IoT-enabled Smart Home Research Articles

An Efficient Online/Offline Signcryption Scheme for Internet of Things in Smart Home

The delivery of unified intelligent services is accomplished through a networked environment comprised of a wide array of electronic devices. Through the use of Internet of Things (IoT) technology, smart homes collect data from their surroundings and use it to improve their tenants’ lives. Remote control, real-time monitoring, and a fire alarm are all characteristics of smart home security. Since smart homes hold personally identifying information about their residents, security is critical to ensure their reliability and prevent data breaches. In this paper, a certificateless online/offline signcryption (COOS) technique for IoT-enabled smart homes is proposed. The proposed solution takes advantage of a resource-constrained smart home device–friendly algorithm known as the Hyperelliptic Curve Cryptosystem (HCC). The suggested approach satisfies the security requirements of unforgeability, confidentiality, resistance to replay attacks, and non-repudiation. The complexity analysis in terms of communicational and computational costs demonstrates the efficiency of the proposed scheme. Finally, we validate the security against Man-In-The-Middle-Attack (MITM) and anti-reply attacks using Automated Validation of Internet Security Protocols and Applications (AVISPA). The data imply that the recommended course of action is safe.

Multi-objective scheduling of IoT-enabled smart homes for energy management based on Arithmetic Optimization Algorithm: A Node-RED and NodeMCU module-based technique

The home energy management system (HEMS) based on advanced internet of things (IoT) technology has attracted the special attention of engineers in the field of smart grid (SG), which has the task of the demand side management (DSM) and helps to control the equality between demand and electricity supply. The main performance of HEMS is based on the optimal scheduling of home appliances because it manages power consumption by automatically controlling the loads and transferring them from peak hours to off-peak hours. This paper presents a multi-objective version of a newly introduced metaheuristic, called Arithmetic Optimization Algorithm (AOA) to discover optimal scheduling of the home appliances, which is called Multi-Objective Arithmetic Optimization Algorithm (MOAOA). Furthermore, the HEMS architecture has been programmed based on the Raspberry Pi minicomputer with Node-RED and NodeMCU modules. HEMS uses the MOAOA algorithm to find the optimal schedule pattern to reduce daily electricity costs, reduce the peak to average ratio (PAR), and increase user comfort (UC). Real-time pricing (RTP) and critical peak pricing (CPP) signals are presumed as energy tariffs. Simulations are performed in two different scenarios: (I) appliance scheduling scheme and (II) appliance scheduling scheme with the integration of renewable energy sources (RES). The results of MOAOA are compared with Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Gray Wolf Optimizer (MOGWO), and Multi-Objective Antlion optimization (MOALO) algorithms. The results demonstrate that the use of the presented scheme remarkably reduces the cost of electricity consumption as well as PAR, in addition to the integration of MOAOA with RES, which greatly increases user comfort.

A Secure and Anonymous User Authentication Scheme for IoT-Enabled Smart Home Environments Using PUF

With the continuous development of Internet of Things (IoT) technology, research on smart home environments is being conducted by many researchers. In smart home environments, home users can remotely access and control a variety of home devices such as smart curtains, lights, and speakers placed throughout the house. Despite providing convenient services, including home monitoring, temperature management, and daily work assistance, smart homes can be vulnerable to malicious attacks because all messages are transmitted over insecure channels. Moreover, home devices can be a target for device capture attacks since they are placed in physically accessible locations. Therefore, a secure authentication and key agreement scheme is required to prevent such security problems. In 2021, Zou <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al</i> . proposed a two-factor-based authentication and key agreement scheme using elliptic curve cryptography (ECC) in smart home environments. They claimed that their scheme provides user anonymity and forward secrecy. However, we prove that their scheme suffers from forgery, ephemeral secret leakage, and session key disclosure attacks. To overcome the security vulnerabilities of Zou <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al</i> .’s scheme and provide home users with secure communication in smart home environments, we propose a secure user authentication scheme using physical unclonable functions (PUF). We utilize Real-or-Random (ROR) model and Burrows-Abadi-Needham (BAN) logic to verify the session key security and mutual authentication of the proposed scheme, respectively. Furthermore, we use the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool to simulate the resistance of our scheme to security attacks. After that, we analyze and compare the communication costs, computational consumption, and security functionalities along with related schemes.

