Lakshmikanth Guntupalli

Bio: Lakshmikanth Guntupalli is an academic researcher from Mid Sweden University. The author has contributed to research in topics: Network packet & Throughput. The author has an hindex of 9, co-authored 17 publications receiving 353 citations. Previous affiliations of Lakshmikanth Guntupalli include University of Agder.

Scalability Analysis of a LoRa Network Under Imperfect Orthogonality

Abstract: Low-power wide-area network (LPWAN) technologies are gaining momentum for Internet-of-things applications since they promise wide coverage to a massive number of battery operated devices using grant-free medium access. LoRaWAN, with its physical (PHY) layer design and regulatory efforts, has emerged as the widely adopted LPWAN solution. By using chirp spread spectrum modulation with qausi-orthogonal spreading factors (SFs), LoRa PHY offers coverage to wide-area applications while supporting high-density of devices. However, thus far its scalability performance has been inadequately modeled and the effect of interference resulting from the imperfect orthogonality of the SFs has not been considered. In this paper, we present an analytical model of a single-cell LoRa system that accounts for the impact of interference among transmissions over the same SF (co-SF) as well as different SFs (inter-SF). By modeling the interference field as Poisson point process under duty cycled ALOHA, we derive the signal-to-interference ratio distributions for several interference conditions. Results show that, for a duty cycle as low as 0.33%, the network performance under co-SF interference alone is considerably optimistic as the inclusion of inter-SF interference unveils a further drop in the success probability and the coverage probability of approximately 10% and 15%, respectively, for 1500 devices in a LoRa channel. Finally, we illustrate how our analysis can characterize the critical device density with respect to cell size for a given reliability target.

Scalability Analysis of a LoRa Network under Imperfect Orthogonality

Abstract: Low-power wide-area network (LPWAN) technologies are gaining momentum for internet-of-things (IoT) applications since they promise wide coverage to a massive number of battery-operated devices using grant-free medium access. LoRaWAN, with its physical (PHY) layer design and regulatory efforts, has emerged as the widely adopted LPWAN solution. By using chirp spread spectrum modulation with qausi-orthogonal spreading factors (SFs), LoRa PHY offers coverage to wide-area applications while supporting high-density of devices. However, thus far its scalability performance has been inadequately modeled and the effect of interference resulting from the imperfect orthogonality of the SFs has not been considered. In this paper, we present an analytical model of a single-cell LoRa system that accounts for the impact of interference among transmissions over the same SF (co-SF) as well as different SFs (inter-SF). By modeling the interference field as Poisson point process under duty-cycled ALOHA, we derive the signal-to-interference ratio (SIR) distributions for several interference conditions. Results show that, for a duty cycle as low as 0.33%, the network performance under co-SF interference alone is considerably optimistic as the inclusion of inter-SF interference unveils a further drop in the success probability and the coverage probability of approximately 10% and 15%, respectively for 1500 devices in a LoRa channel. Finally, we illustrate how our analysis can characterize the critical device density with respect to cell size for a given reliability target.

Aggregated Packet Transmission in Duty-Cycled WSNs: Modeling and Performance Evaluation

Abstract: Duty cycling (DC) is a popular technique for energy conservation in wireless sensor networks (WSNs) that allows nodes to wake up and sleep periodically. Typically, a single-packet transmission (SPT) occurs per cycle, leading to possibly long delay. With aggregated packet transmission (APT), nodes transmit a batch of packets in a single cycle. The potential benefits brought by an APT scheme include shorter delay, higher throughput, and higher energy efficiency. In the literature, different analytical models have been proposed to evaluate the performance of SPT schemes. However, no analytical models for the APT mode on synchronous DC medium access control (MAC) mechanisms exist. In this paper, we first develop a 3-D discrete-time Markov chain (DTMC) model to evaluate the performance of an APT scheme with packet retransmission enabled. The proposed model captures the dynamics of the state of the queue of nodes and the retransmission status and the evolution of the number of active nodes in the network, i.e., nodes with a nonempty queue. We then study the number of retransmissions needed to transmit a packet successfully. Based on the observations, we develop another less-complex DTMC model with infinite retransmissions, which embodies only two dimensions. Furthermore, we extend the 3-D model into a 4-D model by considering error-prone channel conditions. The proposed models are adopted to determine packet delay, throughput, packet loss, energy consumption, and energy efficiency. Furthermore, the analytical models are validated through discrete-event-based simulations. Numerical results show that an APT scheme achieves substantially better performance than its SPT counterpart in terms of delay, throughput, packet loss, and energy efficiency and that the developed analytical models reveal precisely the behavior of the APT scheme.

