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In this paper, a long-term workload management problem for multi-server edge computing with server collaboration is studied. In the considered model, mobile users computation-intensive tasks are generated dynamically over the time and offloaded to associated edge servers according to pre-determined subscription agreements. Upon receiving the subscribed workload, each edge server can then decide to whether participate in server collaboration for enabling workload re-allocation (i.e., workload exchange) with other heterogeneously configured edge servers. Unlike most of the existing work, this paper takes into account both competitions and collaborations among strategic edge servers in sharing their computing capacities. To achieve the equilibrium for each edge server in minimizing its expected cost (including energy consumption, delay, transmission, configuration and pricing costs), a joint optimization is formulated for determining i) its amount of workload to undertake, ii) compensation price charged from peers, and iii) computing speed to adopt. To efficiently solve this problem, we propose a novel cooperative queueing game approach, which integrates a convex optimization, a core cost sharing scheme and a mapping rule. Theoretical analyses and extensive simulations are conducted to evaluate the performance of the proposed solution, and demonstrate its superiority over counterparts.

One-level optimization methods have been proposed to optimize a single user’s load profile or a cluster of users in the smart grids. In this work, two two-level optimization methods are studied, one case considering technical requirements (case 1) and another considering economic criterion (case 2). In the upper level, the supplier optimizes the objective function. Meanwhile, at the lower level, users optimize their electrical costs. The proposed methods are based on Genetic Algorithm methods. In this sense, an indirect control is established in which users react to a price signal. Simulation results illustrate that both cases improve the demand profile and increase the retailer profit concerning an unscheduled case. However, when the supplier tries to maximize the profit, some users receive benefits to detriment of others, concluding that the technical approach is preferable to the economic one.

In Mobile Ad Hoc Networks (MANET), during routing, there may be possibility of route recovery delay and overhead. Also there may be issues due to reduced lifetime of link and node. In order to overcome these issues, in this paper, an optimization technique for fault recovery and fast data transmission in MANET is proposed. In order to avoid the faults due to frequent disconnections and node failures, the path with least mobility and high residual energy is selected for transmission. During data transmission, if majority of the neighbours are changed or if the residual energy is low, then the source will choose any shorter alternate route by applying the optimized recovery mechanism. By simulation results, we show that the proposed technique enhances packet delivery ratio and throughput and minimizes end-to-end delay, energy consumption and control overhead.

Reports show that 40% of global carbon emissions come from energy consumed in buildings, so organizations around the world continuously seek ways to reduce their buildings energy consumption. Research identifies three main approaches; Retrofitting, the use of Low Carbon Buildings (LCB), and implementing Energy Management Systems (EMS). However, other factors could affect the success of these approaches; climate and local weather, building types, social acceptance, and implementation. This paper reviews and evaluates 50 reports on these implementations, and uses that evaluation to identify the most effective approach to reduce buildings carbon footprint. In this evaluation, authors used the reported success rate (in reducing carbon footprint) as key parameter, they then cross-checked this with origin of reports (country, climate region), building type, and implementation thoroughness. This allowed the authors to identify not only the best approach, but also understand the impact of these other factors on the reported success rate. Retrofitting was implemented the most (58%) but with varying success (2% to 49% reductions), this variation is attributed to the current state of the buildings and the level/method of retrofitting applied. LCB showed the highest reductions as energy saving measures were preplanned before building construction, this however limits LCB to new and planned buildings. Even though EMS showed results comparable to Retrofitting and required a fraction of the costs/effort, only 14% implemented it, this is an indication of the level of social awareness in the region on the need to reduce carbon footprint and its impact on the world’s climate.

The computation offloading, where Internet of Things (IoT) devices transfers their task to an external cloud, has several advantages such as low energy consumption of IoT devices and fast response time. To maximize these advantages, IoT devices can exploit the nearest edge cloud. However, frequent offloadings to the nearest edge cloud can cause a location privacy vulnerability due to the proximity of the edge cloud from IoT devices, which is a critical issue in smart city IoT applications. To address this problem, we propose a location privacy-guaranteed offloading algorithm (LPGA) in cache-enabled edge cloud environments. In LPGA, an IoT device decides where to offload the task (i.e., edge cloud or central cloud) with the consideration of the privacy level on its location and the cache hit probability. To minimize the generated traffic volume while maintaining low energy outage probability and providing a sufficient level of location privacy, a constrained Markov decision process (CMDP) problem is developed and it is converted into an equivalent linear programming (LP) model to achieve the optimal policy for offloading. Evaluation results demonstrate LPGA can reduce the traffic volume up to 39% compared to a central cloud-based offloading scheme while maintaining the energy outage probability below a certain level and providing required location privacy level.

Solar distillation is a sustainable and promising technique to generate fresh water. However, the solar vapor generation is a high energy consumption process, resulting in a low water yield under natural sunlight. Hence, developing of advanced evaporators that can simultaneously reduce the energy requirement of water vaporization and accelerate solar water evaporation remains a great challenge. In this study, we report the fabrication of a multifunctional hydrogel of HxMoO3/PNIPAM with PNIPAM as hydratable skeleton and HxMoO3 as the light-absorbing unit for solar water evaporation. The experimental results demonstrate that the as-prepared hydrogel owns excellent photothermal activity. Accurately, the fabricated hydrogel -based solar evaporators achieved high water evaporation rate of 1.65 kg m−2 h−1 with the energy conversion efficiency of 85.87% under 1 kW m−2 irradiation. The enhanced photothermal activity of HxMoO3/PNIPAM hydrogel can be attributed to the synergistic effects of the components composed in this hierarchical architecture that change the water state and further speed up water evaporation. The HxMoO3/PNIPAM evaporators indicate its great potential for practical implementation of solar water evaporation.

Large volumes of video data recorded by the increasing mobile devices and embedded sensors can be leveraged to answer queries of our lives, physical world and our evolving society. Especially, the rapid development of convolutional neural networks (CNNs) in the past few years offers the great advantage for multiple tasks in video analysis. However, adopting running CNNs directly on mobile devices and embedded sensors for video analytics brings heavy burden due to their limited capacity, especially for learning a large volume of data. A promising approach is to outsource the computation-intensive part of CNN to cloud. However, the reveal of data to cloud may cause privacy leakage. In addition, the cloud-assisted approach may also bring some communication efficiency challenges for large volume of data. To address both privacy and efficiency issues, we design a privacy-preserving and computationally efficient framework for mobile video analytics. Extensive experiments demonstrate our proposed system PrivacyEye can effectively protect private information while keep the accuracy of the normal tasks with less than 2% drop, and it saves up to 82.9% execution time and 78.8% energy consumption.

