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The increasing number of Internet of Things (IoT) sensors and the need for real-time data processing have led to the emergence of Fog Computing as a promising paradigm to complement cloud services. However, optimizing energy consumption while ensuring reliable data routing remains a critical challenge. This paper proposes an Energy-aware Two-Way Trust Routing (ETWTR) scheme tailored for fog computing environments. This scheme can perform routing to improve data transmission dynamically, taking into account reliability and energy availability. ETWTR can simultaneously consider security, reliability, quality of service, and energy consumption, overcoming the limitations of fog computing. The ETWTR-based trust management system allows both requesters and service providers to check each other's reliability simultaneously to improve security. To address the challenge of energy optimization, a Deep Q-Network (DQN) is employed to predict available energy levels in nodes and optimize their usage efficiently. Our ETWTR scheme has been compared with baseline schemes to confirm its merit with various criteria. The simulation results show that ETWTR has reached higher reliability and trust with less energy consumption. [ABSTRACT FROM AUTHOR]

Background: The Industrial Internet of Things (IIoT) has revolutionized operations for businesses, and fog computing is a valuable resource management and job scheduling tool that has facilitated the transformation. In this regard, efficient resource usage will be more useful for the performance and energy cost-saving limit of IIoT system. Objective: The article proposed a new energy-aware metaheuristic approach to enhancing the performance and efficiency of IIoT systems in fog computing environments. The study aims to come up with a methodology that strikes a balance between efficiency in compute requirements and energy consumption. Methodology: A meta-heuristic method inspired by natural processes such as genetic algorithms and simulated annealing is applied to optimize the selection of which jobs should be scheduled. This approach takes into account several parameters like when the job is needed, availability of resources, and usage patterns to efficiently schedule jobs across the network. Results: The results show that the proposed approach drastically improves energy efficiency and system performance. In this paper, the fog orchestration master intends to divide the workload between fog and cloud in an excellent manner and resolve specific Issues of fog computing in IIoT environment. This manages to keep energy usage low and the operating efficiency high. Conclusions: Metaheuristic optimization techniques integrate into fog computing environments for IIoT job schedule complexity. This methodology enhances the sustainability of IIoT operations, and their ability to meet robust performance requirements over time. Our findings provide the crucial insight needed to enable industries that need seamless IIoT integration and plan further research in the field of energy-efficient fog computing.

Summary: The rise of the Internet of Things (IoT) has given rise to an era marked by interconnected devices and substantial data generation. This has led to an increased reliance on cloud computing for data processing and storage, primarily due to its cost‐effective pay‐for‐use model. However, this dependence has prompted critical inquiries into the optimal replication of data: what data to replicate, when to replicate it, and where to place new replicas strategically. Conventional cloud data replication often results in resource overutilization, performance bottlenecks, increased workloads, energy consumption, prolonged user wait times, and suboptimal response times. In response to these challenges, this paper introduces a novel approach named Multiobjective Optimization Harris Hawks Optimization with Salp Swarm Algorithm (MOHHOSSA). This approach employs multiobjective optimization (MOO) alongside Harris Hawks Optimization (HHO) and IoT‐based Salp Swarm Algorithm (SSA) for cloud computing environments. MOHHOSSA efficiently identifies data replication opportunities and strategically allocates them across nodes in cloud computing infrastructures. The algorithm aims to enhance key performance metrics, including energy consumption, carbon dioxide emission rate, and mean service time. Extensive experimental validation demonstrates MOHHOSSA's superior performance compared to alternative algorithms. It excels in optimizing energy efficiency, load distribution, mean service time, and the establishment of cost‐effective communication paths between nodes. This research represents a significant advancement in addressing challenges related to IoT data replication in cloud computing, ultimately promoting more sustainable, efficient, and responsive cloud‐based services. [ABSTRACT FROM AUTHOR]

The transmission of point cloud video streaming requires the scheduling of various computing and transmission resources. Existing research rarely considers the energy consumption issues caused by the computing tasks of terminal display devices. To solve this problem, a point cloud video streaming transmission scheme assisted by mobile edge computing (MEC) was proposed, which offloaded part of computing tasks to the MEC server based on the access bandwidth and point cloud video content. A joint optimization model was established in this scheme to maximize the quality of user's viewing experience and minimize the energy consumption of terminal device under the constraints of network resources, terminal and edge computing resources. Experimental results show that the proposed scheme can improve the viewing quality of users and reduce the energy consumption of terminal equipment compared with the contrast scheme under different conditions. [ABSTRACT FROM AUTHOR]

This paper considers the impact of setup time in production scheduling and proposes energy-aware distributed hybrid flow shop scheduling problem with sequence-dependent setup time (EADHFSP-ST) that simultaneously optimizes the makespan and the energy consumption. We develop a mixed integer linear programming model to describe this problem and present a two-stage adaptive memetic algorithm (TAMA) with a surprisingly popular mechanism. First, a hybrid initialization strategy is designed based on the two optimization objectives to ensure the convergence and diversity of solutions. Second, multiple population co-evolutionary approaches are proposed for global search to escape from traditional cross-randomization and to balance exploration and exploitation. Third, considering that the memetic algorithm (MA) framework is less efficient due to the randomness in the selection of local search operators, TAMA is proposed to balance the local and global searches. The first stage accumulates more experience for updating the surprisingly popular algorithm (SPA) model to guide the second stage operator selection and ensures population convergence. The second stage gets rid of local optimization and designs an elite archive to ensure population diversity. Fourth, five problem-specific operators are designed, and non-critical path deceleration and right-shift strategies are designed for energy efficiency. Finally, to evaluate the performance of the proposed algorithm, multiple experiments are performed on a benchmark with 45 instances. The experimental results show that the proposed TAMA can solve the problem effectively.

Unmanned Aerial Vehicles (UAVs) are versatile platforms for carrying communications nodes such as Wi-Fi Access Points and cellular Base Stations. Flying Networks (FNs) offer on-demand wireless connectivity where terrestrial networks are impractical or unsustainable. However, managing communications resources in FNs presents challenges, particularly in optimizing UAV placement to maximize Quality of Service (QoS) for Ground Users (GUs) while minimizing energy consumption, given the UAVs’ limited battery life. Existing multi-UAV placement solutions primarily focus on maximizing coverage areas, assuming static UAV positions and uniform GU distribution, overlooking energy efficiency and heterogeneous QoS requirements. We propose the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm, which defines and optimizes UAV trajectories to reduce energy consumption while ensuring QoS based on Signal-to-Noise Ratio (SNR) in the links with GUs. Additionally, we introduce the Multi-UAV Energy Consumption (MUAVE) simulator to evaluate energy consumption. Using both MUAVE and ns-3 simulators, we evaluate SUPPLY in typical and random networking scenarios, focusing on energy consumption and network performance. Results show that SUPPLY reduces energy consumption by up to 25% with minimal impact on throughput and delay.

