Showing papers on "Smart device published in 2021"

A lightweight remote user authentication scheme for IoT communication using elliptic curve cryptography

Abstract: Internet of things (IoT) has become a new era of communication technology for performing information exchange. With the immense increment of usage of smart devices, IoT services become more accessible. To perform secure transmission of data between IoT network and remote user, mutual authentication, and session key negotiation play a key role. In this research, we have proposed an ECC-based three-factor remote user authentication scheme that runs in the smart device and preserves privacy, and data confidentiality of the communicating user. To support our claim, multiple cryptographic attacks are analyzed and found that the proposed scheme is not vulnerable to those attacks. Finally, the computation and communication overheads of the proposed scheme are compared with other existing protocols to confirm that the proposed scheme is lightweight. A formal security analysis using AVISPA simulation tool has been done that confirms the proposed scheme is robust against relevant security threats.

Designing Secure User Authentication Protocol for Big Data Collection in IoT-Based Intelligent Transportation System

Abstract: Secure access of the real-time data from the Internet-of-Things (IoT) smart devices (e.g., vehicles) by a legitimate external party (user) is an important security service for big data collection in the IoT-based intelligent transportation system (ITS). To deal with this important issue, we design a new three-factor user authentication scheme, called UAP-BCIoT, which relies on elliptic-curve cryptography (ECC). The mutual authentication between the user and an IoT device happens via the semitrusted cloud-gateway (CG) node in UAP-BCIoT. UAP-BCIoT supports several functionality features needed for IoT-based ITS environment including IoT smart device credential validation and big data analytics. A detailed security analysis is conducted based on the defined threat model to show that UAP-BCIoT is resilient against many known attacks. A thorough comparative study reveals that UAP-BCIoT supports better security, offers various functionality attributes, and also provides similar costs in communication as well computation as compared to other relevant schemes Finally, the practical demonstration of the proposed UAP-BCIoT is also provided to measure its impact on the network performance parameters.

A Survey on Sensor-Based Threats and Attacks to Smart Devices and Applications

Abstract: Modern electronic devices have become “smart” as well as omnipresent in our day-to-day lives. From small household devices to large industrial machines, smart devices have become very popular in every possible application domain. Smart devices in our homes, offices, buildings, and cities can connect with other devices as well as with the physical world around them. This increasing popularity has also placed smart devices as the center of attention among attackers. Already, several types of malicious activities exist that attempt to compromise the security and privacy of smart devices. One interesting and noteworthy emerging threat vector is the attacks that abuse the use of sensors on smart devices. Smart devices are vulnerable to sensor-based threats and attacks due to the lack of proper security mechanisms available to control the use of sensors by installed apps. By exploiting the sensors (e.g., accelerometer, gyroscope, microphone, light sensor, etc.) on a smart device, attackers can extract information from the device, transfer malware to a device, or trigger a malicious activity to compromise the device. In this paper, we explore various threats and attacks abusing sensors of smart devices for malicious purposes. Specifically, we present a detailed survey about existing sensor-based threats and attacks to smart devices and countermeasures that have been developed to secure smart devices from sensor-based threats. Furthermore, we discuss security and privacy issues of smart devices in the context of sensor-based threats and attacks and conclude with future research directions.

Online Computation Offloading and Resource Scheduling in Mobile-Edge Computing

Abstract: With the explosion of mobile smart devices, many computation intensive applications have emerged, such as interactive gaming and augmented reality. Mobile-edge computing (EC) is put forward, as an extension of cloud computing, to meet the low-latency requirements of the applications. In this article, we consider an EC system built in an ultradense network with numerous base stations. Heterogeneous computation tasks are successively generated on a smart device moving in the network. An optimal task offloading strategy, as well as optimal CPU frequency and transmit power scheduling, is desired by the device user to minimize both task completion latency and energy consumption in a long term. However, due to the stochastic task generation and dynamic network conditions, the problem is particularly difficult to solve. Inspired by reinforcement learning, we transform the problem into a Markov decision process. Then, we propose an attention-based double deep $Q$ network (DDQN) approach, in which two neural networks are employed to estimate the cumulative latency and energy rewards achieved by each action. Moreover, a context-aware attention mechanism is designed to adaptively assign different weights to the values of each action. We also conduct extensive simulations to compare the performance of our proposed approach with several heuristic and DDQN-based baselines.

