Smart Home Network Research Articles

Design and Implementation of Smart Home Network using Cisco Packet Tracer

Technology has become a critical part of our daily activities and routine life in the present day scenario. The need of the hour is to devise a smart home that will be able to control the operation of electronic devices via a smartphone. This concept, while absurd to imagine, can be easily implemented efficiently using a package tracking software which includes IoT functions for control and simulation of a smart home network. IoT technology has had a huge boom in the tech industry in the recent past. IoT, better known as Internet of Things, can be applied to several real time scenarios, such as home automation, treatment, campus, office, etc. In this paper, we will be focusing on a safe home automation system that will be including modules such as: smoke detection, humiture monitoring, motion detection, temperature monitoring and solar panel controlling using smart battery. The aim of our research is to devise a simulation network of smart devices which will be in control of the end-user remotely and implement the concept of smart home automation. This project will make use of the Cisco Packet Tracer to monitor the IoT devices in the smart home network. Simulation results have shown that these smart devices can be connected in a network to the home portal and objects can be successfully monitored which proves that such a model can be used for real life implementation in the user’s home.

Lightweight capability-token for consent-based authentication protocol for smart sensor nodes

This paper highlights a lightweight token-based authentication scheme for smart sensor nodes in a smart home network. The token holds the capability information of a sensor node in a secure and trusted environment. The capability information adds another layer of security by incorporating the user's consent when registering in the network. The registration is a one-time activity in the node's life cycle, but it prevents unauthorized users or devices from intruding into the network. The method also mitigates the user and token impersonation attacks in the network. It does not need any manual intervention like entering user-id or password, which helps in seamless authentication, identity management, and secure communication. It also incorporates the position of the sensor nodes during the authentication phase. The capability-token mitigates the threats that arise from an independent node attempting to intrude whose position is near the smart home environment. The authors have demonstrated the communication time gain and compared it with the existing methods.

Optimal Planning of Electrical Appliance of Residential Units in a Smart Home Network Using Cloud Services

One of the important aspects of realizing smart cities is developing smart homes/buildings and, from the energy perspective, designing and implementing an efficient smart home area energy management system (HAEMS) is vital. To be effective, the HAEMS should include various electrical appliances as well as local distributed/renewable energy resources and energy storage systems, with the whole system as a microgrid. However, the collecting and processing of the data associated with these appliances/resources are challenging in terms of the required sensors/communication infrastructure and computational burden. Thanks to the internet-of-things and cloud computing technologies, the physical requirements for handling the data have been provided; however, they demand suitable optimization/management schemes. In this article, a HAEMS is developed using cloud services to increase the accuracy and speed of the data processing. A management protocol is proposed that provides an optimal schedule for a day-ahead operation of the electrical equipment of smart residential homes under welfare indicators. The proposed system comprises three layers: (1) sensors associated with the home appliances and generation/storage units, (2) local fog nodes, and (3) a cloud where the information is processed bilaterally with HAEMS and the hourly optimal operation of appliances/generation/storage units is planned. The neural network and genetic algorithm (GA) are used as part of the HAEMS program. The neural network is used to predict the amount of workload corresponding to users’ requests. Improving the load factor and the economic efficiency are considered as the objective function that is optimized using GA. Numerical studies are performed in the MATLAB platform and the results are compared with a conventional method.

Software-Defined Security Architecture for a Smart Home Networks Using Token Sharing Mechanism

Background: Several approaches were proposed earlier to provide a secure infrastructure dependent communication in a smart home network. Some used overlay networks, some used lightweight encryption techniques, and some used honey pot techniques. However, all the approaches are vulnerable to network attacks due to the dependency on the device and server, and due to centralization, there exists a higher chance of attacks. Objective: To develop a security architecture that is more resilient to cyber-attacks and less dependent on any complex network parameter, i.e. an encryption algorithm or an overlay network. Methods: Authentication module along with squid performs token generation, and monitoring module helps devices to communicate with each other. The integrity protection module performs data integrity and the expiration of token is performed by the access module with the clock. Our approach meets with all the security aspects of a smart home network. Results: The analysis of our secure architecture showed that this architecture provides more flexibility, robustness in terms of Load Balancing, Network Lifetime maximization, Failure Management, Energy efficiency, Link quality, and heterogeneity of the network as compared to other existing security policies or architecture. Conclusion: The proposed framework ensures and improves all the security requirements for a smart home network. Token-based authentication is much secure and robust as compared to traditional approaches. This framework is suited for secure communication in a smart home environment, but it lacks for controlling zero-day attacks. In the future, we will improve its resilience against the zero-day attacks and also enhance security features in the current architecture.

