Academic literature on the topic 'Energy-aware Task Planning'

When managing a constellation of nanosatellites, one may leverage this structure to improve the mission’s quality-of-service (QoS) by optimally distributing the tasks during an orbit. In this sense, this research proposes an offline energy-aware task scheduling problem formulation regarding the specifics of constellations, by considering whether the tasks are individual, collective, or stimulated to be redundant. By providing such an optimization framework, the idea of estimating an offline task schedule can serve as a baseline for the constellation design phase. For example, given a particular orbit, from the simulation of an irradiance model, the engineer can estimate how the mission value is affected by the inclusion or exclusion of individuals objects. The proposed model, given in the form of a multi-objective mixed-integer linear programming model, is illustrated in this work for several illustrative scenarios considering different sets of tasks and constellations. We also perform an analysis of the Pareto-optimal frontier of the problem, identifying the feasible trade-off points between constellation and individual tasks. This information can be useful to the decision-maker (mission operator) when planning the behavior in orbit.

Wireless sensor networks find several applications in hard-to-reach areas. As sensors have limited battery power, many energy aware protocols based on negotiation, clustering, and agents have been developed to increase lifetime of the network. This article finds limitation with some multi-agent-based protocols as they place the sink node at the centre of the monitoring region which is quite difficult in hard-to-reach areas. Therefore, a multi-agent-based energy and fault-aware protocol for hard-to-reach territories (MAHT) is proposed which uses technique of impact factor to identify the high power capability of the central node and dynamic itinerary planning to make the protocol fault tolerant. Its agent migration technique results in improvement ofn energy efficiency, task completion time and network lifetime. MAHT is simulated using Castalia simulator and the impact of payload size, network size, node failures, etc., on various performance metrics is analysed. The proposed protocol found outperforming over the existing ones.

The tremendous expansion of the Internet of Things (IoTs) has generated an enormous volume of near and remote sensing data, which is increasing with the emergence of new solutions for sustainable environments. Cloud computing is typically used to help resource-constrained IoT sensing devices. However, the cloud servers are placed deep within the core network, a long way from the IoT, introducing immense data transactions. These transactions require heavy electricity consumption and release harmful CO2 to the environment. A distributed computing environment located at the edge of the network named fog computing has been promoted to reduce the limitation of cloud computing for IoT applications. Fog computing potentially processes real-time and delay-sensitive data, and it reduces the traffic, which minimizes the energy consumption. The additional energy consumption can be reduced by implementing an energy-aware task scheduling, which decides on the execution of tasks at cloud or fog nodes on the basis of minimum completion time, cost, and energy consumption. In this paper, an algorithm called energy-efficient makespan cost-aware scheduling (EMCS) is proposed using an evolutionary strategy to optimize the execution time, cost, and energy consumption. The performance of this work is evaluated using extensive simulations. Results show that EMCS is 67.1% better than cost makespan-aware scheduling (CMaS), 58.79% better than Heterogeneous Earliest Finish Time (HEFT), 54.68% better than Bees Life Algorithm (BLA) and 47.81% better than Evolutionary Task Scheduling (ETS) in terms of makespan. Comparing the cost of the EMCS model, it uses 62.4% less cost than CMaS, 26.41% less than BLA, and 6.7% less than ETS. When comparing energy consumption, EMCS consumes 11.55% less than CMaS, 4.75% less than BLA and 3.19% less than ETS. Results also show that with an increase in the number of fog and cloud nodes, the balance between cloud and fog nodes gives better performance in terms of makespan, cost, and energy consumption.