Comparison of iPad Pro’s LiDAR and TrueDepth capabilities with an industrial 3D scanning solution

Abstract: Today’s smart devices come equipped with powerful hard- and software-enabling professional use cases. The latest hardware by Apple utilizes LiDAR and TrueDepth, which offer the capability of 3D scanning. Devices equipped with these camera systems allow manufacturers to obtain 3D data from their customers at low costs, which potentially enables time-efficient mass customization and product differentiation strategies. However, the utilization is limited by the scanning accuracy. To determine the potential application of LiDAR and TrueDepth as a 3D scanning solution, in this paper an evaluation was performed. For this purpose, different Lego bricks were scanned with the technologies and an industrial 3D scanner. The results were compared according to shape and position tolerances. Even though the industrial 3D scanner consistently delivered more accurate results, the accuracy of the smart device technologies may already be sufficient, depending on the application.

A Secure and Lightweight Authentication Protocol for IoT-Based Smart Homes

Abstract: With the information and communication technologies (ICT) and Internet of Things (IoT) gradually advancing, smart homes have been able to provide home services to users. The user can enjoy a high level of comfort and improve his quality of life by using home services provided by smart devices. However, the smart home has security and privacy problems, since the user and smart devices communicate through an insecure channel. Therefore, a secure authentication protocol should be established between the user and smart devices. In 2020, Xiang and Zheng presented a situation-aware protocol for device authentication in smart grid-enabled smart home environments. However, we demonstrate that their protocol can suffer from stolen smart device, impersonation, and session key disclosure attacks and fails to provide secure mutual authentication. Therefore, we propose a secure and lightweight authentication protocol for IoT-based smart homes to resolve the security flaws of Xiang and Zheng’s protocol. We proved the security of the proposed protocol by performing informal and formal security analyses, using the real or random (ROR) model, Burrows–Abadi–Needham (BAN) logic, and the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. Moreover, we provide a comparison of performance and security properties between the proposed protocol and related existing protocols. We demonstrate that the proposed protocol ensures better security and lower computational costs than related protocols, and is suitable for practical IoT-based smart home environments.

Joint wireless power transfer and task offloading in mobile edge computing: a survey

Abstract: The promising technique of Wireless Power Transfer (WPT) to end devices and sensors has gained the attention of researchers recently. Mobile edge computing (MEC) is also succeeding from Cloud Computing due to its minimum latency constraints. In MEC, smart devices offload computation intensive tasks to the MEC server which achieves low latency. However, limitations exist for smart device battery lifetime and task execution delay because of an effective decision in the offloading scenario necessitating joint WPT and MEC offloading. The joint WPT and MEC offloading decisions are based on real time application requirements, placement of Base Station (BS) with power transfer capabilities for smart devices, and offloading opportunities in the MEC. To meet the energy consumption requirement, a BS integrated with MEC server and power transfer capability transfers wireless power to end devices as an incentive and offers opportunities for offloading. Transferring wireless power to end devices effectively meets the requirement of smart devices while extending battery lifetime. This article encapsulates the state of art work in methodologies of offloading in MEC and WPT to end nodes. We consider MEC offloading techniques with WPT and real time application requirements while summarizing related studies. We formulate a taxonomy of joint WPT and offloading in MEC. We compare the state-of-the-art studies based on parameters identified from taxonomy. Finally, we provide the challenges and debate future research directions relevant to the domain of joint MEC-WPT.

Artificial Intelligence based Smart Door with Face Mask Detection

Abstract: In this pandemic situation, health plays an important role in everyone’s life. Most of the people are not aware of preventing themselves and their surroundings from this pandemic. Face mask is essential to prevent ourselves and others. So, people are in need to wear face mask regularly. People who visit home won’t wear mask due to their unawareness which may affect people. People may not know if someone visits their home when they are not there. AI based smart device (Raspberry pi with AI model with camera) is proposed in this project which identifies whether a person is wearing face mask and gives us an alert message (via mobile app). This device is integrated with a mobile app. Mobile app identifies if someone enters home when people are not physically present in their home. This smart device automatically opens the door only if people wear face mask. This device works both day and night. It can be used in multiple places like malls, shops, hospitals and temples

