Summary Long term evolution (LTE) mobile technology provides high data rate and low latency. 5G Technology is capable of handling the increasing number of IoT devices and provides ultra‐low latency, higher throughput, and higher reliability. Mobile edge computing (MEC) a key 5G technology strengthens the real‐time processing ability, releases the load on the Core Network, and helps in the real‐time processing of data, fulfilling the promise of high data rate and low latency. MEC is used to manage services efficiently to the near user resource. Using Osmotic Computing the services are efficiently scheduled and migrated. The work presented in this article proposes OCTRA‐5G Framework to effectively schedule services and allocate resources using Osmotic Computing (OC) by segregating the services into microservices and macroservices. The results are validated on the sets of 10, 20, and 30 gNBs (base stations) through simulation. OCTRA‐5G is tested on First Come First Serve (FCFS), Priority Scheduling (PS), and Shortest Job First (SJF) algorithm. FCFS provides less time complexity and higher throughput. The results presented using numerical simulations shows better performance by an average of 66.921% with OC than without OC.

Summary Cloud computing and smart gadgets are the need of smart world these days. This often leads to latency and irregular connectivity issues in many situations. In order to overcome this issue, an emerging technique of fog computing is used for cloud and smart devices. A decentralized computing infrastructure in which all the elements, that is, storage, compute, data and the applications in use, are passed in an efficient and logical place between cloud and the data source, is called Fog computing. The cloud computing and services are generally extended by fog computing, which brings the power and advantages of data creation and data analysis at the network edge. Real‐time location based services and applications with mobility support are enabled due to the physical proximity of users and high speed internet connection to the cloud. Fog computing is promoted with leveraging load balancing techniques so as to balance the load which is done in two ways, that is, static load balancing and dynamic load balancing. In this paper, different load balancing algorithms are discussed and their comparative analysis has been carried out. Round Robin load balancing is the simplest and easiest load balancing technique to be implemented in fog computing environments. The major problem of Source IP Hash load balancing algorithm is that each change can redirect to anyone with a different server, and thus, is least preferred in fog networks. The mechanisms to make energy efficient load balancing are also considered as the part of this paper.

Abstract The biggest challenge of task scheduling in Fog computing is to satisfy users' dynamic requirements in real‐time with Fog nodes' limited resource capacities. Fog nodes' heterogeneity and an obligation to complete tasks by the deadline while minimizing cost and energy consumption makes the scheduling process more challenging. This article facilitates a deeper understanding of the research issues through a detailed taxonomy and distinguishes significant challenges in existing work. Furthermore, the paper investigates existing solutions for various challenges, presents a meta‐analysis on quality of service parameters and tools used to implement Fog task scheduling algorithms. This systematic review will help potential researchers easily identify specific research problems and future directions to enhance scheduling efficiency.

Summary It is always desired to improve the response time from cloud servers, which deliver contents without buffering. As the penetration of mobile/fog devices is increasing, the limits of cellular ranges come under question. This question arises in spite of the fact that the current Internet Service Providers and data operators are adding cellular towers frequently to reduce delay and enhance performance. This performance can be improved by increasing Nano‐Cache(s) at the edges of the network for forwarding interrelated contents to remote corner of the earth. In this research work, Nano‐Caches are integrated for delivering contents efficiently, using search‐based optimization techniques, which are energy and response aware in nature. An algorithm, namely, Modified Teaching Learning‐Based Optimization(MTLBO), is devised and implemented in fog zone to find efficient route for forwarding contents using Nano‐Caches and subsequently to improve content retrieval time. Mathematical distribution model of traffic is used for simulation process. MTLBO is compared with existing algorithms, namely, Teaching Learning‐Based Optimization (TLBO) Algorithm and Simulated Annealing (SA) Algorithm. The design of experiments (DOE) was carried out to observe number of iterations, learning rate, and by changing the network size. Java library was used for observing values of memory and execution time. The results show that Modified Teaching Learning‐Based Optimization (MTLBO) approach is better than Teaching Learning‐Based Optimization (TLBO) approach as it has less overheads in terms of memory (considering number of fog caches) and network size for delivering contents at remote areas. In comparison to the Simulated Annealing (SA) algorithm, MTLBO performs better in terms of execution time, overhead in terms of memory, and scalability as function of network size.

