Radiology offers a presumptive diagnosis. The etiology of radiological errors are prevalent, recurrent, and multi-factorial. The pseudo-diagnostic conclusions can arise from varying factors such as, poor technique, failures of visual perception, lack of knowledge, and misjudgments. This retrospective and interpretive errors can influence and alter the Ground Truth (GT) of Magnetic Resonance (MR) imaging which in turn result in faulty class labeling. Wrong class labels can lead to erroneous training and illogical classification outcomes for Computer Aided Diagnosis (CAD) systems. This work aims at verifying and authenticating the accuracy and exactness of the GT of biomedical datasets which are extensively used in binary classification frameworks. Generally such datasets are labeled by only one radiologist. Our article adheres a hypothetical approach to generate few faulty iterations. An iteration here considers simulation of faulty radiologist's perspective in MR image labeling. To achieve this, we try to simulate radiologists who are subjected to human error while taking decision regarding the class labels. In this context, we swap the class labels randomly and force them to be faulty. The experiments are carried out on some iterations (with varying number of brain images) randomly created from the brain MR datasets. The experiments are carried out on two benchmark datasets DS-75 and DS-160 collected from Harvard Medical School website and one larger input pool of self-collected dataset NITR-DHH. To validate our work, average classification parameter values of faulty iterations are compared with that of original dataset. It is presumed that, the presented approach provides a potential solution to verify the genuineness and reliability of the GT of the MR datasets. This approach can be utilized as a standard technique to validate the correctness of any biomedical dataset.

Resource-constrained Internet of Things (IoT) devices depend on remote Cloud/Fog Nodes (FNs) to execute deadline-sensitive services. Offloading computations of real-time services to a remote cloud server results in intolerable latency due to intermittent channels, higher transmission delays, and scarce spectrum resources. Therefore, offloading to nearby FNs is preferable; however, it introduces several significant issues: (i) allocation of limited FN resources, (ii) deadline constraint of heterogeneous services, and (iii) requirement of computationally inexpensive and scalable strategies. This article proposes a M-DAFTO model to tackle the abovementioned issues and generate a fair offloading plan in polynomial time. The offloading problem is modeled as a many-to-one matching game with maximum and minimum quotas at each FN. Because the deferred acceptance (DA) algorithm fails to operate with minimum quotas, we adopt a variant of the DA algorithm, a multistage deferred acceptance (MSDA) algorithm, to solve the offloading problem. The overall goal of M-DAFTO is to reduce the aggregate offloading delay with increased assignment of tasks to FNs. Extensive simulation and analysis confirm a 30.26% and a 93.53% reduction in offloading delay and outages (unassigned tasks) compared to the baselines.

A virtual data center (VDC) comprises multiple virtual machines (VMs) with communication dependencies represented as virtual links (VLs). These virtual components, i.e., VMs and VLs, often experience fluctuating demands across different resource types. In this article, we focus on addressing the issue of dynamic resource expansion that leads to the relocation of solution components (SCs), where a SC comprises a VM and its attached VLs, with either the VM and/or at least one of the VLs facing resource expansion. This is challenging because of the complexity involved in frequently relocating multiple dependent virtual components across the substrate network. This article presents a model calledReMatchthat aims at building an efficient remapping plan with reduced remapping cost and improved resource utilization for service providers (SPs) in polynomial time. The overall relocation problem is formulated as a one-to-many matching game with heterogeneous VM demands. Owing to the inapplicability of the classical deferred acceptance algorithm (DAA) and revised DA (RDA), we propose a modified version of the RDA (MRDA) to obtain a weakly stable assignment. Thorough simulation and analysis show thatReMatchoutperforms the baseline algorithms considering multiple evaluation metrics.

The Internet of Things (IoT) devices are highly reliant on cloud systems to meet their storage and computational demands. However, due to the remote location of cloud servers, IoT devices often suffer from intermittent Wide Area Network (WAN) latency which makes execution of delay-critical IoT applications inconceivable. To overcome this, service providers (SPs) often deploy multiple fog nodes (FNs) at the network edge that helps in executing offloaded computations from IoT devices with improved user experience. As the FNs have limited resources, matching IoT services to FNs while ensuring minimum latency and energy from an end-user’s perspective and maximizing revenue and tasks meeting deadlines from a SP’s standpoint is challenging. Therefore in this paper, we propose a student project allocation (SPA) based efficient task offloading strategy called SPATO that takes into account key parameters from different stakeholders. Thorough simulation analysis shows that SPATO is able to reduce the offloading energy and latency respectively by 29% and 40% and improves the revenue by 25% with 99.3% tasks executing within their deadline.