Multi-Armed Bandit for Energy-Efficient and Delay-Sensitive Edge Computing in Dynamic Networks With Uncertainty

Abstract: In the edge computing paradigm, mobile devices offload the computational tasks to an edge server by routing the required data over the wireless network. The full potential of edge computing becomes realized only if a smart device selects the most appropriate server in terms of the latency and energy consumption, among many available ones. The server selection problem is challenging due to the randomness of the environment and lack of prior information about the same. Therefore, a smart device, which sequentially chooses a server under uncertainty, aims to improve its decision based on the historical time and energy consumption. The problem becomes more complicated in a dynamic environment, where key variables might undergo abrupt changes. To deal with the aforementioned problem, we first analyze the required time and energy to data transmission and processing. We then use the analysis to cast the problem as a budget-limited multi-armed bandit problem, where each arm is associated with a reward and cost, with time-variant statistical characteristics. We propose a policy to solve the formulated problem and prove a regret bound. The numerical results demonstrate the superiority of the proposed method compared to several online learning algorithms.

Certificate Based Authentication Scheme for Smart Homes

Abstract: Smart home networks convey sensitive user private data that requires adequate protection. However, lack of standard security and privacy architectures for smart meters or failure of smart device manufacturers to incorporate security in their designs endear smart home networks as launching pads for attacks such as denial of service. To curb this, techniques such as data aggregation, signcryption, elliptic curve cryptography (ECC) bilinear pairing, public key cryptosystems (PKC) and ECC exponential operations have been utilized. However, the computation complexities of some of these schemes are too high for smart home devices. In addition, privacy and anonymity are rarely considered in most of the conventional authentication protocols. In this paper, an ECC based lightweight authentication scheme is presented. The security evaluation shows that it offers anonymity and untraceability, and is resilient against man-in-the-middle (MitM), impersonation, packet replays and denial of service (DoS) attacks. In terms of performance, the proposed protocol exhibits average communication and computation overheads.

Docker-Based Intelligent Fall Detection Using Edge-Fog Cloud Infrastructure

Abstract: Health sector is a life critical domain, which requires fast and intelligent decisions. Artificial intelligence-based monitoring systems can help the elderly people in situations like fall. In e-health, systems are equipped with wearable devices that aid in remote monitoring with the help of Internet of Things (IoT). Our proposed work ensures fall detection using a three-layer architecture (Edge-Fog-Cloud) which takes advantage of the available smart devices. The edge detection involves vision-based detection using a compressed neural network running on a smart device constructed using transfer learning. Decision making in fog involves ensemble learning methodology using sensor-based data and decision from the edge. The cloud is used for permanent storage and model building. The work also takes advantage of image augmentation for data set building to improve the performance of the model. The model is evaluated based on accuracy, and the advantage of the fog layer is evaluated based on the latency. The proposed model gives an accuracy of 98.5% which is compared with the existing state-of-the-art algorithms available for detection.

Survey on Enterprise Internet-of-Things systems (E-IoT): A security perspective

Abstract: As technology becomes more widely available, millions of users worldwide have installed some form of smart device in their homes or workplaces. These devices are often off-the-shelf commodity systems, such as Google Home or Samsung SmartThings, that are installed by end-users looking to automate a small deployment. In contrast to these “plug-and-play” systems, purpose-built Enterprise Internet-of-Things (E-IoT) systems such as Crestron, Control4, RTI, Savant offer a smart solution for more sophisticated applications (e.g., complete lighting control, A/V management, security). In contrast to commodity systems, E-IoT systems are usually closed source, costly, require certified installers, and are overall more robust for their use cases. Due to this, E-IoT systems are often found in expensive smart homes, government and academic conference rooms, yachts, and smart private offices. However, while there has been plenty of research on the topic of commodity systems, no current study exists that provides a complete picture of E-IoT systems, their components, and relevant threats. As such, lack of knowledge of E-IoT system threats, coupled with the cost of E-IoT systems has led many to assume that E-IoT systems are secure. To address this research gap, raise awareness on E-IoT security, and motivate further research, this work emphasizes E-IoT system components, E-IoT vulnerabilities, solutions, and their security implications. In order to systematically analyze the security of E-IoT systems, we divide E-IoT systems into four layers: E-IoT Devices Layer, Communications Layer, Monitoring and Applications Layer, and Business Layer. We survey attacks and defense mechanisms, considering the E-IoT components at each layer and the associated threats. In addition, we present key observations in state-of-the-art E-IoT security and provide a list of open research problems that need further research.

