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In recent decades, fog computing has contributed significantly to the expansion of smart cities. It generated numerous real‐time data and coped with time‐constraint applications. They use sensors, physical objects, and network standards to monitor health imaging, traffic surveillance, industrial management, and so forth. Interactive applications have been proposed for the Internet of Things (IoT) to control wireless channels and improve communication. However, most of the existing lack of handing network interference and a reliable monitoring process. Moreover, many solutions are vulnerable to external threats, resulting in inconsistent and untrustworthy information for end users. Thus, this article proposes a framework that considers possible shortest paths to provide the most reliable and low‐latency healthcare decision system using Q‐learning. In addition, fog devices offer a trusted transmission interference system and are kept secure. The proposed framework is specially designed for rapid real‐time medical data processing while enforcing robust security throughout the IoT‐based transmission process. To identify the health sensors in pairwise objects with the initial computing cost, the proposed framework applies graph theory. It also extracts the most effective and least loaded communication edges by examining the behaviour of devices. Moreover, the identities of devices are verified using lightweight timestamps and secret information, accordingly, it decreases the privacy threats. [ABSTRACT FROM AUTHOR]

Smart technologies are advancing the development of cutting-edge systems by exploring the future network. The Internet of Things (IoT) and many multimedia sensors interact with each other for collecting and transmitting visual data. However, managing enormous amounts of data from numerous network devices is one of the main research challenges. In this context, various IoT systems have been investigated and have provided efficient data retrieval and processing solutions. For multimedia systems, however, controlling inefficient bandwidth utilization and ensuring timely transmission of vital information are key research concerns. Moreover, to transfer multimedia traffic while balancing communication costs for the IoT system, a sustainable solution with intelligence in real-life applications is demanded. Furthermore, trust must be formed for technological advancement to occur; such an approach provides the smart communication paradigm with the incorporation of edge computing. This study proposed a model for optimizing multimedia using a combination of edge computing intelligence and authentic strategies. Mobile edges analyze network states to discover the system's status and minimize communication disruptions. Moreover, direct and indirect authentication determines the reliability of data forwarders and network stability. The proposed authentication approach minimizes the possibility of data compromise and increases trust in multimedia surveillance systems. Using simulation testing, the proposed model outperformed other comparable work in terms of byte delivery, packet overhead, packet delay, and data loss metrics. [ABSTRACT FROM AUTHOR]

Software-defined networks (SDN) have gained a lot of attention in recent years as a technique to develop smart systems with a help of the Internet of Things (IoT). Its powerful and centralized architecture makes a balanced contribution to the management of sustainable applications through efficient processes. These networks also systematically keep track of mobile devices and decrease the extra overheads in the communication cost. Many solutions are proposed to cope with data transferring for the critical system, however, mobile devices, on the other hand, require long-distance communication links with minimal retransmissions. Furthermore, the mobile network is highly infected by security attacks and compromised the IoT architecture for both the intermediate layers and end-users. Therefore, this paper presents an adaptive routes migration model for sustainable applications with the collaboration of SDN architecture and limits the disconnectivity time in data transporting along with efficient management of network services. Moreover, its centralized controller fetches the updated information from low-level smart devices and supervised their monitoring efficiently. The proposed model also secures the cloud of things (CoTs) from network threats and protects private data. It provides three levels of security algorithms and supports adaptive computing systems. The proposed model was tested using simulations, and the findings showed that it outperformed other existing studies in terms of packet delivery ratio by 13%, packet loss rate by 15%, transmission error by 22%, computing cost by 17%, and latency by 18%. [ABSTRACT FROM AUTHOR]

For the monitoring and processing of network data, wireless systems are widely used in many industrial applications. With the assistance of wireless sensor networks (WSNs) and the Internet of Things (IoT), smart grids are being explored in many distributed communication systems. They collect data from the surrounding environment and transmit it with the support of a multi-hop system. However, there is still a significant research gap in energy management for IoT devices and smart sensors. Many solutions have been proposed by researchers to cope with efficient routing schemes in smart grid applications. But, reducing energy holes and offering intelligent decisions for forwarding data are remain major problems. Moreover, the management of network traffic on grid nodes while balancing the communication overhead on the routing paths is an also demanding challenge. In this research work, we propose a secure edge-based energy management protocol for a smart grid environment with the support of multi-route management. It strengthens the ability to predict the data forwarding process and improves the management of IoT devices by utilizing a technique of correlation analysis. Moreover, the proposed protocol increases the system's reliability and achieves security goals by employing lightweight authentication with sink coordination. To demonstrate the superiority of our proposed protocol over the chosen existing work, extensive experiments were performed on various network parameters. [ABSTRACT FROM AUTHOR]

