Reducing energy consumption in the fog computing environment is both a research and an operational challenge for the current research community and industry. There are several industries such as finance industry or healthcare industry that require a rich resource platform to process big data along with edge computing in fog architecture. As a result, sustainable computing in a fog server plays a key role in fog computing hierarchy. The energy consumption in fog servers depends on the allocation techniques of services (user requests) to a set of virtual machines (VMs). This service request allocation in a fog computing environment is a nondeterministic polynomial-time hard problem. In this paper, the scheduling of service requests to VMs is presented as a bi-objective minimization problem, where a tradeoff is maintained between the energy consumption and makespan. Specifically, this paper proposes a metaheuristic-based service allocation framework using three metaheuristic techniques, such as particle swarm optimization (PSO), binary PSO, and bat algorithm. These proposed techniques allow us to deal with the heterogeneity of resources in the fog computing environment. This paper has validated the performance of these metaheuristic-based service allocation algorithms by conducting a set of rigorous evaluations.

Sustainable Service Allocation Using a Metaheuristic Technique in a Fog Server for Industrial Applications

Robotic technologies for building construction represent a significant departure from conventional construction approaches The use of robots is likely to bring a host of opportunities that transform the way we design and construct buildings To gain an improved understanding of the trend and trajectory of research on robotics application for on-site building construction, this paper provides a systematic review of 52 articles identified through the PRISMA protocol and meta-analysis The results show that robotic technologies for on-site construction is a growing application field, where additive manufacturing (AM), automated installation system, automated robotic assembly system, autonomous robotic assembly, and robotic bricklaying seem to be most studied and have a potential to influence the development of robotics research in building construction While most research discussed single construction activities related to vertical reinforced concrete (RC) elements, masonry walls, steel beams, curtain walls, gypsum boards, and floor tiles, only a few papers proposed an integrated robotized construction site It is suggested that the building construction industry and research organizations could benefit from the current product and work processes that can be improved by taking some measures through innovative construction materials, improved robotics hardware, and more advanced engineering design to streamline construction workflows to achieve a complete on-site robotic system

Robotic technologies for on-site building construction: A systematic review

The unabated flurry of research activities to augment various mobile devices in terms of compute-intensive task execution by leveraging heterogeneous resources of available devices in the local vicinity has created a new research domain called mobile ad hoc cloud MAC or mobile cloud. It is a new type of mobile cloud computing MCC. MAC is deemed to be a candidate blueprint for future compute-intensive applications with the aim of delivering high functionalities and rich impressive experience to mobile users. However, MAC is yet in its infancy, and a comprehensive survey of the domain is still lacking. In this paper, we survey the state-of-the-art research efforts carried out in the MAC domain. We analyze several problems inhibiting the adoption of MAC and review corresponding solutions by devising a taxonomy. Moreover, MAC roots are analyzed and taxonomized as architectural components, applications, objectives, characteristics, execution model, scheduling type, formation technologies, and node types. The similarities and differences among existing proposed solutions by highlighting the advantages and disadvantages are also investigated. We also compare the literature based on objectives. Furthermore, our study advocates that the problems stem from the intrinsic characteristics of MAC by identifying several new principles. Lastly, several open research challenges such as incentives, heterogeneity-ware task allocation, mobility, minimal data exchange, and security and privacy are presented as future research directions. Copyright © 2016 John Wiley & Sons, Ltd.

Mobile ad hoc cloud: A survey

The stereo matching algorithm is one of the important methods for 3D surface reconstruction. A stereo matching process produces a disparity map which provides the depth of information required in 3D reconstruction. This map consists of disparity values of two corresponding points. Furthermore, the accuracy of 3D reconstruction depends on how precise the disparity being estimated on each pixel location. To get a good 3D reconstruction result, the propose stereo matching algorithm must be strong against the radiometric differences and edge distortions. Hence, this article proposes a new stereo matching algorithm with high accuracy for 3D surface reconstruction. First stage, Sum of Gradient Matching (SG) is proposed which uses magnitude differences with fixed window size. The gradient matching is strong against the radiometric distortions due to different characteristics of the input stereo cameras. Second stage, the Adaptive Support Weight (ASW) with iterative Guided Filter (ASW iGF) is proposed to improve the edges of object matching. The last stage, Joint Weighted Guided Filter (JWGF) is suggested to reduce the remaining noise on the disparity map. Based on the standard quantitative benchmarking stereo dataset, the proposed work in this article produces good results and performs much better compared with before the proposed framework. This new algorithm is also competitive with some established methods in the literature.