The energy demand for Internet of Things (IoT) applications is increasing with a rise in IoT devices. Rising costs and energy demands can cause serious problems. Fog computing (FC) has recently emerged as a model for location‐aware tasks, data processing, fast computing, and energy consumption reduction. The Fog computing model assists cloud computing in fast processing at the network's edge, which also exerts a vital role in cloud computing. Due to the fast computing in fog servers, different quality of service (QoS) approaches have been proposed in various sections of the fog system, and several quality factors have been considered in this regard. Despite the significance of QoS in Fog computing, no extensive study has focused on QoS and energy consumption methods in this area. Therefore, this article investigates previous research on the use and guarantee of Fog computing. This article reviews six general approaches that discuss the published articles between 2015 and late May 2023. The focal point of this paper is evaluating Fog computing and the energy consumption strategy. This article further shows the advantages, disadvantages, tools, types of evaluation, and quality factors according to the selected approaches. Based on the reviewed studies, some open issues and challenges in Fog computing energy consumption management are suggested for further study. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are used to collect data and detect phenomena in a real-time environment. There is considerable interest in the deployment of WSNs in remote, inaccessible and inhospitable locations; such use of WSNs throws up many challenges. WSNs come with numerous advantages, yet a notable limitation is that the battery life dictates the lifetime of the sensor node. Two critical factors that determine battery lifetime are the frequency of sensor readings and the transmission range of the sensor nodes. Some energy-efficient routing protocols have been proposed and adopted for use to extend the lifetime of sensor nodes. These protocols aim at optimizing the routes in the network. Given that multi-hop routes are energy inefficient, improving the lifetime of WSNs in a multi-hop routing environment will require the use of route optimization techniques. A modified distance-based energy-aware (mDBEA) routing protocol is proposed which is efficient and capable of minimizing the energy consumption of the sensor nodes and hence, maximizing network lifetime. Our approach addresses the problem by calculating the Euclidian distance between successive nodes to determine the shortest distance that minimizes the energy required for transmission. The simulation results indicate that the mDBEA routing protocol reduced the amount of energy consumed in the network by choosing the minimum transmission distance between the source and its neighbour nodes that significantly prolonged the network's lifetime. Our greedy approach yielded about 95% Packet delivery ratio (PDR). Our next-hop and the direct-to-sink algorithms yielded about 82% PDR. [ABSTRACT FROM AUTHOR]

Due to the rapid utilization of cloud services, the energy consumption of cloud data centres is increasing dramatically. These cloud services are provided by Virtual Machines (VMs) through the cloud data center. Therefore, energy-aware VMs allocation and migration are essential tasks in the cloud environment. This paper proposes a Branch-and-Price based energy-efficient VMs allocation algorithm and a Multi-Dimensional Virtual Machine Migration (MDVMM) algorithm at the cloud data center. The Branch-and-Price based VMs allocation algorithm reduces energy consumption and wastage of resources by selecting the optimal number of energy-efficient PMs at the cloud data center. The proposed MDVMM algorithm saves energy consumption and avoids the Service Level Agreement (SLA) violation by performing an optimal number of VMs migrations. The experimental results demonstrate that our proposed Branch-and-Price based VMs allocation with VMs migration algorithms saves more than 31% energy consumption and improves 21.7% average resource utilization over existing state-of-the-art techniques with a 95% confidence interval. The performance of the proposed approaches outperforms in terms of SLA violation, VMs migration, and Energy SLA Violation (ESV) combined metrics over existing state-of-the-art VMs allocation and migration algorithms.

The exponential growth of the Internet of Things (IoT) has resulted in an unprecedented surge in data, requiring the development of innovative methods to effectively handle and analyze information. This study investigates the 1management of data in a smart manner within the IoT framework. It specifically examines the utilization of Wireless Sensor Networks (WSN) to accomplish effective information processing. The study examines two main elements: the utilization of Run-Length Encoding (RLE) for data compression and the incorporation of energy-aware extensions into the Ad Hoc On-Demand Distance Vector (AODV-EA) routing protocol. The purpose of employing Run-Length Encoding (RLE) is to enhance the efficiency of transmitting and storing data by effectively representing recurring sequences in sensor data. This compression technique is especially applicable for resource-constrained WSN where the conservation of bandwidth and energy is of utmost importance. The study investigates how communication protocols can be improved by integrating energy-conscious extensions into AODV. The objective of this approach is to enhance the energy efficiency of communication in WSN by taking into account the energy levels of each node in real-time when establishing routes. The NS-3 simulation framework is used to assess the proposed methodologies. NS-3 offers a flexible and expandable framework for simulating communication protocols and network scenarios. The study evaluates the performance of the integrated system by using simulation and analyzing important metrics such as accuracy, precision, Latency, data compression and energy efficiency. The research findings provide valuable insights into the field of Smart Data Management in IoT, demonstrating how the combination of data compression and energy-aware routing protocols can improve the efficiency of Wireless Sensor Networks for information processing. [ABSTRACT FROM AUTHOR]

Cloud warehouses have been exploiting multi-tenancy in CPU-FPGA collaborative environments, so clients can share the same infrastructure, achieving scalability and maximizing resource utilization. Therefore, the distribution of tasks across CPU and FPGA must be well-balanced so performance and energy are optimized in a highly variant workload scenario. In this paper, we take a step further and, in contrast to existing approaches, exploit DVFS (Dynamic Voltage and Frequency Scaling) on the CPU, together with an intelligent CPU-FPGA resource provisioning mechanism, to further improve energy. For that, we propose EASER, an end user-transparent framework that employs multiple strategies and dynamically selects the most appropriate one to optimize resource provisioning and DVFS according to the warehouse needs, workload properties, and target architecture. Our synergistic DVFS optimization brings up to 22% additional energy gains over our dynamic provisioning alone. Compared to fixed single strategies with DVFS, EASER brings, on average, 71% of energy gains. • We explore voltage/frequency scaling and provisioning in multi-tenant CPU-FPGA Cloud. • Different strategies are needed depending on the architecture and workload properties. • EASER is end-user transparent and fully-adaptive. • EASER achieves energy improvements without harming makespan. [ABSTRACT FROM AUTHOR]

Features such as the communication scheme, energy awareness, and task distribution amongst others are the key component that characterizes the Flying Ad-hoc Network (FANET). The operational efficiency in FANET surveying a specific region is affected by the nature of the UAVs' node placement, routing protocol, energy-aware task distribution, and node interaction amongst others. In this paper, Drone 1 (D1), Master Drone (DM), and Drone 2 (D2) were used to survey a pipeline of length 12.2 m. This paper aims at minimising energy use by drones during surveillance using energy-aware node exchange technique, task interaction and distribution scheme for each UAV. Due to fast energy depletion of DM due to packets aggregation, its election is based on the UAV with the highest energy before take-off. For two different simulations, 14,697.0 J and 14,836.6 J were obtained for DM. To avoid system failure due to fast energy loss of DM, the drones swapped positions and status. First swapping command comes up when DM loses 50% of its energy, while the second command occurs when it further loses 15%. Return to base threshold energy is computed for the three UAVs to avoid crash due to insufficient energy during surveillance. DM returns to base threshold energy for both single and double swapping simulation were 658.105 J and 652.456 J respectively. From the results obtained the algorithms were able to exchange nodes to maximize energy usage and perform an interaction-based task distribution for cooperative task sharing during surveillance. This translates into longer surveillance time and effective telemetry data aggregation.

