Search

Lightweight communication protocols for wireless sensor networks (WSNs) are unfolding for machine to machine (M2M) communications and thus there is always going to be a possible conflict of interest on which protocol is best suited for any particular application. The two protocols of interest in this study are the message queue telemetry transport protocol for sensor network (MQTT-SN), a variant of message queue telemetry transport (MQTT) protocol and the constrained application protocol (CoAP). There have been studies that reveal that these protocols perform differently based on the underlying network conditions. CoAP experience lower delays than MQTT for higher packet loss and higher delays for lower packet loss. MQTT default communication via a broker is easier to scale compared to CoAP direct request-response paradigm. Although this is a huge advantage over CoAP, it presents the single point-of-failure problem. In this paper we propose an integration of MQTT-CoAP protocol using an abstraction layer that enables both MQTT-SN and CoAP protocol to be used in the same sensor node. Resources are managed by directly modifying sensor node configuration using CoAP protocol. Performance evaluation of these protocols under the integrated scenario shows acceptable levels of latency and energy consumption for internet of thing (IoT) operations. [ABSTRACT FROM AUTHOR]

Wireless electroencephalography (EEG) has emerged as a critical interface between human cognitive processes and machine learning technologies in the burgeoning field of sensor communications. This paper presents a comprehensive review of advancements in wireless EEG communication and analysis, with an emphasis on their role in next-generation green wireless networks and industrial IoT. The review explores the efficacy of modulation techniques, such as amplitude-shift keying (ASK) and frequency-shift keying (FSK) in EEG data transmission, and emphasizes the transformative role of deep learning in the joint transmission and restoration of EEG signals. In addition, we propose a novel, energy-efficient approach to deep learning-based EEG analytics, designed to enhance wireless information transfer for industrial IoT applications. By applying an autoencoder to sample the EEG data and incorporating a hidden layer to simulate a noisy communication channel, we assessed the energy efficiency and reliability of the transmission. Our results demonstrate that the chosen network topology and parameters significantly affect not only data fidelity but also energy consumption, thus providing valuable insights for the development of sustainable and efficient wireless EEG systems in industrial IoT environments. A key aspect of our study is related to symmetry. Our results demonstrate that the chosen network topology and parameters significantly impact not only data fidelity but also energy fidelity and energy consumption, thus providing valuable insights for the development of sustainable and efficient wireless EEG systems in industrial IoT environments. Furthermore, we realized that the EEG data showed mildly marked symmetry. Neural networks must also exhibit asymmetric behavior for better performance. [ABSTRACT FROM AUTHOR]

A low-profile slant ±45° polarized antenna for a 4G/5G base station with a dominant path wave propagation model (DPM) is reported in this paper. For dual-polarization and wide impedance bandwidth, the two dipole radiators are crisscrossed and a square metallic ring is integrated with the dipole arms and screwed with the four shorting vias. The antenna operates in Band 40/41/42/43 with strong isolation and low ECC (Envelop Correlation Coefficient). Fabricated antenna results show that the predicted wideband antenna has 13.38% bandwidth in 4G, which is Band 40/41, and 29.33% bandwidth in 5G sub-6 GHz Band 42/43. The antenna has a consistent radiation pattern, a 40 dB XPD (Cross Pol Discrimination), and a beam width of 69.5° ±1.5° in the entire frequency band. Three base station sites were explored to deploy the radiation pattern of a dual-polarized antenna in the 3D EM (Electromagnetic Analysis) tool. To evaluate signal quality, strength, radio network planning, and wave propagation analysis by analyzing antenna performance in real-time scenarios with 64 and 256 QAM (Quadrature Amplitude Modulation) techniques. For the proposed four-stream MIMO (Multiple-Input-Multiple-Output) antenna arrangement at deployed cell sites for 4G/5G base station applications, the maximum downlink and uplink data rates are 3.826/2.8 Mbps and 1044/800 Mbps respectively. [ABSTRACT FROM AUTHOR]

Due to their much increased spatial resolution and array gain, large-scale antenna arrays have the potential to significantly enhance the spectral efficiency of wireless systems. Recent research in the area of massive Multiple-Input Multiple-Output (MIMO) antennas shows that user channels are enhanced as the number of antennas at the Base Stations (BSs) rises, allowing achieving high signal improvement with minimum inter-user interference. The combined effects of the pilot reuse factor and the BS number of antennas on average overall spectral efficiency in huge MIMO antennas will be investigated. To examine the SE of these receivers, several channel estimators will be utilized, including LMMSE, ZF, and MR methods. Theoretically, BS could offer a substantially higher average total SE if it had more antennas. [ABSTRACT FROM AUTHOR]