Todays large-scale parallel workflows are often processed on heterogeneous distributed computing platforms. From an economic perspective, computing resource providers should minimize the cost while offering high service quality. It has become well-organized that energy consumption accounts for a large part of a computing systems total cost, and timeliness and reliability are two important service indicators. This article studies the problem of scheduling a parallel workflow that minimizes the system energy consumption under the constraints of response time and reliability. We first mathematically formulate this problem as a Non-linear Mixed Integer Programming problem. Since this problem is hard to solve directly, we present some highly-efficient heuristic solutions. Specifically, we first develop an algorithm that minimizes the schedule length while meeting reliability requirement, on top of which we propose a processor-merging algorithm and a slack time reclamation algorithm using a dynamic voltage frequency scaling (DVFS) technique to reduce energy consumption. The processor-merging algorithm tries to turn off some energy-inefficient processors such that energy consumption can be minimized. The DVFS technique is applied to scale down the processor frequency at both processor and task levels to reduce energy consumption. Experimental results on two real-life workflows and extensive synthetic parallel workflows demonstrate their effectiveness.

Datacenters receive dynamic workloads with disparate specifications to be scheduled on virtual machines (VMs). These unpredictable, alarmingly growing workloads with varying resource specifications may bring down the servers of datacenters into an imbalanced state. Thus, resulting in low resource utilization and high energy consumption among the servers. To cater to the need of fluctuating on-demand resource provisioning, it is essential to scale up the ability and capacityof existing infrastructure through virtualization. Moreover, due to the involvement of conflicting scheduling constraints, load scheduling in cloud computing fall under NP-hard problem. An effective scheduling mechanism in amalgamation with a load balancing strategy based on binary JAYA is implemented to alleviate the challenges as mentioned above. This technique not only improves resource utilization but also brings down the degree of energy consumption and makespan while keeping the whole system balanced. At first, it focuses on evoking a load balancing procedure to uniformly disperse the loads among VMs based on the compatibility between the tasks and VMs and secondly, JAYA algorithm is executed to find the best possible mapping of tasks onto VMs. In order to appraise the efficacy of the proposed algorithm, the scheduling of independent and non-preemptive tasks is simulated in CloudSim using a benchmark parallel workload by NASA-iPSC. Experiments are conducted in both homogenous and heterogeneous environments. The proposed algorithm is statistically validated using the Friedman test, followed by a Holm’s test. The proposed approach is compared over other algorithms such as Round Robin (RR), binary Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The simulation results show a notable amelioration over other mentioned algorithms by an increase of resource utilization with 18.47% (GA), 12.65% (BPSO) and 4.18% (GA), 2.51% (BPSO) and a reduction of makespan by 6.47% (GA), 4.35% (BPSO) and 4.17% (GA), 2.20% (BPSO) with an increasing number of tasks and VMs in two different test cases respectively.

Underwater wireless acoustic networks (UWANs) take advantage of acoustic communication to enable many distinct applications. The peculiar features of underwater acoustic channels, such as long propagation delay, high bit error rates, and severely limited bandwidth, make the use of medium access control (MAC) solutions designed for terrestrial radio networks inefficient in UWANs. This paper proposes UW-SEEDEX, a MAC protocol for UWANs that employs random time slot schedules, created from seeds, to avoid collisions. After exchanging seeds, nodes can know other's entire schedules, allowing them to then better plan their transmissions. Simulations evaluate how each of UW-SEEDEX's parameter affects its performance in metrics such as end-to-end delay, energy consumption, transmissions per data reception, and reception rate using different test scenarios. Simulations also show that UW-SEEDEX can perform better than other MAC solutions, delivering more messages than protocols such as UW-Aloha and Slotted FAMA and reduced, on average, up to three times the number of transmissions required for each message reception in networks with grid topologies. UW-SEEDEX presented reception rates close to 100%, low energy cost, and fewer transmissions per data reception. Our code is available at https://gitlab.com/epmcj/ns-3-dev/-/tree/new-uan-mac-protocols.

Unmanned Aerial Vehicles (UAVs) have received a wide range of attention for military and commercial applications. Enhanced with communication capability, UAVs are considered to play important roles in the Sixth Generation (6G) networks due to their low cost and flexible deployment. 6G is supposed to be an all-coverage network to provide ubiquitous connections for space, air, ground and underwater. UAVs are able to provide air-borne wireless coverage flexibly, serving as aerial base stations for ground users, as relays to connect isolated nodes, or as mobile users in cellular networks. However, the onboard energy of small UAVs is extremely limited. Thus, UAVs can be only deployed to establish wireless links temporarily. Prolonging the lifetime and developing green UAV communication with low power consumption becomes a critical challenge. In this article, a comprehensive survey on green UAV communications for 6G is carried out. Specifically, the typical UAVs and their energy consumption models are introduced. Then, the typical trends of green UAV communications are provided. In addition, the typical applications of UAVs and their green designs are discussed. Finally, several promising techniques and open research issues are also pointed out.

Smart body area networks (SmartBAN) is an emerging wireless body area networks (WBAN) standard proposed by the European Telecommunications Standards Institute (ETSI). This paper first examines the potential of SmartBAN medium access control (MAC) layer with scheduled access to support a myriad of WBAN applications, having diverse data rate requirements. Extra scheduled access slots can be allocated to high date rate sensor nodes for managing their data rate requirements. High data rate sensor nodes can also be re-assigned to use the available time slots of low data rate sensor nodes in Inter-Beacon Interval (IBI) by the central hub. But these two schemes incorporate different physical (PHY) and MAC layer overheads related to frame transmission, frame acknowledgement and slot re-assignment. This redundant overhead transmission results in high overhead energy consumption and reduced effective throughput. Therefore, an innovative and flexible enhanced throughput and reduced overhead (FETRO) MAC protocol for scheduled access is proposed in this article. In the proposed scheme, the sensor node data rate requirements are considered while assigning the scheduled access slot duration by allowing minimal changes in the base-line standard implementation. This infers the provision of scheduled access slots with variable slot durations within an IBI. We also evaluate the existing techniques of extra slot allocation and slot re-assignment in SmartBAN as well as the proposed FETRO MAC protocol with variable slot length. The proposed FETRO MAC scheme results in optimizing both the overall throughput and normalized overhead energy consumption per kilo bits per second (Kbps). Additionally, the impact of various WBAN channel models over these throughput management approaches is also investigated. The proposed FETRO MAC protocol with variable slot duration gives an average reduction of 65.5% and 59.16%, respectively, in the hub and nodes normalized overhead energy consumption per Kbps outcomes, as compared to the de-facto SmartBAN MAC scheduling strategies.