Service composition is widely used in multiple scenarios to meet users’ various demands. In a multi-Cloud environment (MCE), a composite request (service request) needs atomic services (service candidates) located in multiple clouds with various functions. Service composition composes atomic services from multiple clouds together as a new service. Prior work focused on how to compose services and ignored energy consumption caused by the execution of atomic services. In this paper, we examine an energy-aware heuristic for service composition (EASC) under a multi-Cloud environment to reduce energy consumption from executing atomic services. To meet our requirements, we try to compose services in one cloud to reduce energy consumption for transferring files between atomic services. Beyond that, we also consider the influence of the split-point positions to energy consumption and other metrics. Simulation results show that our proposed method has shown good performance in reducing execution time and energy consumption.

The rapid advancement of technology has led to the proliferation of devices connected to the Internet of Things (IoT) networks, bringing forth challenges in both energy management and secure data communication. In addition to energy constraints, IoT networks face threats from malicious nodes, which jeopardize the security of communications. To address these challenges, we propose an Energy-aware secure Routing scheme via Two-Way Trust evaluation (ERTWT) for IoT networks. This scheme enhances network protection against various attacks by calculating trust values based on energy trust, direct trust, and indirect trust. The scheme aims to enhance the efficiency of data transmission by dynamically selecting routes based on both energy availability and trustworthiness metrics of fog nodes. Since trust management can guarantee privacy and security, ERTWT allows the service requester and the service provider to check each other's safety and reliability at the same time. In addition, we implement Generative Flow Networks (GFlowNets) to predict the energy levels available in nodes in order to use them optimally. The proposed scheme has been compared with several advanced energy-aware and trust-based routing protocols. Evaluation results show that ERTWT more effectively detects malicious nodes while achieving better energy efficiency and data transmission rates. [ABSTRACT FROM AUTHOR]

Solving remanufacturing process planning and scheduling problem collaboratively and leveraging the complementary attributes of process planning and shop scheduling to attain improved production flow and process routes, are crucial for further enhancing the environmental and economic benefits of remanufacturing. Most of the existing works regard these two segments as independent and solve them separately, which hinder the further improvements of remanufacturing system performance. Besides, studies on energy-aware remanufacturing scheduling have employed machine turn on/off strategy to achieve energy reductions. However, not all machines are suitable for the turn on/off strategy. Therefore, a new energy-aware remanufacturing process planning and scheduling model with process sequence flexibility is proposed. This model not only simultaneously solves the remanufacturing process planning and scheduling problem, but also employs machine speed-switching strategy to reduce energy consumption. To solve this model, a reinforcement learning-based particle swarm optimization algorithm with an efficient multi-dimensional encoding scheme is proposed, in which, a hybrid population initialization strategy, a novel reinforcement learning-based multi-directional guide position-updating mechanism, a local search strategy, and a restart mechanism are devised to enhance the performance. Simulation experiments were conducted on 18 sets of instances with different scales to compare the proposed algorithm with other advanced algorithms. The experimental results confirmed the superiority of the proposed algorithm. • A new energy-aware remanufacturing process planning and scheduling problem is investigated. • Machine, process plan, and process sequence flexibility is considered to improve the practicality and flexibility of the model. • A speed-switching strategy is employed in the model to reduce energy waste. • A reinforcement learning-based particle swarm optimization algorithm is proposed to solve the model. • Experimental results verify the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) have emerged as suitable platforms for transporting and positioning communications nodes on demand, including Wi-Fi Access Points and cellular Base Stations. This paved the way for the deployment of flying networks capable of temporarily providing wireless connectivity and reinforcing coverage and capacity of existing networks. Several solutions have been proposed for the positioning of UAVs acting as Flying Access Points (FAPs). Yet, the positioning of Flying Communications Relays (FCRs) in charge of forwarding the traffic to/from the Internet has not received equal attention. In addition, state of the art works are focused on optimizing both the flying network performance and the energy-efficiency from the communications point of view, leaving aside a relevant component: the energy spent for the UAV propulsion. We propose the Energy and Performance Aware relay Positioning (EPAP) algorithm. EPAP defines target performance-aware Signal-to-Noise Ratio (SNR) values for the wireless links established between the FCR UAV and the FAPs and, based on that, computes the trajectory to be completed by the FCR UAV so that the energy spent for the UAV propulsion is minimized. EPAP was evaluated in terms of both the flying network performance and the FCR UAV endurance, considering multiple networking scenarios. Simulation results show gains up to 25% in the FCR UAV endurance, while not compromising the Quality of Service offered by the flying network.

This paper proposed and implemented an energy-aware routing multi-level and mapping problem (EARMLP) algorithm to minimize the overall power consumption in Software-Defined Networking (SDN)-based core networks. To enforce network utilization toward green policies design for Data Centers (DCs), SDN leverages protocol configurations for routing available in the infrastructure. Therefore, the proposed mechanism aimed to design an optimal routing strategy that considers system configuration and traffic demand between the data and control planes in networks. The problem is then addressed from the perspective of the policy-based EARMLP technique, which is used to carefully determine the optimal assignment between controllers and their switches to optimize network energy savings. Hence, a controller placement problem (CPP) is established to select the optimal locations and number of controllers in core networks and create an optimal mapping and resource allocation between switches and controllers. Since the formulated energy-aware routing algorithm is designed as a multi-objective NP-hardness of the problem, a heuristic approach is developed to find optimal solutions for traffic routing between inter-controllers and controller-switch in terms of energy-aware consumption strategies. Consequently, the proposed optimal routing mechanism can rearrange traffic to meet provisioning criteria by utilizing the capacity-aware design. Remarkably, the energy saved in networks by our suggested method can approach up to 70% of the energy saved in SDN-based networks compared to other methods.

