Internet of Things (IoT) is transforming everyone’s life by providing features, such as controlling and monitoring of the connected smart objects. IoT applications range over a broad spectrum of services including smart cities, homes, cars, manufacturing, e-healthcare, smart control system, transportation, wearables, farming, and much more. The adoption of these devices is growing exponentially, that has resulted in generation of a substantial amount of data for processing and analyzing. Thus, besides bringing ease to the human lives, these devices are susceptible to different threats and security challenges, which do not only worry the users for adopting it in sensitive environments, such as e-health, smart home, etc., but also pose hazards for the advancement of IoT in coming days. This article thoroughly reviews the threats, security requirements, challenges, and the attack vectors pertinent to IoT networks. Based on the gap analysis, a novel paradigm that combines a network-based deployment of IoT architecture through software-defined networking (SDN) is proposed. This article presents an overview of the SDN along with a thorough discussion on SDN-based IoT deployment models, i.e., centralized and decentralized. We further elaborated SDN-based IoT security solutions to present a comprehensive overview of the software-defined security (SDSec) technology. Furthermore, based on the literature, core issues are highlighted that are the main hurdles in unifying all IoT stakeholders on one platform and few findings that emphases on a network-based security solution for IoT paradigm. Finally, some future research directions of SDN-based IoT security technologies are discussed.

An In-Depth Analysis of IoT Security Requirements, Challenges, and Their Countermeasures via Software-Defined Security

A lightweight anonymous user authentication and key establishment scheme for wearable devices

A survey on internet of things security from data perspectives

Wireless body area networks play an indispensable role in the medical Internet of Things. It is a network of several wearables or implantable devices that use wireless technologies to communicate. These devices usually collect the wearer’s physiological data and send it to the server. Some health care providers can access the server over the network and provide medical care to the wearer. Due to the openness and mobility of the wireless network, the adversary can easily steal and forge information, which exchanged in the communication channel that leaks wearer’s privacy. Therefore, a secure and reliable authentication scheme is essential. Most of the existing authentication schemes are based on asymmetric encryption. However, since the sensor devices in wireless body area networks are typically resource-constrained devices, their computing resources cannot afford to use asymmetric encryption. In addition, most of the existing lightweight authentication schemes have various security vulnerabilities, especially the lack of forwarding secrecy. Therefore, we propose a secure lightweight authentication scheme for the wireless body area networks. With this scheme, forward secrecy can be guaranteed without using asymmetric encryption. We use the automatic security verification tool ProVerif to verify the security of our scheme and analyze informal security. The experimental results and the theoretical analysis indicate that our scheme significantly reduces the computational cost compared with the schemes using asymmetric encryption and that it has a lower security risk compared with the lightweight schemes.

A Lightweight Mutual Authentication and Key Agreement Scheme for Medical Internet of Things

A home hospitalization system based on the Internet of things, Fog computing and cloud computing

Anonymous hybrid mutual authentication and key agreement scheme for wireless body area network

Plug and play self-configurable IoT gateway node for telemonitoring of ECG

Smartphones that can support and assist the screening of various cardiovascular diseases are gaining popularity in recent years. The timely detection, diagnosis, and treatment of atrial fibrillation (AF) are critical, especially for those who are at risk of stroke. AF detection via screening with wearable devices should always be confirmed by a standard 12-lead electrocardiogram (ECG). However, the inability to perform on-site AF confirmatory testing results in increased patient anxiety, followed by unnecessary diagnostic procedures and treatments. Also, the delay in confirmation procedure may conclude the condition as non-AF while it was indeed present at the time of screening. To overcome these challenges, we propose an efficient on-site confirmatory testing for AF with 12-lead ECG derived from the reduced lead set (RLS) in a wireless body area network (WBAN) environment. The reduction in the number of leads enhances the comfort level of patients as well as minimizes the hurdles associated with continuous telemonitoring applications such as data transmission, storage, and bandwidth of the overall system. The proposed method is characterized by segment-wise regression and a lead selection algorithm, facilitating improved P-wave reconstruction. Further, an efficient AF detection algorithm is proposed by incorporating a novel three-level P-wave evidence score with an RR irregularity evidence score. The proposed on-site AF confirmation test reduces false positives and false negatives by 88% and 53% respectively, compared to single lead screening. In addition, the proposed lead derivation method improves accuracy, $$F_1$$
 -score, and Matthews correlation coefficient (MCC) for the on-site AF detection compared to existing related methods.

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Efficient on-site confirmatory testing for atrial fibrillation with derived 12-lead ECG in a wireless body area network

In the petrochemical industry, heat integration is one of the most commonly used methods for saving energy in the distillation column. The component of the liquid mixture can be estimated by correctly modeling or identifying the respective heat integrated distillation column (HIDC). Previously, system identification for HIDC has been performed using steady state data in an open loop configuration. However, the data obtained from steady state does not characterize realistic HIDC. Complex nonlinear modeling is required to account for the nonlinearities present in HIDC. In this study, a unique approach is presented to identify a dynamic closed-loop HIDC system using Support Vector Regression (SVR). Here, HYSYS is used to simulate HIDC in a fairly realistic environment and the resulting data is used in MATLAB for system identification. Particle Swarm Optimization (PSO) is used to optimize the SVR parameters, which gives better results than conventional metaheuristic optimization approaches. The proposed approach is also verified by accurately estimating the mole fraction of benzene-toluene mixtures in a dynamic HIDC. In terms of root mean square error (RMSE) and regression coefficients (R), the proposed strategy outperforms artificial neural networks (ANN) and radial basis function (RBF) models. The proposed technique reduces the RMSE by 80% and 47% compared to ANN for the mole fractions of benzene and toluene, respectively. 

Aneesh M. Koya

Papers

Anonymous hybrid mutual authentication and key agreement scheme for wireless body area network

Plug and play self-configurable IoT gateway node for telemonitoring of ECG

Efficient on-site confirmatory testing for atrial fibrillation with derived 12-lead ECG in a wireless body area network

Machine learning based system identification of a realistic heat integrated distillation column using particle swarm optimization