An energy efficient information and communication system is a need of the day. Information technology related industries are making efforts to reduce power consumption by improvising both the hardware infrastructure and software systems. The hardware is driven by software; hence, design and development of software may have a significant impact energy need of the overall system. Many times it is observed that complex software performs useless tasks leading to power consumption, referred to as energy leak. The solution of energy leaks for mobile applications has attracted the interest of researchers, but still handling energy leaks issues for applications developed using Java-based technology need to be explored. While designing software during maintenance phase, code smells are introduced which not only reduce performance of the software also may lead to execution of useless code thus enhancing the energy leak. The common practice of the software industry to remove the code smells is use of refactoring strategy. The basic idea of this study is to understand the impact of refactoring on total energy consumption. To explore this hypothesis a set of Java based applications are selected, code smell present in these applications are discovered and a suitable refactoring strategy is applied to reduce the code smell. Power consumption is estimated using an open source tool Microsoft Joulemeter, it monitors energy usage by machine resources and estimates power consumption. The energy consumption before and after refactoring is recorded and statistical t-test is performed to validate the proposed concept.

Containers are lightweight and provide the potential to reduce more energy consumption of data centers than Virtual Machines (VMs) in container-based clouds. The on-line resource allocation is the most common operation in clouds. However, the on-line Resource Allocation in Container-based clouds (RAC) is new and challenging because of its two-level architecture, i.e. the allocations of containers to VMs and the allocation of VMs to physical machines. These two allocations interact with each other, and hence cannot be made separately. Since on-line container allocation requires a real-time response, most current allocation techniques rely on heuristics (e.g. First Fit and Best Fit), which do not consider the comprehensive information such as workload patterns and VM types. As a result, resources are not used efficiently and the energy consumption is not sufficiently optimized. We first propose a novel model of the on-line RAC problem with the consideration of VM overheads, VM types and an affinity constraint. Then, we design a Cooperative Coevolution Genetic Programming (CCGP) hyper-heuristic approach to solve the RAC problem. The CCGP can learn the workload patterns and VM types from historical workload traces and generate allocation rules. The experiments show significant improvement in energy consumption compared to the state-of-the-art algorithms.

Edge Computing has emerged as a new computing paradigm dedicated for mobile applications for performance enhancement and energy efficiency purposes. Specifically, it benefits today's interactive applications on power-constrained devices by offloading compute-intensive tasks to the edge nodes which is in close proximity. Meanwhile, Field Programmable Gate Array (FPGA) is well known for its excellence in accelerating compute-intensive tasks such as deep learning algorithms in a high performance and energy efficiency manner due to its hardware-customizable nature. In this paper, we make the first attempt to leverage and combine the advantages of these two, and proposed a new network-assisted computing model, namely FPGA-based edge computing. As a case study, we choose three computer vision (CV)-based mobile interactive applications, and implement their backend computation engines on FPGA. By deploying such application-customized accelerator modules for computation offloading at the network edge, we experimentally demonstrate that this approach can effectively reduce response time for the applications and energy consumption for the entire system in comparison with traditional CPU-based edge/cloud offloading approach.

Internet of Things (IoT) connects physical, cyber and human spaces. Event-based system is one of the cornerstones to help IoT achieve real-time monitoring, context-awareness and intelligent control. In the era of big data, the huge amount and high complexity of event inference rule pose a great challenge to traditional event-based system in its efficiency, especially resources-constrained IoT edge systems. This paper proposes a high-efficiency joint event inference model for real-time contextawareness and decision-making in IoT edge systems. We define different kinds of redundancy relations between event inference models and propose a description mechanism, named event containing graph, to support multi-pattern optimization. Three operations on single-pattern event inference models, Merge, Failure and Output are defined respectively. The joint inference model is established by merging sharing patterns, constructing failure transitions and conditional output to eliminate inter-model redundancies. Experimental results prove that the joint model consumes less computational resources and provides higher performance than other benchmarks. It also verifies and proves that joint model has better optimization effect when processing large number of complex events. Especially in edge computing environment, joint inference model improves the real-time performance and significantly reduces the energy consumption in data transmission from edges to data center.

This work is licensed under a Creative Commons Attribution 4.0 International License. Cloud computing represents one of major innovations in Information Technology (IT). It is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources. Elasticity is one of the most important characteristic in the cloud. Due to the enormous amounts of energy consumed by a cloud, it becomes difficult to build an elasticity system that satisfies all the requirements that might arise during its lifetime. Different approaches are proposed to address the energy-efficiency in the modelling of elasticity system at multiple layers of cloud services. Most of the existing approaches measure the overall hardware energy consumption, instead of the energy consumption of the software. Understanding how energy is consumed by cloud with elastic scaling mechanism is a key for managing better energy efficient software. This work proposes an architecture for modelling elasticity and energy-efficiency in the application layer. This architecture combines both the characteristics of adaptation (with Autonomic Computing) and variability (with Feature Model) into a single solution. The feature model considers the variability in the structural modelling and the behaviour one. We show the feasibility of the proposed approach by analysing the smart university applications associated with the Znn.com scenario