In smart cities, the computing power and battery life of terminal devices (TDs) can be effectively enhanced by offloading tasks to nearby base stations (BSs) with richer resources. With the goal of TDs being fully served and achieving low-carbon energy savings for the system, this paper investigates task offloading in cloud-edge collaborative heterogeneous scenarios with multiple BSs and TDs. According to the proportional relationship between the energy and coverage radii of BSs, a complete coverage task offloading model with adjustable BS radii is proposed. The task offloading problem is formulated as an integer linear program with multidimensional resource constraints to minimize the sum of energy consumption of BS coverage, offloading tasks to BSs and the cloud data center (CC). Since this task offloading problem is NP-hard, two approximate algorithms with polynomial time complexity are designed based on the greedy strategy of seeking the most energy-effective disk and the primal–dual method of constructing primal feasible solutions according to dual feasible solutions. Experimental results show that both the greedy and primal–dual algorithms can achieve good approximation performance, but each of them has its own advantages due to different design principles. The former is superior in execution time and energy consumption, while the latter has advantages in balancing loads among BSs and alleviating core network bandwidth pressure. [ABSTRACT FROM AUTHOR]

Summary: The ability to move and hover has made rotary‐wing unmanned aerial vehicles (UAVs) suitable platforms to act as flying communications relays (FCRs), aiming at providing on‐demand, temporary wireless connectivity when there is no network infrastructure available or a need to reinforce the capacity of existing networks. However, since UAVs rely on their on‐board batteries, which can be drained quickly, they typically need to land frequently for recharging or replacing them, limiting their endurance and the flying network availability. The problem is exacerbated when a single FCR UAV is used. The FCR UAV energy is used for two main tasks: Communications and propulsion. The literature has been focused on optimizing both the flying network performance and energy efficiency from the communications point of view, overlooking the energy spent for the UAV propulsion. Yet, the energy spent for communications is typically negligible when compared with the energy spent for the UAV propulsion. In this article, we propose energy‐aware relay positioning (EREP), an algorithm for positioning the FCR taking into account the energy spent for the UAV propulsion. Building upon the conclusion that hovering is not the most energy‐efficient state, EREP defines the trajectory and speed that minimize the energy spent by the FCR UAV on propulsion, without compromising in practice the quality of service offered by the flying network. The EREP algorithm is evaluated using simulations. The obtained results show gains up to 26% in the FCR UAV endurance for negligible throughput and delay degradation. [ABSTRACT FROM AUTHOR]

Rapid developments in radio technology and processors have led to the emergence of small sensor nodes that provide communication over Wireless Sensor Networks (WSNs). The crucial issues in these networks are energy consumption management and reliable data exchange. Due to the limited resources of sensor nodes, WSNs become a vulnerable target against many security attacks. Thus, energy-aware trust-based techniques have become a powerful tool for detecting nodes' behavior and providing security solutions in WSN. Clustering-based routings are one of the most effective methods in increasing the WSN performance. In this paper, an Energy-Aware Trust algorithm based on the AODV protocol and Multi-path Routing approach (EATMR) is proposed to improve the security of WSNs. EATMR consists of two main phases: firstly, the nodes are clustered based on the Open-Source Development Model Algorithm (ODMA), and then in the second phase, clustering-based routing is applied. In this paper, the routing process follows the AODV protocol and multi-path routes approach with considering energy-aware trust. Here, the optimal and safe route is determined based on various parameters, namely energy, trust, hop-count, and distance. In this regard, we emphasize the evaluation of node trust using direct trust, indirect trust, and a multi-objective function. The simulation has been performed in MATLAB software in the presence of a Denial of Service (DoS) attack. The simulation results show that EATMR performs better than the state-of-the-art methods in terms of successfully detecting malicious nodes and enhancing network lifetime, energy consumption, and packet delivery ratio. As a conclusion, EATMR shows an average of 4.3 and 6.1% superiority over M-CSO and SQEER in different scenarios, respectively. [ABSTRACT FROM AUTHOR]

Cloud computing has emerged as the preeminent computing platform for multiple enterprises. All scales of organizations adopt cloud services to leverage cloud technology to drive their businesses ahead. It is prevalent to use the workflow paradigm in modeling a wide variety of problems to compute in distributed environments. Cloud computing is mostly adapting technology to deal with workflow applications, particularly applications with unpredictable workloads. Due to the increased demand for cloud services, excessive power utilization in cloud data centers is a serious issue that needs to be addressed. Scientific workflow applications, in particular, consume high amounts of electrical energy. Many studies have been conducted on the consumption of energy in the cloud environment, and this area of research attracts people from all fields, including both research and business. For this paper, a survey was conducted on existing energy-efficient techniques for scheduling various workflows in a cloud environment. We targeted the methods that minimize energy consumption with assured quality of service constraints. This study on energy-aware and proper workflow scheduling provide extensive knowledge about various energy-aware scheduling paradigms currently going on. The review will help in listing the future directions in this field along with other factors included. [ABSTRACT FROM AUTHOR]

Service composition is widely used in multiple scenarios to meet users' various demands. In a multi-Cloud environment (MCE), a composite request (service request) needs atomic services (service candidates) located in multiple clouds with various functions. Service composition composes atomic services from multiple clouds together as a new service. Prior work focused on how to compose services and ignored energy consumption caused by the execution of atomic services. In this paper, we examine an energy-aware heuristic for service composition (EASC) under a multi-Cloud environment to reduce energy consumption from executing atomic services. To meet our requirements, we try to compose services in one cloud to reduce energy consumption for transferring files between atomic services. Beyond that, we also consider the influence of the split-point positions to energy consumption and other metrics. Simulation results show that our proposed method has shown good performance in reducing execution time and energy consumption. [ABSTRACT FROM AUTHOR]

Supported by the latest evolution of the 5G technologies, Augmented Reality (AR) & Virtual Reality (VR) video streaming services are experiencing an unprecedented growth. However, the transmission issues caused by heterogeneous access and dynamic traffic are still challenging 5G communications. The Internet Engineering Task Force (IETF)’s Multipath Transmission Control Protocol (MPTCP) can aggregate bandwidth and balance traffic across multiple subflows in a heterogeneous network environment. However, in order to support delivery of high quality 5G media services, researchers should also address MPTCP’s inefficient data scheduling to heterogenous sub-paths, consideration of multiple criteria, including energy consumption and its inconsistent behavior when employed along with the Dynamic Adaptive Streaming over HTTP (DASH) adaptive application layer protocol. To address these issues, we propose a Q-Learning driven Energy-aware Data Scheduling (QLE-DS) mechanism for MPTCP-based media services. QLE-DS models the multipath scheduling as a Q-learning process and employs a novel quantum clustering approach to discretize the high dimensional continuous Q-table. An asynchronous framework is designed to improve the learning efficiency of QLE-DS. The simulation results show that QLE-DS performs better than other MPTCP scheduling algorithms in terms of flow completion time (FCT), retransmission rate, and energy consumption. [ABSTRACT FROM AUTHOR]

