Secure key design approaches using entropy harvesting in wireless sensor network: A survey

Physical layer based security design in wireless sensor networks have gained much importance since the past decade. The various constraints associated with such networks coupled with other factors such as their deployment mainly in remote areas, nature of communication etc. are responsible for development of research works where the focus is secured key generation, extraction, and sharing. Keeping the importance of such works in mind, this survey is undertaken that provides a vivid description of the different mechanisms adopted for securely generating the key as well its randomness extraction and also sharing. This survey work not only concentrates on the more common methods, like received signal strength based but also goes on to describe other uncommon strategies such as accelerometer based. We first discuss the three fundamental steps viz. randomness extraction, key generation and sharing and their importance in physical layer based security design. We then review existing secure key generation, extraction, and sharing mechanisms and also discuss their pros and cons. In addition, we present a comprehensive comparative study of the recent advancements in secure key generation, sharing, and randomness extraction approaches on the basis of adversary, secret bit generation rate, energy efficiency etc. Finally, the survey wraps up with some promising future research directions in this area

Design and Implementation of a Hybrid SET-CMOS Based Sequential Circuits

Single Electron Transistor is a hot cake in the present research area of VLSI design and Microelectron-ics technology. It operates through one-by-one tunneling of electrons through the channel, utilizing the Coulomb blockade Phenomenon. Due to nanoscale feature size, ultralow power dissipation, and unique Coulomb blockade oscillation characteristics it may replace Field Effect Transistor FET). SET is very much advantageous than CMOS in few points. And in few points CMOS is advantageous than SET. So it has been seen that Combination of SET and CMOS is very much effective in the nanoscale, low power VLSI circuits. This paper has given a idea to make different sequential circuits using the Hybrid SET-CMOS. The MIB model for SET and BSIM4 model for CMOS are used. The operations of the proposed circuits are verified in Tanner environment. The performances of CMOS and Hybrid SET-CMOS based circuits are compared. The hybrid SET-CMOS circuit is found to consume lesser power than the CMOS based circuit. Further it is established that hybrid SET-CMOS based circuit is much faster compared to CMOS based circuit. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/2777

Radio fingerprinting for anomaly detection using federated learning in LoRa-enabled industrial internet of things

Long Range (LoRa) communications are gaining popularity in the Industrial Internet of Things (IIoT) domain due to their large coverage and high energy efficiency. However, LoRa-enabled IIoT networks are susceptible to cyberattacks mainly due to their wide transmission window and freely operated frequency band. This has led to several categories of cyberattacks. However, existing anomaly detection systems are inefficient in detecting particularly impersonation attacks due to the dense deployment, heterogeneous IIoT devices and manufacturers involved. In this work, we introduce Hawk, a distributed anomaly detection system for detecting compromised devices in LoRa-enabled IIoT. Hawk first measures a device-type specific physical layer feature, Carrier Frequency Offset (CFO) and then leverages the CFO for fingerprinting the device, and consequently detecting anomalous deviations in the device’s CFO behavior, potentially caused by adversaries. To aggregate the device-type specific CFO behavior profile efficiently, Hawk uses federated learning, a distributed machine learning approach. To the best of our knowledge, Hawk is the first to utilise a federated learning method for anomaly-based intrusion detection in LoRa-enabled IIoT. We perform extensive experiments on a real-world dataset collected using 60 LoRa devices, primarily to assess the effectiveness of Hawk against emerging new and unknown attacks. The results show that Hawk improves the detection accuracy by more than 8% compared to the state-of-the-art solutions. Additionally, Hawk reduces the storage overhead by more than 40%, and exhibits significant robustness against cyberattack </p

Enabling secure time-series data sharing via homomorphic encryption in cloud-assisted IIoT

A growing number of Industrial Internet of Things (IIoT) devices and services collect massive time-series data related to production, monitoring and maintenance. To provide ubiquitous access, scalability and sharing possibilities, the IIoT applications utilize the cloud to store collected data streams. However, secure storing of the massive and continuously generated data poses significant privacy risks, including data breaches for IIoT applications. Alongside, we need to protect the utility of the data streams by allowing benign services to access and run analytics securely and selectively. To address this, we propose SmartCrypt, a data storing and sharing system that supports scalable analytics over the encrypted time-series data. SmartCrypt enables users to secure and fine-grain sharing of their encrypted data. Additionally, SmartCrypt guarantees data confidentiality in the presence of unauthorized parties by allowing end-to-end encryption using a novel symmetric homomorphic encryption scheme. We perform extensive experiments on a real-world dataset primarily to assess the feasibility of SmartCrypt for secure storing and sharing of IIoT data streams. The results show that SmartCrypt reduces query time by 17%, reduces range query time by 32%, improves throughput by 9% and scalability by 20% over the best performed scheme in the state-of-the-art.</p

SmartCrypt: secure storing and sharing of time series data streams in IIoT

To provide ubiquitous access, scalability and sharing possibilities, the Industrial Internet of Things (IIoT) applications utilize the cloud to store collected data streams. However, secure storing and sharing of the massive and continuously generated data poses significant privacy risks, including data breaches. This paper proposes SmartCrypt, a data storing and sharing system that supports analytics over the encrypted time series data. SmartCrypt enables users to secure and fine-grain sharing of their encrypted data using a novel symmetric homomorphic encryption scheme. Simulation results show that SmartCrypt reduces query time by 17% and improves through-put by 9% over the benchmark scheme

Distributed On-Demand Clustering Algorithm for Lifetime Optimization in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) play a significant role in Internet of Things (IoT) to provide cost effective solutions for various IoT applications, e.g., wildlife habitat monitoring, but are often highly resource constrained. Hence, preserving energy (or, battery power) of sensor nodes and maximizing the lifetime of WSNs is extremely important. To maximize the lifetime of WSNs, clustering is commonly considered as one of the efficient technique. In a cluster, the role of individual sensor nodes changes to minimize energy consumption, thereby prolonging network lifetime. This paper addresses the problem of lifetime maximization in WSNs by devising a novel clustering algorithm where clusters are formed dynamically. Specifically, we first analyze the network lifetime maximization problem by balancing the energy consumption among cluster heads. Based on the analysis, we provide an optimal clustering technique, in which the cluster radius is computed using alternating direction method of multiplier. Next, we propose a novel On-demand, oPTImal Clustering (OPTIC) algorithm for WSNs. Our cluster head election procedure is not periodic, but adaptive based on the dynamism of the occurrence of events. This on-demand execution of OPTIC aims to significantly reduce computation and message overheads. Experimental results demonstrate that OPTIC improves the energy balance by more than 18% and network lifetime by more than 19% compared to a non-clustering and two clustering solutions in the state-of-the-art