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Cross-layer approach in media access control (MAC) layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in wireless sensor network (WSN) and Internet of Things (IoT) applications. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes. Game theory optimization for distributed may increase the network performance. The purpose of this study is to survey the various operations that can be carried out using distributive and adaptive MAC protocol. Hill climbing distributed MAC does not need a central coordination system and location-based transmission with neighbor awareness reduces transmission power.

Distributed MAC in wireless networks is used to address the challenges like network lifetime, reduced energy consumption and for improving delay performance. In this paper, a survey is made on various cooperative communications in MAC protocols, optimization techniques used to improve MAC performance in various applications and mathematical approaches involved in game theory optimization for MAC protocol.

Spatial reuse of channel improved by 3%–29%, and multichannel improves throughput by 8% using distributed MAC protocol. Nash equilibrium is found to perform well, which focuses on energy utility in the network by individual players. Fuzzy logic improves channel selection by 17% and secondary users’ involvement by 8%. Cross-layer approach in MAC layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in WSN and IoT applications. Cross-layer and cooperative communication give energy savings of 27% and reduces hop distance by 4.7%. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes.

Other optimization techniques can be applied for WSN to analyze the performance.

Game theory optimization for distributed may increase the network performance. Optimal cuckoo search improves throughput by 90% and reduces delay by 91%. Stochastic approaches detect 80% attacks even in 90% malicious nodes.

Channel allocations in centralized or static manner must be based on traffic demands whether dynamic traffic or fluctuated traffic. Usage of multimedia devices also increased which in turn increased the demand for high throughput. Cochannel interference keep on changing or mitigations occur which can be handled by proper resource allocations. Network survival is by efficient usage of valid patis in the network by avoiding transmission failures and time slots’ effective usage.

Literature survey is carried out to find the methods which give better performance.

The purpose of this study is to extensively explore the vehicular network paradigm, challenges faced by them and provide a reasonable solution for securing these vulnerable networks. Vehicle-to-everything (V2X) communication has brought the long-anticipated goal of safe, convenient and sustainable transportation closer to reality. The connected vehicle (CV) paradigm is critical to the intelligent transportation systems vision. It imagines a society free of a troublesome transportation system burdened by gridlock, fatal accidents and a polluted environment. The authors cannot overstate the importance of CVs in solving long-standing mobility issues and making travel safer and more convenient. It is high time to explore vehicular networks in detail to suggest solutions to the challenges encountered by these highly dynamic networks.

This paper compiles research on various V2X topics, from a comprehensive overview of V2X networks to their unique characteristics and challenges. In doing so, the authors identify multiple issues encountered by V2X communication networks due to their open communication nature and high mobility, especially from a security perspective. Thus, this paper proposes a trust-based model to secure vehicular networks. The proposed approach uses the communicating nodes’ behavior to establish trustworthy relationships. The proposed model only allows trusted nodes to communicate among themselves while isolating malicious nodes to achieve secure communication.

Despite the benefits offered by V2X networks, they have associated challenges. As the number of CVs on the roads increase, so does the attack surface. Connected cars provide numerous safety-critical applications that, if compromised, can result in fatal consequences. While cryptographic mechanisms effectively prevent external attacks, various studies propose trust-based models to complement cryptographic solutions for dealing with internal attacks. While numerous trust-based models have been proposed, there is room for improvement in malicious node detection and complexity. Optimizing the number of nodes considered in trust calculation can reduce the complexity of state-of-the-art solutions. The theoretical analysis of the proposed model exhibits an improvement in trust calculation, better malicious node detection and fewer computations.

The proposed model is the first to add another dimension to trust calculation by incorporating opinions about recommender nodes. The added dimension improves the trust calculation resulting in better performance in thwarting attacks and enhancing security while also reducing the trust calculation complexity.

