Multimedia IoT-surveillance optimization model using mobile-edge authentic computing.

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.

Cloud-edge load balancing distributed protocol for IoE services using swarm intelligence.

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.

Efficient and trusted autonomous vehicle routing protocol for 6G networks with computational intelligence.

The Internet of Things (IoT) and wireless sensors have collaborated with many real-time environments for the collection and processing of physical data. Mobile networks with sixth-generation (6G) technologies provide support for emerging applications using Connected and Autonomous Vehicles (CAV) and observe critical conditions. Although, autonomous vehicle-based routing solutions have presented significant development toward reliable and inter-vehicle communications. However, there are numerous research obstacles in terms of data delivery and transmission latency due to the unpredictable environment and changing states of IoT sensors. Therefore, this work presents an efficient and trusted autonomous vehicle routing protocol using 6G networks, which aims to guarantee high quality of service and data coverage. Firstly, the proposed protocol establishes a routing process using a simulated annealing optimization technique and improves energy optimization between IoT-based vehicles, and under difficult circumstances, it statistically guarantees the optimal solution. Secondly, it provides a risk-aware security system due to reliable session-oriented communication with network edges among connected vehicles and avoids uncertainties in the autonomous system. The proposed protocol is verified using simulations for varying vehicles and varying iterations that indicates a green communication system for the autonomous system with authenticity and system intelligence.

Secure Edge-Based Energy Management Protocol in Smart Grid Environments with Correlation Analysis.

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.

Sustainable Data-Driven Secured Optimization Using Dynamic Programming for Green Internet of Things.

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.

Traffic-Aware Secured Cooperative Framework for IoT-Based Smart Monitoring in Precision Agriculture.

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.

Device-to-Device (D2D) Multi-Criteria Learning Algorithm Using Secured Sensors.

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.

An Optimization Model with Network Edges for Multimedia Sensors Using Artificial Intelligence of Things.

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.

Secure and energy-efficient framework using Internet of Medical Things for e-healthcare.

In various fields, the internet of things (IoT) gains a lot of popularity due to its autonomous sensors operations with the least cost. In medical and healthcare applications, the IoT devices develop an ecosystem to sense the medical conditions of the patients' such as blood pressure, oxygen level, heartbeat, temperature, etc. and take appropriate actions on an emergency basis. Using it, the healthcare-related data of patients is transmitted towards the remote users and medical centers for post-analysis. Different solutions have been proposed using Wireless Body Area Network (WBAN) to monitor the medical status of the patients based on low powered biosensor nodes, however, preventing increased energy consumption and communication costs are demanding and interesting problems. The issue of unbalanced energy consumption between biosensor nodes degrades the timely delivery of the patient's information to remote centers and gives a negative impact on the medical system. Moreover, the sensitive data of the patient is transmitting over the insecure Internet and prone to vulnerable security threats. Therefore, data privacy and integrity from malicious traffic are another challenging research issue for medical applications. This research article aims to a proposed secure and energy-efficient framework using Internet of Medical Things (IoMT) for e-healthcare (SEF-IoMT), which primary objective is to decrease the communication overhead and energy consumption between biosensors while transmitting the healthcare data on a convenient manner, and the other hand, it also secures the medical data of the patients against unauthentic and malicious nodes to improve the network privacy and integrity. The simulated results exhibit that the proposed framework improves the performance of medical systems for network throughput by 18%, packets loss rate by 44%, end-to-end delay by 26%, energy consumption by 29%, and link breaches by 48% than other states of the art solutions.

SASC: Secure and Authentication-Based Sensor Cloud Architecture for Intelligent Internet of Things.

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.

An Energy Efficient and Secure IoT-Based WSN Framework: An Application to Smart Agriculture.

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.

Correction: RCER: Reliable Cluster-based Energy-aware Routing protocol for heterogeneous Wireless Sensor Networks.

[This corrects the article DOI: 10.1371/journal.pone.0222009.].

RCER: Reliable Cluster-based Energy-aware Routing protocol for heterogeneous Wireless Sensor Networks.

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.