Ethics Aware Object Oriented Smart City Architecture

A novel initiative in providing advanced civic amenities is the idea of smart city driven by the Internet of Things. Owing to a lack of consensus regarding what constitutes a smart city,diverse smart city architectures have been proposed. However,it is observed that adequate consideration is not given to the most important element of a smart city i.e. its people. In our opinion,energy efficient technologically driven city does not necessarily lead to a smart city. Ethics,tradition and law form essential ingredients of complex social palette that cannot be ignored. In this work we propose Ethics-Aware Object-Oriented Smart City Architecture(EOSCA)that has two distinguishing features. Firstly,we propose an object oriented layered architecture where an object represents an abstraction of a real world thing along with requisite security and ethics parameters. Secondly,we propose to integrate socio-cultural and ethical aspects within the smart city architecture by dedicating a separate ethics layer. Such enhancement not only addresses the challenge of privacy and security of a smart city,but also makes it people friendly by incorporating ethics. Such measures would facilitate social acceptance of smart city paradigm and augment its economic value.

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.

Deep learning for the internet of things:Potential benefits and use-cases

The massive number of sensors deployed in the Internet of Things(IoT)produce gigantic amounts of data for facilitating a wide range of applications.Deep Learning(DL)would undoubtedly play a role in generating valuable inferences from this massive volume of data and hence will assist in creating smarter IoT.In this regard,exploring the potential of DL for IoT data analytics becomes highly crucial.This paper begins with a concise discussion on the Deep Neural Network(DNN)and its different architectures.The potential benefits that DL will bring to the IoT are also discussed.Then,a detailed review of DL-driven IoT use-cases is presented.Moreover,this paper formulates a DL-based model for Human Activity Recognition(HAR).It carries out a performance comparison of the proposed model with other machine learning techniques to delineate the superiority of the DL model over other techniques.Apart from enlightening the potential of DL in IoT applications,this paper will serve as an impetus to encourage advanced research in the realm of DL-driven IoT applications.

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.