Improvement of stereo matching algorithm for 3D surface reconstruction

Cloud Computing is a gathering of physical and virtualized assets gave to the clients according to request and pay per uses bases via internet. Basically, the task scheduling and resource allocation two features are considered such as cost and makespan. In order to achieve better performance in task scheduling, resource allocation and task scheduling must be precisely organized and optimized jointly. Several works have been published in the literature to do the scheduling in cloud. In this paper, for enhancing the scheduling process cuckoo search (CS) and harmony search (HS) algorithm is hybrid as CHSA to improve the optimization problem. These two algorithms are effectively combined to do intelligent process scheduling. According to this, a new multi-objective function is proposed by combining cost, energy consumption, memory usage, credit and penalty. Finally, the performance of the CHSA algorithm is compared with different algorithms such as existing hybrid cuckoo gravitational search algorithm, individual CS and HS algorithm with various multi-objective parameters. By analyzing the result our proposed CHSA algorithm attain minimum cost, minimum memory usage, minimum energy consumption, minimum penalty and maximum credit compared to existing techniques.

A Hybrid Approach for Task Scheduling Using the Cuckoo and Harmony Search in Cloud Computing Environment

An energy-efficient scheduling scheme for time-constrained tasks in local mobile clouds

This paper presents a segmentation-based stereo matching algorithm using an adaptive multi-cost approach, which is exploited for obtaining accuracy disparity maps. The main contribution is to integrate the appealing properties of multi-cost approach into the segmentation-based framework. Firstly, the reference image is segmented by using the mean-shift algorithm. Secondly, the initial disparity of each segment is estimated by an adaptive multi-cost method, which consists of a novel multi-cost function and an adaptive support window cost aggregation strategy. The multi-cost function increases the robustness of the initial raw matching costs calculation and the adaptive window reduces the matching ambiguity effectively. Thirdly, an iterative outlier suppression and disparity plane parameters fitting algorithm is designed to estimate the disparity plane parameters. Lastly, an energy function is formulated in segment domain, and the optimal plane label is approximated by belief propagation. The experimental results with the Middlebury stereo datasets, along with synthesized and real-world stereo images, demonstrate the effectiveness of the proposed approach.

Accurate Dense Stereo Matching Based on Image Segmentation Using an Adaptive Multi-Cost Approach

MANETs(Mobile Ad-Hoc Network) are temporary networks composed of many autonomous nodes. Service discovery is the technology of finding services matching one’s needs in the network, which is crucial to the usability of MANETs. Many service discovery protocols for MANETs have been proposed, but no comprehensive comparative studies have been made. Hence, some typical service discovery protocols are analyzed and compared. The advantages and drawbacks of each protocol are analyzed. The paper provides a perspective overview on service discovery protocols for MANETs.

Service Discovery Protocols for MANETs: A Survey

Compared to the wired distributed computing system, cluster-based ad-hoc wireless networks have certain new characteristics. The transient failure probability of the computing process increases greatly with the enlarging of system scale. If a failure occurs in a process and there is not an appropriate method to protect it, more cost will be wasted for restarting the program. This paper presents an efficient checkpointing and rollback recovery scheme based on CMMP. The graceful fault tolerant scheme can seamlessly cooperate with the cluster-based multi-channel ad-hoc wireless networks. The number of coordinated messages between a cluster head and its ordinary members is small. The recovery scheme has no domino effect and the failure process can rollback from its latest local consistent checkpoint. The simulation results show that the proposed scheme keeps fast recovery upon transient failures and only a low additional overhead is incurred.

An Efficient Checkpointing and Rollback Recovery Scheme for Cluster-Based Multi-channel Ad Hoc Wireless Networks

Segmentation-based stereo matching using combinatorial similarity measurement and adaptive support region

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