IoT Security: Problems and a Centralized Adaptive Approach as a Solution

The Internet of Things (IOT) is the interconnection, and communication of technology devices. This leads to speedy advancements in technology, but unfortunately invites in room for major security vulnerabilities. One facet of the Internet of Things, is smart home devices. Smart home devices are those that are utilized within one's home to improve quality of life. As all these devices communicate with each other, more and more security risks are developed. In this paper we review the existing security issues and solutions for IOT, and propose and approach to centralize the communications through a singular hub, acting as a central command for the smart home devices, allowing that hub to be the main secure point in the smart home network.

A Secure Internet of Things Smart Home Network: Design and Configuration

Many home IoT devices are joining IoT networks by gaining access to some home gateway that configures smart, multimedia, and home networks. To enable secure IoT-based home networking services, (1) an IoT network should be effectively designed and configured with a IoT server, (2) a messaging protocol is required to exchange information between the IoT server and IoT devices, and (3) the home gateway should monitor all safety aspects in both inbound and outbound traffic of the home network. However, not all home network users put in consideration the need for an adequate security posture. Instead, many users still rely on the minimum home network security by setting an easiest-to-guess password to restrict unauthorized access to their home gateway. In this paper, we propose a network design and configuration that enables secure IoT services with MQTT messaging protocol for home networks. With the proposed network design, a home network is interconnected to external networks through a home gateway. To separate the IoT-subnet from other parts of home network, the home gateway subdivides a home network into an inside-subnet and an IoT-subnet with a private IP address using subnet masking. The IoT server, located in the IoT-subnet can be implemented with either a general HTTP server or a security server that acts as an MQTT broker. The secure communications among network entities are governed by a home gateway operating a well-configured extended access control. The effectiveness of the proposed design and configuration is verified through a simulation by showing that it does not impose any significant performance degradation for reinforced security. We expect the proposed configuration to help facilitate interconnection among heterogeneous network entities.

Hardening machine learning denial of service (DoS) defences against adversarial attacks in IoT smart home networks

Machine learning based Intrusion Detection Systems (IDS) allow flexible and efficient automated detection of cyberattacks in Internet of Things (IoT) networks. However, this has also created an additional attack vector; the machine learning models which support the IDS’s decisions may also be subject to cyberattacks known as Adversarial Machine Learning (AML). In the context of IoT, AML can be used to manipulate data and network traffic that traverse through such devices. These perturbations increase the confusion in the decision boundaries of the machine learning classifier, where malicious network packets are often miss-classified as being benign. Consequently, such errors are bypassed by machine learning based detectors, which increases the potential of significantly delaying attack detection and further consequences such as personal information leakage, damaged hardware, and financial loss. Given the impact that these attacks may have, this paper proposes a rule-based approach towards generating AML attack samples and explores how they can be used to target a range of supervised machine learning classifiers used for detecting Denial of Service attacks in an IoT smart home network. The analysis explores which DoS packet features to perturb and how such adversarial samples can support increasing the robustness of supervised models using adversarial training. The results demonstrated that the performance of all the top performing classifiers were affected, decreasing a maximum of 47.2 percentage points when adversarial samples were present. Their performances improved following adversarial training, demonstrating their robustness towards such attacks.

Stacked recurrent neural network for botnet detection in smart homes

Internet of Things (IoT) devices in Smart Home Network (SHN) are highly vulnerable to complex botnet attacks. In this paper, we investigate the effectiveness of Recurrent Neural Network (RNN) to correctly classify network traffic samples in the minority classes of highly imbalanced network traffic data. Multiple layers of RNN are stacked to learn the hierarchical representations of highly imbalanced network traffic data with different levels of abstraction. We evaluate the performance of Stacked RNN (SRNN) model with Bot-IoT dataset. Results show that SRNN outperformed RNN in all classification scenarios. Specifically, SRNN model learned the discriminating features of highly imbalanced network traffic samples in the training set with better representations than RNN model. Also, SRNN model is more robust and it demonstrated better capability to effectively handle over-fitting problem than RNN model. Furthermore, SRNN model achieved better generalization ability in detecting network traffic samples of the minority classes.