Summary The Internet of things (IoT) is an emerging technology for many smart applications due to its efficient resource utilization, scalability, and fast interaction with the physical world. Software‐defined network (SDN), on the other hand, provides dynamic services for controlling and managing real‐time systems. However, collected data are sent to a central location, which requires balancing energy resources with redundant channels to maximize the availability of smart functions. Furthermore, the IoT network faces numerous security vulnerabilities as a result of its open communication space, including malicious messages and privacy concerns. Thus, this paper presents a distributed and artificial intelligence‐based energy‐efficient model for IoT‐SDN architecture, which aims to improve data aggregation and power distribution. It also provides security and authentication for smart communication systems. First, the proposed model introduces the heuristic evaluation using artificial intelligence and decreases the power consumption for sensor nodes in a real‐time system. Moreover, it optimizes the paradigm of distributed processing and efficiently increases the green energy technology with nominal management costs using the mobile edges. Second, the aggregated data of the environment is secured using a centralized controller to attain the most trustworthy data availability. The experimental results show a comparative analysis of the proposed model in terms of energy efficiency, packet drop ratio, and waiting time by 22%, 23%, 40%, and 49% as compared to existing studies. [ABSTRACT FROM AUTHOR]

Wireless networks and the Internet of things (IoT) have proven rapid growth in the development and management of smart environments. These technologies are applied in numerous research fields, such as security surveillance, Internet of vehicles, medical systems, etc. The sensor technologies and IoT devices are cooperative and allow the collection of unpredictable factors from the observing field. However, the constraint resources of distributed battery-powered sensors decrease the energy efficiency of the IoT network and increase the delay in receiving the network data on users' devices. It is observed that many solutions are proposed to overcome the energy deficiency in smart applications; though, due to the mobility of the nodes, lots of communication incurs frequent data discontinuity, compromising the data trust. Therefore, this work introduces a D2D multi-criteria learning algorithm for IoT networks using secured sensors, which aims to improve the data exchange without imposing additional costs and data diverting for mobile sensors. Moreover, it reduces the compromising threats in the presence of anonymous devices and increases the trustworthiness of the IoT-enabled communication system with the support of machine learning. The proposed work was tested and analyzed using broad simulation-based experiments and demonstrated the significantly improved performance of the packet delivery ratio by 17%, packet disturbances by 31%, data delay by 22%, energy consumption by 24%, and computational complexity by 37% for realistic network configurations. [ABSTRACT FROM AUTHOR]

Summary: In modern technologies, the industrial internet of things (IIoT) has gained rapid growth in the fields of medical, transportation, and engineering. It consists of a self‐governing configuration and cooperated with sensors to collect, process, and analyze the processes of a real‐time system. In the medical system, healthcare IIoT (HIIoT) provides analytics of a huge amount of data and offers low‐cost storage systems with the collaboration of cloud systems for the monitoring of patient information. However, it faces certain connectivity, nodes failure, and rapid data delivery challenges in the development of e‐health systems. Therefore, to address such concerns, this paper presents an efficient data uncertainty management model for HIIoT using machine learning (EDM‐ML) with declining nodes prone and data irregularity. Its aim is to increase the efficacy for the collection and processing of real‐time data along with smart functionality against anonymous nodes. It developed an algorithm for improving the health services against disruption of network status and overheads. Also, the multi‐objective function decreases the uncertainty in the management of medical data. Furthermore, it expects the routing decisions using a machine learning‐based algorithm and increases the uniformity in health operations by balancing the network resources and trust distribution. Finally, it deals with a security algorithm and established control methods to protect the distributed data in the exposed health industry. Extensive simulations are performed, and their results reveal the significant performance of the proposed model in the context of uncertainty and intelligence than benchmark algorithms. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) and smart technologies perform a vital role in gathering data to facilitate extreme environments. These systems offer an autonomous management of real-time multimedia activities and are integrated with future networks. Smart communication and Cyber-Physical Systems (CPS) are tightly integrated with various processes to improve the efficiency, safety, and functionality of mobile networks. In recent decades, many approaches have been proposed to cope with the communication gap between physical objects and heterogeneous devices using adaptive routing techniques. Autonomous Multimedia CPS implementation using sensor technologies is significant for environmental monitoring, surveillance, and smart cities. However, reliable and fault-tolerant CPS communication in multimedia networks remains a research challenge. Most existing schemes deal with device mobility, which consumes energy and high latency communication in a constrained environment. In this research work, we proposed an optimized multimedia communication framework using adaptive intelligence for forwarding routes and decreasing the probability of data loss. It examines environmental parameters and circumstances for a prediction and decision-making system based on Particle Swarm Optimization (PSO). Furthermore, the proposed framework uses edge-enabled security to provide trusted sessions for devices with token-based authentication. Compared to existing work, the proposed framework demonstrates notable performance improvement for throughput, data latency, and alive nodes. [ABSTRACT FROM AUTHOR]