Recent advances in computing allows researchers to propose the automation of hydroponic systems to boost efficiency and reduce manpower demands, hence increasing agricultural produce and profit. A completely automated hydroponic system should be equipped with tools capable of detecting plant diseases in real-time. Despite the availability of deep-learning-based plant disease detection models, the existing models are not designed for an embedded system environment, and the models cannot realistically be deployed on resource-constrained IoT devices such as raspberry pi or a smartphone. Some of the drawbacks of the existing models are the following: high computational resource requirements, high power consumption, dissipates energy rapidly, and occupies large storage space due to large complex structure. Therefore, in this paper, we proposed a low-power deep learning model for plant disease detection using knowledge distillation techniques. The proposed low-power model has a simple network structure of a shallow neural network. The parameters of the model were also reduced by more than 90%. This reduces its computational requirements as well as its power consumption. The proposed low-power model has a maximum power consumption of 6.22 w, which is significantly lower compared to the existing models, and achieved a detection accuracy of 99.4%. [ABSTRACT FROM AUTHOR]

Scientific problems can be formulated as workflows to allow them to take advantage of cluster computing resources. Generally, the assumption is that the greater the resources dedicated to completing these tasks the better. This assumption does not take into account the energy cost of performing the computation and the specific characteristics of each workflow. In this paper, we present a unique approach to evaluating the energy consumption of scientific workflows on compute clusters. Two workflows from different domains, Astronomy and Bioinformatics, are presented and their execution is analyzed on a cluster of low powered small board computers. The paper presents a theoretical analysis of an energy-aware execution of workflows that can reduce the energy consumption of workflows by up to 68% compared to normal execution. We demonstrate that there are limitations to the benefits of increasing cluster sizes and there are trade-offs when considering energy vs. performance of the workflows and that the performance and energy consumption of any scientific workflow is heavily dependent on its underlying structure. The study concludes that the energy consumption of workflows can be optimized to improve both aspects of the workflow and motivates the development of an energy-aware scheduler. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have received considerable interest in recent years. These sensor nodes can gather information from the surrounding environment and transmit it to a designated location. Each sensor node in WSN typically has a battery with a limited capacity. Due to their large number and because of various environmental challenges, it is sometimes hard to replace this finite battery. As a result, energy-efficient communication is seen as a critical aspect in extending the lifespan of a sensor node. On the other hand, some applications that require large coverage and generate various sorts of data packets require multi-hop routing and quality of service (QoS) features. Therefore, in order to avoid network failure, these applications need an energy-efficient QoS MAC protocol that can support multiple levels of data packet priority and multi-hop routing features while focusing on energy conservation. An energy-aware QoS MAC protocol based on Prioritized Data and Multi-hop routing (EQPD-MAC) is proposed in this article. The EQPD-MAC protocol offers a simple yet effective cross-layer communication method. It provides timely delivery of multi-priority packets, uses an adaptive active time to limit idle listening, and integrates a robust routing protocol. Finally, the EQPD-MAC protocol's performance was evaluated and compared to three other well-known QoS MAC protocols. The simulation findings show that the proposed protocol significantly decreases sensor node energy consumption by up to 30.3%, per-bit energy consumption by up to 29.6%, sink node energy consumption by up to 27.4% and increases throughput by up to 23.3%. [ABSTRACT FROM AUTHOR]

Resource allocation is an important problem for cloud environments. This paper introduces an energy-aware combinatorial auction-based model for the resource allocation problem in clouds. The proposed model allows users of a cloud to submit their virtual resource requests as bids using the provided bidding language which allows complementarities and substitutabilities among those resources to be declared. The model finds the most profitable mutually satisfiable set of winning bids, and the corresponding allocation of virtual resources to the users while considering the placement of virtual resources to the available physical resources in the cloud by executing an optimization problem. During the optimization, the model also takes account of the non-linear energy requirements of the physical resources based on their utilization levels to find a placement with the lowest energy cost, thus, providing an energy-aware solution to the resource allocation problem. The associated optimization problem is formally defined and formulated using integer programming. Since the optimization problem is intractable, four heuristic methods are also proposed. To evaluate the performance of the model and the proposed heuristic methods, several experiments are conducted on a comprehensive test suite. The results demonstrate the benefits of the proposed model, and the high-quality solutions provided by the proposed methods.

Big data processing, scientific calculations, and multimedia operations are some applications that require very complex time-consuming computations which cannot be performed on personal computers. Utilizing powerful cloud resources is a common method to address this problem. The amount of energy consumption of cloud data centers is an important challenge in these complex calculations, and reducing the energy consumption of cloud data centers is one of the most important goals of the researches in this area. The proposed method of this paper, called multi-agent deep Q-network with coral reefs optimization (MDQ-CR), combines the coral reefs optimization algorithm and multi-agent deep Q-network to reduce the energy consumption of data centers and cloud resources using the dynamic voltage and frequency scaling (DVFS) technique. The MDQ-CR has two main phases. The first phase exploits coral reefs optimization algorithm with a short-term view, and the second phase uses deep Q-network with a long-term view. The Markov game model is used to lead the learning agents to converge to the global optimal solution. Since processors consume the highest amount of energy of cloud compared to the other resources, the proposed method focuses on reducing the processors' energy consumption. Reducing the voltage and frequency of processors, considering expiration times of their tasks, can reduce their energy consumption significantly. The empirical experiments show that the proposed method can save energy about 89% compared to completely randomized methods, and about 20% compared to the two recent methods of the literature. [ABSTRACT FROM AUTHOR]

Cloud computing infrastructures have intended to provide computing services to end-users through the internet in a pay-per-use model. The extensive deployment of the Cloud and continuous increment in the capacity and utilization of data centers (DC) leads to massive power consumption. This intensifying scale of DCs has made energy consumption a critical concern. This paper emphasizes the task scheduling algorithm by formulating the system model to minimize the makespan and energy consumption incurred in a data center. Also, an energyaware task scheduling in the Blockchain-based data center was proposed to offer an optimal solution that minimizes makespan and energy consumption. The established model was analyzed with a target-time responsive precedence scheduling algorithm. The observations were analyzed and compared with the traditional scheduling algorithms. The outcomes exhibited that the developed solution incurs better performance with a response to resource utilization and decreasing energy consumption. The investigation revealed that the applied strategy considerably enhanced the effectiveness of the designed schedule. [ABSTRACT FROM AUTHOR]

Abstract The energy-aware MAC protocol is the basic communication protocol for device-to-device communication in sensor-cloud computing and facilitates data sensing, computing, and sharing for applications. Transmitting high-priority data to control centers quickly to manage emergencies is essential for sensor-cloud applications. In this paper, a Dynamic Wait time-based MAC (DWT-MAC) protocol is proposed for data differentiated services of a sensor-cloud system (SCS). The DWT-MAC protocol is novel in that it changes the receiver wait time, which has been fixed in previous MAC protocols, and it reduces the delay in data transmission by dynamically adjusting the wait time. In the DWT-MAC protocol, the wait time changes according to the number of senders, which can ensure that it always approaches the optimal value. A dynamic time adjustment algorithm, which causes the wait time to always trail the optimal value, is proposed to reduce delays. While the DWT-MAC protocol cannot ensure wait times of the optimal value, it can ensure that it quickly comes close to the optimal value, which is suitable for dynamically changing networks. Extensive experiments show that the DWT-MAC protocol reduces the average delay in the transmission of data of the highest priority by 49.3%.

