Survey on wireless technology trade-offs for the industrial internet of things

Aside from vast deployment cost reduction, Industrial Wireless Sensor and Actuator Networks (IWSAN) introduce a new level of industrial connectivity. Wireless connection of sensors and actuators in industrial environments not only enables wireless monitoring and actuation, it also enables coordination of production stages, connecting mobile robots and autonomous transport vehicles, as well as localization and tracking of assets. All these opportunities already inspired the development of many wireless technologies in an effort to fully enable Industry 4.0. However, different technologies significantly differ in performance and capabilities, none being capable of supporting all industrial use cases. When designing a network solution, one must be aware of the capabilities and the trade-offs that prospective technologies have. This paper evaluates the technologies potentially suitable for IWSAN solutions covering an entire industrial site with limited infrastructure cost and discusses their trade-offs in an effort to provide information for choosing the most suitable technology for the use case of interest. The comparative discussion presented in this paper aims to enable engineers to choose the most suitable wireless technology for their specific IWSAN deployment

Designing Intelligent Energy Efficient Scheduling Algorithm To Support Massive IoT Communication In LoRa Networks

We are about to enter a new world with sixth sense ability – “Network as a sensor -6G”. The driving force behind digital sensing abilities is IoT. Due to their capacity to work in high frequency, 6G devices have voracious energy demand. Hence there is a growing need to work on green solutions to support the underlying 6G network by making it more energy efficient. Low cost, low energy, and long-range communication capability make LoRa the most adopted and promising network for IoT devices. Since LoRaWAN uses ALOHA for multi-access of channels, collision management is an important task. Moreover, in massive IoT, due to the increased number of devices and their Adhoc transmissions, collision becomes and concern. Furthermore, in long-range communication, such as in forests, agriculture, and remote locations, the IoT devices need to be powered using a battery and cannot be attached to an energy grid. LoRaWAN originally has a star network wherein IoT devices communicated to a single gateway. Massive IoT causes increased traffic at a single gateway. To address Massive IoT issues of collision and gateway load handling, we have designed a reinforcement learning-based scheduling algorithm, a Deep Deterministic policy gradient algorithm with channel activity detection (CAD) to optimize the energy efficiency of LoRaWAN in cross-layer architecture in massive IoT with star topology. We also design a CAD-based simulator for evaluating any algorithms with channel sensing. We compare energy efficiency, packet delivery ratio, latency, and signal strength with existing state of art algorithms and prove that our proposed solution is efficient for massive IoT LoRaWAN with star topology

Development of a Random Time-Frequency Access Protocol for M2M Communication

This thesis focuses on the design and development of the random time-frequency access protocol in Machine-to-Machine (M2M) communication systems and covers different aspects of the data collision problem in these systems. The randomisation algorithm, used to access channels in the frequency domain, represents the key factor that affects data collisions. This thesis presents a new randomisation algorithm for the channel selection process for M2M technologies. The new algorithm is based on a uniform randomisation distribution and is called the Uniform Randomisation Channel Selection Technique (URCST). This new channel selection algorithm improves system performance and provides a low probability of collision with minimum complexity, power consumption, and hardware resources. Also, URCST is a general randomisation technique which can be utilised by different M2M technologies. The analysis presented in this research confirms that using URCST improves system performance for different M2M technologies, such as Weightless-N and Sigfox, with a massive number of devices. The thesis also provides a rigorous and flexible mathematical model for the random time-frequency access protocol which can precisely describe the performance of different M2M technologies. This model covers various scenarios with multiple groups of devices that employ different transmission characteristics like the number of connected devices, the number of message copies, the number of channels, the payload size, and transmission time. In addition, new and robust simulation testbeds have been built and developed in this research to evaluate the performance of different M2M technologies that utilise the random time-frequency access protocol. These testbeds cover the channel histogram, the probability of collisions, and the mathematical model. The testbeds were designed to support the multiple message copies approach with various groups of devices that are connected to the same base station and employ different transmission characteristics. Utilising the newly developed channel selection algorithm, mathematical model, and testbeds, the research offers a detailed and thorough analysis of the performance of Weightless-N and Sigfox in terms of the message lost ratio (MLR) and power consumption. The analysis shows some useful insights into the performance of M2M systems. For instance, while using multiple message copies improves the system performance, it might degrade the reliability of the system as the number of devices increases beyond a specific limit. Therefore, increasing the number of message copies can be disadvantageous to M2M communication performance

An Energy Model Using Sleeping Algorithms for Wireless Sensor Networks under Proactive and Reactive Protocols: A Performance Evaluation

The continuous evolution of the Internet of Things (IoT) makes it possible to connect everyday objects to networks in order to monitor physical and environmental conditions, which is made possible due to wireless sensor networks (WSN) that enable the transfer of data. However, it has also brought about many challenges that need to be addressed, such as excess energy consumption. Accordingly, this paper presents and analyzes wireless network energy models using five different communication protocols: Ad Hoc On-Demand Distance Vector (AODV), Multi-Parent Hierarchical (MPH), Dynamic Source Routing (DSR), Low Energy Adaptive Clustering Hierarchy (LEACH) and Zigbee Tree Routing (ZTR). First, a series of metrics are defined to establish a comparison and determine which protocol exhibits the best energy consumption performance. Then, simulations are performed and the results are compared with real scenarios. The energy analysis is conducted with three proposed sleeping algorithms: Modified Sleeping Crown (MSC), Timer Sleeping Algorithm (TSA), and Local Energy Information (LEI). Thereafter, the proposed algorithms are compared by virtue of two widely used wireless technologies, namely Zigbee and WiFi. Indeed, the results suggest that Zigbee has a better energy performance than WiFi, but less redundancy in the topology links, and this study favors the analysis with the simulation of protocols with different nature. The tested scenario is implemented into a university campus to show a real network running