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Mobile crowd computing (MCC) has been fathomed as a high-performance computing system where public-owned smart mobile devices (SMDs) are utilized as computing resources to execute compute-intensive tasks. The overall performance and the integrity of the MCC can be assessed by factors such as execution time, resource utilization, load balancing, etc. An efficient task scheduler should conform to these requirements. Conversely, an inefficient scheduling method will have a negative impact on the QoS of MCC. However, in a dynamic and heterogeneous system like MCC, it is nontrivial to realize such an optimized scheduler, considering the fact that scheduling in a heterogeneous distributed system is an NP-complete problem. In this paper, a heuristic algorithm for resource-aware scheduling in MCC is proposed with the objectives of minimizing makespan and maximizing resource utilization and load balancing. Before scheduling, the resource strength of each SMD is calculated by considering several static and dynamic resource parameters such as CPU clock speed, number of cores, its present load, available RAM and battery, and device temperature. The work is analyzed and validated by extensive simulations with synthetic as well as collected datasets. Experimenting with diverse simulation scenarios confirms the consistency and reliability of the proposed algorithm. The proposed algorithm exhibits significant improvements compared to other popular meta-heuristic algorithms such as particle swarm optimization (PSO), genetic algorithm (GA), and a heuristic algorithm minimum completion time (MCT) in terms of the considered objectives. The statistical hypothesis tests, viz. analysis of variance (ANOVA) and post hoc tests, are carried out to demonstrate the effectiveness of the proposed work. [ABSTRACT FROM AUTHOR]

Mobile crowd computing (MCC) that utilizes public-owned (crowd’s) smart mobile devices (SMDs) collectively can give adequate computing power without any additional financial and ecological cost. However, the major challenge is to cope with the mobility (or availability) issue of SMDs. User’s unpredicted mobility makes the SMDs really unstable resources. Selecting such erratic resources for job schedule would result in frequent job offloading and, in the worst case, job loss, which would affect the overall performance and the quality of service of MCC. In a Local MCC, generally, a set of users are available for a certain period regularly. Based on this information, the chances of a user being available for a certain duration from a given point of time can be predicted. In this paper, we provide an effective model to predict the availability of the users (i.e., their SMDs) in such an MCC environment. We argue that before submitting a job to an SMD, the stability of it is to be assessed for the duration of execution of the job to be assigned. If the predicted availability period is greater than the job size, then only the job should be assigned to the SMD. An accurate prediction will minimize the unnecessary job offloading or job loss due to the early departure of the designated SMD. We propose an advanced convolutional feature extraction mechanism that is applied to LSTM and GRU-based time-series prediction models for predicting SMD availability. To collect user mobility data, we considered a research lab scenario, where real mobility traces were recorded with respect to a Wi-Fi access point. We compared the prediction performances of convolutional LSTM and GRU with the basic LSTM and GRU and ARIMA in terms of MAE, RMSE, R2, accuracy, and perplexity. In all the measurements, the proposed convolutional LSTM exhibited considerably better prediction performance.

In mobile crowd computing (MCC), people’s smart mobile devices (SMDs) are utilized as computing resources. Considering the ever-growing computing capabilities of today’s SMDs, a collection of them can offer significantly high-performance computing services. In a local MCC, the SMDs are typically connected to a local Wi-Fi network. Organizations and institutions can leverage the SMDs available within the campus to form local MCCs to cater to their computing needs without any financial and operational burden. Though it offers an economical and sustainable computing solution, users’ mobility poses a serious issue in the QoS of MCC. To address this, before submitting a job to an SMD, we suggest estimating that particular SMD’s availability in the network until the job is finished. For this, we propose a convolutional GRU-based prediction model to assess how long an SMD is likely to be available in the network from any given point of time. For experimental purposes, we collected real users’ mobility data (in-time and outtime) with respect to a Wi-Fi access point. To build the prediction model, we presented a novel feature extraction method to be applied to the time-series data. The experimental results prove that the proposed convolutional GRU model outperforms the conventional GRU model. [ABSTRACT FROM AUTHOR]