Recent applications in low-power (1-20 mW) near-sensor computing require the adoption of floating-point arithmetic to reconcile high precision results with a wide dynamic range. In this article, we propose a low-power multi-core computing cluster that leverages the fined-grained tunable principles of transprecision computing to provide support to near-sensor applications at a minimum power budget. Our solution - based on the open-source RISC-V architecture - combines parallelization and sub-word vectorization with a dedicated interconnect design capable of sharing floating-point units (FPUs) among the cores. On top of this architecture, we provide a full-fledged software stack support, including a parallel low-level runtime, a compilation toolchain, and a high-level programming model, with the aim to support the development of end-to-end applications. We performed an exhaustive exploration of the design space of the transprecision cluster on a cycle-accurate FPGA emulator, varying the number of cores and FPUs to maximize performance. Orthogonally, we performed a vertical exploration to identify the most efficient solutions in terms of non-functional requirements (operating frequency, power, and area). We conducted an experimental assessment on a set of benchmarks representative of the near-sensor processing domain, complementing the timing results with a post place-&-route analysis of the power consumption. A comparison with the state-of-the-art shows that our solution outperforms the competitors in energy efficiency, reaching a peak of 97 Gflop/s/W on single-precision scalars and 162 Gflop/s/W on half-precision vectors. Finally, a real-life use case demonstrates the effectiveness of our approach in fulfilling accuracy constraints. ISSN:1045-9219 ISSN:1558-2183 ISSN:2161-9883

The primary constraints of Underwater Wireless Acoustic Sensor Networks (UWASNs) are limited bandwidth, node energy and latency. The process of data aggregation will ease the constraints of UWASN. In this paper, we propose a scheme for data aggregation and routing in UWASN using static and mobile agents based on palm tree structure. The main components of palm tree structure include leaflets, spines, rachis and petioles. The leaflets and smaller leaflets (fronds) are connected to the petiole through spines. The junction of petioles connects to the sink node. The proposed scheme works as follows.Firstly, fronds and leaflets connected to the petioles are created through spines. Secondly, master center nodes are selected on petiole junction using mobile agent based on the parameters such as residual energy, Euclidean distance, petiole angle and connectivity. Thirdly, local center nodes are identified on either side of leaflet and connected to the master center using a mobile agent. Fourthly, the process of local aggregation at local centers happens by taking into account of nodes along the leaflets and carry to a connected master center. Finally, the process of master aggregation at the junction of petioles and delivering the aggregated data to the sink node using a mobile agent is performed. To assess the efficacy of the scheme, simulation in different UWASN scenarios are carried out. The parameters of performance analyzed are master and local center selection time, aggregation ratio, aggregation energy, energy consumption, number of local and master centers involved in the aggregation process and lifespan of the network. We observed that proposed scheme performs better than the existing aggregation scheme.

Semi-autogenous grinding (SAG) mill is widely used in the grinding process of coal and ores because of its strong applicability, large capacity and low consumption of grinding media. Therefore, the research of SAG mill is still of great important practical significance. Among those parameters of the SAG mill, the filling level is very important for it is not only directly related to the processing capacity of the mill, but also affects the operation cost and stability of mill. In this paper, combined with shear impact energy model (SIEM) and discrete element method (DEM), the particle collision energy spectrum was applied to analyze the influence of ore filling level and media filling level on the power draw of the mill, the impact energy on ore, and the wear of liner and media in a slice of an industrial SAG mill. It was found that increasing the ore filling level would increase energy consumption and reduce the energy efficiency and the wear of liner and grinding media. Furthermore, the vibration of mill was explored from the collision energy on liner. The results showed that the low frequency but high energy collision between grinding media and liner was the main reason for liner wear and mill vibration.

Based on the nonlinearity of energy consumption systems and the influence of multiple factors, this paper presents a nonlinear multivariable grey prediction model with parameter optimization and estimates the parameters and the approximate time response function of the model. Next, a genetic algorithm is applied to optimize the nonlinear terms of the novel model to seek the optimal parameters, and the modelling steps are outlined. Then, to assess the effectiveness of the novel model, this paper adopts Chinese oil, gas, coal and clean energy as research objects, and three classical grey forecasting models and one time series method are chosen for comparison. The results indicate that the new model attains a high simulation and prediction accuracy, basically higher than that of the three grey prediction models and the time series method.

The Ubiquitous interconnected smart devices enabled by the recent evolution of low-cost, generic, small-size, powerful computing platforms devised the term Internet of Things (IoT), which cross-cuts many areas of our modern day living. IoT applications go way beyond simple sensing and actuation to sophisticated localized processing and decision-making. The recent advances in embedded systems produced a long list of IoT boards equipped with powerful central processing units (CPUs), and graphics processing units (GPUs). Unfortunately, even with the limited energy consumption and high processing power of such GPUs, CPUs are usually the only computational element utilized by the hosted applications, thus hindering the capabilities of the entire board. This is mainly due to the complicated nature of GPU-based programming. In this paper, we are presenting a case study showing the effect of offloading the computationally intensive part of a latency-sensitive educational game to a low-cost Raspberry Pi’s GPU, thus enabling the board to seamlessly host the entire game operations. Relying mainly on the boards CPU shows very long interaction latency i.e., 4.82 s. By efficiently leveraging the powerful coprocessor, VideoCore GPU, we are able to significantly improve the interaction latency to a fraction of a second, making the game conveniently playable.

The advancements in Internet of Things have augmented the importance of innovation and smart technologies, such as self-driving, self-monitoring, and self-controlling as key enablers for future intelligent transport system (ITS). In ITS, critical services such as rescue and disaster, medical emergency, etc., require an efficient route planning by considering performance parameters such as delay, energy, and service-level agreements during peak and off-peak hours. The ignorance of these performance parameters can lead to loss of life, or financial property, or service users. Therefore, Third Eye, a secure information management scheme is proposed by satisfying all the performance parameters as well as satisfying the trust metrics among the vehicles and devices. The proposed scheme is evaluated using various parameters such as qualifying service set mean route between the source and destination (as $s$ to $t$ ), average energy consumption for data transmission, average hop counts, probability of attack strength and error, end-to-end delay, and users query response. The performance results demonstrated the superior performance of the proposed scheme in comparison to the other state-of-the-art schemes.