Energy Efficient Consecutive Packet Transmissions in Receiver-Initiated Wake-Up Radio Enabled WSNs

Abstract: In wake-up radio (WuR)-enabled wireless sensor networks, data communication among nodes is triggered in an on-demand manner, by either a sender or a receiver. For receiver-initiated WuR (RI-WuR), a receiving node wakes up sending nodes through a wake-up call. Correspondingly sending nodes transmit packets in a traditional way by competing with one another multiple times in a single operational cycle. In this paper, we propose a receiver-initiated consecutive packet transmission WuR (RI-CPT-WuR) medium access control (MAC) protocol, which eliminates multiple competitions to achieve higher energy efficiency. Furthermore, we develop two associated discrete time Markov chains (DTMCs) for evaluating the performance of RI-CPT-WuR and an existing RI-WuR MAC protocol. Using the solutions from the DTMC models, closed-form expressions for network throughput, average delay, packet reliability ratio, energy consumption and lifetime, and energy efficiency for both protocols are obtained. Numerical results demonstrate the superiority of the RI-CPT-WuR protocol.

An On-Demand Energy Requesting Scheme for Wireless Energy Harvesting Powered IoT Networks

Abstract: Energy harvesting (EH) delivers a unique technique for replenishing batteries in Internet of Things (IoT) devices. Equipped with an energy harvesting accessory, EH-enabled sensor nodes/IoT devices ...

A Survey of LoRaWAN for IoT: From Technology to Application

Abstract: LoRaWAN is one of the low power wide area network (LPWAN) technologies that have received significant attention by the research community in the recent years. It offers low-power, low-data rate communication over a wide range of covered area. In the past years, the number of publications regarding LoRa and LoRaWAN has grown tremendously. This paper provides an overview of research work that has been published from 2015 to September 2018 and that is accessible via Google Scholar and IEEE Explore databases. First, a detailed description of the technology is given, including existing security and reliability mechanisms. This literature overview is structured by categorizing papers according to the following topics: (i) physical layer aspects; (ii) network layer aspects; (iii) possible improvements; and (iv) extensions to the standard. Finally, a strengths, weaknesses, opportunities and threats (SWOT) analysis is presented along with the challenges that LoRa and LoRaWAN still face.

A Critical Analysis of Research Potential, Challenges, and Future Directives in Industrial Wireless Sensor Networks

Abstract: In recent years, industrial wireless sensor networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems, and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment, and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper, a detailed discussion on design objectives, challenges, and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines, and possible hazards in industrial atmosphere are discussed. This paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. This paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs.

Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges

Abstract: Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart devices connected to the Internet. In the wake of disruptive IoT with a huge amount and variety of data, Machine Learning (ML) and Deep Learning (DL) mechanisms will play a pivotal role to bring intelligence to the IoT networks. Among other aspects, ML and DL can play an essential role in addressing the challenges of resource management in large-scale IoT networks. In this article, we conduct a systematic and in-depth survey of the ML- and DL-based resource management mechanisms in cellular wireless and IoT networks. We start with the challenges of resource management in cellular IoT and low-power IoT networks, review the traditional resource management mechanisms for IoT networks, and motivate the use of ML and DL techniques for resource management in these networks. Then, we provide a comprehensive survey of the existing ML- and DL-based resource management techniques in wireless IoT networks and the techniques specifically designed for HetNets, MIMO and D2D communications, and NOMA networks. To this end, we also identify the future research directions in using ML and DL for resource allocation and management in IoT networks.

Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges

Abstract: Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart devices connected to the Internet. In the wake of disruptive IoT with a huge amount and variety of data, Machine Learning (ML) and Deep Learning (DL) mechanisms will play a pivotal role to bring intelligence to the IoT networks. Among other aspects, ML and DL can play an essential role in addressing the challenges of resource management in large-scale IoT networks. In this article, we conduct a systematic and in-depth survey of the ML- and DL-based resource management mechanisms in cellular wireless and IoT networks. We start with the challenges of resource management in cellular IoT and low-power IoT networks, review the traditional resource management mechanisms for IoT networks, and motivate the use of ML and DL techniques for resource management in these networks. Then, we provide a comprehensive survey of the existing ML- and DL-based resource allocation techniques in wireless IoT networks and also techniques specifically designed for HetNets, MIMO and D2D communications, and NOMA networks. To this end, we also identify the future research directions in using ML and DL for resource allocation and management in IoT networks.

Design architectures for energy harvesting in the Internet of Things

Abstract: An increasing number of objects (things) are being connected to the Internet as they become more advanced, compact, and affordable. These Internet-connected objects are paving the way toward the emergence of the Internet of Things (IoT). The IoT is a distributed network of low-powered, low-storage, light-weight and scalable nodes. Most low-power IoT sensors and embedded IoT devices are powered by batteries with limited lifespans, which need replacement every few years. This replacement process is costly, so smart energy management could play a vital role in enabling energy efficiency for communicating IoT objects. For example, harvesting of energy from naturally or artificially available environmental resources removes IoT networks’ dependence on batteries. Scavenging unlimited amounts of energy in contrast to battery-powered solutions makes IoT systems long-lasting. Thus, here we present energy-harvesting and sub-systems for IoT networks. After surveying the options for harvesting systems, distribution approaches, storage devices and control units, we highlight future design challenges of IoT energy harvesters that must be addressed to continuously and reliably deliver energy.