The Internet of Things (IoT) is an emerging paradigm focusing on the connection of devices, objects, or "things" to each other, to the Internet, and to users. IoT technology is anticipated to become an essential requirement in the development of smart homes, as it offers convenience and efficiency to home residents so that they can achieve better quality of life. Application of the IoT model to smart homes, by connecting objects to the Internet, poses new security and privacy challenges in terms of the confidentiality, authenticity, and integrity of the data sensed, collected, and exchanged by the IoT objects. These challenges make smart homes extremely vulnerable to different types of security attacks, resulting in IoT-based smart homes being insecure. Therefore, it is necessary to identify the possible security risks to develop a complete picture of the security status of smart homes. This article applies the operationally critical threat, asset, and vulnerability evaluation (OCTAVE) methodology, known as OCTAVE Allegro, to assess the security risks of smart homes. The OCTAVE Allegro method focuses on information assets and considers different information containers such as databases, physical papers, and humans. The key goals of this study are to highlight the various security vulnerabilities of IoT-based smart homes, to present the risks on home inhabitants, and to propose approaches to mitigating the identified risks. The research findings can be used as a foundation for improving the security requirements of IoT-based smart homes.

Cyber and Physical Security Vulnerability Assessment for IoT-Based Smart Homes.

In the next few years, smart farming will reach each and every nook of the world. The prospects of using unmanned aerial vehicles (UAV) for smart farming are immense. However, the cost and the ease in controlling UAVs for smart farming might play an important role for motivating farmers to use UAVs in farming. Mostly, UAVs are controlled by remote controllers using radio waves. There are several technologies such as Wi-Fi or ZigBee that are also used for controlling UAVs. However, Smart Bluetooth (also referred to as Bluetooth Low Energy) is a wireless technology used to transfer data over short distances. Smart Bluetooth is cheaper than other technologies and has the advantage of being available on every smart phone. Farmers can use any smart phone to operate their respective UAVs along with Bluetooth Smart enabled agricultural sensors in the future. However, certain requirements and challenges need to be addressed before UAVs can be operated for smart agriculture-related applications. Hence, in this article, an attempt has been made to explore the types of sensors suitable for smart farming, potential requirements and challenges for operating UAVs in smart agriculture. We have also identified the future applications of using UAVs in smart farming.

Unmanned Aerial Vehicles in Smart Agriculture: Applications, Requirements, and Challenges

The human population is growing at a very rapid scale. With this progressive growth, it is extremely important to ensure that healthy food is available for the survival of the inhabitants of this planet. Also, the economy of developing countries is highly dependent on agricultural production. The overall economic balance gets affected if there is a variance in the demand and supply of food or agricultural products. Diseases in plants are a great threat to the yield of the crops thereby causing famines and economy slow down. Our present study focuses on applying machine learning model for classifying tomato disease image dataset to proactively take necessary steps to combat such agricultural crisis. In this work, the dataset is collected from publicly available plant–village dataset. The significant features are extracted from the dataset using the hybrid-principal component analysis–Whale optimization algorithm. Further the extracted data are fed into a deep neural network for classification of tomato diseases. The proposed model is then evaluated with the classical machine learning techniques to establish the superiority in terms of accuracy and loss rate metrics.

A novel PCA–whale optimization-based deep neural network model for classification of tomato plant diseases using GPU

Due to the widespread popularity of Internet-enabled devices, Industrial Internet of Things (IIoT) becomes popular in recent years. However, as the smart devices share the information with each other using an open channel, i.e., Internet, so security and privacy of the shared information remains a paramount concern. There exist some solutions in the literature for preserving security and privacy in IIoT environment. However, due to their heavy computation and communication overheads, these solutions may not be applicable to wide category of applications in IIoT environment. Hence, in this paper, we propose a new biometric-based privacy preserving user authentication (BP2UA) scheme for cloud-based IIoT deployment. BP2UA consists of strong authentication between users and smart devices using preestablished key agreement between smart devices and the gateway node. The formal security analysis of BP2UA using the well-known real-or-random model is provided to prove its session key security. Moreover, an informal security analysis of BP2UA is also given to show its robustness against various types of known attacks. The computation and communication costs of BP2UA in comparison to the other existing schemes of its category demonstrate its effectiveness in the IIoT environment. Finally, the practical demonstration of BP2UA is also done using the NS2 simulation.

