Journal articles on the topic 'Cognitive offloading'

Objective: A distributed cognitive system is a system in which cognitive processes are distributed between brain-based internal and environment-based external resources. In the current experiment, we examined the influence of metacognitive processes on external resource use (i.e., cognitive offloading) in such systems. Background: High-tech working environments oftentimes represent distributed cognitive systems. Because cognitive offloading can both support and harm performance, depending on the specific circumstances, it is essential to understand when and why people offload their cognition. Method: We used an extension of the mental rotation paradigm. It allowed participants to rotate stimuli either internally as in the original paradigm or with a rotation knob that afforded rotating stimuli externally on a computer screen. Two parameters were manipulated: the knob’s actual reliability (AR) and an instruction altering participants’ beliefs about the knob’s reliability (believed reliability; BR). We measured cognitive offloading proportion and perceived knob utility. Results: Participants were able to quickly and dynamically adjust their cognitive offloading proportion and subjective utility assessments in response to AR, suggesting a high level of offloading proficiency. However, when BR instructions were presented that falsely described the knob’s reliability to be lower than it actually was, participants reduced cognitive offloading substantially. Conclusion: The extent to which people offload their cognition is not based solely on utility maximization; it is additionally affected by possibly erroneous preexisting beliefs. Application: To support users in efficiently operating in a distributed cognitive system, an external resource’s utility should be made transparent, and preexisting beliefs should be adjusted prior to interaction.

Modern technical tools such as tablets allow for the temporal externalisation of working memory processes (i.e., cognitive offloading). Although such externalisations support immediate performance on different tasks, little is known about potential long-term consequences of offloading behaviour. In the current set of experiments, we studied the relationship between cognitive offloading and subsequent memory for the offloaded information as well as the interplay of this relationship with the goal to acquire new memory representations. Our participants solved the Pattern Copy Task, in which we manipulated the costs of cognitive offloading and the awareness of a subsequent memory test. In Experiment 1 ( N = 172), we showed that increasing the costs for offloading induces reduced offloading behaviour. This reduction in offloading came along with lower immediate task performance but more accurate memory in an unexpected test. In Experiment 2 ( N = 172), we confirmed these findings and observed that offloading behaviour remained detrimental for subsequent memory performance when participants were aware of the upcoming memory test. Interestingly, Experiment 3 ( N = 172) showed that cognitive offloading is not detrimental for long-term memory formation under all circumstances. Those participants who were forced to offload maximally but were aware of the memory test could almost completely counteract the negative impact of offloading on memory. Our experiments highlight the importance of the explicit goal to acquire new memory representations when relying on technical tools as offloading did have detrimental effects on memory without such a goal.

Many animals manipulate their environments in ways that appear to augment cognitive processing. Adult humans show remarkable flexibility in this domain, typically relying on internal cognitive processing when adequate but turning to external support in situations of high internal demand. We use calendars, calculators, navigational aids and other external means to compensate for our natural cognitive shortcomings and achieve otherwise unattainable feats of intelligence. As yet, however, the developmental origins of this fundamental capacity for cognitive offloading remain largely unknown. In two studies, children aged 4–11 years ( n = 258) were given an opportunity to manually rotate a turntable to eliminate the internal demands of mental rotation––to solve the problem in the world rather than in their heads. In study 1, even the youngest children showed a linear relationship between mental rotation demand and likelihood of using the external strategy, paralleling the classic relationship between angle of mental rotation and reaction time. In study 2, children were introduced to a version of the task where manually rotating inverted stimuli was sometimes beneficial to performance and other times redundant. With increasing age, children were significantly more likely to manually rotate the turntable only when it would benefit them. These results show how humans gradually calibrate their cognitive offloading strategies throughout childhood and thereby uncover the developmental origins of this central facet of intelligence.

