Venkat P. Rangan

Bio: Venkat P. Rangan is an academic researcher from Amrita Vishwa Vidyapeetham. The author has contributed to research in topics: Wireless sensor network & Smart grid. The author has an hindex of 9, co-authored 19 publications receiving 194 citations.

IoT cyber risk: a holistic analysis of cyber risk assessment frameworks, risk vectors, and risk ranking process

Abstract: Security vulnerabilities of the modern Internet of Things (IoT) systems are unique, mainly due to the complexity and heterogeneity of the technology and data. The risks born out of these IoT systems cannot easily fit into an existing risk framework. There are many cybersecurity risk assessment approaches and frameworks that are under deployment in many governmental and commercial organizations. Extending these existing frameworks to IoT systems alone will not address the new risks that have arisen in the IoT ecosystem. This study has included a review of existing popular cyber risk assessment methodologies and their suitability to IoT systems. National Institute of Standards and Technology, Operationally Critical Threat, Asset, and Vulnerability Evaluation, Threat Assessment & Remediation Analysis, and International Standards Organization are the four main frameworks critically analyzed in this research study. IoT risks are presented and reviewed in terms of the IoT risk category and impacted industries. IoT systems in financial technology and healthcare are dealt with in detail, given their high-risk exposure. Risk vectors for IoT and the Internet of Medical Things (IoMT) are discussed in this study. A unique risk ranking method to rank and quantify IoT risk is introduced in this study. This ranking method initiates a risk assessment approach exclusively for IoT systems by quantifying IoT risk vectors, leading to effective risk mitigation strategies and techniques. A unique computational approach to calculate the cyber risk for IoT systems with IoT-specific impact factors has been designed and explained in the context of IoMT systems.

Mobile infrastructure for coastal region offshore communications and networks

Abstract: Marine fishermen risk their lives when they go as far as 120 km from the shore on a fishing trip lasting 5–7 days. They are completely cut off from the mainland. Cellular coverage exists only up to 12–15 km from the shore. In emergency situations, the fishermen have no way to call for help. Even under normal conditions, prolonged isolation from their family and friends causes mental depression. Since the marine fishermen are not economically well off especially in the developing countries, there has not been much commercial interest in addressing this problem. It is not seen as a profitable business proposition. However, addressing this problem will benefit the marine fishermen community immensely. Our center conducted interviews with several fishermen to understand this problem and came up with a cost-effective solution. The solution enables the fishermen to use the smart phones which they own already to get internet at sea using Wi-Fi. The Access Point (AP) on the boat connects over Ethernet to an onboard gateway to long range Wi-Fi backhaul network. The onshore base station is installed on a tower at a height of 50–60 m. Boats are also used as mobile base stations to extend the range of the network. This solution, when tested over the Arabian Sea, provided a range of 40+ km in the first hop and 20+ km every subsequent hop. This network can be operated on a cooperative community basis by the fishermen community at reasonable per capita CAPEX and OPEX. A pilot deployment is in progress in a coastal village community in Kerala, India, to gain operational experience.

Reliable network connectivity in wireless sensor networks for remote monitoring of landslides

Abstract: Providing reliable communications in a remotely monitored large-scale deployment of Wireless Sensor Networks is a challenging task. In this paper, we deal with such a deployment to monitor a landslide prone zone where the nodes sense geological attributes essential for early warning. The deployment area is a hilly mountain with different demographic characteristics. Establishing network connectivity in this deployment site for real-time streaming of sensor data involves dealing with site specific challenges such as asymmetric links, dynamic network conditions due to rough weather, inadequate solar power, network fail-over and re-connection problem etc. Our deployment makes use of only a few number of relay nodes for connecting the wireless sensor network to a field management center through an IoT gateway. The field management center makes use of multiple fault-tolerant WAN networks to relay the data to a remote central data management center for deep data analysis and for generating early warnings prior to a catastrophic event. This paper provides insights into our experiences from successful deployment of a WSN system and reports real-time measurements taken in the field for ensuring network reliability. Evaluation of our system reflects that the network is able to provide reliable communication by providing a fail-over for streaming data with less than a minute to re-establish the connection and the gateway software is capable of handling heterogeneous sensor readings at a rate up-to 1700/s within a latency of 10 s while delivering the data to the data center. The system also achieves highly accurate time synchronization in the order of microseconds throughout the network.

Energy Efficient Data Acquisition Techniques Using Context Aware Sensing for Landslide Monitoring Systems

Abstract: Real-time wireless sensor networks are an emerging technology for continuous environmental monitoring. But real-world deployments are constrained by resources, such as power, memory, and processing capabilities. In this paper, we discuss a set of techniques to maximize the lifetime of a system deployed in south India for detecting rain-fall induced landslides. In this system, the sensing subsystem consumes 77.5%, the communication subsystem consumes 22%, and the processing subsystem consumes 0.45% of total power consumption. Hence, to maximize the lifetime of the system, the sensing subsystem power consumption has to be reduced. The major challenge to address is the development of techniques that reduce the power consumption, while preserving the reliability of data collection and decision support by the system. This paper proposes a wavelet-based sampling algorithm for choosing the minimum sampling rate for ensuring the data reliability. The results from the wavelet sampling algorithm along with the domain knowledge have been used to develop context aware data collection models that enhance the lifetime of the system. Two such models named context aware data management (CAD) and context aware energy management (CAE) have been devised. The results show that the CAD model extends the lifetime by six times and the CAE model does so by 20 times when compared with the continuous data collection model, which is the existing approach. In this paper, we also developed mathematical modeling for CAD and CAE, which have been validated using real-time data collected in the past.