In underwater wireless sensor networks (UWSNs), protocols with efficient energy and reliable communication are challenging, due to the unpredictable aqueous environment. The sensor nodes deployed in the specific region can not last for a long time communicating with each other because of limited energy. Also, the low speed of the acoustic waves and the small available bandwidth produce high latency as well as high transmission loss, which affects the network reliability. To address such problems, several protocols exist in literature. However, these protocols lose energy efficiency and reliability, as they calculate the geographical coordinates of the node or they do not avoid unfavorable channel conditions. To tackle these challenges, this article presents the two novel routing protocol for UWSNs. The first one energy path and channel aware (EPACA) protocol transmits data from a bottom of the water to the surface sink by taking node's residual energy (Re), packet history (Hp), distance (d) and bit error rate (BER). In EPACA protocol, a source node computes a function value for every neighbor node. The most prior node in terms of calculated function is considered as the target destination. However, the EPACA protocol may not always guarantee packet reliability, as it delivers packets over a single path. To maintain the packet reliability in the network, the cooperative-energy path and channel aware (CoEPACA) routing scheme is added which uses relay nodes in packet advancement. In the CoEPACA protocol, the destination node receives various copies from the source and relay(s). The received data at the destination from multiple routes make the network more reliable due to avoiding the erroneous data. The MATLAB simulations results validated the performance of the proposed algorithms. The EPACA protocol consumed 29.01% and the CoEPACA protocol 19.04% less energy than the counterpart scheme. In addition, the overall 12.40% improvement is achieved in the packet's reliability. Also, the EPACA protocol outperforms for packets' latency and network lifetime.

Service composition is always employed to enhance function by composing atomic services that reside in different clouds together. In a multi-cloud environment (MCE), the time and energy consumption of service composition may differ because of the dynamic of the network. The network varies between clouds, or even between composite requests and clouds. A cloud provides a limited variety of services, which makes a composite request that requires multiple clouds to jointly compose all atomic services together. All of this makes it more difficult than ever to implement energy-aware service composite in a MCE. In this paper, we model service composition in the MCE and propose an energy-aware multiple targets service composition method for executing atomic services in a composite request. Since the proposed method gets the scheduling by searching all possible mappings between service request blocks (a set of requests to multiple atomic services) and clouds, we call it “All-Search”. To reduce the complexity of All-Search, we propose “Itersplit”, a heuristics algorithm that can achieve multiple targets. The performance of All-Search, Cloud-SEnergy, and Itersplit is tested through simulations. The results in multiple aspects indicate that Itersplit performs better than other algorithms when we take Itersplit-20%.

Planetary rovers are becoming indispensable for exploration activities and science missions. The rover used in such mission is often limited in its operation time owing to power and computational resources of the rover. In this paper, a trajectory planning method for a planetary rover is proposed that considers vehicle dynamics, and energy management of the rover. The vehicle dynamics is approximated from a dynamic simulation of the rover, which can estimate the power consumption in accordance with terrain traversability of the rover. The power generation of the solar array panel mounted on the rover is also taken into account. The simulation study confirmed the usefulness of the proposed method, especially in scenarios where slopes could be observed, and one result indicated that the energy margin could be improved by 4.1 kJ, 13.9 % at maximum. [ABSTRACT FROM AUTHOR]

In smart cities, the computing power and battery life of terminal devices (TDs) can be effectively enhanced by offloading tasks to nearby base stations (BSs) with richer resources. With the goal of TDs being fully served and achieving low-carbon energy savings for the system, this paper investigates task offloading in cloud-edge collaborative heterogeneous scenarios with multiple BSs and TDs. According to the proportional relationship between the energy and coverage radii of BSs, a complete coverage task offloading model with adjustable BS radii is proposed. The task offloading problem is formulated as an integer linear program with multidimensional resource constraints to minimize the sum of energy consumption of BS coverage, offloading tasks to BSs and the cloud data center (CC). Since this task offloading problem is NP-hard, two approximate algorithms with polynomial time complexity are designed based on the greedy strategy of seeking the most energy-effective disk and the primal–dual method of constructing primal feasible solutions according to dual feasible solutions. Experimental results show that both the greedy and primal–dual algorithms can achieve good approximation performance, but each of them has its own advantages due to different design principles. The former is superior in execution time and energy consumption, while the latter has advantages in balancing loads among BSs and alleviating core network bandwidth pressure.

The functioning of bacterial nanonetworks as a ”drug delivery system” requires the engineered bacteria to track the targets, such as harmful micro-organisms, pathogens, or chemical weapons, to release drug molecules effectively. The coordinated and intelligent movement of energy-constrained engineered bacteria is desired for successful tracking of mobile targets. In this work, first, we analyze the energy consumption by engineered bacteria for releasing molecules and propagating for the tracking process. Then we show that the events of the release of molecules by engineered bacteria and their propagation are interlinked in such a way that the strategy of releasing attractants upon detecting the target is coupled to the energy available with the engineered bacteria. Based on the finding, we propose an energy-aware algorithm, named as EnPoS, which probabilistically selects a group of engineered bacteria among the deployed bacterial population to release signaling molecules over a particular time period in order for engineered bacteria to track the mobile targets. The simulation results show better performance of the proposed algorithm as compared with the basic algorithm incorporating continuous releasing of signaling molecules, concerning the energy expenses, mean displacement over time, and distribution of the engineered bacteria around the targets. [ABSTRACT FROM AUTHOR]

Summary: This paper addresses the problems of relay selection, clustering, and routing to extend the lifetime of wireless body area networks. We first propose an efficient algorithm called "Energy‐aware Relay Selection and Cluster‐based Routing (ERSCR)" to develop a hybrid data aggregation tree in the network. ERSCR has three phases, including the relay selection, clustering and Cluster Head (CH) selection, and data transmission. It divides the biosensors into several clusters and selects an appropriate CH for each cluster. Each biosensor transmits data to its CH or relay node. The aggregated data are then routed to the sink through an energy‐balanced routing tree. The proposed scheme considers both residual energy and distance for routing data of biosensors. It not only reduces the energy consumption in the network but also balances the energy consumed by different biosensors. Subsequently, we improve the ERSCR algorithm and introduce the "Joint Relay Selection, Clustering, and Routing (JRSCR)" algorithm to achieve a better network performance. JRSCR benefits from the advantages of the ERSCR algorithm. Moreover, it reduces the number of transmissions with the direct use of the relay nodes as CH. As another advantage, both ERSCR and JRSCR algorithms are compatible with the natural physical states of the human body. Simulation results demonstrate the efficiency of the proposed algorithms in terms of energy consumption, network lifetime, and maximum hop count. [ABSTRACT FROM AUTHOR]

This paper presents a review of the most recent energy-aware routing and load balancing optimization strategies for traffic flow engineering in Hybrid Software-Defined networks. The purpose is to avail researchers with insights regarding current and future developments in Hybrid SDN to stimulate more research efforts in the area.