With the rapid development of cognitive computing and the Internet of Things (IoT), sensing systems have produced a wide range of real-time communication applications. They use 5G/6G-enabled technologies to connect to the outside world to collect data and process different end-user requests. Wireless systems and artificial intelligence (AI) have led to significant development in the optimization process of network communication. Due to various constraints of wireless systems, many solutions have been presented to cope with routing and connectivity concerns. However, topology awareness and attaining management of quality of services are still demanding research challenges for sustainable development. This study proposes an AI-assisted routing model for mobile wireless sensor networks (MWSN) to optimize energy and detect communication link failures. Moreover, the proposed intelligent security approach increases the trustworthiness of the constraint devices on unpredictable routes. Firstly, it explores a genetic algorithm, a metaheuristic optimization technique to determine the feasible solutions, and based on independent metrics it generates an optimal set of routes. In the proposed model, the genetic algorithm provides a fault-tolerant solution for dynamic environments, specifically under unpredictable conditions. Second, new routes are established using dynamic decisions that satisfy the energy considerations. In the end, the proposed model performs regular auditing to detect malicious devices based on unexpected behavior. The proposed model is tested and it outperforms IMD-EACBR and AGRIC in terms of realistic performance metrics. [ABSTRACT FROM AUTHOR]

Nowadays, because of the unpredictable nature of sensor nodes, propagating sensory data raises significant research challenges in Wireless Sensor Networks (WSNs). Recently, different cluster-based solutions are designed for the improvement of network stability and lifetime, however, most of the energy efficient solutions are developed for homogeneous networks, and use only a distance parameter for the data communication. Although, some existing solutions attempted to improve the selection of next-hop based on energy factor, nevertheless, such solutions are unstable and lack a reducing data delivery interruption in overloaded links. The aim of our proposed solution is to develop Reliable Cluster-based Energy-aware Routing (RCER) protocol for heterogeneous WSN, which lengthen network lifetime and decreases routing cost. Our proposed RCER protocol make use of heterogeneity nodes with respect to their energy and comprises of two main phases; firstly, the network field is parted in geographical clusters to make the network more energy-efficient and secondly; RCER attempts optimum routing for improving the next-hop selection by considering residual-energy, hop-count and weighted value of Round Trip Time (RTT) factors. Moreover, based on computing the measurement of wireless links and nodes status, RCER restore routing paths and provides network reliability with improved data delivery performance. Simulation results demonstrate significant development of RCER protocol against their competing solutions. [ABSTRACT FROM AUTHOR]

The Internet of Health Things (IoHT) has grown in importance for developing medical applications with the support of wireless communication systems. IoHT is integrated with many sensors to capture the patients' records and transmits them to hospital centres for analysis and reporting. Controlling and managing health records has been addressed in several ways, however, it is noted that two key research problems for vital communication systems are reliability and reducing data loss. To enhance the sustainability of health applications and effectively use the network infrastructure when transferring sensitive data, this research provides a machine learning approach. Moreover, data collected from the IoHTs are protected and can be securely received for physical process in hospitals using authentication trees. Firstly, the undirected graphs are explored based on the multi‐parametric machine learning approach to minimize the computation overheads and traffic congestion. Secondly, it evaluates the nodes' level behaviour over the heterogeneous traffic load with efficient identification of redundant links. Finally, in‐depth analysis and simulation results have shown that the proposed protocol is more effective than existing approaches for data accuracy and security analysis. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have grown excessively due to their various applications and low installation cost. In WSN, the main concern is to reduce energy consumption among nodes while maintaining timely and reliable data forwarding. However, most of the existing energy aware routing protocols incur unbalanced energy consumption, which results in inefficient load balancing and compromised network lifetime. Therefore, the main target of this research paper is to present adaptive energy aware cluster-based routing (AECR) protocol for improving energy conservation and data delivery performance. Our proposed AECR protocol differs from other energy efficient routing schemes in some aspects. Firstly, it generates balance sized clusters based on nodes distribution and avoids random clusters formation. Secondly, it optimizes both intra-cluster and inter-cluster routing paths for improving data delivery performance while balancing data traffic on constructed forwarding routes and at the end, in order to reduce the excessive energy consumption and improving load distribution, the role of Cluster Head (CH) is shifted dynamically among nodes by exploit of network conditions. Simulation results demonstrate that AECR protocol outperforms state of the art in terms of various performance metrics. [ABSTRACT FROM AUTHOR]

The interest of research community has significantly increased in wireless sensor networks during last few years due to low-cost solutions for wide range of applications. Most of the times sensor nodes in WSN operate unsupervised which exposes them to variety of security threats, particularly node misbehavior attacks. Therefore, secure data dissemination becomes a challenging task due to unpredictable behavior of nodes. Most of the trust aware routing protocols exclusively focus on identification and isolation of misbehaving nodes over multihop path. However, these schemes do not optimize the route formation by considering important characteristics like path length and energy resources. As a result, these existing schemes exhibits compromised route stability and network lifetime. This paper presents a light-weight trust aware routing protocol that dynamically detects and isolate misbehaving nodes and paves the way for trusted environment. Proposed LTRP scheme employs a multi-facet routing metric integrating node's trust, energy and hop counts for making routing decisions. Simulation based performance evaluation reveals improved network lifetime, throughput, delay, and routing load performance of LTRP when compared to state-of-art. [ABSTRACT FROM AUTHOR]