The coordination between aerial and ground nodes has enhanced the versatility and quality of the traditional networks. The application of aerial systems in mission‐critical operations, as well as civilian applications, brings in the context of safeguarding unmanned aerial systems (UAS) from malicious attackers. This study discusses the threats and attacks mounted on UAS, alongside the challenges introduced by the unmanned aerial vehicle (UAV) network structure itself. A framework for safeguarding UAS against malicious attackers and recovering the rogue UAVs is proposed in the study. The proposed framework enforces a dynamic conceptual grid‐based layout over the actual geographical deployment. The dynamically shuffling grid ascertains the security of transmission channels, as every time the grid is shuffled periodically or based on abnormal behaviour, the safety paradigm is reinitiated. Public key cryptographic algorithms are deployed for securing the communication links. Neural networks‐based predictions are used for detecting abnormality in behavioural, statistical, and mobility patterns. Principal component analysis based on multivariate statistical analysis is used for detecting outliers in the aerial network environment. The behaviour prediction and outlier detection algorithms significantly improve the overall performance of the network and provide immunity against the intruders with reduced false positives, high accuracy, and better detection rate.

Abstract The resource discovery on IoT paradigm requires to be efficient with respect to modeling, storage, processing, and validation of the gathered data. These requirements face challenges like interoperability, heterogeneity, etc, with respect to exponentially growing interconnected resources across distinct application domains and drastically changing search metrics. It leads resource discovery to emerge as a non‐linear constrained‐specific problem that need to be linearized for its optimization with reduced complexity. Keeping the perspective, a context‐aware search optimization framework on the internet of things is introduced, which targets knowledge presentation through schema, discovery via a multi‐modal search algorithm, and its optimization through an Iterative Gradient Descent algorithm. The multi‐modal search algorithm through keywords, value or spatial‐temporal indices performs resource discovery by finding the suited matches as a search set from a search‐space. The search set is further evaluated via the iterative gradient descent algorithm for optimization through the usage of iterative and convergence properties of the gradient descent. The search efficiency is tested using various objective functions and resources on MATLAB and is compared with Newton and Quasi‐Newton methods. The obtained results depict the efficiency of the algorithm graphically with reference to the searching time, such as validate the system performance.

Summary With the growing popularity of cloud computing, different infrastructure as a service cloud frameworks do exists. Each framework has significant impact on robustness, scalability, fault‐tolerance, energy efficiency, and so on of the cloud. To bring good characteristics of open source clouds and commercial clouds under a roof, a six layered green cloud framework namely SERVmegh is proposed. Resource management, green power management, workload analyzer and manager, and on/off control components of SERVmegh framework are designed and developed. Energy‐efficient resource wastage reduction methodology for resource management is proposed. Algorithms for resource management, virtual machine placement, and minimization of virtual machine migrations using resource wastage reduction methodology are implemented. The performance of proposed algorithms is evaluated in CloudSim simulation and OpenNubula open source cloud environment. The results depict significant energy savings under different scenarios. Copyright © 2016 John Wiley & Sons, Ltd.

Summary Integrated frameworks have extended the applications of networks beyond a simple data sharing unit. Simultaneously, operating networks can form a layered structure that can operate as homogeneous as well as dissociated units. Networks using unmanned aerial vehicles follow similar criteria in their operability. Unmanned aerial vehicles can act as single searching unit controlled by human or can form an aerial swarm that can fly autonomously with the capability of forming an aerial network. Such aerial swarms are categorized as aerial ad hoc networks. Cooperation amongst different networks can be realized using various frameworks, models, architectures and middlewares. Several solutions have been developed that can provide easy network deployment of aerial nodes. However, a combined literature is not present that provides a comparison between these approaches. Keeping this in view, various cooperative approaches for similar formation using aerial vehicles have been discussed in this paper. The detailed study and comparative analysis of these approaches have been included. Further, the paper also includes various software solutions and their comparisons based on common parameters. Finally, various open issues have been discussed that can provide insight of ongoing research and problems that are yet to be resolved in these networks. Copyright © 2016 John Wiley & Sons, Ltd.

Summary Data grid provides an efficient solution for data‐oriented applications that need to manage and process large data sets located at geographically distributed storage resources. Data grid relies on data replicas to enhance the performance and to ensure the fault tolerant results to the users. Replicas are developed to increase the availability of data and to provide better data access. Replicas have their own advantages, but there are a number of issues that must be resolved. Among various existing issues, the critical concern is replica consistency. Various replica consistency strategies are available in the literature. These strategies rationalize and investigate various parameters like bandwidth consumption, access cost, scalability, execution time, storage consumption, staleness, and freshness of replicas. In this paper, several asynchronous replica consistencies are classified and analyzed based on various strategies such as topology, level of abstraction, update propagation, and locality. Some other strategies are also discussed and analyzed like adaptive consistency, quorum‐based consistency, load balancing, and agent‐based economically efficient, check‐pointing, fault tolerance, and conflict management. Parameters on which these strategies are analyzed are methodology, replication classification, consistency, grid topology, environment, evaluation parameters, and performance. Copyright © 2016 John Wiley & Sons, Ltd.