The key performance metrics for the Next gener- ation wireless communication system is the Energy efficiency. We use dense Heterogenous Networks(HetNets) which comprises of Macro cell and Small cell where small cells include femto cell, pico cell, and micro cells. The femto cells are deployed in the macrocell area to improve the indoor coverage and provide better user experience. The mass deployment of femtocell faces a number of challenges among which interference management is very important. In this paper we consider the energy aware handovers between macro and femto base stations where the user equipment changes its services from one base station to another since the target one will offer better quality of performance. Handover decision can either be mobile initiated or network initiated and this decision is truly based on the received signal strength (RSS). The experiment is carried out using Network Simulator NS-3 and the results show that the proposed solution exhibits better performance in terms of quality of service metrics.

The diverse architectural evolution and intensive data explosion in the forthcoming 5G will have severe impacts on providing seamless and robust mobility management. In this paper, P-persistent energy-aware handover (HO) decision strategies with mobility robustness are proposed both for intra-handover cases while a femto user equipment (FUE) roams into another femto access point (FAP) and for cross-tier handover cases while a macro user equipment (MUE)/FUE roams into/out of the FAP in a dynamic cell sizing involving macro-femto two-tier networks. To approximate the densely deployed small-cells, a unique RF fingerprint (RFF)-based localization is employed to enable efficient small-cell detection by an RFF matching mechanism. The prediction of the HO trigger is jointly determined by a P-persistent decision mechanism that formulates the specific HO behaviors when an MUE/FUE roams into (HO-in) and out of (HO-out) a femtocell in terms of the correlated coverage variance and UE trajectory features, whereas the target selection follows a utility function in consideration of the UE traveling time and the achievable throughput. The closed-form stationary probabilities of the proposed VHO/HHO decisions are analyzed by a Semi-Markov-based framework. In addition, an adjustable sensing mechanism with dynamic intervals is proposed when UE is located far from the RFF matching region, which can have a positive influence on reducing unnecessary UE energy consumption. Numerical results are presented for the decision accuracy analyses (too early, too late, and ping-pong HOs), energy-efficiency, and resource utilization of the two-tier system. The comprehensive evaluations indicate that the proposed scheme can enhance the mobility robustness and enable an optimal trade-off between the energy efficiency (EE) and system capacity while eliminating the architectural impacts caused by the dynamic topology and the dense deployment for the next-generation macro-femto two-tier networks.

Internet of Things (IoT) devices are basic units in edge computing. Denial of service (DoS) attack is a great threat to low-cost IoT devices, even worse in privacy-preserving authentication protocols for radio frequency identification (RFID) tags. During DoS attacks, the attacker consumes the legal states in a target tag by continuous scanning and further observes its behavior in authentication to break privacy. Due to lack of adequate energy and computing power, it is hard for passive backscattering RFID tags to defend against DoS attacks. In this work, we cast a new insight on the DoS attacks to RFID tags and leverage the malicious scanning behavior as a new energy source. In this way, a passive tag gains more and more energy and can afford more and more complex cryptography computation under a DoS attack. Finally, the victim tag with adequate energy achieves to complete the complicated public key cryptographic computations and defend against the DoS attack. We further propose a protocol, namely the RUND protocol. We define the tracking privacy model and introduce in the notion of tracking interval to define the tracking privacy other than indistinguishable privacy. To guarantee the security and tracking privacy properties of our protocol, we propose 3 rules as designing guidelines. The analysis shows that our approach can achieve $O$ (1) efficiency while providing DoS-defending privacy-preserving authentication. Furthermore, with proper parameters our approach can save about 40% boosting time at least, compared to the directly charging for public key cryptographic computation method.

Virtual Machine (VM) consolidation technique plays an important role in energy management and load-balancing of cloud computing systems. Dynamic VM consolidation is a promising consolidation approach in this direction, which aims at using least active physical machines (PMs) through appropriately migrating VMs to reduce resource consumption. The resulting optimization problem is well-acknowledged to be NP-hard optimization problems. In this paper, we propose a novel merge-and-split-based coalitional game-theoretic approach for VM consolidation in heterogeneous clouds. The proposed approach first partitions PMs into different groups based on their workload levels, then employs a coalitional-game-based VM consolidation algorithm (CGMS) in choosing members from such groups to form effective coalitions, performs VM migrations among the coalition members to maximize the payoff of every coalition, and finally keeps PMs running in a high energy-efficiency state. The simulation results based on three scenarios clearly suggest that our proposed approach outperforms traditional ones in terms of energy-saving, and also achieve a fair level of load balance.

Combining the Internet-of-Things (IoT) technology with cloud computing is a significant alternative for powering the utilization of computing resources in a connected environment. A grand challenge in communications is raised by the emergence of big data, due to the large-sized data transmissions and frequent data exchanges. Applying fog computing is considered an option for resolving the communication challenge. However, a high extent of available heterogeneous computing attached to fog computing servers leads to a restriction of the resource management. This Article addresses the resource management issue by proposing a novel approach - named Energy-aware Fog Resource Optimization (EFRO) model- to optimizing the utilization of connected devices in fog computing. We develop a heuristic algorithm minimizing both energy cost and time consumption in a holistic way. A salient feature of EFRO lies in the integration of the standardization and smart shift operations fueled by a hill-climbing mechanism to produce near-optimal resource allocation solutions. Experimental results demonstrate that our EFRO is adroit at making near-optimal decisions in managing resources in fog computing environments. In particular, EFRO boosts the energy efficiency of the existing MESF and RR schemes by 54.83 and 71.28 percent, respectively. EFRO shortens DECM’s allocation-generation time by up to a factor of 507. [ABSTRACT FROM AUTHOR]

