Intelligent Internet Of Things Devices Research Articles

Enhanced cooperative behavior and fair spectrum allocation for intelligent IoT devices in cognitive radio networks

The extensive utilization of Internet of Things (IoT) devices in a wide range of services prompts spectrum scarcity. Recently, Cognitive Radio Networks (CRNs) have shown promising results in addressing the spectrum scarcity problem. Therefore, CRNs are envisaged to play a crucial role in accommodating the massive number of emerging IoT devices. However, two dominant shortcomings hinder the coalescence of IoT and CRNs. First, the selfish behavior among Secondary Users (SUs) during the spectrum sensing process. Second, the inability to construct a convenient metric for fair spectrum assignment. The contribution of this paper is quadruple: (i) a Direct Strategy Selection (DSS) mechanism that satisfies Aspiration Equilibrium (AE) and enforces cooperation among SUs during the spectrum sensing process; (ii) a spectrum allocation scheme that takes cooperative behavior as a fairness parameter; (iii) studying the effects of three windowing techniques on the proposed allocation scheme and selecting the window that achieves maximum fairness; (iv) exploring the trade-off between the utility function and cooperation among SUs. The results show that DSS yields instant convergence and a threefold increase in the index of cooperation. Regarding spectrum allocation, the proposed scheme combined with the Reverse Attenuation Window (RAW) shows a 22% increase in the Average Satisfaction Ratio (ASR) for SUs. The simulations are conducted in the worst case scenario, where all SUs in the CRN are requesting to access the spectrum at the same time.

A Composite Model for an Intelligent Device-To-Device Communication in the Internet of Things

Similar to how humans communicate, devices will have to communicate with each other in the Internet of Things (IoT) ecosystem. Where the devices themselves become the users of the knowledge or the data that is acquired. Hence we can say that device-to-device communication becomes an intrinsic part of IoT. Devices have to communicate autonomously with each other without any centralized monitoring system. It will be more powerful when the devices can communicate in a multi-hop manner rather than a single-hop manner where the information when and where needed can be accumulated and used. This leads to a need where there must be a group communication between the devices in a particular network. Hence information about the network and the environment in which the devices operate can be identified and the relevant knowledge can be transferred to a peer network or a device for further information gathering or the peer nodes can be used as intermediate nodes in the multi-hop network. Here we are proposing a network of multi-hop interconnected intelligent IoT devices that can communicate among its peer devices gather information and send the information in a multi-hop fashion making using of the adjacent networks or clusters.

Table Recognition for Sensitive Data Perception in an IoT Vision Environment

Internet of Things (IoT) technology allows us to measure, compute, and decide about the physical world around us in a quantitative and intelligent way. It makes all kinds of intelligent IoT devices popular. We are continually perceived and recorded by intelligent IoT devices, especially vision devices such as cameras and mobile phones. However, a series of security issues have arisen in recent years. Sensitive data leakage is the most typical and harmful one. Whether we are just browsing files unintentionally in sight of high-definition (HD) security cameras, or internal ghosts are using mobile phones to photograph secret files, it causes sensitive data to be captured by intelligent IoT vision devices, resulting in irreparable damage. Although the risk of sensitive data diffusion can be reduced by optical character recognition (OCR)-based packet filtering, it is difficult to use it with sensitive data presented in table form. This is because table images captured by the intelligent IoT vision device face issues of perspective transformation, and interferences of circular stamps and irregular handwritten signatures. Therefore, a table-recognition algorithm based on a directional connected chain is proposed in this paper to solve the problem of identifying sensitive table data captured by intelligent IoT vision devices. First, a Directional Connected Chain (DCC) search algorithm is proposed for line detection. Then, valid line mergence and invalid line removal is performed for the searched DCCs to detect the table frame, to filter the irregular interferences. Finally, an inverse perspective transformation algorithm is used to restore the table after perspective transformation. Experiments show that our proposed algorithm can achieve accuracy of at least 92%, and filter stamp interference completely.

Privacy-Preserving Outsourced Speech Recognition for Smart IoT Devices

Most of the current intelligent Internet of Things (IoT) products take neural network-based speech recognition as the standard human–machine interaction interface. However, the traditional speech recognition frameworks for smart IoT devices always collect and transmit voice information in the form of plaintext, which may cause the disclosure of user privacy. Due to the wide utilization of speech features as biometric authentication, the privacy leakage can cause immeasurable losses to personal property and privacy. Therefore, in this paper, we propose an outsourced privacy-preserving speech recognition framework (OPSR) for smart IoT devices in the long short-term memory (LSTM) neural network and edge computing. In the framework, a series of additive secret sharing-based interactive protocols between two edge servers are designed to achieve lightweight outsourced computation. And based on the protocols, we implement the neural network training process of LSTM for intelligent IoT device voice control. Finally, combined with the universal composability theory and experiment results, we theoretically prove the correctness and security of our framework.