To satisfy the explosive growth of computation-intensive vehicular applications, we investigated the computation offloading problem in a cognitive vehicular networks (CVN). Specifically, in our scheme, the vehicular cloud computing (VCC)- and remote cloud computing (RCC)-enabled computation offloading were jointly considered. So far, extensive research has been conducted on RCC-based computation offloading, while the studies on VCC-based computation offloading are relatively rare. In fact, due to the dynamic and uncertainty of on-board resource, the VCC-based computation offloading is more challenging then the RCC one, especially under the vehicular scenario with expensive inter-vehicle communication or poor communication environment. To solve this problem, we propose to leverage the VCC’s computation resource for computation offloading with a perception-exploitation way, which mainly comprise resource discovery and computation offloading two stages. In resource discovery stage, upon the action-observation history, a Long Short-Term Memory (LSTM) model is proposed to predict the on-board resource utilizing status at next time slot. Thereafter, based on the obtained computation resource distribution, a decentralized multi-agent Deep Reinforcement Learning (DRL) algorithm is proposed to solve the collaborative computation offloading with VCC and RCC. Last but not least, the proposed algorithms’ effectiveness is verified with a host of numerical simulation results from different perspectives.

The contribution of the body to cognition and control in natural and artificial agents is increasingly described as “offloading computation from the brain to the body,” where the body is said to perform “morphological computation.” Our investigation of four characteristic cases of morphological computation in animals and robots shows that the “offloading” perspective is misleading. Actually, the contribution of body morphology to cognition and control is rarely computational, in any useful sense of the word. We thus distinguish (1) morphology that facilitates control, (2) morphology that facilitates perception, and the rare cases of (3) morphological computation proper, such as reservoir computing, where the body is actually used for computation. This result contributes to the understanding of the relation between embodiment and computation: The question for robot design and cognitive science is not whether computation is offloaded to the body, but to what extent the body facilitates cognition and control—how it contributes to the overall orchestration of intelligent behavior.

Most research on human cognition has focused on processes “inside the box”. Recently, researchers questioned this monopoly, promoting the relevance of cognitive processing “outside the box”, for instance, when using a GPS to navigate. For processing that is distributed between internal and external resources to work efficiently, humans need good heuristics that help them decide when to use which resource. A novel human-computer-interaction paradigm was employed to explore whether people follow the “minimal memory” heuristic and offload cognitive processing onto external resources whenever possible or the “soft constraint” heuristic and offload cognitive processing only if it is associated with an overall higher speed than internal processing. Participants, despite lower speed, nearly exclusively cognized outside the box, which contradicts the soft constraint heuristic and mostly supports the minimal memory heuristic. Implications for human-technology interaction as well as alternative heuristics relevant for cognitive offloading are discussed.

Recent works recognize musicality is based on and constrained by our cognitive and biological system. Taking in account a concept from cognitive science - cognitive offloading - as a principle for technology-supported musical activities, in this paper we discuss some principles (guidelines) to be taken into account when designing, developing and evaluating computer music technologies, especially those related to ubimus. We think that Ubimus technology can shape the way we think about music and have a positive (or negative) influence on our musicality.

Mobile edge computing (MEC) can augment the computation capabilities of a vehicle terminal (VT) through offloading the computational tasks from the VT to the mobile edge computing-enabled base station (MEC-BS) covering them. However, due to the limited mobility of the vehicle and the capacity of the MEC-BS, the connection between the vehicle and the MEC-BS may be intermittent. If we can expect the availability of MEC-BS through cognitive computing, we can significantly improve the performance in a mobile environment. Based on this idea, we propose a offloading optimization algorithm based on availability prediction. We examine the admission control decision of MEC-BS and the mobility problem, in which we improve the accuracy of availability prediction based on Empirical Mode Decomposition(EMD) and LSTM in deep learning. Firstly, we calculate the availability of MEC, completion time, and energy consumption together to minimize the overall cost. Then, we use a game method to obtain the optimal offloading decision. Finally, the experimental results show that the algorithm can save energy and shorten the completion time more effectively than other existing algorithms in the mobile environment.

In recent decades, mobile media and communication have become integral to human psychology, including how people think and feel. Although the popular press, parents, and educators often voice concerns about the integration of mobile media into everyday life (e.g., “smartphone addiction”), the growing body of scholarship in this area offers a mix of positive, negative, and conditional effects of mobile media use. This review article traverses this variegated scholarship by assembling cognitive and affective implications of mobile media and communication. It identifies information processing, offloading, spatial cognition, habit, attention, and phantom vibrations as cognitive themes, and feelings of pleasure, stress/anxiety, safety/security, connectedness, and control as affective themes. Along the way, it helps bring structure to this growing and interdisciplinary area of scholarship, ground psychological work on mobile media in theorizing on technological embedding, inform academic and public debates, and identify opportunities for future research.