In indoor positioning,the main advantage of pedestrian dead reckoning(PDR)is that the user only needs to have a smart phone to realize positioning,without relying on the external environment.However,there is a large cumulative error.Ge-nerally,it is necessary to combine Bluetooth,WiFi,geomagnetic or other technologies to improve the positioning accuracy.How-ever, this method requires some hardware nodes and a fingerprint database to be built for this purpose.To solve this problem,an indoor positioning method based on correcting PDR in machine to machine(M2M)area is proposed.Firstly,a distance measurement area is set up during pedestrian travel.Secondly,the distance between pedestrian mobile phones and other mobile phones is measured in this region.Finally,the positioning error and accuracy of PDR are corrected by trilateral positioning method.The method has the advantages that no additional hardware facilities are required.Experimental results show that,compared with the traditional PDR positioning,this method is suitable for long-time positioning,and the average positioning error is reduced to 0.36 m,with high positioning accuracy.

The proliferation of devices connected via the Internet of Things (IoT) leads to high energy consumption, e-waste production, and ultimately, additional extra carbon emissions. To maximize the benefits and reduce the detrimental impact of IoT on the environment, the concept of green IoT (GIoT) is presented, which can fulfill the need for a smart and sustainable world. This study aims to investigate the current practices, challenges, and policies related to the implementation of GIoT in Gulf Cooperation Council (GCC) countries (Saudi Arabia, United Arab Emirates (UAE), Qatar, Kuwait, Oman, and Bahrain). GCC countries are placed in the top 14 per capita carbon dioxide (CO 2 ) emissions in the world. This study pioneers discussing the GIoT through the GCC perspective. Therefore, this study reviews the threat of e-waste to the environment and counteract strategies of GCC nations. The impact of living standards in GCC countries on embodied energy and obsolescence overheads were highlighted. This study provides insights into economic diversification plans laid out by GCC countries in their national vision and moving away from dependence on hydrocarbon reserves. The renewable energy (RE) opportunities available in the context of smart city development and GIoT deployment were evaluated. Power consumptions of computing servers at data centers, sensor nodes, and machine-to-machine (M2M) communications were discussed. Integration of artificial intelligence in distributed computing environments was also studied. GCC countries lag in the network readiness index but rank higher on information and communications technology usage (ICT), individual skills, and access to ICT. GCC countries are also making steady progress in building the best international practices and in leveraging opportunities to transform digital government models. [ABSTRACT FROM AUTHOR]

Wireless electroencephalography (EEG) has emerged as a critical interface between human cognitive processes and machine learning technologies in the burgeoning field of sensor communications. This paper presents a comprehensive review of advancements in wireless EEG communication and analysis, with an emphasis on their role in next-generation green wireless networks and industrial IoT. The review explores the efficacy of modulation techniques, such as amplitude-shift keying (ASK) and frequency-shift keying (FSK) in EEG data transmission, and emphasizes the transformative role of deep learning in the joint transmission and restoration of EEG signals. In addition, we propose a novel, energy-efficient approach to deep learning-based EEG analytics, designed to enhance wireless information transfer for industrial IoT applications. By applying an autoencoder to sample the EEG data and incorporating a hidden layer to simulate a noisy communication channel, we assessed the energy efficiency and reliability of the transmission. Our results demonstrate that the chosen network topology and parameters significantly affect not only data fidelity but also energy consumption, thus providing valuable insights for the development of sustainable and efficient wireless EEG systems in industrial IoT environments. A key aspect of our study is related to symmetry. Our results demonstrate that the chosen network topology and parameters significantly impact not only data fidelity but also energy fidelity and energy consumption, thus providing valuable insights for the development of sustainable and efficient wireless EEG systems in industrial IoT environments. Furthermore, we realized that the EEG data showed mildly marked symmetry. Neural networks must also exhibit asymmetric behavior for better performance.

In the twenty-first century, automation has become more prevalent in large part due to the Internet of Things (IoT). The integration of IoT, Wireless Sensor Networks (WSNs) and health monitoring system with multi-cloud environment can optimize a lot of parameters like bandwidth, communication protocols and redundancy of the sensor network. The proposed architecture is introduced keeping in mind the current health infrastructure and monitoring system with different types of network framework. We have also discussed about machine to machine protocols for health monitoring with common entities linked with building communication protocols. The sensors data analytics carried out can sustain multi-level computation is the idea behind this research prospect to give greater depth about direct applicability in multi cloud sensor communication protocols. The offered prototype, when modelled with the unique communication system, produces a more efficient model than the standard way, with a system that is 98 percent more accurate. [ABSTRACT FROM AUTHOR]