A Blockchain-Enabled Framework for mHealth Systems.

Abstract: Presently modern technology makes a significant contribution to the transition from traditional healthcare to smart healthcare systems. Mobile health (mHealth) uses advances in wearable sensors, telecommunications and the Internet of Things (IoT) to propose a new healthcare concept centered on the patient. Patients' real-time remote continuous health monitoring, remote diagnosis, treatment, and therapy is possible in an mHealth system. However, major limitations include the transparency, security, and privacy of health data. One possible solution to this is the use of blockchain technologies, which have found numerous applications in the healthcare domain mainly due to theirs features such as decentralization (no central authority is needed), immutability, traceability, and transparency. We propose an mHealth system that uses a private blockchain based on the Ethereum platform, where wearable sensors can communicate with a smart device (a smartphone or smart tablet) that uses a peer-to-peer hypermedia protocol, the InterPlanetary File System (IPFS), for the distributed storage of health-related data. Smart contracts are used to create data queries, to access patient data by healthcare providers, to record diagnostic, treatment, and therapy, and to send alerts to patients and medical professionals.

Blockchain Technology in Smart-Cities

Abstract: With the fast pace of population, intelligent city wants effective and sustainable smart solutions in transport, climate, energy, and government affairs. Amongst the most sensible solutions is the smart city platform which includes IoT, Big Data and the Internet of Energy. It faces many issues, including such inadequate IoT security, trouble maintaining and improving efficiency, higher operating expenses of large amounts of data center construction, good permeability to damage, difficulty building confidence in electricity internet users, quick leakage of consumer privacy and a business model which is not acceptable etc. Blockchain is one of today’s most disruptive technologies. As part of the overall efforts to shape the urban future, numerous cities around the world are launching blockchain initiatives. With a range of potential advantages, digital transformation poses many key issues like data security and confidentiality. This study proposed a security architecture which utilizes the blockchain-based with smart devices and provide a secure communication system in an intelligent city.

Understanding cloud-based BYOD information security protection behaviour in smart business: in perspective of perceived value

Abstract: This research aims at investigating the critical factors that promote users in protecting the Bring Your Own Device (BYOD) information security. Perceived value serves as the core variable and inte...

An incentive mechanism based on a Stackelberg game for mobile crowdsensing systems with budget constraint

Abstract: The adoption of smart device technologies is steadily increasing. Most of the smart devices in use today have built-in sensors which measure motion, direction, and various environmental conditions. Sensors are able to provide raw data with different quality and accuracy. A large group of smart devices forms a mobile crowdsensing system which is capable of sensing, collecting and sharing the environmental data to perform large scale sensing jobs. This paper aims to study and design an incentive mechanism for a mobile crowdsensing system based on a one-leader multi-follower Stackelberg game. A platform provider, as proponent of the sensing job, will act as the leader, while the mobile users will act as the followers. The final goal is to devise an efficient mechanism able to motivate the smart device users to participate in the sensing activity. Different from existing approaches, we propose a centralized method where the platform provider can estimate users’ parameters very efficiently sending and receiving a few messages. We formulate the optimization problem on the platform provider side as a mixed integer nonlinear program with time constraints for each job and a budget constraint. Finally, a heuristic algorithm based on the derivative-free directional direct search method is designed to solve the platform optimization problem and achieve a close-to-optimal solution for the game. Results show that our Stackelberg game solution is much more scalable than the approach proposed in the work by other authors Zhan et al. (2018) as we can decrease the average number of messages by a factor between 53 to 80 and the average running time between 23 and 650 times. Furthermore, we compared our heuristic algorithm with BARON, a state of the art commercial tool for mixed integer global optimization, to solve the platform optimization problem. Results demonstrated that our proposed algorithm converges to a near-optimal solution much faster especially in large scale systems.