The resource-constrained IoT devices often offload tasks to Fog nodes (FNs) owing to the intermittent WAN delays and multi-hopping by executing at remote cloud servers. An efficient allocation strategy satisfies the users' requirements by ensuring minimum offloading delays and provides a balanced assignment from the service providers' (SPs) viewpoint. This paper presents a model called LETO that reduces the total offloading delay for real-time tasks and achieves a balanced assignment across FNs. The overall problem is modeled as a one-to-many matching game with maximum and minimum quotas. Owing to the deferred acceptance algorithm (DAA) inapplicability, we use a proficient version of the DAA called multi-stage deferred acceptance algorithm (MSDA) to obtain a fair and Pareto-optimal assignment of tasks to FNs. Extensive simulations confirm that LETO can achieve a more balanced assignment compared to the baseline algorithms.

Typical cloud systems are often prone to inherent wide area network (WAN) latency. To address this issue fog computing is proposed that enables resource-constrained Internet-of-Things (IoT) devices, to execute deadline-sensitive tasks at the edge of the network. These devices can extend their battery lifespan by intelligently offloading computations as tasks to fog nodes (FNs) in their vicinity. However, finding an optimal offloading plan in a densely connected IoT-fog network is proven to beNP-Hard. Hence, in this article, we propose a matching theory-based efficient task offloading strategy called METO that aims to reduce the total system energy and number of outages (number of tasks exceeding the deadline) in an IoT-fog interconnection network. As resource allocation involves multiple criteria, their weights are derived using criteria importance though inter criteria correlation (CRITIC). Furthermore, to rank the alternatives we use the technique for order of preference by similarity to ideal solution (TOPSIS). Based on this ranking, we formulate the overall offloading problem as a one-to-many matching game and utilize the deferred acceptance algorithm (DAA) to produce a stable assignment. Simulation is performed in two different settings comprising offloading of homogeneous and heterogeneous tasks. Extensive simulations across both environments confirm that the proposed algorithm outperforms the existing schemes with respect to improved energy consumption, completion time, and execution time. Moreover, METO also shows the reduced number of outages across baselines used for comparison.

A virtual data center (VDC) mostly encapsulates multiple virtual machines (VMs) with communication dependencies. These VDC requests are dynamic in nature and often experience fluctuating demands across different resources. In this paper, we propose a dynamic resource reconfiguration strategy called VMatch that generates an efficient relocation/remapping plan for already assigned virtual links (VLs) facing bandwidth expansion. The overall problem is formulated as a one-to-one matching game that aims to minimize the relocation cost from the users perspective and at the same time improves resource utilization from a service providers (SPs) perspective. By using the concept of preferences in the matching game, different stakeholders, i.e., end-users and SPs express their priorities. Thorough simulation analysis of the proposed approach shows that the model on an average can reduce the remapping cost by 19% and improve server utilization by 21% in comparison with the baselines.

Cloud systems are inefficient in processing delay-sensitive applications due to the WAN latency associated. To augment the processing of cloud services and provide delay-free computation, fog computing is used. The delay sensitivity of the tasks and heterogeneity of the fog-cloud hybrid architecture calls for efficient resource allocation policies. The decision making must be precise and also multiple criteria must be considered while deciding which resources to allocate. In this article, we propose two variants of analytic hierarchy process (AHP)-based resource allocation policies for fog-cloud hybrid systems. The proposed resource allocation policies consider network load, in addition, to the compute load during decision making. The overall aim of the resource allocation policies is to reduce the delay incurred by each task. The allocation policies differ in the way they assign weights to each criterion of optimization. One of the resource allocation policies uses predetermined weights for compute and network while the second method finds the weights dynamically from the overall data. The experimental results show that the proposed approach outperforms existing resource allocation approaches thereby showing the usefulness of AHP-based optimization in fog-cloud hybrid systems.

Wireless Sensor Networks(WSNs) consist of many self organized sensor nodes to monitor various activities like temperature, pressure, health condition, intrusion detection, etc. These sensor nodes mostly sense the events happening around them, process the sensed data, and send it to the base station using multiple hops. The base station is connected to the outside world who wants to access these sensed and processed data. In WSN, one of the most important challenge is to handle the collision during data transmission by multiple sensor nodes at the same point of time. The collision during data transmission is handled by proper MAC protocol. The MAC protocols for WSN are broadly categorized into 3 types, i.e. schedule, random, and hybrid. Among these 3 types of MAC protocols, the hybrid MAC protocols try to combine the advantage of both schedule and random based MAC protocols. In this paper, we proposed a distributed slot scheduling algorithm for hybrid MAC algorithm. This algorithm mainly focuses on preparing a schedule which bridges the gap between a feasible and an optimal schedule to handle the collision during the data transmission. In our proposed approach, first we find out two-hop neighbors of each node, then a particular slot is allotted to each node in order to prepare a feasible schedule using the RD-TDMA algorithm. Finally, the feasible schedule is fine tuned in a novel way to improve the efficiency in handling the collision by reducing the number of allotted slots. The proposed algorithm out performs the existing RD-TDMA algorithm in terms of number of slots required to handle the collision. The performance of the proposed protocol is carried out using Castalia simulator.