In recent decades, cluster-based schemes have emerged as viable solutions for energy conservation problem in wireless sensor networks (WSN). However, most of the existing solutions incur imbalanced energy consumption and high network overheads. In addition, existing cluster-based solutions do not optimize route discovery based on the restricted resources of sensor nodes. Moreover, most of the cluster-based solutions perform periodical re-clustering for load balancing, which results in shortening network lifetime. This research paper presents a Dynamic Energy-aware Fault Tolerant Routing (DEFTR) protocol that exploits uniform-sized network partitioning based on network size and utilizes a multi-facet routing mechanism, which takes into consideration the residual energy, and position and link quality of neighbors. Furthermore, DEFTR not only offers reliable and energy efficient data routing but also supports fault tolerance. Simulation results demonstrate that DEFTR improved the network lifetime by 20.9% and throughput by 35%, also it reduced the delay by 29% and transmission cost by 46% in comparison to the existing work. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) consists of several tiny devices that are dispersed randomly for gathering network field. Clustering mechanism divides the WSN into different sub-regions called clusters. Individual cluster is consisting of cluster head (CH) and member nodes. The main research challenges behind clustering mechanism are to optimize network overheads with efficient data delivery. Sensor nodes are operated by batteries and practically it is not feasible to replace them during sensing the environment so energy should be effectively utilized among sensors for improving overall network performance. This research paper presents an improved energy aware cluster based data routing (i-ECBR) scheme, by dividing the network regions into uniform sized square partitions and localized CH election mechanism. In addition, consistent end-to-end data routing is performed for improving data dissemination. Simulation results illustrate that our proposed scheme outperforms than existing work in terms of different performance metrics. [ABSTRACT FROM AUTHOR]

Rapid development of the Internet of Everything (IoE) and cloud services offer a vital role in the growth of smart applications. It provides scalability with the collaboration of cloud servers and copes with a big amount of collected data for network systems. Although, edge computing supports efficient utilization of communication bandwidth, and latency requirements to facilitate smart embedded systems. However, it faces significant research issues regarding data aggregation among heterogeneous network services and objects. Moreover, distributed systems are more precise for data access and storage, thus machine-to-machine is needed to be secured from unpredictable events. As a result, this research proposed secured data management with distributed load balancing protocol using particle swarm optimization, which aims to decrease the response time for cloud users and effectively maintain the integrity of network communication. It combines distributed computing and shift high cost computations closer to the requesting node to reduce latency and transmission overhead. Moreover, the proposed work also protects the communicating machines from malicious devices by evaluating the trust in a controlled manner. Simulation results revealed a significant performance of the proposed protocol in comparison to other solutions in terms of energy consumption by 20%, success rate by 17%, end-to-end delay by 14%, and network cost by 19% as average in the light of various performance metrics. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) consists of hundred to thousands sensor nodes to gathered the information from physical environment. Different clustering based algorithms have been proposed to improve network lifetime and energy efficiency. Practically it is not feasible to recharge the battery of sensor nodes when they are sensing the data. In such situation energy is crucial resource and it should be improved for life span of WSN. Cluster head (CH) has an important role in hierarchical energy efficient routing protocols because it receives data from nodes and sends towards base station (BS) or sink node. This paper presents a grid based cluster head selection (GBCHS) mechanism by dividing the network field into MXN uniform size partitions that aims to minimize the energy dissipation of sensor nodes and enhancing network lifetime. Simulation experiments have been performed in network simulator (NS2) that show our proposed GBCHS approach outperformed than standard clustering hierarchy LEACH protocol. [ABSTRACT FROM AUTHOR]

The lightweight sensors gain rapid growth for the development of emerging technologies due to multi aspects. Recently, many applications are integrating with the cloud services for storage and processing a large amount of observing data, however, the transitory and time-dependent environment needs valuable performance. Also, reliable and error-free data transmission is an important factor for green systems. Moreover, most of the proposed green networks may be exposed to unspecified events due to the broadcast nature of the unreserved medium. This research paper presents a secured mobile sensor network based protocol for smart data gathering with cloud services (S-SDC), which aims to attain information distribution in dynamic networks using mobile sensors with minimum loss and power consumption. Also, it ensures network availability and consistency for the collected data with the cloud corporations and increases authoritative routing. The simulation results and their analysis are proven the significant performance of the proposed protocol. [Display omitted] [ABSTRACT FROM AUTHOR]