Resource owners in mobile grids, with financial motivations in mind, provide their clients with the resources they possess. The clients can, on the other hand, obtain what they seek through the payments they make. Mobile grid users may also demand cost- and time-optimizing strategies as part of their service quality. As a result, the scheduling system, through making use of a proper algorithm, should allocate resources to users’ independent tasks in such a way that would minimize time and cost parameters. Hence, in this paper, two scheduling algorithms are introduced with regard to user needs based upon Particle Swarm Optimization (PSO), which, through mutation and population expanding, are able to find the best scheduling scheme for user tasks, while preventing quick algorithm convergence.

In mobile crowd computing (MCC), smart mobile devices (SMDs) are utilized as computing resources. To achieve satisfactory performance and quality of service, selecting the most suitable resources (SMDs) is crucial. The selection is generally made based on the computing capability of an SMD, which is defined by its various fixed and variable resource parameters. As the selection is made on different criteria of varying significance, the resource selection problem can be duly represented as an MCDM problem. However, for the real-time implementation of MCC and considering its dynamicity, the resource selection algorithm should be time-efficient. In this paper, we aim to find out a suitable MCDM method for resource selection in such a dynamic and time-constraint environment. For this, we present a comparative analysis of various MCDM methods under asymmetric conditions with varying selection criteria and alternative sets. Various datasets of different sizes are used for evaluation. We execute each program on a Windows-based laptop and also on an Android-based smartphone to assess average runtimes. Besides time complexity analysis, we perform sensitivity analysis and ranking order comparison to check the correctness, stability, and reliability of the rankings generated by each method.

Mobile devices have evolved from single purpose devices, such as mobile phone, into general purpose multi-core computers with considerable unused capabilities. Therefore, several researchers have considered harnessing the power of these battery-powered devices for distributed computing. Despite their ever-growing capabilities, using battery as power source for mobile devices represents a major challenge for applying traditional distributed computing techniques. Particularly, researchers aimed at using mobile devices as resources for executing computationally intensive task. Different job scheduling algorithms were proposed with this aim, but many of them require information that is unavailable or difficult to obtain in real-life environments, such as how much energy would require a job to be finished. In this context, Simple Energy Aware Scheduler (SEAS) is a scheduling technique for computational intensive Mobile Grids that only require easily accessible information. It was proposed in 2010 and it has been the base for a range of research work. Despite being described as easily implementable in real-life scenarios, SEAS and other SEAS-improvements works have always been evaluated using simulations. In this work, we present a distributed computing platform for mobile devices that support SEAS and empirical evaluation of the SEAS scheduler. This evaluation followed the methodology of the original SEAS evaluation, in which Random and Round Robin schedulers were used as baselines. Although the original evaluation was performed by simulation using notebooks profile instead of smartphones and tablets, results confirms that SEAS outperforms the baseline schedulers.

The profusion of mobile devices over the world and their evolved computational capabilities promote their inclusion as resource providers in traditional Grid environments. However, their efficient exploitation requires adapting current schedulers to operate with computing capabilities limited by energy supply and mobile devices that cannot be assumed to be dedicated, among other concerns. We propose a two-phase scheduling approach for running CPU-intensive jobs on mobile devices that combines novel energy-aware criteria with job stealing techniques. The approach was evaluated through an event-based simulator that uses battery consumption profiles extracted from real mobile devices. CPU usage derived from non-Grid processes was also modelled. For evaluating the first phase we compared the number of finalized jobs by all energy-aware criteria, while for the second phase we analyzed the performance boost introduced by job stealing. While the best first phase criteria finalized up to 90 % of submitted jobs, job stealing increased this percentage by up to 9 %. [ABSTRACT FROM AUTHOR]