The ubiquitous adoption of Internet-of-Things (IoT) based applications has resulted in the emergence of the Fog computing paradigm, which allows seamlessly harnessing both mobile-edge and cloud resources. Efficient scheduling of application tasks in such environments is challenging due to constrained resource capabilities, mobility factors in IoT, resource heterogeneity, network hierarchy, and stochastic behaviors. xisting heuristics and Reinforcement Learning based approaches lack generalizability and quick adaptability, thus failing to tackle this problem optimally. They are also unable to utilize the temporal workload patterns and are suitable only for centralized setups. However, Asynchronous-Advantage-Actor-Critic (A3C) learning is known to quickly adapt to dynamic scenarios with less data and Residual Recurrent Neural Network (R2N2) to quickly update model parameters. Thus, we propose an A3C based real-time scheduler for stochastic Edge-Cloud environments allowing decentralized learning, concurrently across multiple agents. We use the R2N2 architecture to capture a large number of host and task parameters together with temporal patterns to provide efficient scheduling decisions. The proposed model is adaptive and able to tune different hyper-parameters based on the application requirements. We explicate our choice of hyper-parameters through sensitivity analysis. The experiments conducted on real-world data set show a significant improvement in terms of energy consumption, response time, Service-Level-Agreement and running cost by 14.4%, 7.74%, 31.9%, and 4.64%, respectively when compared to the state-of-the-art algorithms., Comment: Accepted in IEEE Transaction on Mobile Computing

Resource and environmental management are shaping our society and economy. Virgin mining is rapidly becoming unsustainable as primary resources are in increasingly short supply and energy consumption continues to increase. Urban mining through a circular economy has emerged as a promising option for securing copper and aluminum resources. However, the sustainability and viability of this circular economy industry are heavily dependent on its profitability. Here, we demonstrate the economic benefits of urban mining for anthropogenic minerals: e-waste, end-of-life vehicles, and waste wiring and cables. The material and cost flows associated with urban mining are deeply examined. Through life cycle cost and cost-benefit analysis, the cost of obtaining one ton of copper or aluminum is found to be, on average, 3,000 US$ or 1,660 US$, which is significantly lower than the cost of virgin mining. Moreover, in terms of the costs and benefits, copper may differ tremendously from aluminum depending on the type of anthropogenic mineral. Some additional regulations or policies enactment need to reinforce the urban mining and circular economy.

For real-time computing systems, energy efficiency, Quality of Service, and fault tolerance are among the major design concerns. In this work, we study the problem of reliable and energy-aware (m,k)-deadlines enforcement using standby-sparing under fixed-priority assignment. The standby-sparing systems adopt a primary processor and a spare processor to provide fault tolerance for both permanent and transient faults. In order to reduce energy consumption for such kind of systems, we proposed two novel scheduling schemes under the QoS constraint of (m,k)-deadlines: one for task sets partitioned with deeply-red pattern and one for task sets partitioned with evenly distributed pattern. The evaluation results demonstrate that our proposed approaches significantly outperformed the previous research in energy conservation while assuring (m,k)-deadlines and fault tolerance for real-time systems.

Vehicular Edge Computing (VEC) is a promising paradigm based on the Internet of vehicles to provide computing resources for end users and relieve heavy traffic burden for cellular networks. In this paper, we consider a VEC network with dynamic topologies, unstable connections and unpredictable movements. Vehicles inside can offload computation tasks to available neighboring VEC clusters formed by onboard resources, with the purpose of both minimizing energy consumption of systems and satisfying task latency constraints. For online task scheduling, existing researches either design heuristic algorithms or leverage machine learning, e.g., Deep Reinforcement Learning (DRL). However, these algorithms are not efficient enough because of their low searching efficiency and slow convergence speeds for large-scale networks. Instead, we propose an imitation learning enabled online task scheduling algorithm with near-optimal performance from the initial stage of the system. Specially, an expert can obtain the optimal scheduling policy by solving the formulated optimization problem with a few samples offline. For online learning, we train a learning policy by following the expert's demonstration with an acceptable performance gap in theory. Performance results show that our solution has a significant advantage with more than 50% improvement compared with the benchmark.

In the era of IoT, the energy consumption of sensor nodes in WSN is one of the main challenges. It is crucial to reduce energy consumption due to the limited battery life of the sensor nodes. Recently, Zone-based Energy-Aware data coLlection (ZEAL) routing protocol is proposed to improve energy consumption and data delivery. In this paper, an enhancement to ZEAL is proposed to improve WSN performance in terms of energy consumption and data delivery. Enhanced ZEAL (E-ZEAL) applies the K-means clustering algorithm to find the optimal path for the mobile-sink node. As well, it provides better selections for sub-sink nodes. The experiments are performed using the ns-3 simulator. The performance of E-ZEAL is compared to ZEAL. E-ZEAL reduces the number of hops and distance by more than 50%, Leading to speed up the data-collection phase by more than 30% with complete delivery of data. Moreover, E-ZEAL improves the lifetime of the network by 30%.

Disassembly is an essential step in a remanufacturing process via which valuable parts and material of end-of-life (EOL) products can be well reused and resource waste is reduced. Disassembly sequence planning focuses on finding the best disassembly sequence for a given EOL product by considering economic and environmental performance. In a practical disassembly process, one may face a disassembly operation failure risk due to the difficulty of knowing EOL products' exact information in advance. Despite its importance in impacting disassembly outcomes, the existing work fails to consider it comprehensively. This work proposes a stochastic biobjective DSP problem with the objectives of maximizing disassembly profit and minimizing energy consumption by doing so. A chance-constrained programming model is established, where a chance constraint ensures a fixed confidence level of disassembly failure. To solve it efficiently, a multiobjective multiverse optimization algorithm with stochastic simulation is proposed. Experiments are carried out on four products. Results demonstrate that it outperforms some state-of-the-art algorithms in terms of solution performance.

Radiant floor systems have the potential to reduce energy consumption and the carbon footprint of buildings. This study analyzed a novel radiant panel configuration comprising a metal plate with small spikes that can be pressed into cement board or wood. The behaviour of this configuration was simulated for different materials for the metal plate, spike dimensions, and varying spacing between spikes. An annual energy simulation model compared the radiant panel configuration with traditional concrete-based system. Simulations were run under heating dominant, cooling dominant, and neutral conditions; significant cost savings and greenhouse gas emission reduction were seen across all scenarios. Keywords: Metal plate with spikes; radiant floor heating and cooling; energy efficiency; thermal comfort; computer simulation; economic optimization

Useful Energy consumption is regarded as the main driver of energy system transitions. However, there are few national level estimates of useful energy consumption, mainly due to the lack of data and concerns about uncertainty. The first step to compute the useful energy consumption involves allocating primary energy to end-use conversion devices. A method is developed that employs widely available end-use energy statistics to allocate energy to conversion devices. The method includes an uncertainty analysis which is believe to increase the robustness of the results. In this paper, the method is applied to the United Kingdom in 2013. The results show that boilers are the device converting most primary energy in the UK (2.2 ±0.13 EJ) and that the consumption mix for the UK is much more equally distributed among devices compared to the global level and to China. The uncertainty analysis shows that it is difficult to rank many of the devices in terms of their primary energy consumption because the uncertainty is higher than the difference between the expected values; and that most uncertainty in the allocation is due to the uncertainty in the end use of final energy.