In modern years, network edges have been explored by many applications to lower communication and management costs. They are also integrated with the internet of things (IoT) to achieve network design, in terms of scalability and heterogeneous services for multimedia applications. Many proposed solutions are performing a vital role in the development of robust protocols and reducing the response time for critical networks. However, most of them are not able to support the forwarding processes of high multimedia traffic under dynamic characteristics with constraint bandwidth. Moreover, they increase the rate of data loss in an uncertain environment and compromise network performance by increasing delivery delay. Therefore, this paper presents an optimization model with mobile edges for multimedia sensors using artificial intelligence of things, which aims to maintain the process of real-time data collection with low consumption of resources. Moreover, it improves the unpredictability of network communication with the integration of software-defined networks (SDN) and mobile edges. Firstly, it utilizes the artificial intelligence of things (AIoT), forming the multi-hop network and guaranteed the primary services for constraints network with stable resources management. Secondly, the SDN performs direct association with mobile edges to support the load balancing for multimedia sensors and centralized the management. Finally, multimedia traffic is heading towards applications in an unchanged form and without negotiating using the sharing of subkeys. The experimental results demonstrated its effectiveness for delivery rate by an average of 35%, processing delay by an average of 29%, network overheads by an average of 41%, packet drop ratio by an average of 39%, and packet retransmission by an average of 34% against existing solutions. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) performs a vital role in providing connectivity between computing devices, processes, and things. It significantly increases the communication facilities and giving up-to-date information to distributed networks. On the other hand, the techniques of artificial intelligence offer numerous and valuable services in emerging fields. An IoT-based healthcare solution facilitates patients, hospitals, and professionals to observe real-time and critical data. In the literature, most of the solution suffers from data intermission, high ethical standards, and trustworthiness communication. Moreover, network interruption with recurrent expose of sensitive and personal health data decreases the reliance on network systems. Therefore, this paper intends to propose an IoT solution for AI-enabled privacy-preserving with big data transferring using blockchain. Firstly, the proposed algorithm uses a graph-modeling to develop a scalable and reliable system for gathering and transmitting data. In addition, it extracts the subset of nodes using the artificial intelligence approach and achieves efficient services for the healthcare system. Secondly, symmetric-based digital certificates are utilized to offer authentic and confidential transmission with communication resources using blockchain. The proposed algorithm is explored with existing solutions through multiple simulations and proved improvement in terms of realistic parameters. [ABSTRACT FROM AUTHOR]

The development of smart applications has benefited greatly from the expansion of wireless technologies. A range of tasks are performed, and end devices are made capable of communicating with one another with the support of artificial intelligence technology. The Internet of Things (IoT) increases the efficiency of communication networks due to its low costs and simple management. However, it has been demonstrated that many systems still need an intelligent strategy for green computing. Establishing reliable connectivity in Green-IoT (G-IoT) networks is another key research challenge. With the integration of edge computing, this study provides a Sustainable Data-driven Secured optimization model (SDS-GIoT) that uses dynamic programming to provide enhanced learning capabilities. First, the proposed approach examines multi-variable functions and delivers graph-based link predictions to locate the optimal nodes for edge networks. Moreover, it identifies a sub-path in multistage to continue data transfer if a route is unavailable due to certain communication circumstances. Second, while applying security, edge computing provides offloading services that lower the amount of processing power needed for low-constraint nodes. Finally, the SDS-GIoT model is verified with various experiments, and the performance results demonstrate its significance for a sustainable environment against existing solutions. [ABSTRACT FROM AUTHOR]

In recent decades, networked smart devices and cutting-edge technology have been exploited in many applications for the improvement of agriculture. The deployment of smart sensors and intelligent farming techniques supports real-time information gathering for the agriculture sector and decreases the burden on farmers. Many solutions have been presented to automate the agriculture system using IoT networks; however, the identification of redundant data traffic is one of the most significant research problems. Additionally, farmers do not obtain the information they need in time, such as data on water pressure and soil conditions. Thus, these solutions consequently reduce the production rates and increase costs for farmers. Moreover, controlling all agricultural operations in a controlled manner should also be considered in developing intelligent solutions. Therefore, this study proposes a framework for a system that combines fog computing with smart farming and effectively controls network traffic. Firstly, the proposed framework efficiently monitors redundant information and avoids the inefficient use of communication bandwidth. It also controls the number of re-transmissions in the case of malicious actions and efficiently utilizes the network's resources. Second, a trustworthy chain is built between agricultural sensors by utilizing the fog nodes to address security issues and increase reliability by preventing malicious communication. Through extensive simulation-based experiments, the proposed framework revealed an improved performance for energy efficiency, security, and network connectivity in comparison to other related works. [ABSTRACT FROM AUTHOR]

The Wireless Sensor Network (WSN) has seen rapid growth in the development of real-time applications due to its ease of management and cost-effective attributes. However, the balance between optimization of network lifetime and load distribution between sensor nodes is a critical matter for the development of energy-efficient routing solutions. Recently, many solutions have been proposed for constraint-based networks using the cloud paradigm. However, they achieve network scalability with the additional cost of routing overheads and network latency. Moreover, the sensors' data is transmitted towards application users over the uncertain medium, which leads to compromised data security and its integrity. Therefore, this work proposes a light-weight secure and energy-efficient fog-based routing (SEFR) protocol to minimize data latency and increase energy management. It exploits the Quality of Service (QoS) factors and facilitates time-sensitive applications with network edges. Moreover, the proposed protocol protects real-time data based on two levels of cryptographic security primitives. In the first level, a lightweight data confidentiality scheme is proposed between the cluster heads and fog nodes, and in the second level, a high-performance asymmetric encryption scheme is proposed among fog and cloud layers. The analysis of simulation-based experiments has proven the significant outcomes of the proposed protocol compared to existing solutions in terms of routing, security, and network management. [ABSTRACT FROM AUTHOR]

