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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.

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]

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]

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]

Inherent characteristics of mobile devices make resource discovery in mobile grid challenging. Adding to the complexity is heterogeneity and hence multi-attribute management of these devices. Decentralized resource discovery using DHTs incur maintenance overheads when changes in attributes are continuous and rapid. The paper proposes a novel dual heap non-DHT overlay (DEEPG) for query resolution dealing with inherent mobile characteristics along with a mathematical model to estimate the problem size. Simulation results show that DEEPG reduces search bound on query resolution and results in limited maintenance overheads. The results also indicate that higher the query sum reduced is the number of lookups for exact match query resolution. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose an economics-based distributed negotiation scheme among mobile devices in mobile grid. In our model, there are energy negotiation and transactions between buyer devices and seller devices. Dynamic allocation of energy resources in mobile grid is performed through online transactions within markets. Mobile devices can be sellers and buyers that use optimization algorithms to maximize predefined utility functions during their transactions. Seller device agents sell the underlying energy resources of the mobile device. Buyer device agent makes buying decisions within the budget constraints to acquire energy resources. An economics-based negotiation algorithm among mobile devices is proposed. The proposed algorithm decomposes mobile grid system optimization problem into a sequence of two sub-problems. In the simulation, the performance evaluation of economics-based negotiation algorithm is evaluated. [ABSTRACT FROM AUTHOR]

Mobile grid is a combination of grid computing and mobile computing to build grid systems in a wireless mobile environment. The development of network technology is assisting in realizing mobile grid. Mobile grid based on established grid infrastructures needs effective resource management and reliable job scheduling because mobile grid utilizes not only static grid resources but also dynamic grid resources with mobility. However, mobile devices are considered as unavailable resources in traditional grids. Mobile resources should be integrated into existing grid sites. Therefore, this paper presents a mobile grid middleware interconnecting existing grid infrastructures with mobile resources and a mobile service agent installed on the mobile resources. This paper also proposes a mobile resource reliability-based job scheduling model in order to overcome the unreliability of wireless mobile devices and guarantee stable and reliable job processing. In the proposed job scheduling model, the mobile service agent calculates the mobile resource reliability of each resource by using diverse reliability metrics and predicts it. The mobile grid middleware allocated jobs to mobile resources by predicted mobile resource reliability. We implemented a simulation model that simplifies various functions of the proposed job scheduling model by using the DEVS (Discrete Event System Specification) which is the formalism for modeling and analyzing a general system. We also conducted diverse experiments for performance evaluation. Experimental results demonstrate that the proposed model can assist in improving the performance of mobile grid in comparison with existing job scheduling models. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents joint contexts optimization in mobile grid. The paper describes device context information for context-aware services in the mobile device collaboration. The objective of the paper is to dynamically deliver services to mobile grid users according to current context of mobile grid environment. A utility function is used as objective function that expresses values for the current contexts. The optimization is carried out by the joint context parameter optimizer with respect to an objective function. A joint contexts optimization algorithm is proposed which decomposes mobile grid system optimization problem into sub-problems. In the experiment, the performance evaluation of joint contexts optimization algorithm is conducted. [ABSTRACT FROM AUTHOR]

Energy efficient computing has recently become hot research area. Many works have been carried out on conserving energy, but considering energy efficiency in grid computing is few. This paper proposes energy efficient resource management in mobile grid. The objective of energy efficient resource management in mobile grid is to maximize the utility of the mobile grid which is denoted as the sum of grid application utility. The utility function models benefits of application and system. By using nonlinear optimization theory, energy efficient resource management in mobile grid can be formulated as multi objective optimization problem. In order to derive a distributed algorithm to solve global optimization problem in mobile grid, we decompose the problem into the sub problems. The proposed energy efficient resource management algorithm decomposes the optimization problem via iterative method. To test the performance of the proposed algorithm, the simulations are conducted to compare proposed energy efficient resource management algorithm with other energy aware scheduling algorithm. [ABSTRACT FROM AUTHOR]

Abstract: A mobile grid incorporates mobile devices into Grid systems. But mobile devices at present have severe limitations in terms of processing, memory capabilities and energy. Minimizing the energy usage in mobile devices poses significant challenges in mobile grids. This paper presents energy constrained resource allocation optimization for mobile grids. The goal of the paper is not only to reduce energy consumption, but also to improve the application utility in a mobile grid environment with a limited energy charge, ensuring battery lifetime and the deadlines of the grid applications. The application utility not only depends on its allocated resources including computation and communication resources, but also on the consumed energy, this leads to a coupled utility model, where the utilities are functions of allocated resources and consumed energy. Energy constrained resources allocation optimization is formulated as a utility optimization problem, which can be decomposed into two subproblems, the interaction between the two sub-problems is controlled through the use of a pricing variable. The paper proposes a price-based distributed energy constrained resources allocation optimization algorithm. In the simulation, the performance evaluation of our energy constrained resources allocation optimization algorithm is conducted. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]