Summary The test case optimisation is an NP‐complete, knowledge‐driven, data‐driven, and multidimensional search space partitioning and dimension reduction problem. In the multifaceted test case classification, partitioning and reducing the multidimensional test case fitness search space is the critical problem. The vague nature of fitness parameters, conflicting nature objectives, and ambiguity in the test case fitness evaluation have created and increased the uncertainty, the imprecision, and the incompleteness in the test case classification and selection. Because of the increasing ambiguity, the complexity, and the cost of software testing, automated test case classification and selection has emerged as an appropriate tool to classify test cases into predefined categories using the multifaceted concept. Most of the test cases affecting the performance of the classifier are irrelevant and redundant. A strong need therefore exists to devise an intelligent technique to identify and remove test cases affecting the performance of the classifier. For increasing the performance of classifier, multifaceted test case selection is used to reduce fitness search space to be searched. In this paper, a three‐tier sequential framework is proposed for a multifaceted test case classification and selection. The first stage of the proposed framework is the fuzzy synthesis‐based filtration approach for multifaceted test case fitness evaluation and classification. The second stage of the proposed framework is the fuzzy entropy‐based filtration technique with a backward search strategy, used for estimating and reducing the ambiguity in test case fitness evaluation, classification, and selection. The third stage of the proposed framework is the ant colony optimisation‐based wrapper technique with a forward search strategy, employed to select test cases from the output (reduced) test suite by the second stage. The proposed framework is tested on artefacts of benchmark applications. The results of the empirical study clearly show that the third stage of our proposed method outperforms the second and first stages, and the performance of the algorithms used in all three stages increases on average as the stages are escalating. The classification accuracy is enhanced by reducing the ambiguity in fitness and the classification of test cases, increasing the number of test cases accurately classified, and reducing the number in the test case pool to be exercised. Copyright © 2014 John Wiley & Sons, Ltd.

SUMMARY In component‐based development, software systems are built by assembling components already developed and prepared for integration. To estimate the quality of components, complexity, reusability, dependability, and maintainability are the key aspects. The quality of an individual component influences the quality of the overall system. Therefore, there is a strong need to select the best quality component, both from functional and nonfunctional aspects. The present paper produces a critical analysis of metrics for various quality aspects for components and component‐based systems. These aspects include four main quality factors: complexity, dependency, reusability, and maintainability. A systematic study is applied to find as much literature as possible. A total of 49 papers were found suitable after a defined search criteria. The analysis provided in this paper has a different objective as we focused on efficiency and practical ability of the proposed approach in the selected papers. The various key attributes from these two are defined. Each paper is evaluated based on the various key parameters viz. metrics definition, implementation technique, validation, usability, data source, comparative analysis, practicability, and extendibility. The paper critically examines various quality aspects and their metrics for component‐based systems. In some papers, authors have also compared the results with other techniques. For characteristics like complexity and dependency, most of the proposed metrics are analytical. Soft computing and evolutionary approaches are either not being used or much less explored so far for these aspects, which may be the future concern for the researchers. In addition, hybrid approaches like neuro‐fuzzy, neuro‐genetic, etc., may also be examined for evaluation of these aspects. However, to conclude that one particular technique is better than others may not be appropriate. It may be true for one characteristic by considering different set of inputs and dataset but may not be true for the same with different inputs. The intension in the proposed work is to give a score for each metric proposed by the researchers based on the selected parameters, but certainly not to criticize any research contribution by authors. Copyright © 2012 John Wiley & Sons, Ltd.

Aspect-Oriented Programming (AOP) is an emerging technique that provides a means to clearly encapsulate and implement aspects that crosscut other modules. However, despite an interesting body of work for measuring cohesion in Aspect-Oriented (AO) Systems, there is poor understanding of cohesion in the context of AOP. Most of the proposed cohesion assessment frameworks and metrics for AOP are for AspectJ programming language. In this paper, we have defined a generic cohesion framework that takes into account, two well-known families of available AOP languages viz, AspectJ and CaesarJ. This unified framework contributes in better understanding of cohesion in AO technology, which can contribute in (i) comparing measures and their potential use, (ii) integrating different existing measures, which examine the same concept in different ways, and (iii) defining new cohesion metrics, which in turn permits the analysis and comparison of Java, AspectJ and CaesarJ implementations. Correlation analysis between cohesion and changeability has also been performed. The correlation factor value indicates that cohesion cannot be used as an indicator of changeability.