Abstract We propose the self-regulation view in writing-to-learn as a promising theoretical perspective that draws on models of self-regulated learning theory and cognitive load theory. According to this theoretical perspective, writing has the potential to scaffold self-regulated learning due to the cognitive offloading written text generally offers as an external representation and memory aid, and due to the offloading, that specifically results from the genre-free principle in journal writing. However, to enable learners to optimally exploit this learning opportunity, the journal writing needs to be instructionally supported. Accordingly, we have set up a research program—the Freiburg Self-Regulated-Journal-Writing Approach—in which we developed and tested different instructional support methods to foster learning outcomes by optimizing cognitive load during self-regulated learning by journal writing. We will highlight the main insights of our research program which are synthesized from 16 experimental and 4 correlative studies published in 16 original papers. Accordingly, we present results on (1) the effects of prompting germane processing in journal writing, (2) the effects of providing worked examples and metacognitive information to support students in effectively exploiting prompted journal writing for self-regulated learning, (3) the effects of adapting and fading guidance in line with learners’ expertise in self-regulated learning, and (4) the effects of journal writing on learning motivation and motivation to write. The article closes with a discussion of several avenues of how the Freiburg Self-Regulated-Journal-Writing Approach can be developed further to advance research that integrates self-regulated learning with cognitive load theory.

Human memory benefits from information clustering, which can be accomplished by chunking. Chunking typically relies on expertise and strategy, and it is unknown whether perceptual clustering over time, through temporal integration, can also enhance working memory. The current study examined the attentional and working memory costs of temporal integration of successive target stimulus pairs embedded in rapid serial visual presentation. ERPs were measured as a function of behavioral reports: One target, two separate targets, or two targets reported as a single integrated target. N2pc amplitude, reflecting attentional processing, depended on the actual number of successive targets. The memory-related CDA and P3 components instead depended on the perceived number of targets irrespective of their actual succession. The report of two separate targets was associated with elevated amplitude, whereas integrated as well as actual single targets exhibited lower amplitude. Temporal integration thus provided an efficient means of processing sensory input, offloading working memory so that the features of two targets were consolidated and maintained at a cost similar to that of a single target.

Proactively coordinating one's actions is an important aspect of multitasking performance due to overlapping task sequences. In this study, we used fMRI to investigate neural mechanisms underlying monitoring of multiple overlapping task sequences. We tested the hypothesis that temporal control demands in multiple-task monitoring are offloaded onto spatial processes by representing patterns of temporal deadlines in spatial terms. Results showed that increased demands on time monitoring (i.e., responding to concurrent deadlines of one to four component tasks) increasingly activated regions in the left inferior parietal lobe and the precuneus. Moreover, independent measures of spatial abilities correlated with multiple-task performance beyond the contribution of working memory. Together, these findings suggest that monitoring and coordination of temporally overlapping task timelines rely on cortical processes involved in spatial information processing. We suggest that the precuneus is involved in tracking of multiple task timelines, whereas the inferior parietal lobe constructs spatial representations of the temporal relations of these overlapping timelines. These findings are consistent with the spatial offloading hypothesis and add new insights into the neurocognitive mechanisms underlying the coordination of multiple tasks.