Ad hoc mobile networks are commonplace in every aspect of our everyday life. They become essential in many industries and have uses in logistics, science and the military. However, because they operate mostly in open spaces, they are exposed to a variety of dangers. The purpose of this study is to introduce a novel method for detecting the MAC layer misbehavior.

The proposed novel approach is based on exponential smoothing for throughput prediction to address this MAC layer misbehavior. The real and expected throughput are processed using an exponential smoothing algorithm to identify this attack, and if these metrics exhibit a trending pattern, an alarm is then sent.

The effect of the IEEE 802.11 MAC layer misbehavior on throughput was examined using the NS-2 network simulator, as well as the approval of our novel strategy. The authors have found that a smoothing factor value that is near to 0 provides a very accurate throughput forecast that takes into consideration the recent history of the updated values of the real value. As for the smoothing factor values that are near to 1, they are used to identify MAC layer misbehavior.

According to the authors’ modest knowledge, this new scheme has not been proposed in the state of the art for the detection of greedy behavior in mobile ad hoc networks.

The internet of vehicles (IoV) communication has recently become a popular research topic in the automotive industry. The growth in the automotive sector has resulted in significant standards and guidelines that have engaged various researchers and companies. In IoV, routing protocols play a significant role in enhancing communication safety for the transportation system. The high mobility of nodes in IoV and inconsistent network coverage in different areas make routing challenging. This paper aims to provide a lane-based advanced forwarding protocol for internet of vehicles (LAFP-IoV) for efficient data distribution in IoV. The proposed protocol’s main feature is that it can identify the destination zone by using position coordinates and broadcasting the packets toward the direction of destination. The novel suppression technique is used in the broadcast method to reduce the network routing overhead.

The proposed protocol considers the interferences between different road segments, and a novel lane-based forwarding model is presented. The greedy forwarding notion, the broadcasting mechanism, and the suppression approach are used in this protocol to reduce the overhead generated by standard beacon forwarding procedures. The SUMO tool and NS-2 simulator are used for the vehicle's movement pattern and to simulate LAFP-IoV.

The simulation results show that the proposed LAFP-IoV protocol performs better than its peer protocols. It uses a greedy method for forwarding data packets and a carry-and-forward strategy to recover from the local maximum stage. This protocol's low latency and good PDR make it ideal for congested networks.

The proposed paper provides a unique lane-based forwarding for IoV. The proposed work achieves a higher delivery ratio than its peer protocols. The proposed protocol considers the lanes while forwarding the data packets applicable to the highly dense scenarios.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

The current and modern electrical distribution networks, named smart grids (SGs), use advanced technologies to accomplish all the technical and nontechnical challenges naturally demanded by energy applications. Energy metering collecting is one of these challenges ranging from the most basic (i.e., visual assessment) to the expensive advanced metering infrastructure (AMI) using intelligent meters networks. The AMIs’ data acquisition and system monitoring environment require enhancing some routine tasks. This paper aims to propose a methodology that uses a distributed and sustainable approach to manage wide-range metering networks, focused on using current public or private telecommunication infrastructure, optimizing the implementation and operation, increasing reliability and decreasing costs.

Inspired by blockchain technology, a collaborative metering system architecture is conceived, managing massive data sets collected from the grid. The use of cryptography handles data integrity and security issues.

A robust proof-of-concept simulation results are presented concerning the resilience and performance of the proposed distributed remote metering system.

The methodology proposed in this work is an innovative AMI solution related to SGs. Regardless of the implementation, operation and maintenance of AMIs, the proposed solution is unique, using legacy and new technologies together in a reliable way.

Digital transformation using Industry 4.0 technologies can address various challenges in food supply chains (FSCs). However, the integration of emerging technologies to achieve digital transformation in FSCs is unclear. This study aims to establish how the digital transformation of FSCs can be achieved by adopting key technologies such as the Internet of Things (IoTs), cloud computing (CC) and big data analytics (BDA).