Lightweight Three-Factor-Based Privacy- Preserving Authentication Scheme for IoT-Enabled Smart Homes

Smart homes are an emerging paradigm of Internet of Things (IoT) in which users can remotely control various home devices via the internet anytime and anywhere. However, smart home environments are vulnerable to security attacks because an attacker can inject, insert, intercept, delete, and modify transmitted messages over an insecure channel. Thus, secure and lightweight authentication protocols are essential to ensure useful services in smart home environments. In 2021, Kaur and Kumar presented a two-factor based user authentication protocol for smart homes using elliptic curve cryptosystems (ECC). Unfortunately, we demonstrate that their scheme cannot resist security attacks such as impersonation and session key disclosure attacks, and also ensure secure user authentication. Moreover, their scheme is not suitable in smart home environments because it utilizes public-key cryptosystems such as ECC. Hence, we design a secure and lightweight three-factor based privacy-preserving authentication scheme for IoT-enabled smart home environments to overcome the security problems of Kaur and Kumar’s protocol. We prove the security of the proposed scheme by using informal and formal security analyses such as the ROR model and AVISPA simulation. In addition, we compare the performance and security features between the proposed scheme and related schemes. The proposed scheme better provides security and efficiency compared with the previous schemes and is more suitable than previous schemes for IoT-enabled smart home environments.

Towards Secure and Privacy-Preserving IoT Enabled Smart Home: Architecture and Experimental Study.

Internet of Things (IoT) technology is increasingly pervasive in all aspects of our life and its usage is anticipated to significantly increase in future Smart Cities to support their myriad of revolutionary applications. This paper introduces a new architecture that can support several IoT-enabled smart home use cases, with a specified level of security and privacy preservation. The security threats that may target such an architecture are highlighted along with the cryptographic algorithms that can prevent them. An experimental study is performed to provide more insights about the suitability of several lightweight cryptographic algorithms for use in securing the constrained IoT devices used in the proposed architecture. The obtained results showed that many modern lightweight symmetric cryptography algorithms, as CLEFIA and TRIVIUM, are optimized for hardware implementations and can consume up to 10 times more energy than the legacy techniques when they are implemented in software. Moreover, the experiments results highlight that CLEFIA significantly outperforms TRIVIUM under all of the investigated test cases, and the latter performs 100 times worse than the legacy cryptographic algorithms tested.

TSFS: An Integrated Approach for Event Segmentation and ADL Detection in IoT Enabled Smarthomes

With the recent advances in Internet of Things, smarthomes containing a variety of sensors are expected to monitor and react to the activities of daily living (ADL) of the residents with the goal of improving convenience, comfort, and safety. However, a single human activity may trigger multiple sensors, and each sensor is triggered by multiple activities. Also, the very human nature of interleaving activities along with the presence of multiple inhabitants makes ADL detection a complex problem. This paper tackles the problem with a data driven de-multiplexing approach termed TSFS (Temporal-Sensor-Frequency-Stitch) that disentangles each individual activity from the sensor stream and thus simplifies the ADL recognition problem. TSFS leverages temporal($T$T), sensory($S$S), and frequency($F$F) information to discriminate between two consecutive/parallel activities, followed by a stitch($S$S) to enhance segmentation. The multi-faceted approach helps us in correctly identifying segments; extensive evaluation on 10 public datasets reveals that TSFS is better by 185 percent when compared to a simple temporal segmentation approach. The activity detection accuracy yields within 93 percent of the best possible (an oracle) approach.

Privacy-Preserving and Residential Context-Aware Online Learning for IoT-Enabled Energy Saving With Big Data Support in Smart Home Environment

Energy-saving (ES) systems developed on the basis of the Internet-of-Things (IoT) by heavily relying on automated understanding of human behaviors and activities recognition is of paramount importance in smart home. However, classic approaches are incapable to understand the relations among users’ contexts and ES of appliances very well, and they cannot handle massive metering and time-varying user context datasets. Moreover, privacy concern is thoroughly aroused from both the residential and utility provider sides as to its essentiality. To tackle these problems, we propose a privacy-preserving and residential context-aware online ES (PRCOES) system in an IoT-enabled smart home environment. We model the repeated interaction of ES of appliances and the activity recognition of user context as a contextual multiarmed bandits (CMAB) problem, where the context-aware online learning algorithm can predict appropriate energy offers (EOs) that could meet the users’ satisfaction, task completion rate, and ES purposes for appliances. We utilize a tree-based structure expanding from top to bottom to recommend EOs, which supports ever-increasing big metering datasets with user context-awareness. Theoretical analysis shows that our proposal achieves sublinear regret and differential privacy for both residents and utility provider. Experiments results validate that PRCOES could enhance users’ experience and prolong users’ engagement in everyday ES while guarantee the privacy for both residents and utility provider.

