Mitigating and Monitoring Smart City Using Internet of Things

The present trends in smart world reflects the extensive use of limited resources through information and communication technology.The limited resources like space,mobility,energy,etc.,have been consumed rigorously towards creating optimized but smart instances.Thus,a new concept of IoT integrated smart city vision is yet to be proposed which includes a combination of systems like noise and air loss monitoring,web monitoring and fire detection systems,smart waste bin systems,etc.,that have not been clearly addressed in the previous researches.This paper focuses on developing an effective system for possible monitoring of losses,traffic management,thus innovating smart city at large with digitalized and integrated systems and software for fast and effective implementations.In our proposed system,a real time data analysis is performed.These data are collected by various sensors to analyze different factors that are responsible for such losses.The proposed work is validated on a real case study.

A Stacking-Based Deep Neural Network Approach for Effective Network Anomaly Detection

An anomaly-based intrusion detection system(A-IDS)provides a critical aspect in a modern computing infrastructure since new types of attacks can be discovered.It prevalently utilizes several machine learning algorithms(ML)for detecting and classifying network traffic.To date,lots of algorithms have been proposed to improve the detection performance of A-IDS,either using individual or ensemble learners.In particular,ensemble learners have shown remarkable performance over individual learners in many applications,including in cybersecurity domain.However,most existing works still suffer from unsatisfactory results due to improper ensemble design.The aim of this study is to emphasize the effectiveness of stacking ensemble-based model for A-IDS,where deep learning(e.g.,deep neural network[DNN])is used as base learner model.The effectiveness of the proposed model and base DNN model are benchmarked empirically in terms of several performance metrics,i.e.,Matthew’s correlation coefficient,accuracy,and false alarm rate.The results indicate that the proposed model is superior to the base DNN model as well as other existing ML algorithms found in the literature.

Analysis of identifiers in IoT platforms

The Internet of Things(IoT)provides new opportunities for different IoT platforms connecting various devices together.The need to identify those devices is the foremost important to perform any kind of operation.Many organizations and standard bodies that provide specifications and frameworks for the IoT currently have their own identification mechanisms.Some existing industrial identification mechanisms can also be used in the IoT.There is no common Identification Scheme(IS)for the IoT as yet,because of the political and commercial differences amongst the standard bodies.The unavailability of a unified IS method makes the inter-working among IoT platforms challenging.This paper analyses and compares ISs used by several selected IoT platforms.This work will help in understanding the need for a common identification mechanism to provide inter-working among different IoT platforms.

Blockchain-Enabled EHR Framework for Internet of Medical Things

The Internet of Medical Things(IoMT)offers an infrastructure made of smart medical equipment and software applications for healthcare services.Through the internet,the IoMT is capable of providing remote medical diagnosis and timely health services.The patients can use their smart devices to create,store and share their electronic health records(EHR)with a variety of medical personnel including medical doctors and nurses.However,unless the underlying commination within IoMT is secured,malicious users can intercept,modify and even delete the sensitive EHR data of patients.Patients also lose full control of their EHR since most healthcare services within IoMT are constructed under a centralized platform outsourced in the cloud.Therefore,it is appealing to design a decentralized,auditable and secure EHR system that guarantees absolute access control for the patients while ensuring privacy and security.Using the features of blockchain including decentralization,auditability and immutability,we propose a secure EHR framework which is mainly maintained by the medical centers.In this framework,the patients’EHR data are encrypted and stored in the servers of medical institutions while the corresponding hash values are kept on the blockchain.We make use of security primitives to offer authentication,integrity and confidentiality of EHR data while access control and immutability is guaranteed by the blockchain technology.The security analysis and performance evaluation of the proposed framework confirms its efficiency.

Fog-Based Secure Framework for Personal Health Records Systems

The rapid development of personal health records(PHR)systems enables an individual to collect,create,store and share his PHR to authorized entities.Health care systems within the smart city environment require a patient to share his PRH data with a multitude of institutions’repositories located in the cloud.The cloud computing paradigm cannot meet such a massive transformative healthcare systems due to drawbacks including network latency,scalability and bandwidth.Fog computing relieves the load of conventional cloud computing by availing intermediate fog nodes between the end users and the remote servers.Assuming a massive demand of PHR data within a ubiquitous smart city,we propose a secure and fog assisted framework for PHR systems to address security,access control and privacy concerns.Built under a fog-based architecture,the proposed framework makes use of efficient key exchange protocol coupled with ciphertext attribute based encryption(CP-ABE)to guarantee confidentiality and fine-grained access control within the system respectively.We also make use of digital signature combined with CP-ABE to ensure the system authentication and users privacy.We provide the analysis of the proposed framework in terms of security and performance.