Efficiency and reliable turnaround time are core features of modern aircraft transportation and key to its future sustainability. Given the connected aircraft cabin, the deployment of digitized and interconnected sensors, devices and passengers provides comprehensive state detection within the cabin. More specifically, passenger localization and occupancy detection can be monitored using location-aware communication systems, also known as wireless sensor networks. These multi-purpose communication systems serve a variety of capabilities, ranging from passenger convenience communication services, over crew member devices, to maintenance planning. In addition, radio-based sensing enables an efficient sensory basis for state monitoring; e.g., passive seat occupancy detection. Within the scope of the connected aircraft cabin, this article presents a multipath-assisted radio sensing (MARS) approach using the propagation information of transmitted signals, which are provided by the channel impulse response (CIR) of the wireless communication channel. By performing a geometrical mapping of the CIR, reflection sources are revealed, and the occupancy state can be derived. For this task, both probabilistic filtering and k-nearest neighbor classification are discussed. In order to evaluate the proposed methods, passenger occupancy detection and state detection for the future automation of passenger safety announcements and checks are addressed. Therefore, experimental measurements are performed using commercially available wideband communication devices, both in close to ideal conditions in an RF anechoic chamber and a cabin seat mockup. In both environments, a reliable radio sensing state detection was achieved. In conclusion, this paper provides a basis for the future integration of energy and spectrally efficient joint communication and sensing radio systems within the connected aircraft cabin.

Efficient resource planning is recognized as one of the key enablers making the large-scale deployment of next-generation wireless networks available for mass usage. Modelling, planning, and software simulation tools reduce both the time needed and costs of their tuning and realization. In this paper, we propose a model-driven framework for proactive network planning relying on synergy of deep learning and multiobjective optimization. The predictions about service demand and energy consumption are taken into account. Also, the impact of degradations resulting from fading and cochannel interference (CCI) effects is also considered. The optimization task is treated as a component allocation problem (CAP) aiming to find the best possible base station allocation for the considered smart city locations with respect to performance and service demand constraints. The goal is to maximize Quality of Service (QoS) while keeping the costs and energy consumption as low as possible. The adoption of a model-driven approach in combination with model-to-model transformations and automated code generation does not only reduce the complexity, making experimentation more rapid and convenient at the same time, but also increase the overall reusability and expandability of the planning tool. According to the obtained results, the proposed solution seems to be promising not only due to achieved benefits but also regarding the execution time, which is shorter than that achieved in our previous works, especially for larger distances. Further, we adopt model-based representation of handover strategies within the planning tool, enabling examination of the dynamic behavior of user-created plan, which is not exploited in other similar works. The main contributions of the paper are (1) wireless network planning (WNP) metamodel, a modelling notation for network plans; (2) model-to-model transformation for conversion of WNP to generalized CAP metamodel; (3) prediction problem (PP) metamodel, high-level abstraction for representation of prediction-related regression and classification problems; (4) code generator that creates PyTorch neural network from PP representation; (5) service demand and energy consumption prediction modules performing regression; (6) multiobjective optimization model for base station allocation; (7) Handover Strategy (HS) metamodel used for description of dynamic aspects and adaptability relevant to network planning.