BackgroundMedical team leaders in cardiac arrest teams are routinely subjected to disproportionately high levels of cognitive burden. This simulation-based study explored whether the introduction of a dedicated ‘nursing team leader’ is an effective way of cognitively offloading medical team leaders of cardiac arrest teams. It was hypothesised that reduced cognitive load may allow medical team leaders to focus on high-level tasks resulting in improved team performance.MethodsThis randomised controlled trial used a series of in situ simulations performed in two Australian emergency departments in 2018–2019. Teams balanced on experience were randomised to either control (traditional roles) or intervention (designated nursing team leader) groups. No crossover between groups occurred with each participant taking part in a single simulation. Debriefing data were collected for thematic analysis and quantitative evaluation of self-reported cognitive load and task efficiency was evaluated using the NASA Task Load Index (NTLX) and a ‘task time checklist’ which was developed for this trial.ResultsTwenty adult cardiac arrest simulations (120 participants) were evaluated. Intervention group medical team leaders had significantly lower NTLX scores (238.4, 95% CI 192.0 to 284.7) than those in control groups (306.3, 95% CI 254.9 to 357.6; p=0.02). Intervention group medical team leaders working alongside a designated nursing leader role had significantly lower cognitive loads than their control group counterparts (206.4 vs 270.5, p=0.02). Teams with a designated nurse leader role had improved time to defibrillator application (23.5 s vs 59 s, p=0.004), faster correction of ineffective compressions (7.5 s vs 14 s, p=0.04), improved compression fraction (91.3 vs 89.9, p=0.048), and shorter time to address reversible causes (107.1 s vs 209.5 s, p=0.002).ConclusionDedicated nursing team leadership in simulation based cardiac arrest teams resulted in cognitive offload for medical leaders and improved team performance.

Complex spatial forms like topography can be challenging to understand, much less intentionally shape, given the heavy cognitive load of visualizing and manipulating 3D form. Spatiotemporal processes like the flow of water over a landscape are even more challenging to understand and intentionally direct as they are dependent upon their context and require the simulation of forces like gravity and momentum. This cognitive work can be offloaded onto computers through 3D geospatial modeling, analysis, and simulation. Interacting with computers, however, can also be challenging, often requiring training and highly abstract thinking. Tangible computing – an emerging paradigm of human-computer interaction in which data is physically manifested so that users can feel it and directly manipulate it – aims to offload this added cognitive work onto the body. We have designed Tangible Landscape, a tangible interface powered by an open source geographic information system (GRASS GIS), so that users can naturally shape topography and interact with simulated processes with their hands in order to make observations, generate and test hypotheses, and make inferences about scientific phenomena in a rapid, iterative process. Conceptually Tangible Landscape couples a malleable physical model with a digital model of a landscape through a continuous cycle of 3D scanning, geospatial modeling, and projection. We ran a flow modeling experiment to test whether tangible interfaces like this can effectively enhance spatial performance by offloading cognitive processes onto computers and our bodies. We used hydrological simulations and statistics to quantitatively assess spatial performance. We found that Tangible Landscape enhanced 3D spatial performance and helped users understand water flow.

Complex spatial forms like topography can be challenging to understand, much less intentionally shape, given the heavy cognitive load of visualizing and manipulating 3D form. Spatiotemporal processes like the flow of water over a landscape are even more challenging to understand and intentionally direct as they are dependent upon their context and require the simulation of forces like gravity and momentum. This cognitive work can be offloaded onto computers through 3D geospatial modeling, analysis, and simulation. Interacting with computers, however, can also be challenging, often requiring training and highly abstract thinking. Tangible computing – an emerging paradigm of human-computer interaction in which data is physically manifested so that users can feel it and directly manipulate it – aims to offload this added cognitive work onto the body. We have designed Tangible Landscape, a tangible interface powered by an open source geographic information system (GRASS GIS), so that users can naturally shape topography and interact with simulated processes with their hands in order to make observations, generate and test hypotheses, and make inferences about scientific phenomena in a rapid, iterative process. Conceptually Tangible Landscape couples a malleable physical model with a digital model of a landscape through a continuous cycle of 3D scanning, geospatial modeling, and projection. We ran a flow modeling experiment to test whether tangible interfaces like this can effectively enhance spatial performance by offloading cognitive processes onto computers and our bodies. We used hydrological simulations and statistics to quantitatively assess spatial performance. We found that Tangible Landscape enhanced 3D spatial performance and helped users understand water flow.

