN. Information regarding existence of pests is aggregated at Cluster-Head (CH) and is conveyed to base station. CH is selected using five states of each node: i-band, o-band, cluster-head request, idle and cluster-head. CH performs data aggregation with residual energy, time stamp using Kolmogorov’s zero-one law to eliminate redundancy. Simulation analysis of CBDA is compared with fast local clustering, energy-efficient reliable data aggregation technique and energy-efficient data aggregation transfer in terms of aggregation ratio, message overhead, control overhead, packet delivery ratio, algorithmic complexity, delay, energy consumption, time-out period and clustering time. The CBDA simulation results outperform compared to the corresponding techniques. [ABSTRACT FROM AUTHOR]

In this paper the design of a novel high-speed, low-cost autonomous line following robot that combines human expert knowledge and experiential data extracted through neural network training has been proposed and implemented. The proposed autonomous robot overcomes the disadvantages of earlier designs like oscillations in motion, exception handling, high-cost and high-energy consumption. The robot uses a novel square-topology infrared sensor matrix that enables it to anticipate a turn by sensing the track ahead. Three different strategies to control high-speed line following robots are presented: a neural network based control strategy, a learning vector quantization based strategy and a reinforcement learning based strategy. The final design based on a hybrid neural network based control strategy smoothly followed the benchmark track with just 4 neurons and used a low-cost 8-bit microcontroller to implement the control system. [ABSTRACT FROM AUTHOR]

The Ultraviolet (UV) Non-line-of-sight (NOLS) communications are becoming a research hotspot in recent years. For wireless UV networks, there are some well-known existing issues such as network connectivity, power control and channel assignment. In this paper, a Fast Power Control and Channel Assignment scheme (FPCCA) is proposed to reduce interference and save energy. Firstly, the FPCCA is compared with the traditional channel assignment which without power control. It is proved that the approach of FPCCA can save energy more than 10%. Then, the FPCCA is both applied in the UV and Wireless Mesh Networks (WMN). The performance of FPCCA with different angles and network traffic loads is simulated and analyzed. When the traffic load is fixed, the performance metrics of FPCCA in UV are better than that in WMN. Meanwhile, the FPCCA not only ensures the quickness and accuracy of channel assignment, but also saves energy and improves the throughput. [ABSTRACT FROM AUTHOR]

Wireless sensor networks comprise nodes with limited power supply. The dense deployment of nodes on a terrain for monitoring the environment causes contention in wireless channels that leads to interference and high energy consumption. The topology of the network changes frequently because of failure of nodes, addition of new nodes, and channel fading. Topology control is an important technique for reducing energy consumption, interference, and maintaining connectivity in the network. This paper provides a survey on state scheduling-based topology control techniques for unattended wireless sensor networks. Topology control algorithms for both flat and hierarchical networks are discussed. The algorithms are further categorized according to the key parameters used for state scheduling. The advantages, disadvantages, and results of the algorithms are tabulated. In addition, we highlight future research directions in designing state scheduling-based topology control algorithms. [ABSTRACT FROM AUTHOR]

This paper presents the design of a compact Radar for real-time detection of targets in smart mobility applications. The Radar integrates Fabry–Perot resonating antennas, X-band and configurable continuous wave transceiver, high-speed analog-digital-converter and low-power/low-cost FPGA for the baseband signal processing. The latter includes region-of-interest selection, 2D Fast Fourier Transform for range-Doppler map extraction, peak detection and alarm decision logic. The transmitted power can be configured from few mW to 1.8 W. This allows for a trade-off between the maximum detection range, from few hundreds of meters up to 1.54 km, and the Radar power consumption, from 2.56 W to 11.66 W. The measured speed is up to 40 m/s. The speed and distance resolutions are 0.3 m/s and 37.5 cm, respectively. The configurable Radar features increased robustness vs. laser-scanners, video cameras, or induction loops detection techniques, and stands for its better trade-off in terms of covered range, size, and low-power consumption vs. state-of-the-art surveillance Radars. [ABSTRACT FROM AUTHOR]

This paper proposes two architectures, including an Opportunistic Large Array Concentric Routing Algorithm with Geographic Relay Nodes (OLACRA-GRN) architecture, and an Opportunistic Large Array Concentric Routing Algorithm with Relay Nodes (OLACRA-RN) architecture. First, the OLACRA-GRN architecture with the geographic information of relay nodes reduces node energy consumption and finds the optimum number of relay nodes to forward the data; an analysis of the characteristics of the energy model is also presented. Besides, the OLACRA-RN architecture without the geographic information of relay nodes is proposed, which can find the number layer of concentric circles in the sensing field. The optimal number layer of concentric circles is calculated according to the distance between the sink and field boundary. Simulation results show that our proposed OLACRA-GRN and OLACRA-RN architectures can effectively reduce node energy consumption more than Opportunistic Large Array Concentric Routing Algorithm (OLACRA) architecture. [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]

In this paper, we address the problem of improving the performance of real-time embedded Multiprocessor System-on-Chip (MPSoC). Such MPSoCs often execute applications composed of multiple tasks. The tasks on each processor are scheduled by a Real-Time Operating System (RTOS) instance. To improve performance, we reduce the Worst Case Execution Time (WCET) of the RTOS by new processor-coprocessor execution models using reconfigurable hardware. Furthermore, we integrate the proposed contributions on an MPSoC platform, where the processor-coprocessor execution models are applied on three reconfigurable coprocessors - Hardware Task-Status Manager (HWTSM), Thread Interrupt State Controller (TISC), and Remote Slack Manager (RSM). As a case study, we investigate the HWTSM, which determines the execution eligibility of tasks. The experimental results suggest overall system improvement up to 13.3% with the help of the HWTSM. Moreover, the TISC can boost further the performance and RSM can significantly reduce the energy consumption. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology for improving efficiency and power consumption of networks-on-chip (NoCs). The proposed approach applies queue length considerations of a modified version of RED algorithm. Moreover, a stochastic learning-automata-based algorithm has been used to optimize the threshold values required in RED algorithm. Furthermore, a new architecture has been provided for dynamic flow control of virtual channels. The proposed method contributes to reduction in queue blockages and power consumption in addition to determining an appropriate size for virtual channels. The proposed algorithm was evaluated under various synthetic traffic patterns for different injection rates and trace-driven SPLASH-2 benchmark suite. The experimental results demonstrate that the algorithm reduces latency and power consumption by 23% and 52%, respectively, compared to the conventional NoC. Further, compared to Express Virtual Channels (EVC) scheme, it showed 13% and 36% improvement in latency and power consumption, respectively. [ABSTRACT FROM AUTHOR]