Recent Trends and Future of Fog and Edge Computing, Services, and Enabling Technologies Cloud computing has been established as the most popular as well as suitable computing infrastructure providing on-demand, scalable and pay-as-you-go computing resources and services for the state-of-the-art ICT applications which generate a massive amount of data. Though Cloud is certainly the most fitting solution for most of the applications with respect to processing capability and storage, it may not be so for the real-time applications. The main problem with Cloud is the latency as the Cloud data centres typically are very far from the data sources as well as the data consumers. This latency is ok with the application domains such as enterprise or web applications, but not for the modern Internet of Things (IoT)-based pervasive and ubiquitous application domains such as autonomous vehicle, smart and pervasive healthcare, real-time traffic monitoring, unmanned aerial vehicles, smart building, smart city, smart manufacturing, cognitive IoT, and so on. The prerequisite for these types of application is that the latency between the data generation and consumption should be minimal. For that, the generated data need to be processed locally, instead of sending to the Cloud. This approach is known as Edge computing where the data processing is done at the network edge in the edge devices such as set-top boxes, access points, routers, switches, base stations etc. which are typically located at the edge of the network. These devices are increasingly being incorporated with significant computing and storage capacity to cater to the need for local Big Data processing. The enabling of Edge computing can be attributed to the Emerging network technologies, such as 4G and cognitive radios, high-speed wireless networks, and energy-efficient sophisticated sensors. Different Edge computing architectures are proposed (e.g., Fog computing, mobile edge computing (MEC), cloudlets, etc.). All of these enable the IoT and sensor data to be processed closer to the data sources. But, among them, Fog computing, a Cisco initiative, has attracted the most attention of people from both academia and corporate and has been emerged as a new computing-infrastructural paradigm in recent years. Though Fog computing has been proposed as a different computing architecture than Cloud, it is not meant to replace the Cloud. Rather, Fog computing extends the Cloud services to network edges for providing computation, networking, and storage services between end devices and data centres. Ideally, Fog nodes (edge devices) are supposed to pre-process the data, serve the need of the associated applications preliminarily, and forward the data to the Cloud if the data are needed to be stored and analysed further. Fog computing enhances the benefits from smart devices operational not only in network perimeter but also under cloud servers. Fog-enabled services can be deployed anywhere in the network, and with these services provisioning and management, huge potential can be visualized to enhance intelligence within computing networks to realize context-awareness, high response time, and network traffic offloading. Several possibilities of Fog computing are already established. For example, sustainable smart cities, smart grid, smart logistics, environment monitoring, video surveillance, etc. To design and implementation of Fog computing systems, various challenges concerning system design and implementation, computing and communication, system architecture and integration, application-based implementations, fault tolerance, designing efficient algorithms and protocols, availability and reliability, security and privacy, energy-efficiency and sustainability, etc. are needed to be addressed. Also, to make Fog compatible with Cloud several factors such as Fog and Cloud system integration, service collaboration between Fog and Cloud, workload balance between Fog and Cloud, and so on need to be taken care of. It is our great privilege to present before you Volume 20, Issue 2 of the Scalable Computing: Practice and Experience. We had received 20 Research Papers and out of which 14 Papers are selected for Publication. The aim of this special issue is to highlight Recent Trends and Future of Fog and Edge Computing, Services and Enabling technologies. The special issue will present new dimensions of research to researchers and industry professionals with regard to Fog Computing, Cloud Computing and Edge Computing. Sujata Dash et al. contributed a paper titled “Edge and Fog Computing in Healthcare- A Review” in which an in-depth review of fog and mist computing in the area of health care informatics is analysed, classified and discussed. The review presented in this paper is primarily focussed on three main aspects: The requirements of IoT based healthcare model and the description of services provided by fog computing to address then. The architecture of an IoT based health care system embedding fog computing layer and implementation of fog computing layer services along with performance and advantages. In addition to this, the researchers have highlighted the trade-off when allocating computational task to the level of network and also elaborated various challenges and security issues of fog and edge computing related to healthcare applications. Parminder Singh et al. in the paper titled “Triangulation Resource Provisioning for Web Applications in Cloud Computing: A Profit-Aware” proposed a novel triangulation resource provisioning (TRP) technique with a profit-aware surplus VM selection policy to ensure fair resource utilization in hourly billing cycle while giving the quality of service to end-users. The proposed technique use time series workload forecasting, CPU utilization and response time in the analysis phase. The proposed technique is tested using CloudSim simulator and R language is used to implement prediction model on ClarkNet weblog. The proposed approach is compared with two baseline approaches i.e. Cost-aware (LRM) and (ARMA). The response time, CPU utilization and predicted request are applied in the analysis and planning phase for scaling decisions. The profit-aware surplus VM selection policy used in the execution phase for select the appropriate VM for scale-down. The result shows that the proposed model for web applications provides fair utilization of resources with minimum cost, thus provides maximum profit to application provider and QoE to the end users. Akshi kumar and Abhilasha Sharma in the paper titled “Ontology driven Social Big Data Analytics for Fog enabled Sentic-Social Governance” utilized a semantic knowledge model for investigating public opinion towards adaption of fog enabled services for governance and comprehending the significance of two s-components (sentic and social) in aforesaid structure that specifically visualize fog enabled Sentic-Social Governance. The results using conventional TF-IDF (Term Frequency-Inverse Document Frequency) feature extraction are empirically compared with ontology driven TF-IDF feature extraction to find the best opinion mining model with optimal accuracy. The results concluded that implementation of ontology driven opinion mining for feature extraction in polarity classification outperforms the traditional TF-IDF method validated over baseline supervised learning algorithms with an average of 7.3% improvement in accuracy and approximately 38% reduction in features has been reported. Avinash Kaur and Pooja Gupta in the paper titled “Hybrid Balanced Task Clustering Algorithm for Scientific workflows in Cloud Computing” proposed novel hybrid balanced task clustering algorithm using the