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The performance of a Wireless Body Area Network (WBAN) depends on the Quality of Service (QoS) and energy efficiency. The traffic generated by WBAN is heterogeneous in nature, and consists of both periodic and emergency events. One crucial challenge in designing a WBAN Medium Access Control (MAC) protocol is guaranteeing high-reliability transmission while satisfying diverse QoS requirements. Therefore, this paper proposes a QoS-aware MAC protocol named the Adaptive MAC (ADT-MAC) that accommodates dynamic medical traffic by addressing emergency and periodic traffic requirements. ADT-MAC utilizes a hybrid and adaptive superframe structure based on the IEEE 802.15.6 standard. Additionally, an M/M/1 queuing algorithm with a non-preemptive priority is modeled using SimEvents in MATLAB to validate the packet delay of the priority queues. The proposed ADT-MAC protocol is simulated using Castalia and OMNeT++ to evaluate its performance against state-of-the-art MAC protocols. Simulation findings reveal that ADT-MAC achieves lower packet delay, higher PDR, and increased network throughput while reducing energy consumption compared to its benchmarks. Furthermore, the result of packet delay from priority queues validates the accuracy of the proposed ADT-MAC and queueing algorithm. The two-fold simulation approach using Castalia and SimEvents demonstrated that the packet delay for each priority level remains below the 125 ms threshold set by the IEEE 802.15.6 specifications.

The dynamic nature of energy harvesting rate, arising because of ever changing weather conditions, raises new concerns in energy harvesting based wireless sensor networks (EH-WSNs). Therefore, this drives the development of energy aware EH solutions. Formerly, many Medium Access Control (MAC) protocols have been developed for EH-WSNs. However, optimizing MAC protocol performance by incorporating predicted future energy intake is relatively new in EH-WSNs. Furthermore, existing MAC protocols do not fully harness the high harvested energy to perform aggressively despite the availability of sufficient energy resources. Therefore, a prediction-based adaptive duty cycle (PADC) MAC protocol has been proposed, called PADC-MAC, that incorporates current and future harvested energy information using the mathematical formulation to improve network performance. Furthermore, a machine learning model, namely nonlinear autoregressive (NAR) neural network, is employed that achieves good prediction accuracy under dynamic harvesting scenarios. As a result, it enables the receiver node to perform aggressively better when there is sufficient inflow of incoming harvesting energy. In addition, PADC-MAC uses a self-adaptation technique that reduces energy consumption. The performance of PADC-MAC is evaluated using GreenCastalia in terms of packet delay, network throughput, packet delivery ratio, energy consumption per bit, receiver energy consumption, and total network energy consumption using realistic harvesting data for 96 consecutive hours under dynamic solar harvesting conditions. The simulation results show that PADC-MAC provides lower average packet delay of the highest priority packets and all packets, energy consumption per bit, and total energy consumption by more than 10.7%, 7.8%, 81%, and 76.4%, respectively when compared to three state-of-the-art protocols for EH-WSNs.

The requirement of high quality of service (QoS) in multi-priority industrial and domestic sensor networks poses new challenges to the increasing adoption of Internet of Things (IoT). In Multi-event Wireless Sensor Networks (MWSNs), nodes generate different types of data packets i.e., urgent (high priority) or normal (low priority), with different traffic proportions. High priority packets require an assurance of faster transmission and higher reliability in the network. In the literature, the existing medium access control (MAC) protocols for MWSNs have limited consideration in supporting data priority with different traffic proportions. Therefore, this paper proposes an energy efficient MAC scheme that incorporates multi-priority of data packets with dynamic traffic proportion, called PriTraCon-MAC. PriTraCon-MAC supports multi-events by considering four different priority levels of data packets and uses a novel approach that adjusts the contention window adaptively. Due to that, Request-To-Send frame of higher priority data can be sent earlier in the contention window, resulting in the corresponding faster acceptance by the receiver. Furthermore, mathematical delay analysis with different priority traffic proportions has also been undertaken. In addition, PriTraCon-MAC has been implemented in OMNET++ Castalia and its performance has been evaluated in terms of packet delay, reliability, and energy consumption, and compared with the existing Timeout Multi-priority based-MAC (TMPQ-MAC) under various network conditions. The simulation results demonstrate that PriTraCon-MAC offers lower average delay and achieves significantly higher packet success rate, while reducing energy consumption by up to 80% when compared with TMPQ-MAC protocol.

The rapid growth of the world population has increased the food demand as well as the need for assurance of food quality, safety, and sustainability. However, food security can easily be compromised by not only natural hazards but also changes in food preferences, political conflicts, and food frauds. In order to contribute to building a more sustainable food system—digitally visible and processes measurable—within this review, we summarized currently available evidence for various information and communication technologies (ICTs) that can be utilized to support collaborative actions, prevent fraudulent activities, and remotely perform real-time monitoring, which has become essential, especially during the COVID-19 pandemic. The Internet of Everything, 6G, blockchain, artificial intelligence, and digital twin are gaining significant attention in recent years in anticipation of leveraging the creativity of human experts in collaboration with efficient, intelligent, and accurate machines, but with limited consideration in the food supply chain. Therefore, this paper provided a thorough review of the food system by showing how various ICT tools can help sense and quantify the food system and highlighting the key enhancements that Industry 5.0 technologies can bring. The vulnerability of the food system can be effectively mitigated with the utilization of various ICTs depending on not only the nature and severity of crisis but also the specificity of the food supply chain. There are numerous ways of implementing these technologies, and they are continuously evolving.

