Network Security System in Mobile Edge Computing-to-IoMT Networks Using Distributed Approach

Internet of Things (IoT) has widely been accepted as a promising paradigm for connecting a large number of resource-constrained miniature sensor nodes that have the ability to sense the deployed environment. They have found their applications in various aspect of our daily lives. However, these nodes are mostly restricted to sense only the scalar data. Nowadays, multimedia sensor nodes are gaining significant attention due to their ability to collect scalar as well as multi-dimensional data. These nodes are considered as the foundation of Internet of Multimedia Things (IoMT) and are shaping the perception of IoT. Multimedia data have stringent requirements in terms of reliability, latency, storage, bandwidth, and Quality of Service (QoS). To provide seamless and interoperable communication in IoMT, the underlying protocol stacks need to fulfill these stringent requirements. However, the heterogeneous nature of multimedia sensors makes interoperability a challenging task to fulfill. To understand the challenges faced by seamless and interoperable communication in IoMT, we provide a comprehensive review of the existing protocol stacks of IoMT and analyze their feasibility for multimedia streaming applications. Data storage of multimedia applications is another area that requires immediate attention of the research community. For this purpose, we study cloud as an entity to facilitate multimedia applications of IoMT. The instances of multimedia cloud are analyzed and a number of shortcomings are identified that pave the way for edge computing in IoMT. Finally, we present a case study that shows the significance of our work. The case study portrays an in-home patient monitoring system with an interoperable communication among the connected multimedia streaming devices at home, and healthcare practitioners at hospital. The case study also highlights the importance of uninterrupted data storage and retrieval at the network edge and multimedia sensor nodes.

With the immersive growth of the Internet of Things (IoT) and real-time adaptability, quality of life for people is improving. IoT applications are diverse in nature and one crucial aspect of it is multimedia sensors and devices. These IoT multimedia devices form the Internet of Multimedia Things (IoMT). It generates a massive volume of data with different characteristics and requirements than the IoT. The real-time deployment scenarios vary from smart traffic monitoring to smart hospitals. Hence, Timely delivery of IoMT data and decision making is critical as it directly involves the safety of human beings. In this paper, we present a brief overview of IoMT and future research directions. Afterward, we provide an overview of the accepted articles in our special issue on the IoMT: Opportunities, Challenges, and Solutions.

The Internet of Things (IoT) is growing fast and is gaining significant adoption in areas such as smart cities and manufacturing. The variety and low-cost of IoT devices with excellent audio/visual sensors is fueling the growth of multimedia IoT applications, many of which are bandwidth-hungry and time-sensitive (i.e., must produce their results within an application specific time-sensitive requirement). While a large body of related work has studied distribution of such Time Sensitive Multimedia IoT (TS-MIoT) applications in simulated environments, there is lack of a platform that can be used to experiment with techniques for meeting their time-sensitive and computing resource requirements on real-world IoT infrastructure, i.e., a combination of IoT devices, close by computers and a cloud data centre connected by a variety of networks. This paper proposes APOLLO, a platform for experimental analysis of TS-MIoT applications. APOLLO provides mechanisms to load TS-MIoT application execution plans and execute the plans on available IoT infrastructure. We describe a proof-of-concept implementation using Orleans and present experimental evaluations to validate APOLLO's ability to support the experimental analysis of TS-MIoT applications.

This paper addresses the issue of evaluating the Quality of Experience (QoE) for Internet of Things (IoT) applications, with particular attention to the case where multimedia content is involved. A layered IoT architecture is firstly analyzed to understand which QoE influence factors have to be considered in relevant application scenarios. We then introduce the concept of Multimedia IoT (MIoT) and define a layered QoE model aimed at evaluating and combining the contributions of each influence factor to estimate the overall QoE in MIoT applications. Finally, we present a use case related to the remote monitoring of vehicles during driving practices, which is used to validate the proposed layered model, and we discuss a second use case for smart surveillance, to emphasize the generality of the proposed framework. The effectiveness in evaluating classes of influence factors separately is demonstrated.

This paper goes beyond classic Internet of Things (IoT) and focuses on the concept of Multimedia-based Internet of Things (M-IoT). Smart heterogeneous multimedia things can interact and cooperate with each another and with other things connected to the Internet to facilitate multimedia-based services and applications that are globally available to the users. Nowadays, the access network selection for heterogeneous multimedia devices is not managed taking into account the reputation that a given network has at a given instant. In order to solve these issues ReMIoT, a Reputation-based M-IoT architecture is introduced in this paper and presented alongside a novel solution to improve multimedia content delivery quality. The solution includes three distinct operational stages: (i) multimedia service detection, (ii) network reputation detection, and (iii) M-IoT network selection. The performance of the proposed solution is compared against that of a traffic type-based differentiated reputation algorithm for radio resource allocation (TYDER). This performance was evaluated in terms of QoS parameters such as throughput, packet delay and packet loss ratio.