In recent times, the field of wireless sensor networks (WSNs) has attained a growing popularity in observing the environment due to its dynamic factors. Sensor data are gathered and forwarded to the base station (BS) through a wireless transmission medium. The data from the BS is further distributed to end-users using the Internet for their post analysis and operations. However, all sensors except the BS have limited constraints in terms of memory, energy and computational resources that degrade the network performance concerning the network lifetime and trustworthy routing. Therefore, improving energy efficiency with reliable and secure transmissions is a valuable debate among researchers for critical applications based on low-powered sensor nodes. In addition, security plays a significant cause to achieve responsible communications among sensors due to their unfixed and variable infrastructures. Keeping in view the above-mentioned issues, this paper presents an energy-aware graph clustering and intelligent routing (EGCIR) using a supervised system for WSNs to balance the energy consumption and load distribution. Moreover, a secure and efficient key distribution in a hierarchy-based mechanism is adopted by the proposed solution to improve the network efficacy in terms of routes and links integrity. The experimental results demonstrated that the EGCIR protocol enhances the network throughput by an average of 14%, packet drop ratio by an average of 50%, energy consumption by an average of 13%, data latency by an average of 30.2% and data breaches by an average of 37.5% than other state-of-the-art protocols. [ABSTRACT FROM AUTHOR]

Nowadays, the integration of Wireless Sensor Networks (WSN) and the Internet of Things (IoT) provides a great concern for the research community for enabling advanced services. An IoT network may comprise a large number of heterogeneous smart devices for gathering and forwarding huge data. Such diverse networks raise several research questions, such as processing, storage, and management of massive data. Furthermore, IoT devices have restricted constraints and expose to a variety of malicious network attacks. This paper presents a Secure Sensor Cloud Architecture (SASC) for IoT applications to improve network scalability with efficient data processing and security. The proposed architecture comprises two main phases. Firstly, network nodes are grouped using unsupervised machine learning and exploit weighted-based centroid vectors for the development of intelligent systems. Secondly, the proposed architecture makes the use of sensor-cloud infrastructure for boundless storage and consistent service delivery. Furthermore, the sensor-cloud infrastructure is protected against malicious nodes by using a mathematically unbreakable one-time pad (OTP) encryption scheme to provide data security. To evaluate the performance of the proposed architecture, different simulation experiments are conducted using Network Simulator (NS3). It has been observed through experimental results that the proposed architecture outperforms other state-of-the-art approaches in terms of network lifetime, packet drop ratio, energy consumption, and transmission overhead. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have demonstrated research and developmental interests in numerous fields, like communication, agriculture, industry, smart health, monitoring, and surveillance. In the area of agriculture production, IoT-based WSN has been used to observe the yields condition and automate agriculture precision using various sensors. These sensors are deployed in the agricultural environment to improve production yields through intelligent farming decisions and obtain information regarding crops, plants, temperature measurement, humidity, and irrigation systems. However, sensors have limited resources concerning processing, energy, transmitting, and memory capabilities that can negatively impact agriculture production. Besides efficiency, the protection and security of these IoT-based agricultural sensors are also important from malicious adversaries. In this article, we proposed an IoT-based WSN framework as an application to smart agriculture comprising different design levels. Firstly, agricultural sensors capture relevant data and determine a set of cluster heads based on multi-criteria decision function. Additionally, the strength of the signals on the transmission links is measured while using signal to noise ratio (SNR) to achieve consistent and efficient data transmissions. Secondly, security is provided for data transmission from agricultural sensors towards base stations (BS) while using the recurrence of the linear congruential generator. The simulated results proved that the proposed framework significantly enhanced the communication performance as an average of 13.5% in the network throughput, 38.5% in the packets drop ratio, 13.5% in the network latency, 16% in the energy consumption, and 26% in the routing overheads for smart agriculture, as compared to other solutions. [ABSTRACT FROM AUTHOR]

Due to the advancement of information and communication technologies, the use of Internet of Things (IoT) devices has increased exponentially. In the development of IoT, wireless sensor networks (WSNs) perform a vital part and comprises of low-cost smart devices for information gathering. However, such smart devices have constraints in terms of computation, processing, memory and energy resources. Along with such constraints, one of the fundamental challenges for WSN is to achieve reliability with the security of transmitted data in a vulnerable environment against malicious nodes. This paper aims to develop an energy-efficient and secure routing protocol (ESR) for intrusion avoidance in IoT based on WSN to increase the network period and data trustworthiness. Firstly, the proposed protocol creates different energy-efficient clusters based on the intrinsic qualities of nodes. Secondly, based on the (k,n) threshold-based Shamir secret sharing scheme, the reliability and security of the sensory information among the base station (BS) and cluster head are achieved. The proposed security scheme presents a light-weight solution to cope with intrusions generated by malicious nodes. The experimental results using the network simulator (NS-2) demonstrate that the proposed routing protocol achieved improvement in terms of network lifetime as 37%, average end-to-end delay as 24%, packet delivery ratio as 30%, average communication cost as 29%, network overhead as 28% and the frequency of route re-discoveries as 38% when compared with the existing work under dynamic network topologies. [ABSTRACT FROM AUTHOR]