Bandwidth management framework for smart homes using SDN: ISP perspective

This paper proposed a software-defined networking-based (SDN) quality of service (QoS) aware bandwidth management framework for thousands of IoT-enabled smart homes from ISP perspective. Each smart home equips with tens of IoT devices, and each smart home exploits a variety of services. The overall system architecture consists of SDN smart home cloud at ISP side, and massive SDN enabled smart homes. The smart home cloud interconnects with Smart Homes through OpenFlow protocol. With this architecture, we proposed a bandwidth allocation for smart home (BASH) strategy to calculate appropriate bandwidth for each service category according to distinct QoS requirements. ISP can thus use BASH to optimise bandwidth allocation of aggregated and classified services of smart homes. The experiment results show that the proposed framework can effectively enhance QoS and outperform the traditional ISP bandwidth allocation strategy in terms of average transfer ratio, average throughput, average delay and average jitter.

Bandwidth management framework for smart homes using SDN: ISP perspective

This paper proposed a software-defined networking-based (SDN) quality of service (QoS) aware bandwidth management framework for thousands of IoT-enabled smart homes from ISP perspective. Each smart home equips with tens of IoT devices, and each smart home exploits a variety of services. The overall system architecture consists of SDN smart home cloud at ISP side, and massive SDN enabled smart homes. The smart home cloud interconnects with Smart Homes through OpenFlow protocol. With this architecture, we proposed a bandwidth allocation for smart home (BASH) strategy to calculate appropriate bandwidth for each service category according to distinct QoS requirements. ISP can thus use BASH to optimise bandwidth allocation of aggregated and classified services of smart homes. The experiment results show that the proposed framework can effectively enhance QoS and outperform the traditional ISP bandwidth allocation strategy in terms of average transfer ratio, average throughput, average delay and average jitter.

Context-Aware Service Provisioning via Agentized and Reconfigurable Multimodel Cooperation for Real-Life IoT-Enabled Smart Home Systems

For the upcoming Internet of Things (IoT) enabled era, context-aware service provisioning (CaSP) can be realized by first analyzing new input data, followed by inferring contexts from the input data, and providing new services based on the inferred contexts. Over time, further new contextual models will also be incorporated into CaSP due to growing data collected by existing or new IoT devices. Furthermore, these contextual models require ongoing adaptation because a real-life environment is dynamic in nature. It becomes more challenging to maintain and adapt these ever-increasing contextual models as the system evolves. To address these concerns, this paper proposes a CaSP infrastructure along with an agentized and reconfigurable design to improve system adaptability and extensibility. The proposed middleware-enhanced CaSP infrastructure can keep as much previously learned knowledge as possible to share among all integrated components. This design reduces the overhead from integrating and adapting multiple contextual models in response to inevitable uncertainties in a dynamically changing IoT-enabled smart home environment. Our agentized design generalizes the scheme of all smart components integrated with the CaSP infrastructure, thus facilitating reciprocal cooperation among all initially independent components. The CaSP infrastructure can facilitate multilevel rather than single-level information reuse via message queues residing in the middleware. This reconfigurable design enables all of the smart components to become loosely coupled and flexibly interconnect to form reconfigurable agents. Such a design allows temporal reconfiguration by reusing as many existing or even new features and contexts on the CaSP infrastructure, thus improving extensibility. Our experimental results show that the proposed CaSP infrastructure can improve the overall adaptability (by about 20%) and extensibility (by 44% to 95%) of CaSP in a dynamic environment.

Pervasive Energy Monitoring and Control Through Low-Bandwidth Power Line Communication

The Internet of Things (IoT) is growing rapidly, with increasingly sophisticated networking, sensing, and actuation functions embedded into everyday devices. One important IoT application is managing a building’s energy usage by monitoring and controlling its electrical devices. Many existing IoT-enabled devices operate through low-cost and convenient power line networks, using protocols such as X10 and Insteon for communication. However, as these technologies have traditionally targeted low-bandwidth device control, they are often not readily suited to higher bandwidth uses such as continuous energy monitoring. In this paper, we consider the challenge of leveraging existing low-bandwidth power line communication networks for energy monitoring, and present several techniques that enable reliable and high-resolution monitoring in such networks. As a case study, we consider the popular Insteon protocol and show that intelligent polling and event detection methods can reduce the bandwidth requirements and undetected power events in a real-world Insteon network by 50% or more versus naive methods. Our techniques have been employed in a real IoT-enabled smart home, which has collected much of the data publicly released in the UMass Smart* energy dataset.