Encrypted Network Traffic Classification and Resource Allocation with Deep Learning in Software Defined Network

The climate has changed absolutely in every area in just a few years as digitized, making high-speed internet service a significant need in the future. Future Internet is supposed to face exponential growth in traffic, and highly complicated infrastructure, threatening to make conventional NTC approaches unreliable and even counterproductive. In recent days, AI Stimulated state-of-the-art breakthroughs with the ability to tackle extensive and multifarious challenges, and the network community is initiated by considering the NTC prototype from legacy rule-based towards a novel AI-based. Design and execution are applied to interdisciplinary become more essential. A smart home network supports various applications and smart devices within the proposed work, including e-health devices, regular computing devices, and home automation devices. Many devices accessible through the Internet by Home GateWay for Congestion (HGC) in a smart home. Throughout this paper, a Software-Defined Network Home GateWay for Congestion (SDNHGC) architecture for improved management of remote smart home networks and protection of the significant network's SDN controller. It enables effective network capacity regulation, focused on real-time traffic analysis and core network resource allocation. It cannot control the Network in dispersed smart homes. Our innovative SDNHGC expands power across the connectivity network, a smart home network enabling improved end-to-end monitoring of networks. The planned SDNHGC directly will gain centralized device identification by classifying traffic through a smart home network. Several of the current traffic classifications approach, checking deep packets, cannot have this real-time device knowledge for encrypted data to solve this issue.

Smart Home Environment Future Challenges and Issues - A Survey

The smart home automation is that the exploitation internet enabled devices remotely and mechanically management appliances such as lighting, heating system and security measures in and around your home. This papers talks about relative emission effects in Home Energy Management. Also the result outcome is that consumption of the electricity will be reduced towards green environment. Moreover, the research paper is considering the analysis of calculate the negative effects in environment due to full home automation system. While calculating these negative effects, the Life Cycle Assessment (LCA) should be in sum total. This study uses to analysis the electricity consumption for environment impact of Home Energy Management system (HEMs). The research article discusses home automation system consumes the energy for different devices connected for smart home. The maximum energy consumption in smart home network is smart plugs due to an uninterrupted supply. Therefore this research article comprises about home automation energy management that shows the balance energy consumption between the devices in a regular interval. Also this research article provides a future challenge tasks in security issues in smart home environment. Also the perception for smart home environment focuses the Interoperability, Reliability, Integration of smart homes and term privacy in context, term security and privacy vulnerabilities to smart home.

Home Network Traffic Control Scheme Based on Two-Level Bargaining Game Model

This article presents a new smart home network (SHN) traffic control scheme to adaptively handle different quality of service (QoS) traffic services. According to their special characteristics, we categorize the SHN traffic services into three classes - class I, II and III data services - to share the limited SHN bandwidth resource. To effectively share the limited resource, we adopt the bridging and iterated egalitarian bargaining solutions, and formulate a two-level cooperative bargaining game. At the over bargaining process, the idea of bridging bargaining solution is used to compromise the conflicting views of real-time and non real-time data services. At the under bargaining process, class I and class II data services share the assigned bandwidth based on the concept of iterated egalitarian bargaining solution. These both processes are implemented to take full advantages of collaborative SHN traffic services. This interactive coordinated paradigm explores the mutual benefits under dynamically changing SHN environments. The most important novelties of our two-level bargaining approach is to compromise diverse QoS requirements while leveraging a reciprocal consensus among different traffic services. In a coordinated manner, we can get a globally desirable solution to challenge the relatively fair-efficient gains. Finally, simulation testbed is constructed and the numerical analysis is conducted to demonstrate the performance improvement of our proposed method.

Data Integrity Time Optimization of a Blockchain IoT Smart Home Network Using Different Consensus and Hash Algorithms