In recent decades, artificial intelligence techniques have been adopted for many real-time applications. The Internet of Things (IoT) network comprises many sensing devices and physical objects for information gathering and further transmission. In addition to being sent to the receiving nodes, the collected data also needs to be received promptly. Also, many solutions have been proposed for IoT-based embedded systems using edge computing but they are not fully protected against unidentified communication threats. In such circumstances, such systems decrease the trust ratio, and communication performance is compromised. In this research, we describe an optimization model based on IoT-edged technology that incorporates cloud computational intelligence. Furthermore, edge nodes employ artificial intelligence algorithms to provide the optimal outcome for selecting trustworthy forwarded data and lengthen the connected time for smart devices. Firstly, the edge devices extract useful information from the IoT nodes, and accordingly, it provides a decision module based on optimization computing. Secondly, utilizing cryptographic approaches, edge technology secures the multi-layers of the IoT system and ensures data privacy with integrity. Finally, the proposed model is tested and verified for its performance than other related studies in terms of energy consumption, packet delivery ratio, and data delay. [ABSTRACT FROM AUTHOR]

• The proposed model overcomes information loss and inaccuracy by utilizing the decision-making function. • The proposed model achieves trust in collecting the agricultural data by integrating secured IoT systems and supporting reliable communication. • The proposed model is simulated and verified its significance against state-of-the-art solutions in terms of performance metrics. • The proposed model decreases the overheads on IoT sensors using the lightweight optimization technique of artificial intelligence. Artificial intelligence (AI) is gaining demanding growth in the field of smart cities, agriculture, food management, and weather forecasting due to the lack of computing power on sensing devices. The applications of artificial intelligence are integrated with various Internet of Things (IoT) and ubiquitous sensors for the improvement of the agriculture sector and to decrease its management cost. Due to the bounded resources of wireless technologies, most of the solutions are designed for efficient delivery of agriculture data to cloud systems, however, still optimizing the resources management and data load for forwarding nodes, especially those closest to edge boundaries is a challenging issue. Moreover, due to the collection of incorrect environmental data, the decision-making process leads to a decrease in the productivity of the optimization process. To overcome such issues, this work proposes a trustworthy and intelligent agricultural model that uses metaheuristic optimization to enhance resource management to address these problems. The proposed model approach employs the decision-making function to overcome information loss and inconsistency. Moreover, it builds trust in agricultural data collection by using secure IoT devices and facilitating reliable communication. In terms of performance metrics, the proposed model is simulated to assess its importance in comparison to state-of-the-art solutions. It not only collects updated data from agricultural land but also uses artificial intelligence's lightweight optimization technique to reduce the overheads on IoT devices. The experiment findings demonstrate the importance of the proposed model for resource monitoring and overheads on the IoT system. [ABSTRACT FROM AUTHOR]

• Agricultural systems are made of smart things and wireless sensors. They can collaborate to facilitate the farmers life. • The tight constraints of the network devices are offering bounded services in critical situations. • Security is one of the major research challenges for agricultural data over the wireless technologies. • A smart solution is demanded with trustworthiness to increase the productivity of the agriculture with less communication cost and delay. The growth of Internet of Agriculture Things (IoAT) with wireless technologies has resulted in significant advances for smart farming systems. However, various techniques have been presented to predict the soil and crop conditions. Nonetheless providing a quality-enabled autonomous system is one of the important research challenges. Furthermore, in the event of network overloading, most existing work needs help to handle trustworthy communication. As a result, this paper proposes a smart optimization model to develop reliable and quality-aware sustainable agriculture using machine learning. Firstly, the proposed model utilizes intelligent devices to automate the data collection and transmission. It analyzes the independent performance variables to support the consistent decision-making process for the forwarding scheme. Secondly, the proposed model investigated blockchain-based security principles for integrating the trusted system to reduce communication interference. The proposed model has been validated through simulations, and numerous experiments have demonstrated its efficacy regarding network parameters. [Display omitted] [ABSTRACT FROM AUTHOR]