Review of Autonomous Intelligent Vehicles for Urban Driving and Parking

Abstract: With the concept of Internet-of-Things, autonomous vehicles can provide higher driving efficiency, traffic safety, and freedom for the driver to perform other tasks. This paper first covers enabling technology involving a vehicle moving out of parking, traveling on the road, and parking at the destination. The development of autonomous vehicles relies on the data collected for deployment in actual road conditions. Research gaps and recommendations for autonomous intelligent vehicles are included. For example, a sudden obstacle while the autonomous vehicle executes the parking trajectory on the road is discussed. Several aspects of social problems, such as the liability of an accident affecting the autonomous vehicle, are described. A smart device to detect abnormal driving behaviors to prevent possible accidents is briefly discussed.

Live Emergency and Warning Alerts Through Android Application for Vehicular Ad Hoc Network Communication (Android VANET)

Abstract: The technology is growing towards smart communication through smart devices. This smart communication leads to the development of Vehicular Ad Hoc Network (VANET). These days each vehicle is acting as a smart device which could establish a smart communication among the vehicles. The growth of VANET communication passes through various stages such as RoadSide Unit (RSU), Vehicle to Vehicle (V2V), Cluster based, Internet of Vehicles (IoV), and Web VANET (WVANET) Communication models. All these advancements in VANET architecture provide a smart communication among the vehicles. The vital aim of VANET architecture is to provide an efficient and effective emergency and warning alerts to the vehicles so that the vehicle can take appropriate decisions without any delay to safeguard the safety of the passengers. However, it will be more reliable to the vehicles if the VANET architecture could provide a live emergency and warning alerts to the vehicles in well ahead of time. In order to provide live emergency and warning alerts the communication as well as the device should be smart. In this paper, we have developed a live emergency and warning alerts to the vehicles though android application. Each vehicle will have the android application installed on it, in which the entire live driving scenario is provided. Once, the live driving scenario is provided, the live emergency and warning alerts can be shown to the vehicles in well ahead of time. As live emergency alerts are shown to the vehicles, it will help the vehicles to take the right decision more effectively.

mHealth for Mental Health

Abstract: An individual’s physiological and social well-being shapes their mental health. Mental health is indicative of one’s cognitive behaviour and emotional well-being. Everything around us is connected through virtue of a network—Internet. The Internet is a tool for information, communication and connectivity. It has been predicted that three-quarters of Internet users will access Internet solely via smartphones in the coming years. This brings an opportunity to monitor health care using smartphone technology. mHealth holds enormous untapped potential in the near future, especially, in the arena of personalised health care and eventually personalised medicine. With data-intensive diagnostics and imaging coming into play, a gigantic amount of data is being collected and stored. In this paper, we have mentioned the importance of mental health, its characteristics and how mHealth devices (smartphone, smart devices, bio-sensors etc.) can play a key role in managing and coping with mental health problems. We have further analysed the behavioural patterns of active smartphone and smart device users globally as opposed to the active users in a particular region. An analysis of the percentage of population suffering from different mental health problems has been undertaken and a solution to these using mHealth architecture and data analytics proposed. We have also highlighted how mHealth sensory system with its 5 potential P’s (Preventive, Personalised, Predictive, Participatory, Psycho cognitive) can revolutionise the mental health landscape.

Wildlife Monitoring on the Edge: A Performance Evaluation of Embedded Neural Networks on Microcontrollers for Animal Behavior Classification.

Abstract: Monitoring animals' behavior living in wild or semi-wild environments is a very interesting subject for biologists who work with them. The difficulty and cost of implanting electronic devices in this kind of animals suggest that these devices must be robust and have low power consumption to increase their battery life as much as possible. Designing a custom smart device that can detect multiple animal behaviors and that meets the mentioned restrictions presents a major challenge that is addressed in this work. We propose an edge-computing solution, which embeds an ANN in a microcontroller that collects data from an IMU sensor to detect three different horse gaits. All the computation is performed in the microcontroller to reduce the amount of data transmitted via wireless radio, since sending information is one of the most power-consuming tasks in this type of devices. Multiples ANNs were implemented and deployed in different microcontroller architectures in order to find the best balance between energy consumption and computing performance. The results show that the embedded networks obtain up to 97.96% ± 1.42% accuracy, achieving an energy efficiency of 450 Mops/s/watt.