parameter of impact factor of workflows along with the structure of workflow and using this technique, tasks can be considered for clustering either vertically or horizontally based on value of impact factor. The testing of the algorithm proposed is done on Workflowsim- an extension of CloudSim and DAG model of workflow was executed. The Algorithm was tested on variables- Execution time of workflow and Performance Gain and compared with four clustering methods: Horizontal Runtime Balancing (HRB), Horizontal Clustering (HC), Horizontal Distance Balancing (HDB) and Horizontal Impact Factor Balancing (HIFB) and results stated that proposed algorithm is almost 5-10% better in makespan time of workflow depending on the workflow used. Pijush Kanti Dutta Pramanik et al. in the paper titled “Green and Sustainable High-Performance Computing with Smartphone Crowd Computing: Benefits, Enablers and Challenges” presented a comprehensive statistical survey of the various commercial CPUs, GPUs, SoCs for smartphones confirming the capability of the SCC as an alternative to HPC. An exhaustive survey is presented on the present and optimistic future of the continuous improvement and research on different aspects of smartphone battery and other alternative power sources which will allow users to use their smartphones for SCC without worrying about the battery running out. Dhanapal and P. Nithyanandam in the paper titled “The Slow HTTP Distributed Denial of Service (DDOS) Attack Detection in Cloud” proposed a novel method to detect slow HTTP DDoS attacks in cloud to overcome the issue of consuming all available server resources and making it unavailable to the real users. The proposed method is implemented using OpenStack cloud platform with slowHTTPTest tool. The results stated that proposed technique detects the attack in efficient manner. Mandeep Kaur and Rajni Mohana in the paper titled “Static Load Balancing Technique for Geographically partitioned Public Cloud” proposed a novel approach focused upon load balancing in the partitioned public cloud by combining centralized and decentralized approaches, assuming the presence of fog layer. A load balancer entity is used for decentralized load balancing at partitions and a controller entity is used for centralized level to balance the overall load at various partitions. Results are compared with First Come First Serve (FCFS) and Shortest Job First (SJF) algorithms. In this work, the researchers compared the Waiting Time, Finish Time and Actual Run Time of tasks using these algorithms. To reduce the number of unhandled jobs, a new load state is introduced which checks load beyond conventional load states. Major objective of this approach is to reduce the need of runtime virtual machine migration and to reduce the wastage of resources, which may be occurring due to predefined values of threshold. Mukta and Neeraj Gupta in the paper titled “Analytical Available Bandwidth Estimation in Wireless Ad-Hoc Networks considering Mobility in 3-Dimensional Space” proposes an analytical approach named Analytical Available Bandwidth Estimation Including Mobility (AABWM) to estimate ABW on a link. The major contributions of the proposed work are: i) it uses mathematical models based on renewal theory to calculate the collision probability of data packets which makes the process simple and accurate, ii) consideration of mobility under 3-D space to predict the link failure and provides an accurate admission control. To test the proposed technique, the researcher used NS-2 simulator to compare the proposed technique i.e. AABWM with AODV, ABE, IAB and IBEM on throughput, Packet loss ratio and Data delivery. Results stated that AABWM performs better as compared to other approaches. R.Sridharan and S. Domnic in the paper titled “Placement Strategy for Intercommunicating Tasks of an Elastic Request in Fog-Cloud Environment” proposed a novel heuristic IcAPER,(Inter-communication Aware Placement for Elastic Requests) algorithm. The proposed algorithm uses the network neighborhood machine for placement, once current resource is fully utilized by the application. The performance IcAPER algorithm is compared with First Come First Serve (FCFS), Random and First Fit Decreasing (FFD) algorithms for the parameters (a) resource utilization (b) resource fragmentation and (c) Number of requests having intercommunicating tasks placed on to same PM using CloudSim simulator. Simulation results shows IcAPER maps 34% more tasks on to the same PM and also increase the resource utilization by 13% while decreasing the resource fragmentation by 37.8% when compared to other algorithms. Velliangiri S. et al. in the paper titled “Trust factor based key distribution protocol in Hybrid Cloud Environment” proposed a novel security protocol comprising of two stages: first stage, Group Creation using the trust factor and develop key distribution security protocol. It performs the communication process among the virtual machine communication nodes. Creating several groups based on the cluster and trust factors methods. The second stage, the ECC (Elliptic Curve Cryptography) based distribution security protocol is developed. The performance of the Trust Factor Based Key Distribution protocol is compared with the existing ECC and Diffie Hellman key exchange technique. The results state that the proposed security protocol has more secure communication and better resource utilization than the ECC and Diffie Hellman key exchange technique in the Hybrid cloud. Vivek kumar prasad et al. in the paper titled “Influence of Monitoring: Fog and Edge Computing” discussed various techniques involved for monitoring for edge and fog computing and its advantages in addition to a case study based on Healthcare monitoring system. Avinash Kaur et al. elaborated a comprehensive view of existing data placement schemes proposed in literature for cloud computing. Further, it classified data placement schemes based on their assess capabilities and objectives and in addition to this comparison of data placement schemes. Parminder Singh et al. presented a comprehensive review of Auto-Scaling techniques of web applications in cloud computing. The complete taxonomy of the reviewed articles is done on varied parameters like auto-scaling, approach, resources, monitoring tool, experiment, workload and metric, etc. Simar Preet Singh et al. in the paper titled “Dynamic Task Scheduling using Balanced VM Allocation Policy for Fog Computing Platform” proposed a novel scheme to improve the user contentment by improving the cost to operation length ratio, reducing the customer churn, and boosting the operational revenue. The proposed scheme is learnt to reduce the queue size by effectively allocating the resources, which resulted in the form of quicker completion of user workflows. The proposed method results are evaluated against the state-of-the-art scene with non-power aware based task scheduling mechanism. The results were analyzed using parameters-- energy, SLA infringement and workflow execution delay. The performance of the proposed schema was analyzed in various experiments particularly designed to analyze various aspects for workflow processing on given fog resources. The LRR (35.85 kWh) model has been found most efficient on the basis of average energy consumption in comparison to the LR (34.86 kWh), THR (41.97 kWh), MAD (45.73 kWh) and IQR (47.87 kWh). The LRR model has been also observed as the leader when compared on the basis of number of VM migrations. The LRR (2520 VMs) has been observed as best contender on the basis of mean of number of VM migrations in comparison with LR (2555 VMs), THR (4769 VMs), MAD (5138 VMs) and IQR (5352 VMs).