With the Advancement of Technology, IoT has gained momentum by providing Machine to Machine (M2M) Communication bringing smart devices closer to the proximity of end users. Although IoT is expected to bring together 75.4 billion of devices by 2025, improving the quality of life beyond expectations, but we will have to pay a bigger price for all this in the form of expatiated energy consumption and carbon footprints. These Constraints have motivated researchers to put forward a solution for energy efficient green communication. However, the increasing number of devices annually is also a matter of concern. To deal with such an invidious situation, we have brought together two magnanimous technologies- NB-IoT and Green IoT. The concept of Green IoT has been of paramount importance as it addresses a very important IoT challenge by restricting energy usage of IoT devices and bringing forwards new tactics to deal with it. LPWA technology has come to the rescue, by connecting numerous devices over a wide domain with optimized battery life, utilizing very less power for connectivity and hence curbing the cost to a great extent. Various LPWA technologies like LoRA, Zigbee, Sigfox etc offer prominent solutions to the users by reducing Carbon emissions and making efficient Utilization of power resources but to provide a maximum global outreach of devices to the users and avoid Infrastructure cost, 3GPP in Release-13 provided a cellular based Licensed Technology called NB-IoT. This Technology provides features which can bring maximum benefits to LPWA technology in Market like Low power Consumption, massive connection to IoT devices and enhanced Indoor Coverage. In our research paper, we have highlighted the NB-IoT Technology, its technical features and applications. We have also made an effort in proposing a green NB-IoT model for Smart agriculture as an initiative to promote use of green IoT features and applications. Also Proposed NB-IoT model for Smart agriculture towards green IoT.

Understanding customers’ energy consumption at the individual appliances level is crucial for the planning and im-plementation of demand response (DR) programs. The appliances’ usage profiles can be disaggregated from whole-house energy consumption data using non-intrusive load monitoring (NILM) methods. The appliance load patterns of each customer are considera-bly different, which make it challenging to train a model with strong generalization ability. In this paper, a novel methodology using transfer knowledge between domains for NILM is proposed. A temporal convolutional network is developed to learn the dynamic features of individual appliance load. A domain adaption loss is used to quantify the domain distribution discrepancy between source and target domain representation. By jointly optimizing domain adaptation and energy disaggregation, an invariant representation across domains for the individual appliance states can be learned. Data experiments on ground truth data validate the accuracy and the robustness of the proposed model, and demonstrate its superior transferability and application potential under those scenarios of data shortage.

The convergence of Software-Defined Networking (SDN) and Internet of Vehicular (IoV) integrated with fog computing, known as software-defined vehicle network-based fog computing (SDV-F), has recently been established to take advantage of both paradigms and efficiently control the wireless networks. SDV-F tackles numerous problems, such as scalability, load-balancing, energy consumption, and security. It lags, however, in providing a promising approach to enable ultra-reliable and delay-sensitive applications with high vehicle mobility over SDV-F. We propose ARTNet, an AI-based Vehicle-to-Everything (V2X) framework for resource distribution and optimized communication using the SDV-F architecture. ARTNet offers ultra-reliable and low-latency communications, particularly in highly dynamic environments, which is still a challenge in IoV. ARTNet is composed of intelligent agents/controllers, to make decisions intelligently about i) maximizing resource utilization at the fog layer, and ii) minimizing the average end-to-end delay of time-critical IoV applications. Moreover, ARTNet is designed to assign a task to fog nodes based on their states. Our experimental results show that considering a dynamic IoV environment, ARTNet can efficiently distribute the fog layer tasks while minimizing the delay.

Modular Solar-Powered Aircraft (M-SPA) is a kind of High-Altitude Long-Endurance (HALE) aircraft which exploits the mission advantage of swarm UAV and the HALE advantage of large aspect-ratio SPA. M-SPA’s separated mode and combined mode give it the potential to maximize the mission efficiency with limited solar energy. In this paper, firstly, oriented by the mission of maximizing the cruise area, the overall design of the M-SPA is modeled, including the energy model, the aerodynamic model and the flight environment settings. Secondly, by analyzing the energy consumption of the flight modes, we design a multi-phase flight mission strategy. Then, a 24-hour three-dimensional (3D) flight profile of the M-SPA is optimized, including the sub-SPA cooperative path planning in the separation mode. Finally, inspired by the Traveling Salesman Problem (TSP), an improved Ant Colony Algorithm (ACA) is exploited to find the optimal path for each sub-SPA, which is further developed into a dynamic separation and combination scheme for the M-SPA. The simulation results show that the mission performance of the M-SPA outperforms that of the conventional SPA, and explicitly, the mission coverage of the M-SPA is slightly less than a linear increase under comparable simulation conditions.

This study analyses the relationship between economic growth and environmental degradation in Nigeria (1981–2019). It also examines the effect of industrialization and energy consumption on the environmental quality in the nation. The study utilizes mainly a secondary data; the unit root test shows that carbon emission is stationary at level, while economic growth, industrialization and energy consumption achieve stationary at first difference. Consequently, the Auto Regressive Distributed Lag (ARDL) model, results reveal that economic growth and energy consumption have positive relationship with environmental degradation, while the industrialization has negative or inverse relationship with it. Therefore, the study recommends that there should be adoption of sink enhancement strategy which involves filtering out greenhouse gases.

With the prosperity of cloud computing and video services, the demand for network resources has increased dramatically, leading to the remarkable growth in the amount of network energy consumption, a key factor restricting the development of data centers. Numerous existing works reduce network energy consumption by optimizing data transmission, but they ignore the energy consumption for data transmission preparation, such as activating devices and installing rules. In this paper, we jointly optimize device activation, rule installation and data transmission to minimize network energy consumption. Specifically, we first formulate the minimization problem of the energy consumption of device activation, rule installation, and data transmission. We then prove that it is NP-complete to get the optimal solution of the minimization problem, furthermore, we propose a heuristic algorithm to plan the path with minimum network energy consumption for each flow. The simulation results show that the energy consumption of our algorithm is close to the optimal solution solved by Gurobi, and our algorithm has lower complexity. Compared with the state-of-the-art algorithm, our algorithm always consumes less energy and has shorter flow completion time.