In the recent few years, due to its significant deployment to meet global demand for smart cities, the Internet of Things (IoT) has gained a lot of attention. Environment energy harvesting devices, which use ambient energy to generate electricity, could be a viable option in near future for charging or powering stand-alone IoT sensors and electronic devices. The key advantages of such energy harvesting gadgets are that they are environmentally friendly, portable, wireless, cost-effective, and compact. It is significant to propos and fabricate an improved, high-quality, economical, and efficient energy harvesting systems to overcome power supply to tiny IoT devices at the remote locations. In this article, various types of mechanisms for harvesting renewable energies that can power sensor enabled IoT locally, as well as its associated wireless sensor networks (WSNs), are reviewed. These methods are discussed in terms of their advantages and applications, as well as their drawbacks and limitations. Furthermore, methodological performance analysis for the decade 2005 to 2020 is surveyed in order to identify the methods that delivered high output power for each device. Furthermore, the outstanding breakthrough performances of each of the aforementioned micro-power generators during this time period are emphasized. According to the research, thermoelectric modules can convert up to 2500×10^(-3) W/cm^2, thermo-photovoltaic 10.9%, piezoelectric 10,000 mW/cm^3 and microbial fuel cell 6.86 W/m^2 of energy. Doi: 10.28991/esj-2021-SP1-08 Full Text: PDF

AbstractThis paper applies learning automata to improve the performance of a Q‐learning based random access channel (QL‐RACH) scheme in a cellular machine‐to‐machine (M2M) communication system. A prioritized learning automata QL‐RACH (PLA‐QL‐RACH) access scheme is proposed. The scheme employs a prioritized learning automata technique to improve the throughput performance by minimizing the level of interaction and collision of M2M devices with human‐to‐human devices sharing the RACH of a cellular system. In addition, this scheme eliminates the excessive punishment suffered by the M2M devices by controlling the administration of a penalty. Simulation results show that the proposed PLA‐QL‐RACH scheme improves the RACH throughput by approximately 82% and reduces access delay by 79% with faster learning convergence when compared with QL‐RACH.

In the twenty-first century, the Internet of Things (IoT) has assisted in the mainstreaming of automation. By boosting bandwidth, communication protocols, and redundancy, the integration of IoT, WSNs, and health monitoring systems with a multi-cloud environment can improve sensor networks. The proposed design is compared to current health infrastructure and monitoring systems, as well as alternative network architectures. Machine-to-machine health monitoring methods have also been investigated, as well as how they relate to communication protocols. The objective of this study is to find out more about how sensor data analytics may be used to multi-cloud sensor communication protocols directly. When the supplied prototype and the one-of-a-kind communication mechanism are merged, a model is created that is 98 percent more accurate than traditional techniques. [ABSTRACT FROM AUTHOR]

This paper applies learning automata to improve the performance of a Q‐learning based random access channel (QL‐RACH) scheme in a cellular machine‐to‐machine (M2M) communication system. A prioritized learning automata QL‐RACH (PLA‐QL‐RACH) access scheme is proposed. The scheme employs a prioritized learning automata technique to improve the throughput performance by minimizing the level of interaction and collision of M2M devices with human‐to‐human devices sharing the RACH of a cellular system. In addition, this scheme eliminates the excessive punishment suffered by the M2M devices by controlling the administration of a penalty. Simulation results show that the proposed PLA‐QL‐RACH scheme improves the RACH throughput by approximately 82% and reduces access delay by 79% with faster learning convergence when compared with QL‐RACH. [ABSTRACT FROM AUTHOR]

Summary: A wireless sensor network (WSN) can give a coordinated and minimal effort checking and control answer for such keen emotionally supportive systems. In this paper, we have discussed about the SEP in comparison with LEACH for smart home applications. Smart home scenario of an industry is taken into consideration in our research. The normal sink node is represented in the name of link node that connects the multiple sectors in the smart home scenario. In addition, it will act as a cluster head for the entire smart home setup. Moreover, the present research work proposes the plan of a low energy and high throughput and various levelled cluster‐based directing convention for smart home sensor organize application. Proposed protocol performs dynamic cluster head choice in light of various node qualities like node enduring energy, remoteness of the node from the link (sink), and average distance of the node from various neighbor nodes. Cluster head appropriation is even and brings about upgraded organization lifetime. Sensor nodes process the sense information utilizing nearby on‐board processors and exclude the repetitive information from transmission. In the final section, the energy level and the through put can be compared with the LEACH for getting a better understanding. The simulation results of the proposed technique clarify the energy consumed and the energy remaining in the stream. The energy will be measured in Joules. Moreover, the dead node will be tracked and intimated through our proposed work. The packet delivery and the delayed process of each and every node will be discussed in the proposed simulation result. The energy level remained will be calculated in Joules measurement. The SEP level will be more when compared with LEACH protocol. The energy level will be improved in case of SEP as 5, 10, 15, and 20 J/ ms. [ABSTRACT FROM AUTHOR]

The ecosystem for an Internet of Things (IoT) generally comprises endpoint clients, network devices, and cloud servers. Thus, data transfers within the network present multiple security concerns. The recent boom in IoT applications has accelerated the need for a network infrastructure that provides timely and safe information exchange services. A shortcoming of many existing networks is the use of static key authentication. To enable the use of automatic key update mechanisms in IoT devices and enhance security in lightweight machine-to-machine (M2M) communications, we propose a key update mechanism, namely, double OTP (D-OTP), which combines both one-time password (OTP) and one-time pad to achieve an IoT ecosystem with theoretically unbreakable security. The proposed D-OTP was implemented into the Constrained Application Protocol (CoAP) through the commonly used libcoap library. The experimental results revealed that an additional 8.93% latency overhead was required to obtain an unbreakable guarantee of data transfers in 100 CoAP communication sessions.