Ocean ecosystems have spatiotemporal variability and dynamic complexity that require a long-term deployment of an autonomous underwater vehicle for data collection. A new generation of long-range autonomous underwater vehicles (LRAUVs), such as the Slocum glider and Tethys-class AUV, has emerged with high endurance, long-range, and energy-aware capabilities. These new vehicles provide an effective solution to study different oceanic phenomena across multiple spatial and temporal scales. For these vehicles, the ocean environment has forces and moments from changing water currents which are generally on the order of magnitude of the operational vehicle velocity. Therefore, it is not practical to generate a simple trajectory from an initial location to a goal location in an uncertain ocean, as the vehicle can deviate significantly from the prescribed trajectory due to disturbances resulted from water currents. Since state estimation remains challenging in underwater conditions, feedback planning must incorporate state uncertainty that can be framed into a stochastic energy-aware path planning problem. This article presents an energy-aware feedback planning method for an LRAUV utilizing its kinematic model in an underwater environment under motion and sensor uncertainties. Our method uses ocean dynamics from a predictive ocean model to understand the water flow pattern and introduces a goal-constrained belief space to make the feedback plan synthesis computationally tractable. Energy-aware feedback plans for different water current layers are synthesized through sampling and ocean dynamics. The synthesized feedback plans provide strategies for the vehicle that drive it from an environment’s initial location toward the goal location. We validate our method through extensive simulations involving the Tethys vehicle’s kinematic model and incorporating actual ocean model prediction data.

Recent advances in computing allows researchers to propose the automation of hydroponic systems to boost efficiency and reduce manpower demands, hence increasing agricultural produce and profit. A completely automated hydroponic system should be equipped with tools capable of detecting plant diseases in real-time. Despite the availability of deep-learning-based plant disease detection models, the existing models are not designed for an embedded system environment, and the models cannot realistically be deployed on resource-constrained IoT devices such as raspberry pi or a smartphone. Some of the drawbacks of the existing models are the following: high computational resource requirements, high power consumption, dissipates energy rapidly, and occupies large storage space due to large complex structure. Therefore, in this paper, we proposed a low-power deep learning model for plant disease detection using knowledge distillation techniques. The proposed low-power model has a simple network structure of a shallow neural network. The parameters of the model were also reduced by more than 90%. This reduces its computational requirements as well as its power consumption. The proposed low-power model has a maximum power consumption of 6.22 w, which is significantly lower compared to the existing models, and achieved a detection accuracy of 99.4%.

Nowadays, multi-core processing systems have to perform complex functionalities on densely packed multi-million gate platforms, making design issues like resource usage efficiency, energy consumption, temperature management of cores, etc. more challenging to handle. Since many of these processing platforms use batteries as their primary source of energy and are prone to an uncontrolled surge in temperatures, researchers have started focusing on these criteria in great detail. In this work, we propose a three-phase hierarchical resource allocation strategy called RESET for scheduling periodic tasks with a bounded number of migrations and context-switches. The first phase divides time into distinct intervals/windows based on deadlines of tasks. The exact proportional fairness needed for the progress of task executions is maintained at all window boundaries. The second phase performs energy-aware task-to-core assignments based on task execution requirements and operating frequency levels of cores. In the last phase, it splits tasks based on an input parameter δ and prepares a temperature-aware schedule for individual cores. Our experimental analysis shows that the presented strategy improves upon the state-of-the-art in terms of resource utilisation and reduces dynamic energy consumption and the average temperature of cores in the system. • A real-time scheduler for energy and temperature aware heterogeneous platforms. • It delivers high resource utilisation while incurring bounded context-switches. • It is able to schedule more tasks compared to state-of-the-art. [ABSTRACT FROM AUTHOR]

The rest of the paper is arranged as follows: Section 2 explains motivation and literature review on energy-aware routing protocol in mobile sink-based WSN, and Section 3 illustrates the mobile sink-based WSN. "Energy-Aware Data Gathering Mechanism for Mobile Sink in Wireless Sensor Networks Using Particle Swarm Optimization." Keywords: a sink node; cluster head; energy-aware; routing; wireless sensor network EN a sink node cluster head energy-aware routing wireless sensor network 37 54 18 01/27/22 20210101 NES 210101 Introduction In the Internet of Things (IoT) system, wireless sensor network (WSN) is a significant task and involves connectivity, sensing, data processing, and data acquisition. [Extracted from the article]

Wireless sensor networks (WSNs) have received considerable interest in recent years. These sensor nodes can gather information from the surrounding environment and transmit it to a designated location. Each sensor node in WSN typically has a battery with a limited capacity. Due to their large number and because of various environmental challenges, it is sometimes hard to replace this finite battery. As a result, energy-efficient communication is seen as a critical aspect in extending the lifespan of a sensor node. On the other hand, some applications that require large coverage and generate various sorts of data packets require multi-hop routing and quality of service (QoS) features. Therefore, in order to avoid network failure, these applications need an energy-efficient QoS MAC protocol that can support multiple levels of data packet priority and multi-hop routing features while focusing on energy conservation. An energy-aware QoS MAC protocol based on Prioritized Data and Multi-hop routing (EQPD-MAC) is proposed in this article. The EQPD-MAC protocol offers a simple yet effective cross-layer communication method. It provides timely delivery of multi-priority packets, uses an adaptive active time to limit idle listening, and integrates a robust routing protocol. Finally, the EQPD-MAC protocol’s performance was evaluated and compared to three other well-known QoS MAC protocols. The simulation findings show that the proposed protocol significantly decreases sensor node energy consumption by up to 30.3%, per-bit energy consumption by up to 29.6%, sink node energy consumption by up to 27.4% and increases throughput by up to 23.3%.

Abstract Network virtualization is a vital technology that helps overcome shortcomings such as network ossification of the current Internet architecture. However, virtual network embedding (VNE) involving the allocation of resources for heterogeneous virtual network requests (VNRs) on the substrate network (SN) is considered as NP-hard problem. VNE process may involve conflicting objectives, including energy saving and VNR acceptance rate as the most critical. In this paper, we propose a virtual network multi-objective embedding algorithm based on Q-learning and curiosity-driven (Q-CD-VNE) for improving the performance of the system by optimizing conflicting objectives, namely energy saving and acceptance rate. The proposed algorithm employs Q-learning and curiosity-driven mechanism by considering other non-deterministic factors to avoid falling into a local optimum. The major contributions of this work involve (1) modeling of the multi-objective deterministic factors as binary (0, 1) integer programming problem, (2) formulating the virtual node mapping problem using the Markov decision process (MDP), (3) solving the VNE problem using Q-learning algorithm, (4) mining non-deterministic factors using curiosity-driven mechanism for avoiding prematurely falling into the Exploration-Exploitation dilemma and local optimal. Experimental results in comparison with representative researches in the field prove that the proposed algorithm can reduce energy consumption, improve the request acceptance rate, and improve the long-term average income.