Data security is a major issue for smart home networks. Yet, different existing tools and techniques have not been proven highly effective for home networks’ data security. Blockchain is a promising technology because of the distributed computing infrastructure network that makes it difficult for hackers to intrude into the systems through the use of cryptographic signatures and smart contracts. In this paper, an architecture for smart home networks that could guarantee data integrity, robust security, and the ability to protect the validity of the blockchain transactions has been investigated. The system model is tested using various sizes of realistic datasets (30, 3 k, and 30 k to represent a small, medium, and large number of transactions, respectively). Four different consensus algorithms were considered, the conventional schemes concatenated hash transactions (CHT) and Merkle hash tree (MHT), as well as the newly proposed odd and even modified MHT (O&E MHT) and modified MHT (MMHT). Moreover, 15 hash functions were also examined and compared to understand the effects of each consensus algorithms on the data integrity verification check execution time and the time optimization provided by the proposed MMHT algorithm. The results show that even though the CHT algorithm gives the lowest execution time, it is impractical for a blockchain implementation due to the requirement to copy the entire blockchain ledger in real time. Meanwhile, the O&E MHT does not give any tangible benefit in the execution time. However, the proposed MMHT offers a minimum of 30% gain in time optimization than the conventional MHT algorithm typically used in blockchains. This work shows that the proposed MMHT consensus algorithm not only can identify malicious codes but has an improved data integrity check performance in smart homes, all while ensuring network stability.

A Novel Framework to Enterprise Smart City with IOT and Analytics

The use of IoT devices like sensors, actuators, smartphones etc. is the very rapid and useful source in order to cope smartly with the public and community growth requirements. Nevertheless, when you connect thousands of IoT devices to create a smart network as you communicate over the Internet, you produce a massive amount of data, known as Big Data. Integrating IoT services to receive city data in real time and then efficiently processing large amounts of data to create a smart city is a challenge. Therefore, in this paper the smart town framework based on IoT using Big Data Analytics was proposed and developed. We use sensors such as smart home sensors , network cars, water and weather sensors, smart parking sensors, tracking objects, etc. The entire design and implementation model is proposed and implemented in a specific world using Hadoop ecosystems. The system is implemented in various steps , starting from data collection , aggregation, filtering, classification, preprocessing, computing and decision-making. Spark over Hadoop achieves reliability in the production of big data. The program is realistic for building smart cities by using intelligent systems as the city data base.

An energy efficient hybrid MAC protocol for smart home networks

In this paper cluster-based hybrid MAC (CB-HMAC) protocol, based on IEEE 802.15.4 MAC is proposed for smart home networks. The CB-HMAC is specially designed for short packet transmission in a dense home network environment. Simulation results show that the proposed CB-HMAC is more energy-efficient than other medium access control (MAC) protocols without sacrificing much delay and throughput.

An ECC-based authentication protocol for fog-IoT enabled smart home environment

Internet of things (IoT)-enabled smart home means all the home appliances are connected to the internet. All these devices are monitored and controlled using a mobile device, iPhone, or iPad regardless of location or time constraint. In an IoT-based smart home network, associated users and devices need to be recognised otherwise unauthorised access reduce the security and privacy of the smart environment. In this paper, contributions are first proposed as an elliptical curve cryptography-based authentication protocol for fog nodes and users in the IoT-based smart home environment. Secondly, the simulation for security analysis of the proposed authentication protocol using AVISPA tools has been done. Thirdly, BAN logic is used to verify that the devices in the smart home environment have achieved the security goals. Finally, a comparison with some existing works has been done and it has been seen that proposed protocol achieved better security goals in a smart home environment.

Towards Energy Efficient Home Automation: A Deep Learning Approach.

Home Automation Systems (HAS) attracted much attention during the last decade due to the developments in new wireless technologies, such as Bluetooth 4.0, 5G, WiFi 6, etc. In order to enable automation as a service in smart homes, a number of challenges must be addressed, such as fulfilling the electrical energy demands, scheduling the operational time of appliances, applying machine learning models in real-time, optimal human appliances interaction, etc. In order to address the aforementioned challenges and control the wastage of energy due to the lifestyle of the home users, we propose a system for automatically controlling the energy consumption by employing machine and deep learning techniques to smart home networks. The proposed system works in three phases, (1) feature extraction and classification based on 1-dimensional Deep Convolutional Neural Network (1D-DCNN) which extract important energy patterns from the historic energy data, (2) a load forecasting system based on Long-short Term Memory (LSTM) is proposed to forecast the load based on the extracted features in phase 1 and (3) a scheduling algorithm based on the forecasted data obtained from phase 2 is designed to schedule the operational time of smart home appliances. The proposed scheme efficiently automates the smart home appliances to consume less energy while adapting to the lifestyle of smart home users. The validation of the proposed scheme is tested with a number of simulation scenarios incorporating datasets from authentic data sources. The simulation results show that the proposed smart home automation system can be a game-changer in fulfilling the energy demands of the home users without installing renewable and other energy sources in the future.