Floods are the most common disaster in the world and cause deaths, damages to houses, buildings, and possessions, as well as disruption to communications. In such dynamic scenarios, it is needed to search and track mobile objects transported by the water flows, such as humans, animals, vehicles, and debris. The use of Unmanned Aerial Vehicles (UAVs) is essential in disaster scenarios to help first responders determine correct procedures in terms of searching, tracking, and rescuing the victims, as well as in defining the actions to minimize the risks in a sustainable and timely manner. However, the tracking of mobile objects and the delivery of real-time video sequences from UAVs with energy constraints toward first responders is still a hard task. Therefore, it is necessary to orchestrate a swarm of UAVs for searching and tracking objects while reacting fast to frequent changes in the topology and trajectory, as well as distributing real-time video flows with quality level support to the ground team. This article proposes an energy-aware swarm-based and mobility prediction scheme for UAVs, called SUAV. SUAV provides a unique UAV-based system for rescue scenarios with mobile objects and with support for route and path planning, searching and tracking procedures, mobility prediction and multi-hop communication management, video delivery with Quality of Service (QoS)/Quality of Experience (QoE) support, and energy-efficiency. Simulation results show the efficiency of SUAV in transmitting real-time video flows with QoS/QoE support while minimizing the energy consumption in flooding scenarios with mobile targets.

In this paper, we present a real-world case study on deploying a face recognition application, using MTCNN detector and FaceNet recognizer. We report the challenges faced to decide on the best deployment strategy. We propose three inference architectures for the deployment, including cloud-based, edge-based, and hybrid. Furthermore, we evaluate the performance of face recognition inference on different cloud-based and edge-based GPU platforms. We consider different types of Jetson boards for the edge, and various GPUs for the cloud. We also investigate the effect of deep learning model optimization using TensorRT and TFLite compared to a standard Tensorflow GPU model, and the effect of input resolution. We provide a benchmarking study for all these devices in terms of frame per second, execution times, energy and memory usages. After conducting a total of 294 experiments, the results demonstrate that the TensorRT optimization provides the fastest execution on all cloud and edge devices, at the expense of a significantly larger energy consumption (up to +40% and +35% for edge and cloud devices respectively, compared to Tensorflow). Whereas TFLite is the most efficient framework in terms of memory and power consumption, while providing significantly less (-4% to -62%) processing acceleration than TensorRT.

Improving the energy efficiency of data centers while guaranteeing Quality of Service (QoS), together with detecting performance variability of servers caused by either hardware or software failures, are two of the major challenges for efficient resource management of large-scale cloud infrastructures. Previous works in the area of dynamic Virtual Machine (VM) consolidation are mostly focused on addressing the energy challenge, but fall short in proposing comprehensive, scalable, and low-overhead approaches that jointly tackle energy efficiency and performance variability. Moreover, they usually assume over-simplistic power models, and fail to accurately consider all the delay and power costs associated with VM migration and host power mode transition. These assumptions are no longer valid in modern servers executing heterogeneous workloads and lead to unrealistic or inefficient results. In this paper, we propose a centralized-distributed low-overhead failure-aware dynamic VM consolidation strategy to minimize energy consumption in large-scale data centers. Our approach selects the most adequate power mode and frequency of each host during runtime using a distributed multi-agent Machine Learning (ML) based strategy, and migrates the VMs accordingly using a centralized heuristic. Our Multi-AGent machine learNing-based approach for Energy efficienT dynamIc Consolidation (MAGNETIC) is implemented in a modified version of the CloudSim simulator, and considers the energy and delay overheads associated with host power mode transition and VM migration, and is evaluated using power traces collected from various workloads running in real servers and resource utilization logs from cloud data center infrastructures. Results show how our strategy reduces data center energy consumption by up to 15% compared to other works in the state-of-the-art (SoA), guaranteeing the same QoS and reducing the number of VM migrations and host power mode transitions by up to 86% and 90%, respectively. Moreover, it shows better scalability than all other approaches, taking less than 0.7% time overhead to execute for a data center with 1500 VMs. Finally, our solution is capable of detecting host performance variability due to failures, automatically migrating VMs from failing hosts and draining them from workload.

In an energy harvesting sensor actor network, a node recharges its battery from harvestable sources, such as solar, wind and vibrations. Sustainability of the network till next recharge time is one of the most important challenges in harvesting sensor networks. In this paper, a fuzzy based adaptive duty cycling algorithm has been proposed to achieve the network sustainability in harvesting sensor actor networks. In this work, current residual energy, predicted harvesting energy (for a futuristic time slot) and predicted residual energy parameters are considered as fuzzy input variables to estimate duty cycle for a sensor node. In this work, a harvesting model has been adopted to predict the harvesting energy. Further, residual energy has been estimated for future time slot using predicted harvesting energy, energy consumption model and current residual energy. Simulation results are presented to show the efficacy of the proposed mechanism by considering network sustainability metrics, such as number of rounds in which network is connected, the round at which first node dies, maximum number of dead nodes and average number of received packets at the actor node.

In data mining, outlier detection is applied in different domains. It has very large applications such as energy consumption analysis, forecasting hurricanes in meteorological data, fraud and intrusion detection, event detection and system monitoring in sensor networks, etc. Most of existing outlier detection techniques depend on the properties of a particular type of data and can not deal with a large volume of data well, which mean that there is a necessity for improved methodologies and techniques to be applied to a large amount of data with different types in other application areas. In this paper, a parallel outlier detection technique is developed to detect the outliers in the sequential data. Although there are many types of outliers, this paper concentrates on the contextual anomalies. The proposed technique uses a graph approach to detect the outliers. It is very flexible, fast, and no labeled data is needed comparing to many previous approaches. The experimental results show the detected contextual outliers in the sequential data, as well as the efficient scaling up to handle the massive data by increasing the number of processors. The results prove that the parallelism of the proposed technique is very valuable.