Given the relentless growing number of mobile devices, researchers have pointed out that distributed computing environments, such as clusters or even computational Grids, could increase the available resources by scavenging devices’ capabilities. However, this negatively impacts on their batteries. We study centralized job schedulers that aim at exploiting clusters of mobile resources with rather stable devices in terms of connection time. These schedulers outperform a previous centralized scheduler, finishing up to 30% more jobs with the same amount of energy. We also show that the schedulers perform competitively compared to schedulers based on job stealing, which are more difficult to implement and consume more network resources and hence energy. [ABSTRACT FROM AUTHOR]

Grid computing has migrated from traditional high performance and distributed computing to pervasive and utility computing based on the advanced capabilities of the wireless networks and the lightweight, thin devices. This has as result the emergence of a new computing paradigm which is the Mobile Grid. This paper presents the simulation results in order to choose the best routing protocol to give the highest performance when implement the routing protocols in the target mobile grid application. This simulation gives the results for three ad hoc routing protocols named DSDV, DSR and AODV. In this paper these results are shown or simulated by Network Simulator (NS 2.34).The simulations have shown that the conventional routing protocols like DSR have a dramatic decrease in performance when mobility is high. However the AODV and DSDV are perform very well when mobility is high. [ABSTRACT FROM AUTHOR]

Mobile grid is a branch of grid computing that incorporates mobile devices into the grid infrastructure. It poses new challenges because mobile devices are typically resource-constrained and exhibit unique characteristics such as instability in network connections. New scheduling strategies are imperative in mobile grid to efficiently utilize the devices. This paper presents a scheduling algorithm that considers dynamic properties of mobile devices such as availability, reliability, maintainability, and usage pattern in mobile grid environments. In particular, usage patterns caused by voluntarily or involuntarily losing a connection, such as switching off the device or a network interruption could be important criteria for choosing the best resource to execute a job. The experimental results show that our scheduling algorithm provides superior performance in terms of execution time, as compared to the other methods that do not consider usage pattern. Throughout the experiments, we found it essential to consider usage pattern for improving performance in the mobile grid. [ABSTRACT FROM AUTHOR]

The Present Grid community is trying to incorporate mobile devices such as PDA, Laptops, and sensor devices supporting abile functionalities in to their Grid network. The most challenging aspect in mobile Grid are job scheduling and load balancing under the unreliable communication environment. The existing job scheduling algorithms lag to verify the node's capability and to optimize the job execution time. This deficiency leads to increased network overhead causing poor network performance. To overcome these issues, we propose a mobility aware load balanced scheduling algorithm. Our approach categories the jobs into Computing-focused and Communication-focused jobs. The computing-intensive jobs are allocated to the resources with shorter round-trip-time, high CPU speed and capacity. The communication-intensive jobs are allocated to resources with low mobility and high reliability. The outcome of this approach results in most excellent balanced job allocation across the mobile nodes. By simulation results, we show that the proposed approach gives optimized results compared to existing techniques. [ABSTRACT FROM AUTHOR]

Mobile devices have evolved from simple electronic agendas and mobile phones to small computers with great computational capabilities. In addition, there are more than 2 billion mobile devices around the world. Taking these facts into account, mobile devices are a potential source of computational resources for clusters and computational Grids. In this work, we present an analysis of different schedulers based on job stealing for mobile computational Grids. These job stealing techniques have been designed to consider energy consumption and battery status. As a result of this work, we present empirical evidence showing that energy-aware job stealing is more efficient than traditional random stealing in this context. In particular, our results show that mobile Grids using energy-aware job stealing might finish up to 11 % more jobs than when using random stealing, and up to 24 % more jobs than when not using any job stealing technique. This means that using energy-aware job stealing increases the energy efficiency of mobile computational Grids because it increases the number of jobs that can be executed using the same amount of energy. [ABSTRACT FROM AUTHOR]