Education has a crucial role to play in helping meet the Sustainable Development Goals, for which the initial training of university teachers, and its evaluation, are all essential. In this context, the authors developed an outdoor work task, consisting of an orientation game in ‘medieval Madrid’. The main objective was to show future teachers how they can enable their own students to value cultural heritage in order to acquire sustainability competencies. The task was evaluated by participants using a questionnaire, in order to make them aware of the acquired competencies. A gamification component was added to the outdoor task to create a healthy competitive environment. In this way, future teachers were able to observe how a teaching activity is evaluated; learn how to organize a didactic activity that can be extrapolated to other territorial and heritage realities; and employ their mobile devices to learn the foundations of sustainability in heritage management. Additionally, they acquired teaching competencies that promoted quality education and contributed towards two of the Sustainable Development Goals, specifically: 4 “Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all” and 11 “Make cities and human settlements inclusive, safe, resilient and sustainable”.

The significance of contextual data has been recognized by analysts and specialists in numerous disciplines such as customization, data recovery, ubiquitous and versatile processing, information mining, and management. While a generous research has just been performed in the zone of recommender frameworks, by far most of the existing approaches center on prescribing the most relevant items to customers. It usually neglects extra-contextual information, for example time, area, climate or the popularity of different locations. Therefore, we proposed a deep long-short term memory (LSTM) based context-enriched hierarchical model. This proposed model had two levels of hierarchy and each level comprised of a deep LSTM network. In each level, the task of the LSTM was different. At the first level, LSTM learned from user travel history and predicted the next location probabilities. A contextual learning unit was active between these two levels. This unit extracted maximum possible contexts related to a location, the user and its environment such as weather, climate and risks. This unit also estimated other effective parameters such as the popularity of a location. To avoid feature congestion, XGBoost was used to rank feature importance. The features with no importance were discarded. At the second level, another LSTM framework was used to learn these contextual features embedded with location probabilities and resulted into top ranked places. The performance of the proposed approach was elevated with an accuracy of 97.2%, followed by gated recurrent unit (GRU) (96.4%) and then Bidirectional LSTM (94.2%). We also performed experiments to find the optimal size of travel history for effective recommendations.