The Internet of Things (IoT) has already penetrated an ever-increasing array of daily aspects of life. IoTs bridge the analog and digital worlds in an unprecedented manner and degree by providing in situ sensing. Adding to the IoT the capability to collect interrelated multi-modal sensing, the use of the Internet of Multimedia Things (IoMTs) has recently been exhibited to significantly enhance the role of Information and Communication Technologies (ICT) in numerous applications, and most importantly in agrifood systems. In this work, we review key recent works in the conjunction of the three domains of IoMT, agrifood and precision agriculture and present open research directions.

Multimedia Internet of Things (IoT) devices, especially, the smartphones are embedded with sensors including Global Positioning System (GPS), barometer, microphone, accelerometer, etc. These sensors working together, present a fairly complete picture of the citizens' daily activities, with implications for their privacy. With the internet, Citizens in Smart Cities are able to perform their daily life activities online with their connected electronic devices. But, unfortunately, computer hackers tend to write automated malicious applications to attack websites on which these citizens perform their activities. These security threats sometime put their private information at risk. In order to prevent these security threats on websites, Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHAs) are generated, as a form of security mechanism to protect the citizens' private information. But with the advancement of deep learning, text-based CAPTCHAs can sometimes be vulnerable. As a result, it is essential to conduct performance evaluation on the CAPTCHAs that are generated before they are deployed on multimedia web applications. Therefore, this work proposed an optimal and light-weight Convolutional Neural Network (CNN) to solve both numerical and alpha-numerical complex text-based CAPTCHAs simultaneously. The accuracy of the proposed CNN model has been accelerated based on Cyclical Learning Rates (CLRs) policy. The proposed CLR-CNN model achieved a high accuracy to solve both numerical and alpha-numerical text-based CAPTCHAs of 99.87% and 99.66%, respectively. In real-time, we observed that the speed of the model has increased, the model is lightweight, stable, and flexible as compared to other CAPTCHA solving techniques. The result of this current work will increase awareness and will assist multimedia security Researchers to continue and develop more robust text-based CAPTCHAs with their security mechanisms capable of protecting the private information of citizens in Smart Cities.

In Multimedia Internet of Things (IoT), in order to reduce the bandwidth consumption of wireless channels, Motion-Compensated Frame Rate Up-Conversion (MC-FRUC) is often used to support the low-bitrate video communication. In this paper, we propose a spatial predictive algorithm which is used to improve the performance of MC-FRUC. The core of the proposed algorithm is a predictive model to split a frame into two kinds of blocks: basic blocks and absent blocks. Then an improved bilateral motion estimation is proposed to compute the Motion Vectors (MVs) of basic blocks. Finally, with the spatial correlation of Motion Vector Field (MVF), the MV of an absent block is predicted based on the MVs of its neighboring basic blocks. Experimental results show that the proposed spatial prediction algorithm can improve both the objective and the subjective quality of the interpolated frame, with a low computational complexity.

Forecasting daily PM2.5 concentrations in Wuhan with a spatial-autocorrelation-based long short-term memory model

Random linear network coding (RLNC) has the potential to improve the performance of current and future Internet of Things (IoT) communication systems, but is computationally demanding due to matrix multiplications and inversions. Some single-core RLNC implementations achieve already sufficient coding speeds for contemporary multimedia streaming formats. However, advances in multimedia streaming formats and IoT applications will require the exploitation of heterogeneous multicore architectures, which are becoming common for a wide range of IoT nodes, including smartphones. In this paper, we introduce and evaluate efficient RLNC computing strategies for IoT node architectures, including the emerging heterogeneous big.LITTLE multicore architectures with multiple big (fast) cores and multiple LITTLE (slow) cores. In contrast to existing RLNC implementation strategies, we build on and adapt highly optimized dense matrix operations from the high performance computing field to RLNC on heterogeneous multicore IoT nodes. Our approach includes the optimization of RLNC matrix operations through optimized operations on matrix blocks with single instruction multiple data instructions. We schedule block operations on the heterogeneous cores through a directed acyclic graph that avoids artificial synchronization points while ensuring the data dependencies. We examine priority scheduling according to the number of outgoing dependencies of a task and data locality of cached blocks. Our extensive measurements with several heterogeneous big.LITTLE multicore IoT node and smartphone processor boards demonstrate higher RLNC encoding and decoding throughputs than existing approaches. Moreover, our measurements indicate that the utilization of more cores decreases energy consumption, which is an important goal for IoT nodes.