In future multi-tier cellular networks, cognitive radio (CR) compatible with device-to-device (D2D) communication can be an aid to enhance system spectral efficiency (SE) and energy efficiency (EE). Users in proximity can establish a direct connection with D2D communication and bypass the base stations (BSs), thereby offloading the network infrastructure and providing EE improvement. We use stochastic geometry to model and analyze cognitive D2D communication underlying a multi-tier/multi-channel cellular network where the D2D transmitters are capable of harvesting RF energy from ambient interference resulting from simultaneous cellular downlink transmissions. For further improvement in EE, small cells (SCs) can be put into a power-saving mode by specifying a load-dependent transmission power coefficient (TPC) for SC BSs. In addition, to consider practical D2D communication scenarios, we propose a wireless video sharing framework where cache-enabled users can store and exchange popular video files through D2D communication. We investigate the potential effects of the TPC and the introduced D2D layer on the network EE and SE. We will also observe that the energy-harvesting CR-based D2D communication network design will not only ease the spectrum shortage problem but will also result in a greener network thanks to its reliance on ambient energies.

Abstract The theory of embodiment (Lakoff and Johnson 2003; Gibbs et al. 2004) explains the origin of meaning by postulating that thought is influenced by sensorimotor experience (Robbins and Aydede 2009). However, the relation between the body, mind and environment is not unidirectional. Not only do we derive information from the world, but we are also able to use it as an extension of the mind through epistemic actions, strategies that minimize the cognitive load by offloading it onto the environment (Kirsh and Maglio 1994). This paper investigates the potential of gesture as epistemic action. 12 blind and severely visually impaired children and young adults, as well as a control group of 7 young adults were interviewed for the purpose of the study. Participants were asked to explain a set of abstract and concrete concepts while their speech and gestures were recorded. If gesture indeed plays a role in reducing the mental load by externalizing thought, more gestures should be produced for concepts that are more difficult to describe (in this case: abstract, intangible concepts). Qualitative data analysis, as well as simple statistical analyses of gesture type, number and gesture per word rates show that abstract concepts do not generate more gestures, but do prompt blind and visually impaired speakers to use simulation gestures. These gestures constitute reenactments of situations associated with a given concept by the respondent. They are also thought to confirm the embodied cognition hypothesis (Hostetter and Alibali 2008). A number of examples demonstrates that abstract concepts in blind children are strongly grounded in their experience of real-world situations. Findings suggest that gesture is not merely a tool for communication, but a way of extending the capabilities of the mind.

The key attributes envisioned for LTE-Advanced pertaining to 5G Networks are ubiquitous presence, device convergence, massive machine connectivity, ultrahigh throughput and moderated carbon footprint of the network and the user equipment actuated by offloading cellular data traffic and by enabling device to device communication. The present method of mobility management and addressing as the authors have foreseen in LTE Advanced can solve some issues of cellular traffic backhaul towards the access and core network by actuating a local breakout and enabling communication directly between devices. But most of the approaches look forward towards an enhancement in the radio resource allocation process and prone to interference. Besides, most of these proposals delve in Device to Device (D2D) mode initiation from the device end, but no research has so far addressed the concept of a network initiated D2D process, which can optimise the channel utilisation and network operations further. In their attempt to knot these loose ends together, the auhtors furnish the concept of WISDOM (Wireless Innovative System for Dynamic Operating Mega communications) (Badoi Cornelia-I., Prasad N., Croitoru V., Prasad R., 2011) (Prasad R., June 2013) (Prasad R.,December 2013) and SMNAT (Sanyal, R., Cianca, E. and Prasad,R.,2012a) () () () (. Further, the authors explore how SMNAT (Smart Mobile Network Access Topology) can engage with WISDOM in cooperative communication to actuate D2D communication initiated by the device or the network. WISDOM is an architectural concept for 5G Networks based on cognitive radio approach. The cognition, sustained by adaptation techniques, is a way to provide communication, convergence, connectivity, co-operation, and content, anytime and anywhere. Though D2D communication using a dedicated spectrum in multi cell environment is possible through advanced network coding or by use of fractional frequency reuse, but physical proximity of the 2 devices is still a key requisite. In this paper the authors will discuss SMNAT which employs physical layer addressing to enable D2D communication agnostic to the spatial coordinates of the devices.

