Towards the Design of Ethics Aware Systems for the Internet of Things

The Internet of Things promises to offer numerous societal benefits by providing a spectrum of user applications.However,ethical ramifications of adopting such pervasive technology on a society-wide scale have not been adequately considered.Smart things endowed with artificial intelligence may carry out decisions that entail ethical consequences.It is assumed that the functioning of a smart device does not involve any ethical responsibility vis-a-vis its application context.Such a perspective may precipitate situations that endanger essential human values or cause physical or emotional harm.Therefore,it is necessary to consider the design of ethics within intelligent systems to safeguard human interests.In order to address these concerns,we propose a novel method based on Boolean algebra that enables a machine to exhibit varying ethical behaviour by employing the concept of ethics categories and ethics modes.Such enhancement of smart things offers a way to design ethically compliant smart devices and paves way for human friendly technology ecosystems.

Idea plagiarism detection with recurrent neural networks and vector space model

Purpose-Natural languages have a fundamental quality of suppleness that makes it possible to present a single idea in plenty of different ways.This feature is often exploited in the academic world,leading to the theft of work referred to as plagiarism.Many approaches have been put forward to detect such cases based on various text features and grammatical structures of languages.However,there is a huge scope of improvement for detecting intelligent plagiarism.Design/methodology/approach-To realize this,the paper introduces a hybrid model to detect intelligent plagiarism by breaking the entire process into three stages:(1)clustering,(2)vector formulation in each cluster based on semantic roles,normalization and similarity index calculation and(3)Summary generation using encoder-decoder.An effective weighing scheme has been introduced to select terms used to build vectors based on K-means,which is calculated on the synonym set for the said term.If the value calculated in the last stage lies above a predefined threshold,only then the next semantic argument is analyzed.When the similarity score for two documents is beyond the threshold,a short summary for plagiarized documents is created.Findings-Experimental results show that this method is able to detect connotation and concealment used in idea plagiarism besides detecting literal plagiarism.Originality/value-The proposed model can help academics stay updated by providing summaries of relevant articles.It would eliminate the practice of plagiarism infesting the academic community at an unprecedented pace.The model will also accelerate the process of reviewing academic documents,aiding in the speedy publishing of research articles.

Adaptive task scheduling in IoT using reinforcement learning

Purpose-The intelligence in the Internet of Things(IoT)can be embedded by analyzing the huge volumes of data generated by it in an ultralow latency environment.The computational latency incurred by the cloud-only solution can be significantly brought down by the fog computing layer,which offers a computing infrastructure to minimize the latency in service delivery and execution.For this purpose,a task scheduling policy based on reinforcement learning(RL)is developed that can achieve the optimal resource utilization as well as minimum time to execute tasks and significantly reduce the communication costs during distributed execution.Design/methodology/approach-To realize this,the authors proposed a two-level neural network(NN)-based task scheduling system,where the first-level NN(feed-forward neural network/convolutional neural network[FFNN/CNN])determines whether the data stream could be analyzed(executed)in the resourceconstrained environment(edge/fog)or be directly forwarded to the cloud.The second-level NN(RL module)schedules all the tasks sent by level 1 NN to fog layer,among the available fog devices.This real-time task assignment policy is used to minimize the total computational latency(makespan)as well as communication costs.Findings-Experimental results indicated that the RL technique works better than the computationally infeasible greedy approach for task scheduling and the combination of RL and task clustering algorithm reduces the communication costs significantly.Originality/value-The proposed algorithm fundamentally solves the problem of task scheduling in realtime fog-based IoT with best resource utilization,minimum makespan and minimum communication cost between the tasks.

Exploring compression and parallelization techniques for distribution of deep neural networks over Edge-Fog continuum-a review

Purpose-The trend of“Deep Learning for Internet of Things(IoT)”has gained fresh momentum with enormous upcoming applications employing these models as their processing engine and Cloud as their resource giant.But this picture leads to underutilization of ever-increasing device pool of IoT that has already passed 15 billion mark in 2015.Thus,it is high time to explore a different approach to tackle this issue,keeping in view the characteristics and needs of the two fields.Processing at the Edge can boost applications with realtime deadlines while complementing security.Design/methodology/approach-This review paper contributes towards three cardinal directions of research in the field of DL for IoT.The first section covers the categories of IoT devices and how Fog can aid in overcoming the underutilization of millions of devices,forming the realm of the things for IoT.The second direction handles the issue of immense computational requirements of DL models by uncovering specific compression techniques.An appropriate combination of these techniques,including regularization,quantization,and pruning,can aid in building an effective compression pipeline for establishing DL models for IoT use-cases.The third direction incorporates both these views and introduces a novel approach of parallelization for setting up a distributed systems view of DL for IoT.Findings-DL models are growing deeper with every passing year.Well-coordinated distributed execution of such models using Fog displays a promising future for the IoT application realm.It is realized that a vertically partitioned compressed deep model can handle the trade-off between size,accuracy,communication overhead,bandwidth utilization,and latency but at the expense of an additionally considerable memory footprint.To reduce the memory budget,we propose to exploit Hashed Nets as potentially favorable candidates for distributed frameworks.However,the critical point between accuracy and size for such models needs further investigation.Originality/value-To the best of our knowledge,no study has explored the inherent parallelism in deep neural network architectures for their efficient distribution over the Edge-Fog continuum.Besides covering techniques and frameworks that have tried to bring inference to the Edge,the review uncovers significant issues and possible future directions for endorsing deep models as processing engines for real-time IoT.The study is directed to both researchers and industrialists to take on various applications to the Edge for better user experience.