• This research explores machine learning techniques in providing quality-aware data acquisition with reliable computing devices. • Extracted the devices' information in terms of their updated factors and offer an intelligent learning strategy with low-cost processing. • Used simple cryptographic techniques for secure collaboration of all devices with dual authentication to protect data retrieval. • Finally, simulations are verified to validate the proposed system under dynamic and unpredictable situations. Wireless communication systems offer a dynamic infrastructure with efficient data sensing and forwarding services using digital networks and the Internet of Things (IoT). Many schemes have been proposed to cope with a smart communication system by integrating medical devices. However, lowering the processing overheads with efficient utilization of network services are challenging tasks. Thus, this paper presents a smart health analysis system using machine learning techniques for IoT network, which aims to handle big data with balancing the communication load for green technologies. Firstly, using regression prediction, the proposed system offers quality-aware services, secondly, by exploring intelligent methods it provides a delay-tolerant scheme to give the least overhead communication paradigm using mobile agents. Finally, the big data is secured using cryptographic techniques and collaborative devices to maintain its trustworthiness with the cloud. The proposed system has revealed a noteworthy performance in terms of network parameters against existing studies. [Display omitted] [ABSTRACT FROM AUTHOR]

Breast cancer is one of the common disease in female gender population all over the world. The classical methods of segmentation and classification for malignant cells are not only repetitive but also very time-consuming. Therefore, a computer-aided diagnosis is needed for automatic segmentation and classification of malignant cells in breast cytology images. In this article, a machine learning-based approach is proposed for malignant cell segmentation and classification in breast cytology images. In the proposed approach, the segmentation of cells is performed by a level set algorithm which is used to extract statistical information related to the malignant and benign cells. Similarly, the gray level co-occurrence matrix is computed to exploit the texture information, and support vector machine-based classification is used for the classification of malignant and benign cells. It has been observed through experiments that the proposed approach achieved high accuracy (96.3%) in the classification of malignant and benign cells. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is a developing technology for supporting heterogeneous physical objects into smart things and improving the individuals living using wireless communication systems. Recently, many smart healthcare systems are based on the Internet of Medical Things (IoMT) to collect and analyze the data for infectious diseases, i.e., body fever, flu, COVID-19, shortness of breath, etc. with the least operation cost. However, the most important research challenges in such applications are storing the medical data on a secured cloud and make the disease diagnosis system more energy efficient. Additionally, the rapid explosion of IoMT technology has involved many cyber-criminals and continuous attempts to compromise medical devices with information loss and generating bogus certificates. Thus, the increase in modern technologies for healthcare applications based on IoMT, securing health data, and offering trusted communication against intruders is gaining much research attention. Therefore, this study aims to propose an energy-efficient IoT e-health model using artificial intelligence with homomorphic secret sharing, which aims to increase the maintainability of disease diagnosis systems and support trustworthy communication with the integration of the medical cloud. The proposed model is analyzed and proved its significance against relevant systems. [ABSTRACT FROM AUTHOR]

A wireless body area network is a collection of Internet of Things–based wearable heterogeneous computing devices primarily used in healthcare monitoring applications. A lot of research is in process to reduce the cost and increase efficiency in medical industry. Low power sensor nodes are often attached to high-risk patients for real-time remote monitoring. These sensors have limited resources such as storage capacity, battery life, computational power, and channel bandwidth. The current work proposes a multi-hop Priority-based Congestion-avoidance Routing Protocol using IoT based heterogeneous sensors for energy efficiency in wireless body area networks. The objective is to devise a routing protocol among sensor nodes such that it has minimum delay and higher throughput for emergency packets using IoT based sensor nodes, optimal energy consumption for longer network lifetime, and efficient scarce resource utilization. In our proposed work, data traffic is categorized into normal and emergency or life-critical data. For normal data traffic, next-hop selection will be selected based upon three parameters; residual energy, congestion on forwarder node, and signal-to-noise ratio of the path between source and forwarder node. We use the data aggregation and filtration technique to reduce the network traffic load and energy consumption. A priority-based routing scheme is also proposed for life-critical data to have less delay and greater throughput in emergency situations. Performance of the proposed protocol is evaluated with two cutting-edge routing techniques iM-SIMPLE and Optimized Cost Effective and Energy Efficient Routing. The proposed model outperforms in terms of network throughput, traffic load, energy consumption, and lifespan. [ABSTRACT FROM AUTHOR]

Wireless sensor networks have gained remarkable appreciation over the last few years. Despite significant advantages and tremendous applications, WSN is vulnerable to variety of attacks. Due to resource constraint nature of WSN, applicability of traditional security solutions is debatable. Although cryptography, authentication and confidentiality measures help in preventing specific types of attacks but they cannot safeguard against node misbehavior attacks and come at significant cost. To address this problem, we propose a Trust Based Secure Routing Protocol (TBSRP) which relies on distributed trust model for the detection and isolation of misbehaving nodes. The TBSRP aims to establish shortest path that contain all trusted nodes, identify packet forwarding misbehavior caused by malicious and faulty nodes and reroute the traffic to other reliable paths. The performance of TBSRP is evaluated in terms of packet delivery ratio, average end-to-end delay and normalized routing load. Simulations results show that TBSRP can achieve both high delivery ratio and throughput in presence of various numbers of misbehaving and faulty nodes. [ABSTRACT FROM AUTHOR]