Objective To examine the effects of interruptions and retention interval on prospective memory for deferred tasks in simulated air traffic control. Background In many safety-critical environments, operators need to remember to perform a deferred task, which requires prospective memory. Laboratory experiments suggest that extended prospective memory retention intervals, and interruptions in those retention intervals, could impair prospective memory performance. Method Participants managed a simulated air traffic control sector. Participants were sometimes instructed to perform a deferred handoff task, requiring them to deviate from a routine procedure. We manipulated whether an interruption occurred during the prospective memory retention interval or not, the length of the retention interval (37–117 s), and the temporal proximity of the interruption to deferred task encoding and execution. We also measured performance on ongoing tasks. Results Increasing retention intervals (37–117 s) decreased the probability of remembering to perform the deferred task. Costs to ongoing conflict detection accuracy and routine handoff speed were observed when a prospective memory intention had to be maintained. Interruptions did not affect individuals’ speed or accuracy on the deferred task. Conclusion Longer retention intervals increase risk of prospective memory error and of ongoing task performance being impaired by cognitive load; however, prospective memory can be robust to effects of interruptions when the task environment provides cuing and offloading. Application To support operators in performing complex and dynamic tasks, prospective memory demands should be reduced, and the retention interval of deferred tasks should be kept as short as possible.

Access to the digital “all-knowing cloud” has become an integral part of our daily lives. It has been suggested that the increasing offloading of information and information processing services to the cloud will alter human cognition and metacognition in the short and long term. A much-cited study published in Science in 2011 provided first behavioral evidence for such changes in human cognition. Participants had to answer difficult trivia questions, and subsequently showed longer response times in a variant of the Stroop task with internet-related words (“Google Stroop effect”). The authors of this study concluded that the concept of the Internet is automatically activated in situations where information is missing (e.g., because we might feel the urge to “google” the information). However, the “Google Stroop effect” could not be replicated in two recent replication attempts as part of a large replicability project. After the failed replication was published in 2018, the first author of the original study pointed out some problems with the design of the failed replication. In our study, we therefore aimed to replicate the “Google Stroop effect” with a research design closer to the original experiment. Our results revealed no conclusive evidence in favor of the notion that the concept of the Internet or internet access (via computers or smartphones) is automatically activated when participants are faced with hard trivia questions. We provide recommendations for follow-up research.

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).

Abstract Background Cognitive offloading is the use of physical action to reduce the cognitive demands of a task. Everyday memory relies heavily on this practice; for example, when we write down to-be-remembered information or use diaries, alerts, and reminders to trigger delayed intentions. A key goal of recent research has been to investigate the processes that trigger cognitive offloading. This research has demonstrated that individuals decide whether or not to offload based on a potentially erroneous metacognitive evaluation of their mental abilities. Therefore, improving the accuracy of metacognitive evaluations may help to optimise offloading behaviour. However, previous studies typically measure participants’ use of an explicitly instructed offloading strategy, in contrast to everyday life where offloading strategies must often be generated spontaneously. Results We administered a computer-based task requiring participants to remember delayed intentions. One group of participants was explicitly instructed on a method for setting external reminders; another was not. The latter group spontaneously set reminders but did so less often than the instructed group. Offloading improved performance in both groups. Crucially, metacognition (confidence in unaided memory ability) guided both instructed and spontaneous offloading: Participants in both groups set more reminders when they were less confident (regardless of actual memory ability). Conclusions These results show that the link between metacognition and cognitive offloading holds even when offloading strategies need to be spontaneously generated. Thus, metacognitive interventions are potentially able to alter offloading behaviour, without requiring offloading strategies to be explicitly instructed.

AbstractThe cognitive load of many everyday life tasks exceeds known limitations of short-term memory. One strategy to compensate for information overload is cognitive offloading which refers to the externalization of cognitive processes such as reminder setting instead of memorizing. There appears to be remarkable variance in offloading behavior between participants which poses the question whether there is a common factor influencing offloading behavior across different tasks tackling short-term memory processes. To pursue this question, we studied individual differences in offloading behavior between two well-established offloading paradigms: the intention offloading task which tackles memory for intentions and the pattern copy task which tackles continuous short-term memory load. Our study also included an unrelated task measuring short-term memory capacity. Each participant completed all tasks twice on two consecutive days in order to obtain reliability scores. Despite high reliability scores, individual differences in offloading behavior were uncorrelated between the two offloading tasks. In both tasks, however, individual differences in offloading behavior were correlated with the individual differences in an unrelated short-term memory task. Our results therefore show that offloading behavior cannot simply be explained in terms of a single common factor driving offloading behavior across tasks. We discuss the implications of this finding for future research investigating the interrelations of offloading behavior across different tasks.