Family farm sustainability traditionally focuses on economic and environmental issues. However, sustaining family farms also relies on understanding how to sustain the relationships contained therein. Emotional intelligence (EI) is an important means through which family farm members can sustain relationships, especially when handing conflict between members. This paper focused on how four EI dimensions (awareness of own emotion, management of own emotion, awareness of others’ emotions, management of others’ emotions) could prevent four types of conflict within family farms (task, relational, process, and status). Family farm participants (N = 204) were recruited through social media posts and emails to specialty agricultural groups and agencies, and students at a university. Hierarchical regression results showed that awareness of own emotions, management of own emotions, and management of others’ emotions negatively predicted task, relational, process, and status conflict. Awareness of others’ emotions did not predict any conflict types. Theoretically, this article points to the importance of considering all four EI dimensions, since they impact conflict types differently. For the family farm members, being aware of their own emotions and being able to manage emotional responses in themselves and others can help prevent conflict from occurring, thereby sustaining both family and business relationships for the future.

Although hazard perception is an important skill found to be crucial for negotiating traffic among various types of road users, few studies have systematically investigated e-bike riders’ ability to perceive potential unmaterialized hazardous situations or aimed to enhance these abilities through training. The present study explored the formation of two hazard perception training interventions based upon exposing young complete-novice e-bike riders to a vast array of materialized traffic hazards, with the aim of evaluating their effectiveness in enriching the ability to anticipate unmaterialized hazards. Young complete-novice e-bike riders were allocated into one of two intervention modes (‘Act and Anticipate Training’ or ‘Predictive and Commentary Training’) or a control group (ten in each group). AAT members underwent a theoretical tutorial, then observed clips depicting real-time hazardous situations footage taken from an e-bike rider’s perspective and were asked to perform a hazard detection task. PCT members underwent a theoretical tutorial, then a ‘what might happen next?’ task, followed by observation of video footage with expert commentary. A week later, participants were requested to complete a hazard perception test, during which they viewed ten videos and pressed a response button whenever they identified a hazardous situation. Overall, participants in both interventions were more aware of potential unmaterialized hazards compared to the control in both their response sensitivity and verbal descriptions. Trainees were responsive to the developed training interventions. Thus, actively detecting materialized hazards may produce effective training that enriches these road users’ hazard perception skills and allows them to safely negotiate traffic. Advantages of each of the training methodologies along with implications for intervention strategies, licensing, and policy development are discussed.

Sustainable building will be the major guiding principle for renewal of building and spatial planning practice. Kropman being one of the major Dutch building services contractors is aware of this trend. It is because of this rowing awareness of sustainability that the company puts effort towards knowledge transfer and research with the Dutch knowledge and research institutes. The principles of sustainable building within the IFD (Industrial Flexible Dismountable) concept are applied in the planning of a new building for the Kropman Utrecht department. Aim was an integrated approach within the design process to reach a maximum level of integration between building elements and climate conditioning elements. A newly developed methodology for structuring and documenting integral design processes enables verification and reproduction of decisions made during designing. Its conscious use by professionals results in iteration cycles within and between their own domains. The gradual emergence of a design language that helps structuring design tasks and solutions further stimulates the multidisciplinary exchange of ideas and concepts. This approach of structuring tasks and solutions is tested within a professional context of a building design project. Investigated is if the approach improves and supports the building design process. Besides its added value for building design process, the integral design methodology directly stimulates application of sustainable energy in the built environment.