Guest Editorial Special Issue on Enabling a Smart City: Internet of Things Meets AI

Future cities are to be not only an intelligent and green living environment but also provide human-centered public services at a lower cost. The Internet of Things (IoT) and artificial intelligence (AI) are two cornerstone technologies enabling the smart city concept, which are fusing into an organic whole in recent years. Some particular joint points where IoT meets AI are intelligent IoT devices, smart sensing boosted by AI, and IoT big data mining with AI. Such “IoT meets AI” trend is already casting significant impact to enable a smart city. Some examples are: smartphones are able to learn the touching pattern of users; home Wi-Fi router can intelligently detect and locate an intruder; vehicles locations and movement information can be exploited for traffic control; video cameras installed in the street can perform face recognition locally.

An Approach to Share Self-Taught Knowledge between Home IoT Devices at the Edge

The traditional Internet of Things (IoT) paradigm has evolved towards intelligent IoT applications which exploit knowledge produced by IoT devices using artificial intelligence techniques. Knowledge sharing between IoT devices is a challenging issue in this trend. In this paper, we propose a Knowledge of Things (KoT) framework which enables sharing self-taught knowledge between IoT devices which require similar or identical knowledge without help from the cloud. The proposed KoT framework allows an IoT device to effectively produce, cumulate, and share its self-taught knowledge with other devices at the edge in the vicinity. This framework can alleviate behavioral repetition in users and computational redundancy in systems in intelligent IoT applications. To demonstrate the feasibility of the proposed concept, we examine a smart home case study and build a prototype of the KoT framework-based smart home system. Experimental results show that the proposed KoT framework reduces the response time to use intelligent IoT devices from a user’s perspective and the power consumption for compuation from a system’s perspective.

A Prediction Mechanism of Energy Consumption in Residential Buildings Using Hidden Markov Model

Internet of Things (IoT) is considered as one of the future disruptive technologies, which has the potential to bring positive change in human lifestyle and uplift living standards. Many IoT-based applications have been designed in various fields, e.g., security, health, education, manufacturing, transportation, etc. IoT has transformed conventional homes into Smart homes. By attaching small IoT devices to various appliances, we cannot only monitor but also control indoor environment as per user demand. Intelligent IoT devices can also be used for optimal energy utilization by operating the associated equipment only when it is needed. In this paper, we have proposed a Hidden Markov Model based algorithm to predict energy consumption in Korean residential buildings using data collected through smart meters. We have used energy consumption data collected from four multi-storied buildings located in Seoul, South Korea for model validation and results analysis. Proposed model prediction results are compared with three well-known prediction algorithms i.e., Support Vector Machine (SVM), Artificial Neural Network (ANN) and Classification and Regression Trees (CART). Comparative analysis shows that our proposed model achieves 2.96 % better than ANN results in terms of root mean square error metric, 6.09 % better than SVM and 9.03 % better than CART results. To further establish and validate prediction results of our proposed model, we have performed temporal granularity analysis. For this purpose, we have evaluated our proposed model for hourly, daily and weekly data aggregation. Prediction accuracy in terms of root mean square error metric for hourly, daily and weekly data is 2.62, 1.54 and 0.46, respectively. This shows that our model prediction accuracy improves for coarse grain data. Higher prediction accuracy gives us confidence to further explore its application in building control systems for achieving better energy efficiency.

A Reconfigurable Streaming Deep Convolutional Neural Network Accelerator for Internet of Things

Convolutional neural network (CNN) offers significant accuracy in image detection. To implement image detection using CNN in the Internet of Things (IoT) devices, a streaming hardware accelerator is proposed. The proposed accelerator optimizes the energy efficiency by avoiding unnecessary data movement. With unique filter decomposition technique, the accelerator can support arbitrary convolution window size. In addition, max-pooling function can be computed in parallel with convolution by using separate pooling unit, thus achieving throughput improvement. A prototype accelerator was implemented in TSMC 65-nm technology with a core size of 5 mm2. The accelerator can support major CNNs and achieve 152GOPS peak throughput and 434GOPS/W energy efficiency at 350 mW, making it a promising hardware accelerator for intelligent IoT devices.

Co-locating services in IoT systems to minimize the communication energy cost

Ubiquitous sensing and actuating devices are now everywhere in our living environment as part of the global cyber–physical ecosystem. Sensing and actuating capabilities can be modeled as services to compose intelligent Internet of Things (IoT) applications. An issue for perpetually running and managing these IoT devices is the energy cost. One energy saving strategy is to co-locate several services on one device in order to reduce the computing and communication energy. In this paper, we propose a service merging strategy for mapping and co-locating multiple services on devices. In a multi-hop network, the service co-location problem is formulated as a quadratic programming problem. We show a reduction method that reduces it to the integer programming problem. In a single hop network, the service co-location problem can be modeled as the Maximum Weighted Independent Set (MWIS) problem. We show the algorithm to transform a service flow to a co-location graph, then use known heuristic algorithms to find the maximum independent set which is the basis for making service co-location decisions. The performance of different co-location algorithms are evaluated by simulation in this paper.