Abstract The ubiquitous availability of technological aids requires individuals to constantly decide between either externalizing cognitive processes into these aids (i.e. cognitive offloading) or relying on their own internal cognitive resources. With the present research, we investigated the influence of metacognitive beliefs on individuals’ offloading behavior in an experimental setup (N = 159). We manipulated participants’ metacognitive beliefs about their memory abilities by providing fake performance feedback: below-average feedback, above-average feedback, or no feedback (control-group). We then measured offloading behavior, using a pattern copying task in which participants copied a color pattern from a model window into a workspace window. While solving this task, participants could rely either more on an internal memory strategy or more on an offloading strategy. Fake performance feedback affected the participants’ metacognitive evaluations about their memory abilities (below-group < control-group < above-group). Although fake performance feedback did not affect actual offloading behavior, the participants receiving below-average performance feedback reported that they had relied more on an offloading strategy than those participants receiving above-average performance feedback. Furthermore, the participants in the below-group reported lower general memory abilities than the other groups at the end of the experiment. We conclude that while fake performance feedback strongly influenced metacognitive beliefs, this did not transfer into a change of strategy selection, thus not influencing offloading behavior. We propose to consider not only metacognitive beliefs but also metacognitive experiences as potential determinants of cognitive offloading.

AbstractCognitive offloading refers to the act of reducing the mental processing requirements of a task through physical actions like writing down information or storing information on a cell phone or computer. Offloading can lead to improved performance on ongoing tasks with high cognitive demand, such as tasks where multiple pieces of information must be simultaneously maintained. However, less is known about why some individuals choose to engage in offloading and under what conditions they might choose to do so. In the present study, offloading behavior is investigated in a short-term memory task requiring memory for letters. The present study is a replication and extension of a previous study conducted by Risko and Dunn, and tests the new prediction that individuals with lower working memory capacity will be more likely to offload. Here, we find that offloading information confers a performance advantage over relying on internal memory stores, particularly at higher memory loads. However, we fail to observe that those with poorer memory abilities have a greater propensity for offloading or benefit more from it. Instead, our findings suggest that cognitive offloading may be a valid compensatory strategy to improve performance of memory-based tasks for individuals with a wide range of memory ability.

Objective Human problem solvers possess the ability to outsource parts of their mental processing onto cognitive “helpers” ( cognitive offloading). However, suboptimal decisions regarding which helper to recruit for which task occur frequently. Here, we investigate if understanding and adjusting a specific subcomponent of mental models—beliefs about task-specific expertise—regarding these helpers could provide a comparatively easy way to improve offloading decisions. Background Mental models afford the storage of beliefs about a helper that can be retrieved when needed. Methods Arithmetic and social problems were solved by 192 participants. Participants could, in addition to solving a task on their own, offload cognitive processing onto a human, a robot, or one of two smartphone apps. These helpers were introduced with either task-specific (e.g., stating that an app would use machine learning to “recognize faces” and “read emotions”) or task-unspecific (e.g., stating that an app was built for solving “complex cognitive tasks”) descriptions of their expertise. Results Providing task-specific expertise information heavily altered offloading behavior for apps but much less so for humans or robots. This suggests (1) strong preexisting mental models of human and robot helpers and (2) a strong impact of mental model adjustment for novel helpers like unfamiliar smartphone apps. Conclusion Creating and refining mental models is an easy approach to adjust offloading preferences and thus improve interactions with cognitive environments. Application To efficiently work in environments in which problem-solving includes consulting other people or cognitive tools (“helpers”), accurate mental models—especially regarding task-relevant expertise—are a crucial prerequisite.