Biometrics-Based Privacy-Preserving User Authentication Scheme for Cloud-Based Industrial Internet of Things Deployment

Information-Centric Networking (ICN) is an appealing architecture that has received a remarkable interest from the research community thanks to its friendly structure. Several projects have proposed innovative ICN models to cope with the Internet practice, which moves from host-centrism to receiver-driven communication. A worth mentioning component of these novel models is in-network caching, which provides flexibility and pervasiveness for the upturn of swiftness in data distribution. Because of the rapid Internet traffic growth, cache deployment and content caching have been unanimously accepted as conspicuous ICN issues to be resolved. In this article, a survey of cache management strategies in ICN is presented along with their contributions and limitations, and their performance is evaluated in a simulation network environment with respect to cache hit, stretch ratio, and eviction operations. Some unresolved ICN caching challenges and directions for future research in this networking area are also discussed.

Caching in Information-Centric Networking: Strategies, Challenges, and Future Research Directions

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart’s rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients’ acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.

Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart's rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients' acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11\%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.

To cope with the ongoing changing demands of the internet, ‘in-network caching’ has been presented as an application solution for two decades. With the advent of information-centric network (ICN) architecture, ‘in-network caching’ becomes a network level solution. Some unique features of the ICNs, e.g., rapidly changing cache states, higher request arrival rates, smaller cache sizes, and other factors, impose diverse requirements on the content eviction policies. In particular, eviction policies should be fast and lightweight. In this paper, we propose cache replication and eviction schemes, conditional leave cope everywhere (CLCE) and least frequent recently used (LFRU), which are well suited for the ICN type of cache networks (CNs). The CLCE replication scheme reduces the redundant caching of contents; hence improves the cache space utilization. LFRU approximates the least frequently used scheme coupled with the least recently used scheme and is practically implementable for rapidly changing cache networks like ICNs.

A Cache Management Scheme for Efficient Content Eviction and Replication in Cache Networks

Authentication is one of the essential security services in Wireless Sensor Networks (WSNs) for ensuring secure data sessions. Sensor node authentication ensures the confidentiality and validity of data collected by the sensor node, whereas user authentication guarantees that only legitimate users can access the sensor data. In a mobile WSN, sensor and user nodes move across the network and exchange data with multiple nodes, thus experiencing the authentication process multiple times. The integration of WSNs with Internet of Things (IoT) brings forth a new kind of WSN architecture along with stricter security requirements; for instance, a sensor node or a user node may need to establish multiple concurrent secure data sessions. With concurrent data sessions, the frequency of the re-authentication process increases in proportion to the number of concurrent connections. Moreover, to establish multiple data sessions, it is essential that a protocol participant have the capability of running multiple instances of the protocol run, which makes the security issue even more challenging. The currently available authentication protocols were designed for the autonomous WSN and do not account for the above requirements. Hence, ensuring a lightweight and efficient authentication protocol has become more crucial. In this paper, we present a novel, lightweight and efficient key exchange and authentication protocol suite called the Secure Mobile Sensor Network (SMSN) Authentication Protocol. In the SMSN a mobile node goes through an initial authentication procedure and receives a re-authentication ticket from the base station. Later a mobile node can use this re-authentication ticket when establishing multiple data exchange sessions and/or when moving across the network. This scheme reduces the communication and computational complexity of the authentication process. We proved the strength of our protocol with rigorous security analysis (including formal analysis using the BAN-logic) and simulated the SMSN and previously proposed schemes in an automated protocol verifier tool. Finally, we compared the computational complexity and communication cost against well-known authentication protocols.

An Authentication Protocol for Future Sensor Networks.

The information centric networks (ICN) can be viewed as a network of caches. Conversely, ICN type of cache networks has distinctive features e.g, contents popularity, usability time of content and other factors inflicts some diverse requirements for cache eviction policies. In this paper we defined four important characteristics of a suitable eviction policy for ICN. We analysed well known eviction policies in view of defined characteristics. Based upon analysis we propose a new eviction scheme which is well suitable for ICN type of cache networks.

/pdf/time-aware-least-recent-used-tlru-cache-management-policy-in-mlert7uq3z.pdf

Muhammad Bilal

Papers

Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

A Cache Management Scheme for Efficient Content Eviction and Replication in Cache Networks

An Authentication Protocol for Future Sensor Networks.

Time Aware Least Recent Used (TLRU) cache management policy in ICN