A Secure and Lightweight Three-Factor Remote User Authentication Protocol for Future IoT Applications

Abstract: With the booming integration of IoT technology in our daily life applications such as smart industrial, smart city, smart home, smart grid, and healthcare, it is essential to ensure the security and privacy challenges of these systems. Furthermore, time-critical IoT applications in healthcare require access from external parties (users) to their real-time private information via wireless communication devices. Therefore, challenges such as user authentication must be addressed in IoT wireless sensor networks (WSNs). In this paper, we propose a secure and lightweight three-factor (3FA) user authentication protocol based on feature extraction of user biometrics for future IoT WSN applications. The proposed protocol is based on the hash and XOR operations, including (i) a 3-factor authentication (i.e., smart device, biometrics, and user password); (ii) shared session key; (iii) mutual authentication; and (iv) key freshness. We demonstrate the proposed protocol’s security using the widely accepted Burrows–Abadi–Needham (BAN) logic, Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool, and the informal security analysis that demonstrates its other features. In addition, our simulations prove that the proposed protocol is superior to the existing related authentication protocols, in terms of security and functionality features, along with communication and computation overheads. Moreover, the proposed protocol can be utilized efficiently in most of IoT’s WSN applications, such as wireless healthcare sensor networks.

Open-source multi-purpose remote laboratory for IoT education

Abstract: With the constant growth of devices connected to the internet, many researchers focus their interest on the development of Remote Laboratories, taking advantage of the numerous electronic open-source platforms. The aim of this work is to propose a compact solution (hardware and software) for the implementation of a low-cost open-access remote laboratory. The key concept is using Python, a programming language that is strong, flexible and rich of free external packages. Specifically, Python is used at the same time as: (i) a microframework with server functionalities, (ii) a control unit that drives an Arduino microcontroller and a Raspberry Pi microcomputer. The software to access the laboratory is realized as a web client interface by using HTML5 and JavaScript. This software is light enough to be run on an average personal computer. The concept of Remote Laboratory allows students to be able to carry out didactic experiences without constraints with regard to space and time, being it accessible from everywhere with just a PC or even a smart device (smartphone, tablet, etc.). With this remote laboratory, students can do real measurements of physical quantities and perform real activities while accessing the system from any place, thus allowing students of distance learning universities to do real experiments.

Automated Smart Utilization of Background Lights and Daylight for Green Building Efficient and Economic Indoor Lighting Intensity Control

Abstract: The ways which are used today in order to light houses, offices, and most of the indoor areas are inefficient as a lot of energy is consumed unnecessarily during the day time. Mainly this problem because the interior lighting design consider the worst case when the light service is at night, which is not always valid. Also in most cases the lighting system design relies on people to control the lights switching on and off. This problem is also one of the design concerns in Green Building. In this paper, a solution to this problem and a method for people’s comfort who use the indoor facilities in industrial buildings is presented. In the proposed smart lighting system, lights switch on automatically when there is somebody in the room or in the occupied space and switch off when there is no occupancy. In addition to this known technique, adjustment of the brightness level of the lights will be possible via the personal computer or any other smart device. In this method, for the illumination level in the area, where is needed to be controlled for better energy saving, the light automatically is measured by the sensor and considering the amount of background lights coming from outside, automatically the brightness of lights is controlled to reach the preset level that determined for that room. By the means of this method, it is possible to provide better user comfort, avoid human forcedness to switch the light on and off, and hence effective energy saving. Arduino controller is used to build the controller and to demonstrate the results. Economic analysis was done to calculate the percentage of the energy saving that can be obtained by implementing the proposed smart lighting controller. As an outcome of the economic analysis, energy saving norm for an office with a standard size was calculated.

Optimal task partitioning, Bit allocation and trajectory for D2D-assisted UAV-MEC systems

Abstract: Unmanned aerial vehicle is a promising technology in upcoming wireless networks due to its potential aerial feature and line-of-sight capability. Mobile edge computing systems that rely on unmanned aerial vehicles consider offloading computationally intensive tasks to unmanned aerial vehicle to be executed via a powerful edge server. In this paper, we exploit the power of device-to-device communications in terms of low latency and low-power transmission as an additional option to edge offloading. Specifically, any smart device that have a nearby device partner can offload part of its task to be executed by his partner. Additionally, we aim to minimize the total energy consumption during the offloading and local computing procedures at smart devices via jointly optimizing the trajectory of the unmanned aerial vehicle, number of bits allocated for both unmanned aerial vehicle and the partner device, and finally task partitioning. We divide the non-convex major problem into two sub-problems which are efficiently solved via alternative optimization. Simulation results reveal the superiority of device-to-device-assisted mobile edge computing systems that rely on unmanned aerial vehicles over counterparts with no device-to-device assistance.