The future of 5G smart home network security is micro-segmentation

IDC predicts that there will be around 41.6 billion connected IoT devices in 2025, and GSMA predicts 20% of connections will be 5G by the same year. 1 In a 5G world, many IoT devices will connect directly to the network, which will bring new cyber security challenges and opportunities alike. For carriers, providing strong cyber security built into their networks will be a competitive advantage at first, and ultimately will become a must-have for all carriers. IDC predicts that there will be around 41.6 billion connected IoT devices in 2025, and GSMA predicts 20% of connections will be 5G by the same year. In a 5G world, many IoT devices will connect directly to the network, which will bring new cyber security challenges and opportunities. For carriers, providing strong cyber security built into their networks will be a competitive advantage at first, and ultimately will become a must-have for all, according to Armin Wasicek of Avast.

Adaptive Packet Padding Approach for Smart Home Networks: A Tradeoff Between Privacy and Performance

The presence of connected devices in homes introduces numerous threats to privacy via the analysis of the encrypted traffic these devices generate. Prior works have shown that traffic attributes such as packet size combined with machine learning techniques enable the inference of private information from Internet of Things users. One of the commonly used techniques to mitigate those privacy threats is traffic obfuscation, such as packet padding. Most padding mechanisms that were previously proposed statically select the number of bytes inserted in the packets, which incurs high overhead and ineffective privacy improvement. These static mechanisms are particularly unsuitable for networks whose traffic patterns are significantly dynamic, such as smart homes. This article proposes an adaptive packet padding approach based on software-defined networking (SDN) that adjusts the number of bytes inserted into packets in response to variations in the home network utilization. The proposed technique monitors the network to instruct a padding mechanism through a representational state transfer (REST) interface proposed in this article. This mechanism ensures that the length of packets generated by connected devices is modified. The evaluation includes four supervised learning mechanisms, random forest (RF), support vector machine (SVM), decision tree, and k-nearest neighbors (KNNs), to measure privacy improvement through the metrics accuracy, recall, and F1-score. Goodput, jitter, and packet loss induced by the proposal are also evaluated. Our proposal is shown to overcome the state-of-the-art solutions in privacy preservation with a significantly lower overhead. For instance, the accuracy of RF on identifying devices decreases from 96% to 4.96%.

Multi-Sensor Based MK/HyperK/1/M Queuing Model for Heterogeneous Traffic

Integration of wireless sensor networks (WSNs) with Internet of Things (IoT) has given rise to applications such as Smart Home Networks, Vehicular Ad hoc Sensor Networks and Health and Wellness Wireless Sensor Networks among others. These applications tend to use multi-sensor nodes that have multiple sensors mounted on a single radio board and may require handling heterogeneous traffic. Modern WSNs are therefore no longer homogeneous in nature and have different quality of service (QoS) requirements. Modeling the performance of heterogeneous data is a challenging task owing to the complexity involved. In this paper we propose a MK/HyperK/1/M queuing model for multi-sensor nodes. Queues with Poisson arrivals and hyper-exponentially distributed service time and their applications have been widely discussed in literature however hardly any work can be found that uses them for modeling WSNs. In this paper we consider that the traffic from multi-sensor node is emanating from different sensor types within the node and is hence heterogeneous. The model captures the impact of varying arrival rates, mean packet lengths, buffer size and duty cycle on throughput. The model can be used with any duty cycle-based medium access control (MAC) protocols for WSNs. To test the model we apply it to IEEE 802.15.4 in beacon-enabled mode with star topology. Analytical results are corroborated by ns-2.35 simulations. Results indicate that the heterogeneity of traffic has a significant impact on the throughput and this may be useful in improving the QoS of applications.

A session key authentication system for smart homes

The Internet of Things (IoT) is a rapidly growing emerging topic of social, economic and technical significance. The spread of today’s wireless information and communication technologies has changed people’s lifestyle and social interactions - the next frontier is the smart home environments. A smart home consists of low capacity devices (sensors, for example) and wireless networks. These devices are networked together and they need to all work together as a secure system. The proposed scheme aims to offer a secure session key establishment scheme which can then be implemented in smart home networks. Every node (sensor and control unit) uses a small authentication token, along with a random number to establish a session key which is both secure and dynamic. A few popular attacks like denial-of-service and eavesdropping are prevented by the proposed scheme.