This article aims at identifying and analyzing opinions referring to the necessity, opportunity and efficiency of the implementation of the strategies and sustainable development practices in the wine industry as food safety is considered a component of sustainability. The main objective of the paper is to create a clear picture of the existing willingness of companies to adopt marketing strategies that should contribute to increasing food security by increasing the quality of wine and which should be based on the principles of sustainability. In this sense, the authors conducted some qualitative marketing research in which 15 managers from wine producing companies from Romania's wine-growing areas were interviewed. The research highlights the relationship between responsible consumption, food safety and sustainability, and the authors believe that such an association is relevant to the economic and social context of the European Union and other parts of the world. The novelty of this research refers to the analysis of the relationship between the responsible wine consumption and sustainability, to the identification of the opinions of the wine companies managers regarding the benefits and the limits of M2M and IoT technologies, to the emphasis of the marketing strategies and techniques considered innovative in the field of viticulture, as well as to the association of the concept of food safety by wine consumption to the aspects related to education and culture.

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays, the installation of efficient lighting sources and Information and Communications Technologies can provide economic benefits, energy efficiency, and visual comfort requirements. More advantages can be derived if the public lighting infrastructure integrates a smart grid. This study presents an experimental multifunctional infrastructure for public lighting, installed in Palermo. The system is able to provide smart lighting functions (hotspot Wi-Fi, video-surveillances, car and pedestrian access control, car parking monitoring and support for environmental monitoring). A remote control and monitoring platform called “Centro Servizi” processes the information coming from different installations as well as their status in real time, and sends commands to the devices (e.g. to control the luminous flux), each one provided with a machine to machine interface. Data can be reported either on the web or on a customised app. The study has shown the efficient operation of such new infrastructure and its capability to provide new functions and benefits to citizens, tourists, and public administration. Thus, this system represents a starting point for the implementation of many other lighting infrastructure features typical of a “smart city.”

In this paper, we discuss educational courses for Internet of Things and related areas such as Machine to Machine communications and Smart Cities. The Internet of Things models introduce many new business prospects and, of course, they should be presented in the university curriculum. The purpose of the discussed educational program is to examine issues related to information and communication technologies used in Internet of Things projects and related areas based on them, such as Smart Cities. The educational course proposed in this paper aims to introduce students to modern information technology, standing for such areas as Machine to Machine communications, Internet of Things, and Smart Cities.

In recent years, the growing number of devices connected to the internet has increased significantly. These devices can interact with the external environment and with human beings through a wide range of sensors that, perceiving reality through the digitization of some parameters of interest, can provide an enormous amount of data. All this data is then shared on the network with other devices and with different applications and infrastructures. This dynamic and ever-changing world underlies the Internet of Things (IoT) paradigm. To date, countless applications based on IoT have been developed; think of Smart Cities, smart roads, and smart industries. This article analyzes the current architectures, technologies, protocols, and applications that characterize the paradigm.

All over the world, road congestion is among the most prevalent transport challenges usually in urban environments which not only increases fuel consumption and emission of harmful gases, but also causes stress for the drivers. Intelligent Transportation System (ITS) enables a better use of the infrastructure by connecting vehicles to other vehicles as well as infrastructure and thus delivers a faster communication opportunity to ensure safe and secure driving. Machine-to-machine (M2M) communication is one of the latest information and communication technologies which offers ubiquitous connectivity among several smart devices. The use of mobile (cellular) M2M communications has emerged due to the wide range, high reliability, increased data rates, decreased costs as well as easy and short-term deployment opportunities. Since the radio spectrum is a scarce resource, M2M traffic can potentially degrade the performance of mobile networks due to the large number of devices sending small-sized data. This paper presents an efficient data multiplexing scheme by using Long-Term Evolution Advanced (LTE-Advanced) Relay Nodes, which aggregates M2M traffic to maximize radio resource utilization. Extensive system-level simulations are performed using an LTE-Advanced-based model developed in the RIVERBED modeler to evaluate the performance of the proposed data multiplexing scheme. Simulation results show that approximately 40 % more smart M2M devices used in ITS and logistics are served per LTE-Advanced cell under the present system settings.