Long short-term memory - Fully connected (LSTM-FC) neural network for PM2.5 concentration prediction

Traffic safety has been thought of as a basic feature of transportation, recent developments in civil aviation have emphasized the need for risk identification and safety prediction. This study aims to increase en-route flight safety through the development of prediction models for flight conflicts. Firstly, flight conflicts time series and traffic parameters are extracted from historical ADS-B data. In the second step, a Long Short-Term Memory (LSTM) model is trained to make a one-step-ahead prediction on the flight conflict time series. The results show that the LSTM model has the greatest prediction effect (MAE 0.3901) with comparison to other models. Based on that, we add traffic parameters (volume, density, velocity) into the LSTM model as new input variables and issue a comprehensive analysis of the relative predictive power of traffic parameters. The accuracy of prediction model is validated with a mean error of less than 3%. Based on the improvements of model performance brought by traffic parameters, LSTM models with a single traffic parameter are proposed for further discussion. The results illustrate that volume is the most important factor in promoting prediction accuracy and density has an advantage of improvement in the aspect of model stability.

Evapotranspiration (ET) is the fundamental component of efficient water resource management. Accurate forecasting of ET is essential for efficient water utilization in agriculture. ET forecasting is a complex process due to the requirements of large meteorological variables. The recommended approach is based on the Internet of Things (IoT) and an ensemble-learning-based approach for meteorological data collection and ET forecasting with limited meteorological conditions. IoT is part of the recommended approach to collect real-time data on meteorological variables. The daily maximum temperature (T), mean humidity (Hm), and maximum wind speed (Ws) are used to forecast evapotranspiration (ET). Long short-term memory (LSTM) and ensemble LSTM with bagged and boosted approaches are implemented and evaluated for their accuracy in forecasting ET values using meteorological data from 2001 to 2023. The results demonstrate that the bagged LSTM approach accurately forecasts ET with limited meteorological conditions in Riyadh, Saudi Arabia, with the coefficient of determination (R2) of 0.94 compared to the boosted LSTM and off-the-shelf LSTM with R2 of 0.91 and 0.77, respectively. The bagged LSTM model is also more efficient with small values of root mean squared error (RMSE) and mean squared error (MSE) of 0.42 and 0.53 compared to the boosted LSTM and off-the-shelf LSTM models.

The Internet of Things has advanced in such a way that smart homes become more of a reality. One of the advancements that make this possible is the use of efficient and cost-effective sensors that collect environmental data that can be used for machine learning. Environmental sensors are non-intrusive, so they can be deployed to homes, offices, and other similar environments with minimum risk of privacy. Recurrent learning models such as long short-term memory (LSTM) can be used to predict future occupancy information using environmental data for smart environment and security with high accuracy. This trained model can be used in other applications related to building occupancy. In this work, we trained a long short-term memory model using a publicly available dataset to forecast the occupancy count and achieved a high accuracy of 97.3%. We present the training process, the hyperparameters, and the results. This model can be used in other building occupancy estimation applications.

The accurate prediction of carbon dioxide (CO2) emissions in the building industry can provide data support and theoretical insights for sustainable development. This study proposes a hybrid model for predicting CO2 emissions that combines a multi-strategy improved particle swarm optimization (MSPSO) algorithm with a long short-term memory (LSTM) model. Firstly, the particle swarm optimization (PSO) algorithm is enhanced by combining tent chaotic mapping, mutation for the least-fit particles, and a random perturbation strategy. Subsequently, the performance of the MSPSO algorithm is evaluated using a set of 23 internationally recognized test functions. Finally, the predictive performance of the MSPSO-LSTM hybrid model is assessed using data from the building industry in the Yangtze River Delta region as a case study. The results indicate that the coefficient of determination (R2) of the model reaches 0.9677, which is more than 10% higher than that of BP, LSTM, and CNN non-hybrid models and demonstrates significant advantages over PSO-LSTM, GWO-LSTM, and WOA-LSTM hybrid models. Additionally, the mean square error (MSE) of the model is 2445.6866 Mt, and the mean absolute error (MAE) is 4.1010 Mt, both significantly lower than those of the BP, LSTM, and CNN non-hybrid models. Overall, the MSPSO-LSTM hybrid model demonstrates high predictive accuracy for CO2 emissions in the building industry, offering robust support for the sustainable development of the industry.