A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

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. The 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. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

Energy Harvesting Networked Nodes: Measurements, Algorithms, and Prototyping

Recent advances in ultra-low-power wireless communications and in energy harvesting will soon enable energetically self-sustainable wireless devices. Networks of such devices will serve as building blocks for different Internet of Things (IoT) applications, such as searching for an object on a network of objects and continuous monitoring of object configurations. Yet, numerous challenges need to be addressed for the IoT vision to be fully realized. This thesis considers several challenges related to ultra-low-power energy harvesting networked nodes: energy source characterization, algorithm design, and node design and prototyping. Additionally, the thesis contributes to engineering education, specifically to project-based learning. We summarize our contributions to light and kinetic (motion) energy characterization for energy harvesting nodes. To characterize light energy, we conducted a first-of-its kind 16 month-long indoor light energy measurements campaign. To characterize energy of motion, we collected over 200 hours of human and object motion traces. We also analyzed traces previously collected in a study with over 40 participants. We summarize our insights, including light and motion energy budgets, variability, and influencing factors. These insights are useful for designing energy harvesting nodes and energy harvesting adaptive algorithms. We shared with the community our light energy traces, which can be used as energy inputs to system and algorithm simulators and emulators. We also discuss resource allocation problems we considered for energy harvesting nodes. Inspired by the needs of tracking and monitoring IoT applications, we formulated and studied resource allocation problems aimed at allocating the nodes' time-varying resources in a uniform way with respect to time. We mainly considered deterministic energy profile and stochastic environmental energy models, and focused on single node and link scenarios. We formulated optimization problems using utility maximization and lexicographic maximization frameworks, and introduced algorithms for solving the formulated problems. For several settings, we provided low-complexity solution algorithms. We also examined many simple policies. We demonstrated, analytically and via simulations, that in many settings simple policies perform well. We also summarize our design and prototyping efforts for a new class of ultra-low-power nodes - Energy Harvesting Active Networked Tags (EnHANTs). Future EnHANTs will be wireless nodes that can be attached to commonplace objects (books, furniture, clothing). We describe the EnHANTs prototypes and the EnHANTs testbed that we developed, in collaboration with other research groups, over the last 4 years in 6 integration phases. The prototypes harvest energy of the indoor light, communicate with each other via ultra-low-power transceivers, form small multihop networks, and adapt their communications and networking to their energy harvesting states. The EnHANTs testbed can expose the prototypes to light conditions based on real-world light energy traces. Using the testbed and our light energy traces, we evaluated some of our energy harvesting adaptive policies. Our insights into node design and performance evaluations may apply beyond EnHANTs to networks of various energy harvesting nodes. Finally, we present our contributions to engineering education. Over the last 4 years, we engaged high school, undergraduate, and M.S. students in more than 100 research projects within the EnHANTs project. We summarize our approaches to facilitating student learning, and discuss the results of evaluation surveys that demonstrate the effectiveness of our approaches

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Electrical and Computer Engineering Research Report 2008

Department Research New Chair Publications Enterprisehttps://digitalcommons.mtu.edu/ece-annualreports/1005/thumbnail.jp

A Self-organizing Hybrid Sensor System With Distributed Data Fusion For Intruder Tracking And Surveillance

A wireless sensor network is a network of distributed nodes each equipped with its own sensors, computational resources and transceivers. These sensors are designed to be able to sense specific phenomenon over a large geographic area and communicate this information to the user. Most sensor networks are designed to be stand-alone systems that can operate without user intervention for long periods of time. While the use of wireless sensor networks have been demonstrated in various military and commercial applications, their full potential has not been realized primarily due to the lack of efficient methods to self organize and cover the entire area of interest. Techniques currently available focus solely on homogeneous wireless sensor networks either in terms of static networks or mobile networks and suffers from device specific inadequacies such as lack of coverage, power and fault tolerance. Failing nodes result in coverage loss and breakage in communication connectivity and hence there is a pressing need for a fault tolerant system to allow replacing of the failed nodes. In this dissertation, a unique hybrid sensor network is demonstrated that includes a host of mobile sensor platforms. It is shown that the coverage area of the static sensor network can be improved by self-organizing the mobile sensor platforms to allow interaction with the static sensor nodes and thereby increase the coverage area. The performance of the hybrid sensor network is analyzed for a set of N mobile sensors to determine and optimize parameters such as the position of the mobile nodes for maximum coverage of the sensing area without loss of signal between the mobile sensors, static nodes and the central control station. A novel approach to tracking dynamic targets is also presented. Unlike other tracking methods that are based on computationally complex methods, the strategy adopted in this work is based on a computationally simple but effective technique of received signal strength indicator measurements. The algorithms developed in this dissertation are based on a number of reasonable assumptions that are easily verified in a densely distributed sensor network and require simple computations that efficiently tracks the target in the sensor field. False alarm rate, probability of detection and latency are computed and compared with other published techniques. The performance analysis of the tracking system is done on an experimental testbed and also through simulation and the improvement in accuracy over other methods is demonstrated

Next-generation IoT devices: sustainable eco-friendly manufacturing, energy harvesting, and wireless connectivity

This invited paper presents potential solutions for tackling some of the main underlying challenges toward developing sustainable Internet-of-things (IoT) devices with a focus on eco-friendly manufacturing, sustainable powering, and wireless connectivity for next-generation IoT devices. The diverse applications of IoT systems, such as smart cities, wearable devices, self-driving cars, and industrial automation, are driving up the number of IoT systems at an unprecedented rate. In recent years, the rapidly-increasing number of IoT devices and the diverse application-specific system requirements have resulted in a paradigm shift in manufacturing processes, powering methods, and wireless connectivity solutions. The traditional cloud-centering IoT systems are moving toward distributed intelligence schemes that impose strict requirements on IoT devices, e.g., operating range, latency, and reliability. In this article, we provide an overview of hardware-related research trends and application use cases of emerging IoT systems and highlight the enabling technologies of next-generation IoT. We review eco-friendly manufacturing for next-generation IoT devices, present alternative biodegradable and eco-friendly options to replace existing materials, and discuss sustainable powering IoT devices by exploiting energy harvesting and wireless power transfer. Finally, we present (ultra-)low-power wireless connectivity solutions that meet the stringent energy efficiency and data rate requirements of future IoT systems that are compatible with a batteryless operation