A novel solution for high speed internet over the oceans

Abstract: The cellular network range over the oceans is limited to about 15 km from the shore in most places. The service providers do not have any incentive to extend the coverage further. However, marine fishermen who routinely spend 5 to 7 days at the ocean on a single fishing trip are severely impacted by this. They go as far away as 120 km from the shore on some occasions and are completely cut off from the land during their fishing trips. Also, they are generally poor and need an economical solution to stay connected. We have developed and successfully prototyped an affordable solution based on a multi-level auto-reconfigurable backhaul infrastructure network. It uses long range (LR) Wi-Fi and extends the connectivity to 60 km and beyond over the oceans using heterogeneous networks and relay nodes. Multiple field trials involving up to four boats over the Arabian Sea have yielded consistent and repeatable results even under rough sea states. Additional experiments conducted over the backwaters by scaling up the network further have also yielded positive results. This work provides a detailed description of the architected solution and the results obtained.

ISO (International Organization for Standardization ; 国際標準化機構)

Abstract: ISO 3166-3 Newsletters are issued by the secretariat of the ISO 3166/MA when changes in the code lists of ISO 3166-3 have been decided upon by the ISO 3166/MA. ISO 3166-3 Newsletters are identified by a two-component number, stating the currently valid edition of ISO 3166-3 in Roman numerals (e g "I") and a consecutive order number (in Latin numerals) starting with "1" for each new edition of ISO 3166-3. The headers of the table below refer to the list part of ISO 31663:1999.

Application of deep learning algorithms in geotechnical engineering: a short critical review

Abstract: With the advent of big data era, deep learning (DL) has become an essential research subject in the field of artificial intelligence (AI). DL algorithms are characterized with powerful feature learning and expression capabilities compared with the traditional machine learning (ML) methods, which attracts worldwide researchers from different fields to its increasingly wide applications. Furthermore, in the field of geochnical engineering, DL has been widely adopted in various research topics, a comprehensive review summarizing its application is desirable. Consequently, this study presented the state of practice of DL in geotechnical engineering, and depicted the statistical trend of the published papers. Four major algorithms, including feedforward neural (FNN), recurrent neural network (RNN), convolutional neural network (CNN) and generative adversarial network (GAN) along with their geotechnical applications were elaborated. In addition, a thorough summary containing pubilished literatures, the corresponding reference cases, the adopted DL algorithms as well as the related geotechnical topics was compiled. Furthermore, the challenges and perspectives of future development of DL in geotechnical engineering were presented and discussed.

Joint Position and Travel Path Optimization for Energy Efficient Wireless Data Gathering Using Unmanned Aerial Vehicles

Abstract: Unnamed aerial vehicles (UAVs) or drones have attracted growing interest in the last few years for multiple applications; thanks to their advantages in terms of mobility, easy movement, and flexible positioning. In UAV-based communications, mobility and higher line-of-sight probability represent opportunities for the flying UAVs while the limited battery capacity remains its major challenge. Thus, they can be employed for specific applications where their permanent presence is not mandatory. Data gathering from wireless sensor networks is one of these applications. This paper proposes an energy-efficient solution minimizing the UAV and/or sensors energy consumption while accomplishing a tour to collect data from the spatially distributed wireless sensors. The objective is to determine the positions of the UAV “stops” from which it can collect data from a subset of sensors located in the same neighborhood and find the path that the UAV should follow to complete its data gathering tour in an energy-efficient manner. A non-convex optimization problem is first formulated then, an efficient and low-complex technique is proposed to iteratively achieve a sub-optimal solution. The initial problem is decomposed into three sub-problems: The first sub-problem optimizes the positioning of the stops using linearization. The second one determines the sensors assignment to stops using clustering. Finally, the path among these stops is optimized using the travel salesman problem. Selected numerical results show the behavior of the UAV versus various system parameters and that the achieved energy is considerably reduced compared to the one of existing approaches.

Multi-hazard disaster studies: monitoring, detection, recovery, and management, based on emerging technologies and optimal techniques

Abstract: Every year man-made and natural disasters impact the lives of millions of people. The frequency of occurrence of such disasters is steadily increasing since the last 50 years, and this has resulted in considerable loss of life, destruction of infrastructure, and social and economic disruption. A focussed and comprehensive solution is needed encompassing all aspects, including early detection of disaster scenarios, prevention, recovery, and management to minimize the losses. This survey paper presents a critical analysis of the existing methods and technologies that are relevant to a disaster scenario, such as WSN, remote sensing technique, artificial intelligence, IoT, UAV, and satellite imagery, to encounter the issues associated with disaster monitoring, detection, and management. In case of emergency conditions arising out of a typical disaster scenario, there is a strong likelihood that the communication networks will be partially disrupted; thus the alternate networks can play a vital role in disaster detection and management. It focuses on the role of the alternate networks and the associated technologies in maintaining connectivity in various disaster scenarios. It presents a comprehensive study on multiple disasters such as landslide, forest fire, and an earthquake based on the latest technologies to monitor, detect, and manage the various disasters. It focuses on several parameters that are necessary for disaster detection and monitoring and offers appropriate solutions. It also touches upon big data analytics for disaster management. Several techniques are explored, along with their merits and demerits. Open challenges are highlighted, and possible future directions are given.