Machines consume intensive energy in some production systems. Although substantial efforts have been devoted to performance analysis, continuous improvement, and design of production systems, the research on reducing the energy consumption in these systems is limited. In this article, the problem of minimizing the energy consumed by machines in the two-machine Bernoulli serial line, which has been formulated as nonlinear programming with production rate constraint, is investigated. Specifically, structural characteristics and optimality conditions of the problem are analyzed, and two nonlinear algebraic optimality equations are established. To solve the optimality equations, their properties are explored, and an effective algorithm based on the binary search method is developed. Furthermore, the sensitivity of the optimal solution with respect to system parameters is quantitatively analyzed. Based on the sensitivity analysis, some useful insights on the optimal objective value are extracted.

Long-term evolution advanced (LTE-A) heterogeneous networks have been observed to offer reliable and service-differentiated communication, thereby enabling numerous mobile applications such as smart meters, remote sensors, and vehicular applications. This fact envisions the trend of Internet of things (IoT) underlaying heterogeneous small cell networks. On this basis, this paper proposes an energy-efficient framework for such a scenario, where multitier heterogeneous small cell networks provide wireless connection and seamless coverage for mobile users and IoT nodes. In our proposed framework, an elastic cell-zooming algorithm based on the quality of service and traffic loads of end-users is performed by adaptively adjusting the transmission power of small cells in order to reduce energy consumption. In addition, aiming at the high energy efficiency of IoT underlaying small cell networks, a clustering-based IoT structure is used, where a SWIPT-CH selection algorithm is proposed to maximize the average residual energy of IoT nodes and to mitigate resource competition between IoT nodes and mobile users.

Residential flexible resource is attracting much attention in demand response (DR) for peak load shifting. This paper proposes a scenario for multi-stage DR project to schedule energy consumption of residential communities considering the incomplete information. Community in the scenario can decide whether to participate in DR in each stage, but the decision is private information that is unknown to other communities. To optimize the energy consumption, a Bayesian game approach is formulated, in which probability characteristic of community's decision-making is described with Markov chain considering human behavior of bounded rationality. Simulation results show that the proposed approach can benefit all communities and grid, but the optimization effect is slightly inferior to that in complete information game approach.

Mitigation of global warming, energy security and industrial competitiveness urge the energy-intensive pulp and paper industry (PPI) to transform energy use practices. This study investigates how the PPI has responded to the need for the energy transition in the 2000s. Finland and Sweden as forerunners of energy-efficient operation and decarbonization of the PPI are used as target countries. Understanding of changes in energy consumption is complemented using decomposition analysis (Logarithmic Mean Divisia Index Method) and the energy efficiency index approach. Analysis of companies’ investments in energy technologies is used for explaining changes in energy production. Evidence of significant development towards the more sustainable operation of the PPI was found. Energy consumption per produced unit has decreased, i.e., energy efficiency has improved. Fossil fuels have been partially replaced with bio-based alternatives. Thus, the CO2 intensity has decreased substantially. The generation of renewable electricity has increased in both countries. Examples of Finland and Sweden indicate that the PPI has great potential to contribute to CO2 emission reduction worldwide in the future as energy efficiency can be further improved, and the share of fossil fuels can be decreased increasing the use of biofuels and self-generated green electricity at least in kraft pulp mills.

Different formalisms and modeling frameworks have been developed to capture the discrete behavior of manufacturing systems. However a purely discrete model does not capture the continuous variables of machine-level operation. Previous research in modeling manufacturing systems has studied the quantity-quality and quantity-reliability coupling of production environments but their relationship to energy consumption is often not considered. This work presents a framework that extends the state-of-the-art in modeling manufacturing systems to support decision making for control at both machine- and system-level variables using hybrid models. The modeling strategy considers the coupling between productivity, quality, reliability and energy consumption and leverages the current plant floor data extraction capabilities to develop data-driven models. A novel control strategy is presented that considers the effect of various machine- and system-level variables over different performance metrics and balances them using multi-objective optimization. This framework was validated using a combination of real and simulated data of a production representative environment. The optimal set of control variables is obtained using simulation-based optimization to support plant floor decision making by studying process variables, maintenance actions, or system reconfiguration. Results show the effect of different control variables and the ability to reduce energy consumption while improving productivity. The implementation of the modeling and control framework presented here has the potential to impact the operations of manufacturing system by reducing cost and improving productivity.

Efficient transmission protocols are required to minimize the energy consumption of mobile devices for ubiquitous connectivity in the next-generation of wireless networks. In this article, we analyze the energy consumption performance of a two-hop opportunistic device-select relaying (ODSR) scheme, where a device can either transmit data directly to a base station (BS) or relay the data to a nearby device, which forwards the data to the BS. We select a single device opportunistically from a device-to-device (D2D) network based on the energy required for transmission, including the energy consumed in the circuitry of the devices. By considering the log-normal shadowing as the dominant factor between devices and the BS, and Rayleigh fading in D2D links, we derive analytical bounds and scaling laws on average energy consumption. The derived analytical expressions show that the energy consumption of the ODSR decreases logarithmically with an increase in the number of devices, and achieves near-optimal performance only with a few nearby devices. This is an important design criterion to reduce latency and overhead energy consumption in a relay-assisted large-scale network. We also demonstrate the performance of the ODSR using simulations in realistic scenarios of a wireless network.

In this paper, we aim to make the best joint decision of device selection and computing and spectrum resource allocation for optimizing federated learning (FL) performance in distributed industrial Internet of Things (IIoT) networks. To implement efficient FL over geographically dispersed data, we introduce a three-layer collaborative FL architecture to support deep neural network (DNN) training. Specifically, using the data dispersed in IIoT devices, the industrial gateways locally train the DNN model and the local models can be aggregated by their associated edge servers every FL epoch or by a cloud server every a few FL epochs for obtaining the global model. To optimally select participating devices and allocate computing and spectrum resources for training and transmitting the model parameters, we formulate a stochastic optimization problem with the objective of minimizing FL evaluating loss while satisfying delay and long-term energy consumption requirements. Since the objective function of the FL evaluating loss is implicit and the energy consumption is temporally correlated, it is difficult to solve the problem via traditional optimization methods. Thus, we propose a “ Reinforcement on Federated ” (RoF) scheme, based on deep multi-agent reinforcement learning, to solve the problem. Specifically, the RoF scheme is executed decentralizedly at edge servers, which can cooperatively make the optimal device selection and resource allocation decisions. Moreover, a device refinement subroutine is embedded into the RoF scheme to accelerate convergence while effectively saving the on-device energy. Simulation results demonstrate that the RoF scheme can facilitate efficient FL and achieve better performance compared with state-of-the-art benchmarks.