Taking mobile grid as background, the problem of scheduling related tasks on dynamic resource environment was studied. During scheduling, not only dependencies between tasks, but also resources' dynamic behavior such as joining, leaving and performance changes are considered. Dynamic scheduling policy was proposed, which includes task subset scheduling and re-scheduling. Dynamic partition of subset mainly focuses on dependencies between tasks and helps reduce the number of rescheduling. Subset scheduling objective and constraints were described and particle swarm scheduling algorithm was put forward, which integrates the idea of simulated annealing. Re-scheduling further improves the adaptability of scheduling policy to the dynamic behavior of resources. Re-scheduling trigger conditions was explained. The complete process of related tasks scheduling in mobile grid was given, as well as complexity analysis and experimental analysis of the proposed algorithm. Experiment results demonstrate the effectiveness of scheduling policy. [ABSTRACT FROM AUTHOR]

The social force model (SF) is able to reproduce many emergent phenomena observed in real crowds. Unfortunately, in some situations, such as low density environments, SF may produce counterintuitive results where the trajectories simulated look more like particles than to real people. We modify the SF model through the use of a mobile grid to allow the simulated pedestrians to change the direction of their desired velocity at reasonable times, thus avoiding nearby blocked or crowded areas smoothly. Our experiments focus on qualitative behavior, and verify that our model produces the desired trajectories of the pedestrians, achieving softer and more coherent trajectories when compared to the pure SF model solution. Like SF, our model reproduces the 'faster-is-slower' and the arching underlying the clogging effects. Finally, we examine the occupation rates of the space when pedestrians were submitted to narrowed corridors and observe the 'edge effect.' [ABSTRACT FROM AUTHOR]

The use of mobile devices in grid environments may have two interaction aspects: devices are considered as users of grid resources or as grid resources providers. Due to the limitation constraints on energy and processing capacity of mobile devices, their integration into the Grid is difficult. In this paper, we investigate the cooperation among mobile devices to balance the energy consumption and computation workloads. Mobile devices can have different roles such as buyer devices and seller devices. In the mobile grid, the energies of mobile devices are uneven, energy-poor devices can exploit other devices with spare energy. Our model consists of two actors: A buyer device agent represents the benefits of mobile buyer device that intends to purchase energy from other devices. A seller device agent represents the profits of mobile seller device that is willing to sell spare energy to other devices. The objective of optimal energy allocation in mobile grid is to maximize the utility of the system without exceeding the energy capacity, expense budget and the deadline. A collaboration algorithm among mobile agents for efficient energy allocation is proposed. In the simulation, the performance evaluation of collaboration algorithm among mobile agents is conducted. [ABSTRACT FROM AUTHOR]

The interaction between mobile and grid environments in building a transparent infrastructure for mobile users, involves several factors from both wired and wireless networks. These include the following: coordinated application executions, the management of inherent characteristics of mobile devices (disconnections and the problem of a reduced battery lifetime), and making a selection of suitable heterogeneous resources in wired network, which are distinctly in terms of their virtual organizations. For this reason, the main goal of the architecture is the provision of more complete transparency for mobile users, when they need to access the processing capacity of the grid computing environment. To achieve this, this paper outlines an architecture, called MITSIO, together with a prototype implementation that takes these factors into consideration. Furthermore, the study examines two case studies that provide evidence of the capacities of the proposed architecture. The first case study shows how the architecture enables mobile devices to consume less battery power (approximately 31%, for submitting and monitoring applications). The second case provides evidence of its capacity to select resources from different virtual organizations through the semantic integration of multiple ontologies. [ABSTRACT FROM AUTHOR]

Abstract: One of the frustrations of the INDIAN government is the nation’s lack of voter participation. Many voters claim issues are “not relevant” to them. Taken within the context of mobile grid voting, when an individual is within a time and location that is highly related to the ballot’s issue, we predict that they will be more likely to participate in the voting process. In this work, we proposed a deniable authentication mobile voting scheme that not only meets the general requirements of a secure voting system but also is suitable for application over mobile ad-hoc networks. VoteGrid is an online system for public debate and expression of opinions through voting facilities based on Digital Signature. It functions as a national “debate and voting portal” allowing citizens, businesses, politicians and journalists to participate in debates and votes organised by levels of government, subject, etc. It can handle debates and votes at the local, regional and national levels and it is possible to authentify user identity through login with the Digital Signature. [Copyright &y& Elsevier]