A systematic literature review (SLR) resulted in 57 articles from 2008 to 2022. Following descriptive and thematic analysis, a conceptual framework based on the diffusion of innovation (DOI) theory and the context-intervention-mechanism-outcome (CIMO) logic is established, along with avenues for future research.

The combination of DOI theory and CIMO logic provides the theoretical foundation for linking the general innovation process to the digital transformation process. A novel conceptual framework for achieving digital transformation in FSCs is developed from the initiation to implementation phases. Objectives and principles for digitally transforming FSCs are identified for the initiation phase. A four-layer technology implementation architecture is developed for the implementation phase, facilitating multiple applications for FSC digital transformation.

The study contributes to the development of theory on digital transformation in FSCs and offers managerial guidelines for accelerating the growth of the food industry using key Industry 4.0 emerging technologies. The proposed framework brings clarity into the “neglected” intermediate stage of data management between data collection and analysis. The study highlights the need for a balanced integration of IoT, CC and BDA as key Industry 4.0 technologies to achieve digital transformation successfully.

In recent times, the construction industry is being influenced by technological innovations when delivering a better, more effective and efficient desired project, cyber-physical systems (CPSs) offer a coupling of the physical and engineered systems by monitoring, coordinating, controlling and integrating their operations. This study aims to examine the level of awareness of professionals and usage of CPSs for construction projects in Nigerian construction industry.

The target population for this study was the professionals in the construction industry consisting Architects, Quantity Surveyors, Engineers and Builders. Data collection was through the use of a structured questionnaire administered to the target population. The data was analyzed by using statistical tools.

This study concluded that the construction professionals in the Nigerian construction industry are mostly aware about the heating, ventilation and air conditioning (HVAC) systems, global positioning system, microphone, speakers and camera as the most widely used CPSs in construction industry. HVAC systems was also found to be the mostly adopted technologies in the construction industry.

This study recommended that platforms that increase the awareness and encourage the usage of CPSs in construction industry should be encouraged by stakeholders concerned with management of construction projects. Such include electronic construction and adoption of blockchain technology.

The Cloud of Things (IoT) that refers to the integration of the Cloud Computing (CC) and the Internet of Things (IoT), has dramatically changed the way treatments are done in the ubiquitous computing world. This integration has become imperative because the important amount of data generated by IoT devices needs the CC as a storage and processing infrastructure. Unfortunately, security issues in CoT remain more critical since users and IoT devices continue to share computing as well as networking resources remotely. Moreover, preserving data privacy in such an environment is also a critical concern. Therefore, the CoT is continuously growing up security and privacy issues. This paper focused on security and privacy considerations by analyzing some potential challenges and risks that need to be resolved. To achieve that, the CoT architecture and existing applications have been investigated. Furthermore, a number of security as well as privacy concerns and issues as well as open challenges, are discussed in this work.

In the final step, the trust model is applied to the on-demand federated multipath distance vector routing protocol (AOMDV) to introduce path trust as a foundation for routing selection in the route discovery phase, construct a trusted path, and implement a path warning mechanism to detect malicious nodes in the route maintenance phase, respectively.

A trust-based on-demand multipath distance vector routing protocol is being developed to address the problem of flying ad-hoc network being subjected to internal attacks and experiencing frequent connection interruptions. Following the construction of the node trust assessment model and the presentation of trust evaluation criteria, the data packet forwarding rate, trusted interaction degree and detection packet receipt rate are discussed. In the next step, the direct trust degree of the adaptive fuzzy trust aggregation network compute node is constructed. After then, rely on the indirect trust degree of neighbouring nodes to calculate the trust degree of the node in the network. Design a trust fluctuation penalty mechanism, as a second step, to defend against the switch attack in the trust model.

When compared to the lightweight trust-enhanced routing protocol (TEAOMDV), it significantly improves the data packet delivery rate and throughput of the network significantly.

Additionally, it reduces the amount of routing overhead and the average end-to-end delay.