Power Analysis of Household Appliances using IoT

Abstract: In recent years, significant changes have occurred in the fields of the power sector, from producing electricity to power consumption, with smart technology having a major role. In the field of IoT there are a lot of smart devices and smart homes as a solution, like that only there are Smart Energy Meters which can be used as a solution in our homes. It is possible to use non-intrusive (contactless) load tracking using smart meters to control our home appliances' power usage. In this work, the electrical energy meter is used to calculate the energy usage and performance profile of the inner components of the electrical devices by checking how and what amount of electricity is passing through an electric wire in real-time. After calculating, it will save everything in its cache and be ready to upload it to the cloud and the microcontroller via sensors to any display device the consumer desires. Which can be stored for a longer period and accessed from any smart device to which it has previously been connected. This allows the consumer to understand how and which appliances use more electricity, allowing them to make plans on how to reduce usage, or set some rules and come up with new ideas to save electricity, power usage criteria, and the environment, or simply to use the appropriate appliances efficiently.

Tennis Online Teaching Information Platform Based on Android Mobile Intelligent Terminal

Abstract: With the development of the information age, there is almost one mobile smart device based on android, which is inseparable from mobile smart devices in both learning and life. In order to explore the effects of mobile smart devices on tennis teaching, this article mainly introduces the research on the tennis online teaching information platform based on android mobile smart terminals. This article first uses the android system framework and software architecture to design the technical aspects of the tennis online teaching information platform and then analyzes and improves the functional and nonfunctional requirements of the tennis online teaching information platform and students’ needs for mobile learning. For the design of aspects other than the technology of the online teaching information platform, the control group and the experimental group were designed to carry out teaching experiments, comparing the traditional teaching methods and the teaching methods of the online teaching information platform to bring out different teaching effects to tennis teaching. The results show that online teaching has a better learning effect on tennis skills and tennis theory knowledge, and the academic performance of the traditional teaching method is improved by 20%. In terms of increasing interest in tennis courses, the online teaching information platform has improved 70% of students’ interest.

IoT-based smart environment using intelligent intrusion detection system

Abstract: One of the most basic characteristic features of every smart device in a network based on the Internet of Things (IoT) is to gather a larger set of data that has been created and then transfer the gathered data to the destination/receiver server through the internet. Thus, IoT-based networks are most vulnerable to simple or complex attacks that need to be identified in the early stage of data transmission for saving the network from these malicious attacks. The chief goal of the proposed work is to design and form the intelligent intrusion detection system (I-IDS) using the machine learning models such that the attacks can be identified in the IoT network. The model is built considering the normal and malicious attacks on the data that are generated in IoT smart environment. To simulate such a model, a testbed is built where a wireless router, a DHT11 sensor, and a node MCU are being used during the design phase. An attacker or adversarial system is built to perform poisoning and sniffing attacks using a laptop system. The node captures the sensor values and transmits the data to the ThinkSpeak platform, during the normal phase via the wireless gateway, and in the attack phase, the malicious attacker interprets the data, modifies it while transmitting from node to the ThinkSpeak server. Thus, the attack called Man-In-The-Middle (MITM) is performed and classified as abnormal data. Various machine learning algorithms are performed on the data, and finally, the results obtained using a probabilistic model called as Markov model have a high performance evaluated based on the I-IDS IoT network. The results obtained during the experimental analysis show that the Markov model has obtained a 100% detection rate and 19% of false alarm rate (FAR) with high precision and low error rate. The algorithms such as naive Bayes classifier, support vector machine (SVM), decision tree, and Adaboost are considered in comparison with the Markov model. The optimal solution is obtained concerning other evaluation metrics like sensitivity, F1, and true-positive rate (TPR). Therefore, the integrated network of IoT-WSN with its performance metrics is tabulated to show the potentials of securing a network system. Additionally, the proposed work gives a high level of security for IoT smart environment as compared with the other machine learning algorithms using the novel technique of intelligent IDS.