Wireless sensor networks (WSNs) have received considerable interest in recent years. These sensor nodes can gather information from the surrounding environment and transmit it to a designated location. Each sensor node in WSN typically has a battery with a limited capacity. Due to their large number and because of various environmental challenges, it is sometimes hard to replace this finite battery. As a result, energy-efficient communication is seen as a critical aspect in extending the lifespan of a sensor node. On the other hand, some applications that require large coverage and generate various sorts of data packets require multi-hop routing and quality of service (QoS) features. Therefore, in order to avoid network failure, these applications need an energy-efficient QoS MAC protocol that can support multiple levels of data packet priority and multi-hop routing features while focusing on energy conservation. An energy-aware QoS MAC protocol based on Prioritized Data and Multi-hop routing (EQPD-MAC) is proposed in this article. The EQPD-MAC protocol offers a simple yet effective cross-layer communication method. It provides timely delivery of multi-priority packets, uses an adaptive active time to limit idle listening, and integrates a robust routing protocol. Finally, the EQPD-MAC protocol’s performance was evaluated and compared to three other well-known QoS MAC protocols. The simulation findings show that the proposed protocol significantly decreases sensor node energy consumption by up to 30.3%, per-bit energy consumption by up to 29.6%, sink node energy consumption by up to 27.4% and increases throughput by up to 23.3%.

Wireless sensors networks (WSNs) are characterized by flexibility and scalability in any environment. These networks are increasingly used in agricultural and industrial environments and have a dual role in data collection from sensors and transmission to a monitoring system, as well as enabling the management of the monitored environment. Environment management depends on trust in the data collected from the surrounding environment, including the time of data creation. This paper proposes a trust model for monitoring humidity and moisture in agricultural and industrial environments. The proposed model uses a digital signature and public key infrastructure (PKI) to establish trust in the data source, i.e., the trust in the sensor. Trust in data generation is essential for real-time environmental monitoring and subsequent analyzes, thus timestamp technology is implemented here to further ensure that gathered data are not created or changed after the assigned time. Model validation is performed using the Castalia network simulator by testing energy consumption at the receiver and sender nodes and the delay incurred by creating or validating a trust token. In addition, validation is also performed using the Ascertia TSA Crusher application for the time consumed to obtain a timestamp from the free TSA. The results show that by applying different digital signs and timestamps, the trust entity of the WSN improved significantly with an increase in power consumption of the sender node by up to 9.3% and receiver node by up to 126.3% for a higher number of nodes, along with a packet delay of up to 15.6% and an average total time consumed up to 1.186 s to obtain the timestamp from the best chosen TSA, which was as expected.

This study aims to discuss the synthesis and fabrication of SnO2-In2O3-based thick-films and their biosensing applications. The structural characterization of SnO2-In2O3 nanocomposites was performed using X-ray diffraction, Raman spectroscopy and transmission electron microscopy. Furthermore, the screen-printing technology was used in the fabrication of conductive electrodes to form an interdigitated capacitive structure, and the sensor layer based on the mixture of SnO2 and In2O3. Moreover, the sensing performance of the developed structure was tested using Pseudomonas aeruginosa (P. aeruginosa) and Staphylococcus aureus (S. aureus) bacteria. In addition, the validation of sensing characteristics was performed by electrochemical impedance spectroscopic and self-resonant frequency analysis. Finally, the sensing properties were analyzed for two consecutive days, and changes in both P. aeruginosa and S. aureus pathogens growing media were also studied.

Nowadays, wireless sensor networks (WSN) are widely used in agriculture monitoring to improve the quality and productivity of farming. In this application, sensors gather different types of data (i.e., humidity, carbon dioxide level, and temperature) in real-time scenarios. Thus, data gathering, transmission, and rapid response to new circumstances require a secured data mechanism to avoid malicious adversaries. Therefore, this paper focuses on data security from the data origin source to the end-user, and proposes a general data security model that is independent of the network topology and structure, and can be widely used in the agriculture monitoring application. The developed model considers practical aspects, the architecture of the sensor node, as well as the necessity to save energy while ensuring data security, and optimize the model through the application of organizational and technical measures. The model evaluation is conducted through simulation in terms of energy consumption. The result shows that the proposed model ensures good data security at the cost of a slight increase in energy consumption at receiver and sender nodes, and energy consumption per bit, up to 2%, 7%, and 1.3%, respectively, due to overhead added for authentication in the network.